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ENDE, JOHN F., WALTER HUDA, PABLO R. ROS, and ANTHONY L. LITWILLER. "Image Mottle in Abdominal CT." Investigative Radiology 34, no. 4 (1999): 282. http://dx.doi.org/10.1097/00004424-199904000-00005.
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Song, Xiao Long, Qiao Wang, and Zhen Gang Jiang. "Three-Dimensional Segmentation Research of CT Abdominal Artery Image Sequence." Advanced Materials Research 791-793 (September 2013): 2048–52. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.2048.
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As the role of medical imaging in clinical diagnoses and treatment has been more and more important, CT scan has been applied more widely. How to extract abdominal artery from CT image automatically and accurately has a significant value for abdominal artery disease. Because the medical image is of complexity and diversity, traditional segmentation method cannot complete the segmentation task very well. Therefore, this paper presents a method for extracting abdominal artery from CT images using three-dimensional region growing algorithm combined with image morphology. The experimental results show that the proposed method is an effective way for improving the accuracy of abdominal artery segmentation.
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Apisarnthanarak, Piyaporn, Anawat Sriwaleephun, Sastrawut Thammakittiphan, et al. "Abdominal CT radiation dose reduction at Siriraj Hospital (Phase III)." ASEAN Journal of Radiology 22, no. 1 (2021): 5–19. http://dx.doi.org/10.46475/aseanjr.v22i1.82.
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OBJECTIVE: To compare the image quality and the radiation dose between fixed tube current (FTC) low dose abdominal CT currently performed at our hospital and new automatic tube current modulation (ATCM) low dose abdominal CT. MATERIALS AND METHODS: We prospectively performed ATCM low dose abdominal CT in 88 participants who had prior FTC low dose CT for comparison. Four experienced abdominal radiologists independently and blindly assessed the quality of FTC and ATCM low dose CT images by using a 5-point-scale satisfaction score (1 = unacceptable, 2 = poor, 3 = average, 4 = good, and 5 = excellent image quality). Each reader selected the preferred image set between FTC and ATCM low dose techniques for each participant. The image noise of the liver and the aorta in both techniques was measured. The volume CT dose index (CTDIvol) of both techniques was compared. RESULTS: The mean satisfaction scores (SD) for FTC and ATCM low dose CT were 4.38 (0.66) and 4.38 (0.64), respectively with the ranges of 3 to 5 in both techniques, which were all acceptable for CT interpretation. The preferred image set between FTC and ATCM low dose techniques of each participant randomly selected by each reader were varied, depending on the readers’ opinions. The mean image noise of the aorta on FTC and ATCM low dose CT accounted for 34.75 and 36.46, respectively, while the mean image noise of the liver was 28.86 and 29.81, respectively. The mean CTDIvol (SD) of FTC and ATCM low dose CT were 8.42 (0.32) and 8.12 (0.43) mGy, respectively. CONCLUSION: FTC and ATCM low dose abdominal CT provided comparable acceptable image quality and showed no clinical significance in radiation dose optimization.
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Apisarnthanarak, Piyaporn, Chosita Buranont, Chulaluck Boonma, et al. "Abdominal CT radiation dose optimization at Siriraj Hospital." ASEAN Journal of Radiology 21, no. 2 (2020): 28–43. http://dx.doi.org/10.46475/aseanjr.v21i2.80.
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OBJECTIVE: To compare radiation dose and image quality between standard dose abdominal CT currently performed at our hospital and new low dose abdominal CT using various percentages (0%, 10%, 20%, and 30%) of Adaptive Statistical Iterative Reconstruction (ASiR). MATERIALS AND METHODS: We prospectively performed low dose abdominal CT (30% reduction of standard tube current) in 119 participants. The low dose CT images were post processed with four parameters (0%, 10%, 20% and 30%) of ASiR. The volume CT dose index (CTDIvol) of standard and low dose CT were compared. Four experienced abdominal radiologists independently assessed the quality of low dose CT with aforementioned ASiR parameters using a 5-point-scale satisfaction score (1 = unacceptable, 2 = poor, 3 = average, 4 = good, and 5 = excellent image quality) by using prior standard dose CT as a reference of excellent image quality (5). Each reader selected the preference ASiR parameter for each participant. The image noise of the liver and the aorta in all 5 (1 prior standard dose and 4 current low dose) image sets was measured. RESULTS: The mean CTDIvol of low dose CT was significantly lower than of standard dose CT (7.17 ± 0.08 vs 12.02 ±1.61 mGy, p<0.001). The mean satisfaction scores for low dose CT with 0%, 10%, 20% and 30% ASiR were 3.95, 3.99, 3.91 and 3.87, respectively with the ranges of 3 to 5 in all techniques. The preferred ASiR parameters of each participant randomly selected by each reader were varied, depending on the readers’ opinions. The mean image noise of the aorta on standard dose CT and low dose CT with 0%, 10%, 20%, and 30% ASiR was 29.07, 36.97, 33.92, 31.49, and 29.11, respectively, while the mean image noise of the liver was 24.60, 30.21, 28.33, 26.25, and 24.32, respectively. CONCLUSION: Low dose CT with 30% reduction of standard mA had acceptable image quality with significantly reduced radiation dose. The increment of ASiR was helpful in reducing image noise.
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Siri, Sangeeta K., and Mrityunjaya V. Latte. "Universal Liver Extraction Algorithm: An Improved Chan–Vese Model." Journal of Intelligent Systems 29, no. 1 (2018): 237–50. http://dx.doi.org/10.1515/jisys-2017-0362.
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Abstract Liver segmentation is important to speed up liver disease diagnosis. It is also useful for detection, recognition, and measurement of objects in liver images. Sufficient work has been carried out until now, but common methodology for segmenting liver image from CT scan, MRI scan, PET scan, etc., is not available. The proposed methodology is an effort toward developing a general algorithm to segment liver image from abdominal computerized tomography (CT) scan and magnetic resonance imaging (MRI) scan images. In the proposed algorithm, pixel intensity range of the liver portion is obtained by cropping a random section of the liver. Using its histogram, threshold values are calculated. Further, threshold-based segmentation is performed, which separates liver from abdominal CT scan image/abdominal MRI scan image. Noise in the liver image is reduced using median filter, and the quality of the image is improved by sigmoidal function. The image is then converted into binary image. The Chan–Vese (C–V) model demands an initial contour, which evolves outward. A novel algorithm is proposed to identify the initial contour inside the liver without user intervention. This initial contour propagates outward and continues until the boundary of the liver is identified accurately. This process terminates by itself when the entire boundary of the liver is detected. The method has been validated on CT images and MRI images. Results on the variety of images are compared with existing algorithms, which reveal its robustness, effectiveness, and efficiency.
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Apisarnthanarak, Piyaporn, Suchanya Hongpinyo, Krittya Saysivanon, et al. "Abdominal CT radiation dose reduction at Siriraj Hospital (Phase II)." ASEAN Journal of Radiology 21, no. 3 (2020): 5–24. http://dx.doi.org/10.46475/aseanjr.v21i3.81.
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Objective: To compare radiation dose, radiologists’ satisfaction, and image noise between the standard dose abdominal CT currently performed at our hospital and the new automatic tube current modulation (ATCM) low dose abdominal CT, using various parameters (0%, 10%, 20%, and 30%) of the Adaptive Statistical Iterative Reconstruction (ASiR). Materials and Methods: We prospectively performed the ATCM low dose abdominal CT in 111 participants who had prior standard dose CT for comparison. The ATCM low dose CT images were post processed with 4 parameters (0%, 10%, 20% and 30%) of ASiR on a CT workstation. The volume CT dose index (CTDIvol) of the ATCM low dose and the standard dose CT were compared. Four experienced abdominal radiologists independently assessed the quality of the ATCM low dose CT with the aforementioned ASiR parameters using a 5-point-scale satisfaction score (1 = unacceptable, 2 = poor, 3 = average, 4 = good, and 5 = excellent image quality) by using the prior standard dose CT as a reference of an excellent image quality (5). Each reader selected the preferred ASiR parameter for each participant. The image noise of the liver and the aorta in all 5 techniques (1 prior standard dose and 4 current ATCM low dose techniques) was measured. The correlation between the image quality vs the participants’ body mass index (BMI) and waist circumferences were analyzed. Results: The mean CTDIvol of the ATCM low dose CT was significantly lower than of the standard dose CT (7.29 ± 0.20 vs 11.28 ± 0.23 mGy, p<0.001). The mean satisfaction score for the ATCM low dose CT with 0%, 10%, 20% and 30% ASiR were 4.14, 4.16, 4.17, and 4.26, respectively with the ranges of 3 to 5 in all techniques. The preferred ASiR parameters of each participant randomly selected by each reader were varied, depending on the readers’ opinions. The mean image noise of the aorta on the standard dose CT and the ATCM low dose CT with 0%, 10%, 20%, and 30% ASiR was 30.69, 36.60, 34.05, 31.43, and 29.09, respectively, while the mean image noise of the liver was 24.96, 29.90, 27.86, 25.66, and 23.68, respectively. There was a correlation between the image quality (satisfaction score and image noise) vs the participants’ BMI and waist circumferences. Conclusion: The ATCM low dose CT received acceptable radiologists’ satisfaction with significant radiation dose reduction. The increment of ASiR was helpful in reducing the image noise and had a tendency to increase the radiologists’ satisfaction score.
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Mohan, Ramya, S. P. Chokkalingam, Kirupa Ganapathy, and A. Rama. "Comparative Image Quality Analysis of Spatial Filters for Pre-processing of CT Abdominal Images." Webology 18, Special Issue 04 (2021): 1449–69. http://dx.doi.org/10.14704/web/v18si04/web18283.
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Aim: To determine the efficient noise reduction filter for abdominal CT images. Background: Image enrichment is the first and foremost step that has to be done in all image processing applications. It is used to enhance the quality of digital images. Digital images are liable to addition of noise from various sources such as error in instrument calibration, excess staining of images, etc., Image de-noising is an enhancement technique used to remove / reduce noise present in an image. Reducing the noise of images and preserving its edges are always critical and challenging in image processing. Materials and Method: In this paper, four different spatial filters namely Mean, Median, Gaussian and Wiener were used on 100 CT abdominal images and their performances were compared against the following four parameters: Peak signal to noise ratio (PSNR), Mean Square Error (MSE), Normalised correlation coefficient (NCC) and Normalised Absolute Error (NAE) to determine the best denoising filter for the abdominal CT images. Result: Based on the experimental parameters, the median filter had the maximum efficiency in managing salt and pepper noise than the other three filters. Both Median and Wiener filters showed efficiency in removing the Gaussian noise. Whereas, the Wiener filter demonstrated higher efficiency in reducing both Poisson and Speckle noise. Conclusion: Based on the results of this study, we can conclude that the median filter can be used to reduce Salt and Pepper noises. Median and Wiener filters are significantly better for Gaussian Noise and the Wiener filter can be used to reduce both Poisson & Speckle noise in abdominal CT images.
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Steuwe, Andrea, Marie Weber, Oliver Thomas Bethge, et al. "Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography." British Journal of Radiology 94, no. 1117 (2021): 20200677. http://dx.doi.org/10.1259/bjr.20200677.
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Objectives: Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)]. Methods: In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.9 ± 1.8 mGy) were reconstructed with IR, FBP and, furthermore, post-processed using a novel software. For the three reconstructions, qualitative and quantitative image quality was evaluated by means of CT numbers, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in six different ROIs. Additionally, the reconstructions were compared using SNR, peak SNR, root mean square error and mean absolute error to assess structural differences. Results: On average, CT numbers varied within 1 Hounsfield unit (HU) for the three assessed methods in the assessed ROIs. In soft tissue, image noise was up to 42% lower compared to FBP and up to 27% lower to IR when applying the novel software. Consequently, SNR and CNR were highest with the novel software. For both IR and the novel software, subjective image quality was equal but higher than the image quality of FBP-images. Conclusion: The assessed software reduces image noise while maintaining image information, even in comparison to IR, allowing for a potential dose reduction of approximately 20% in abdominal CT imaging. Advances in knowledge: The assessed software reduces image noise by up to 27% compared to IR and 48% compared to FBP while maintaining the image information. The reduced image noise allows for a potential dose reduction of approximately 20% in abdominal imaging.
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Ogawa, Kazuya, Hiromitsu Onishi, Masatoshi Hori, et al. "Visualization of small visceral arteries on abdominal CT angiography using ultra-high-resolution CT scanner." Japanese Journal of Radiology 39, no. 9 (2021): 889–97. http://dx.doi.org/10.1007/s11604-021-01124-6.
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Abstract Purpose To evaluate the image quality and ability to delineate the small visceral arteries of high-resolution (HR) abdominal CT angiography (CTA) using an ultra-high-resolution computed tomography (UHR CT) scanner. Materials and methods Thirty-seven patients were enrolled who underwent abdominal CTA using a UHR CT scanner. The images were reconstructed with a matrix of 1024 × 1024 and 0.25 mm thickness for HR CTA and with a matrix of 512 × 512 and 0.5 mm thickness for normal resolution (NR) CTA. Maximum CT value, image quality, and delineation of the small arteries were compared between HR CTA and NR CTA. Results HR CTA showed significantly higher maximum CT value, higher image quality, and better delineation of the small arteries than did NR CTA (P < .005). Conclusion HR CTA using a UHR CT scanner showed higher image quality than NR CTA and enhanced the delineation of visceral arteries.
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Aurumskjöld, Marie-Louise, Marcus Söderberg, Fredrik Stålhammar, Kristina Vult von Steyern, Anders Tingberg, and Kristina Ydström. "Evaluation of an iterative model-based reconstruction of pediatric abdominal CT with regard to image quality and radiation dose." Acta Radiologica 59, no. 6 (2017): 740–47. http://dx.doi.org/10.1177/0284185117728415.
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Background In pediatric patients, computed tomography (CT) is important in the medical chain of diagnosing and monitoring various diseases. Because children are more radiosensitive than adults, they require minimal radiation exposure. One way to achieve this goal is to implement new technical solutions, like iterative reconstruction. Purpose To evaluate the potential of a new, iterative, model-based method for reconstructing (IMR) pediatric abdominal CT at a low radiation dose and determine whether it maintains or improves image quality, compared to the current reconstruction method. Material and Methods Forty pediatric patients underwent abdominal CT. Twenty patients were examined with the standard dose settings and 20 patients were examined with a 32% lower radiation dose. Images from the standard examination were reconstructed with a hybrid iterative reconstruction method (iDose4), and images from the low-dose examinations were reconstructed with both iDose4 and IMR. Image quality was evaluated subjectively by three observers, according to modified EU image quality criteria, and evaluated objectively based on the noise observed in liver images. Results Visual grading characteristics analyses showed no difference in image quality between the standard dose examination reconstructed with iDose4 and the low dose examination reconstructed with IMR. IMR showed lower image noise in the liver compared to iDose4 images. Inter- and intra-observer variance was low: the intraclass coefficient was 0.66 (95% confidence interval = 0.60–0.71) for the three observers. Conclusion IMR provided image quality equivalent or superior to the standard iDose4 method for evaluating pediatric abdominal CT, even with a 32% dose reduction.
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Li, Junjun, Blessed Kondowe, Rong Wang, et al. "Enhancement of abdominal Low-Dose CT image quality utilizing Clear View reconstruction technique at Mzuzu Central Hospital, Malawi." Malawi Medical Journal 36, no. 5 (2025): 308–12. https://doi.org/10.4314/mmj.v36i5.3.
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Objective This study aimed to investigate the impact of Clear View dual-domain iterative reconstruction (IR) technology on the quality of low-dose abdominal CT images and to determine the optimal weight ratio to optimize image quality.Methods We studied 40 patients (28 males, 12 females, aged 19-69) undergoing low-dose abdominal CT scans (CTDI = 5.32 ± 0.89 mGy). The scanning parameters were set as follows: tube voltage of 120 kVp, tube current modulation based on Signal to Noise Ratio (SNR) at 0.5 mode (O-Dose automatic tube current modulation technology), pitch of 0.9, rotation time of 0.6 s/r, matrix size of 512 × 512, and collimation width of 16 × 1.25 mm. We applied Clear View IR with four weight ratios (20%, 40%, 60%, 80%) and filtered back projection (FBP). Conventional scanning uses with 120 kVp, 280 mAs, pitch of 0.9, rotation time of 0.6 s/r, matrix size of 512 × 512, and collimation width of 16 × 1.25 mm. Conventional dose abdominal CT scans (CTDI = 11.95 ± 0.00 mGy).CT values, standard deviations (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for liver, spleen, pancreas, kidneys, and erector spinae muscles. Two deputy chief physicians blindly evaluated image quality on a 1-5 scale. Statistical analysis was done using SPSS 22.0 with P < 0.05 considered significant.ResultsSubjective evaluations revealed the highest diagnostic score with a 40% Clear View reconstruction weight ratio. Higher weight ratios significantly reduced subjective image noise, with the highest noise scores at 80%. Moreover, compared to FBP, especially Clear View reconstruction weight ratios of 20% to 60%, significantly improved the image quality of abdominal solid organs, reducing image artifacts and improving diagnostic acceptability (P < 0.05). Objective evaluation showed that with increasing Clear View reconstruction weight ratios, image noise SD values decreased, while SNR and CNR values increased, and the differences in SD, SNR, and CNR for different reconstruction weight ratios of abdominal solid organs were statistically significant (P < 0.05).Conclusion Compared to FBP algorithm, Clear View demonstrates greater potential in low-dose abdominal CT, effectively reducing image noise and artifacts while maintaining image clarity. Based on combined subjective and objective evaluations, a 40% Clear View reconstruction weight ratio provides optimal image quality for abdominal solid organs.
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Tamura, Akio, Eisuke Mukaida, Yoshitaka Ota, Masayoshi Kamata, Shun Abe, and Kunihiro Yoshioka. "Superior objective and subjective image quality of deep learning reconstruction for low-dose abdominal CT imaging in comparison with model-based iterative reconstruction and filtered back projection." British Journal of Radiology 94, no. 1123 (2021): 20201357. http://dx.doi.org/10.1259/bjr.20201357.
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Objective: This study aimed to conduct objective and subjective comparisons of image quality among abdominal computed tomography (CT) reconstructions with deep learning reconstruction (DLR) algorithms, model-based iterative reconstruction (MBIR), and filtered back projection (FBP). Methods: Datasets from consecutive patients who underwent low-dose liver CT were retrospectively identified. Images were reconstructed using DLR, MBIR, and FBP. Mean image noise and contrast-to-noise ratio (CNR) were calculated, and noise, artifacts, sharpness, and overall image quality were subjectively assessed. Dunnett’s test was used for statistical comparisons. Results: Ninety patients (67 ± 12.7 years; 63 males; mean body mass index [BMI], 25.5 kg/m2) were included. The mean noise in the abdominal aorta and hepatic parenchyma of DLR was lower than that in FBP and MBIR (p < .001). For FBP and MBIR, image noise was significantly higher for obese patients than for those with normal BMI. The CNR for the abdominal aorta and hepatic parenchyma was higher for DLR than for FBP and MBIR (p < .001). MBIR images were subjectively rated as superior to FBP images in terms of noise, artifacts, sharpness, and overall quality (p < .001). DLR images were rated as superior to MBIR images in terms of noise (p < .001) and overall quality (p = .03). Conclusions: Based on objective and subjective comparisons, the image quality of DLR was found to be superior to that of MBIR and FBP on low-dose abdominal CT. DLR was the only method for which image noise was not higher for obese patients than for those with a normal BMI. Advances in knowledge: This study provides previously unavailable information on the properties of DLR systems and their clinical utility.
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Kiran, Malhari Napte, and Mahajan Anurag. "Liver segmentation using marker controlled watershed transform." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1541–49. https://doi.org/10.11591/ijece.v13i2.pp1541-1549.
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The largest organ in the body is the liver and primarily helps in metabolism and detoxification. Liver segmentation is a crucial step in liver cancer detection in computer vision-based biomedical image analysis. Liver segmentation is a critical task and results in under-segmentation and over-segmentation due to the complex structure of abdominal computed tomography (CT) images, noise, and textural variations over the image. This paper presents liver segmentation in abdominal CT images using marker-based watershed transforms. In the pre-processing stage, a modified double stage gaussian filter (MDSGF) is used to enhance the contrast, and preserve the edge and texture information of liver CT images. Further, marker controlled watershed transform is utilized for the segmentation of liver images from the abdominal CT images. Liver segmentation using suggested MDSGF and marker-based watershed transform help to diminish the under-segmentation and over-segmentation of the liver object. The performance of the proposed system is evaluated on the LiTS dataset based on Dice score (DS), relative volume difference (RVD), volumetric overlapping error (VOE), and Jaccard index (JI). The proposed method gives (Dice score of 0.959, RVD of 0.09, VOE of 0.089, and JI of 0.921).
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Samuel Ibe, Blessing, Akpama Egwu Egong, Akwa Egom Erim, et al. "Acquisition Of Size-Specific Dose Estimates For Abdominal Computed Tomography Examination In Nigeria: A Preliminary Study Using A Water Equivalent Diameter." Global Journal of Pure and Applied Sciences 31, no. 1 (2025): 113–21. https://doi.org/10.4314/gjpas.v31i1.10.
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Background Size-specific dose estimates is an important metric for personalizing dose measurements during abdominal computed tomography (CT) examination. This study aimed to establish patient size-specific dose data as a guide for dose monitoring of abdominal computed tomography examinations among Nigerians. Methods Abdominal CT images of adult subjects obtained from two CT scanners - a light speed VCT –ZTe; (GE Healthcare) 16 – Slice and a Brivo CT 385 series; (GC Healthcare) 16-slice scanners were used in the study. The estimated computed tomography dose index volume (CTDIvol) and dose length product (DLP) were extracted from the CT dose report on the patients’ electronic Image folders. The effective size of the abdomen was obtained by using electronic caliper on the scanner console to measure the anterior-posterior and lateral dimensions at the level of the widest diameter on the image. With Table1A from the AAPM report 220, conversion factors were determined for a total of 264 abdominal CT images. The corresponding conversion factor was multiplied by the CTDIvol to obtain the size specific dose estimates (SSDE). The relationships between effective diameter (ED), CTDIVOL and age on SSDE were analyzed using minitab statistical software version 17. Results The mean CTDlvol was 6.94+ 1.63mGy, while SSDE was 9.76 + 2.56mGy. The SSDE decreased significantly with effective diameter, and increased significantly with the CTDI vol. The effective diameter measured between 8.72.90 and 37.70cm. Conclusion The study concludes that the CTDvol and patient’s abdominal size are determinant factors in the development of a size-specific radiation protection protocol and optimization of patient dose during abdominal CT examinations based on scanner output.
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Li, Lu-Lu, Huang Wang, Jian Song, Jin Shang, Xiao-Ying Zhao, and Bin Liu. "A feasibility study of realizing low-dose abdominal CT using deep learning image reconstruction algorithm." Journal of X-Ray Science and Technology 29, no. 2 (2021): 361–72. http://dx.doi.org/10.3233/xst-200826.
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OBJECTIVES: To explore the feasibility of achieving diagnostic images in low-dose abdominal CT using a Deep Learning Image Reconstruction (DLIR) algorithm. METHODS: Prospectively enrolled 47 patients requiring contrast-enhanced abdominal CT scans. The late-arterial phase scan was added and acquired using lower-dose mode (tube current range, 175–545 mA; 80 kVp for patients with BMI ≤24 kg/m2 and 100 kVp for patients with BMI > 24 kg/m2) and reconstructed with DLIR at medium setting (DLIR-M) and high setting (DLIR-H), ASIR-V at 0% (FBP), 40% and 80% strength. Both the quantitative measurement and qualitative analysis of the five types of reconstruction methods were compared. In addition, radiation dose and image quality between the early-arterial phase ASIR-V images using standard-dose and the late-arterial phase DLIR images using low-dose were compared. RESULTS: For the late-arterial phase, all five reconstructions had similar CT value (P > 0.05). DLIR-H, DLIR-M and ASIR-V80% images significantly reduced the image noise and improved the image contrast noise ratio, compared with the standard ASIR-V40% images (P < 0.05). ASIR-V80% images had undesirable image characteristics with obvious “waxy” artifacts, while DLIR-H images maintained high spatial resolution and had the highest subjective image quality. Compared with the early-arterial scans, the late-arterial phase scans significantly reduced the radiation dose (P < 0.05), while the DLIR-H images exhibited lower image noise and good display of the specific image details of lesions. CONCLUSIONS: DLIR algorithm improves image quality under low-dose scan condition and may be used to reduce the radiation dose without adversely affecting the image quality.
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Steuwe, Andrea, Birte Valentin, Oliver T. Bethge, et al. "Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones." Diagnostics 12, no. 7 (2022): 1627. http://dx.doi.org/10.3390/diagnostics12071627.
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Deep-learning (DL) noise reduction techniques in computed tomography (CT) are expected to reduce the image noise while maintaining the clinically relevant information in reduced dose acquisitions. This study aimed to assess the size, attenuation, and objective image quality of reno-ureteric stones denoised using DL-software in comparison to traditionally reconstructed low-dose abdominal CT-images and evaluated its clinical impact. In this institutional review-board-approved retrospective study, 45 patients with renal and/or ureteral stones were included. All patients had undergone abdominal CT between August 2019 and October 2019. CT-images were reconstructed using the following three methods: filtered back-projection, iterative reconstruction, and PixelShine (DL-software) with both sharp and soft kernels. Stone size, CT attenuation, and objective image quality (signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)) were evaluated and compared using Bonferroni-corrected Friedman tests. Objective image quality was measured in six regions-of-interest. Stone size ranged between 4.4 × 3.1–4.4 × 3.2 mm (sharp kernel) and 5.1 × 3.8–5.6 × 4.2 mm (soft kernel). Mean attenuation ranged between 704–717 Hounsfield Units (HU) (soft kernel) and 915–1047 HU (sharp kernel). Differences in measured stone sizes were ≤1.3 mm. DL-processed images resulted in significantly higher CNR and SNR values (p < 0.001) by decreasing image noise significantly (p < 0.001). DL-software significantly improved objective image quality while maintaining both correct stone size and CT-attenuation values. Therefore, the clinical impact of stone assessment in denoised image data sets remains unchanged. Through the relevant noise suppression, the software additionally offers the potential to further reduce radiation exposure.
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Abu-Qasmieh, Isam F., Ihssan S. Masad, Hiam Alquran, and Khaled Z. Alawneh. "Generation of synthetic FLAIR MRI image from real CT image for accurate synovial fluid segmentation in human knee image." Neural Network World 33, no. 3 (2023): 191–203. http://dx.doi.org/10.14311/nnw.2023.33.012.
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Synthetic MRI FLAIR images of an abdominal 3D multimodality phantom and in vivo human knee have been generated from real CT images using predefined mapping functions of CT mean and standard deviation with the corresponding proton density PD, T1 and T2 that were previously generated from spin-echo sequence. First, the validity of generating synthetic MR images from different sequences were tested and the same PD, T1 and T2 maps that were generated from the real CT image have been used in the simulation of MRI inversion-recovery (IR) sequence. The similarity results between the real and synthetic IR sequence images, using different inversion times TI, showed a very good agreement. After confirming the feasibility of generating synthetic IR images from the PD, T1 and T2-maps, that were originally obtained from spin-echo sequence using the phantom, the simulation of a knee image has been generated from the corresponding knee CT real image using the steady-state transverse magnetization formula of the inversion-recovery sequence. The simulated FLAIR IR sequence MR image are generated using proper TI for nulling the signal from the synovial fluid, where the image complement is used as a mask for segmenting the corresponding tissue region in the real CT image.
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Napte, Kiran Malhari, and Anurag Mahajan. "Liver segmentation using marker controlled watershed transform." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1541. http://dx.doi.org/10.11591/ijece.v13i2.pp1541-1549.
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<span lang="EN-US">The largest organ in the body is the liver and primarily helps in metabolism and detoxification. Liver segmentation is a crucial step in liver cancer detection in computer vision-based biomedical image analysis. Liver segmentation is a critical task and results in under-segmentation and over-segmentation due to the complex structure of abdominal computed tomography (CT) images, noise, and textural variations over the image. This paper presents liver segmentation in abdominal CT images using marker-based watershed transforms. In the pre-processing stage, a modified double stage gaussian filter (MDSGF) is used to enhance the contrast, and preserve the edge and texture information of liver CT images. Further, marker controlled watershed transform is utilized for the segmentation of liver images from the abdominal CT images. Liver segmentation using suggested MDSGF and marker-based watershed transform help to diminish the under-segmentation and over-segmentation of the liver object. The performance of the proposed system is evaluated on the LiTS dataset based on Dice score (DS), relative volume difference (RVD), volumetric overlapping error (VOE), and Jaccard index (JI). The proposed method gives (Dice score of 0.959, RVD of 0.09, VOE of 0.089, and JI of 0.921).</span>
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Gautam, Shubham, Anuradha Sharma, Charu Paruthi, Rohini Gupta Ghasi, and Krishna Bhardwaj. "Comparison of image quality of split-bolus computed tomography versus dual-phase computed tomography in abdominal trauma." Polish Journal of Radiology 90 (March 31, 2025): 151–60. https://doi.org/10.5114/pjr/200756.
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PurposeTo compare the image quality in single-pass split-bolus abdominal computed tomography (CT) and conventional biphasic CT in abdominal trauma patients.Material and methodsSixty-six consecutive abdominal trauma patients referred for CT were randomised into 2 groups: the study group (n = 33), scanned using the split-bolus technique; and the control group (n = 33), scanned using the conventional biphasic technique. CT image quality was analysed subjectively by 2 observers based on a 5-point Likert scale. The images were also analysed quantitatively for attenuation values achieved by region of interest (ROI) placements in major arteries, veins, and solid organs. In addition, the radiation dose in terms of the dose length product (DLP) was compared between the 2 groups.ResultsThe image quality in both groups ranged from good to excellent in most cases. There was no statistically significant difference in subjective image quality in both the groups as assessed by Likert score. Attenuation values in solid organs and major venous structures were significantly higher in the split-bolus group (p < 0.001). Arterial attenuation values were significantly higher in the control group (p < 0.001), but diagnostic levels were achieved in all patients. There was a reduction of 31.1% in DLP in the split-bolus group.ConclusionsThe split-bolus technique offers comparable image quality and higher solid organ and venous enhancement than conventional biphasic protocol at a reduced radiation dose.
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Rusandu, Albertina, Adrian Beck, Atle Hegge, and Gabriele Engh. "Image quality in abdominal CT: A comparison of two reconstruction algorithms in Filtered Back Projection (FBP)." MEDICAL IMAGING AND RADIOTHERAPY JOURNAL 39, no. 1 (2022): 5–11. http://dx.doi.org/10.47724/mirtj.2022.i02.a001.
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Objectives: The aim of this study was to evaluate the effect of the choice of kernel on the image quality in abdominal CT images with focus on liver lesion visibility. Methods: In this comparative study 84 abdominal CT examinations of patients with liver lesions that included parallel series reconstructed with two different kernels (B30 and B45) were analyzed. The subjective assessment of image quality was performed using visual grading analysis based on anatomical criteria, liver lesion visibility and perceived image quality. Objective image quality was assessed by measurements of Hounsfield unit (HU) values (average and standard deviation) in abdominal organs and calculations of contrast-to-noise ratios (CNR). Results: B30 kernel performed significantly better than B45 in all criteria except for sharpness. The most considerable improvement of the image quality was in terms of subjective experienced image noise, overall diagnostic image quality and visually sharp reproduction of liver lesions. The physical measurements showed that CNR increased by up to 46% when using B30. Conclusions: Using a B30 kernel algorithm for image reconstruction reduces noise and by that improves image quality and diagnostic accuracy significantly when compared to B45.
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Heinrich, Andra, Felix Streckenbach, Ebba Beller, Justus Groß, Marc-André Weber, and Felix G. Meinel. "Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta." Diagnostics 11, no. 11 (2021): 2037. http://dx.doi.org/10.3390/diagnostics11112037.
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To evaluate the impact of a novel, deep-learning-based image reconstruction (DLIR) algorithm on image quality in CT angiography of the aorta, we retrospectively analyzed 51 consecutive patients who underwent ECG-gated chest CT angiography and non-gated acquisition for the abdomen on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction (ASIR-V) and DLIR. Intravascular image noise, the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) were quantified for the ascending aorta, the descending thoracic aorta, the abdominal aorta and the iliac arteries. Two readers scored subjective image quality on a five-point scale. Compared to ASIR-V, DLIR reduced the median image noise by 51–54% for the ascending aorta and the descending thoracic aorta. Correspondingly, median CNR roughly doubled for the ascending aorta and descending thoracic aorta. There was a 38% reduction in image noise for the abdominal aorta and the iliac arteries, with a corresponding improvement in CNR. Median subjective image quality improved from good to excellent at all anatomical levels. In CT angiography of the aorta, DLIR substantially improved objective and subjective image quality beyond what can be achieved by state-of-the-art iterative reconstruction. This can pave the way for further radiation or contrast dose reductions.
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Kim, Daehong, Pil-Hyun Jeon, Chang-Lae Lee, and Myung-Ae Chung. "Effect of Tube Voltage and Radiation Dose on Image Quality in Pediatric Abdominal CT Using Deep Learning Reconstruction: A Phantom Study." Symmetry 15, no. 2 (2023): 501. http://dx.doi.org/10.3390/sym15020501.
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Background: Children have a potential risk from radiation exposure because they are more sensitive to radiation than adults. Objective: The purpose of this work is to estimate image quality according to tube voltage (kV) and radiation dose in pediatric computed tomography (CT) using deep learning reconstruction (DLR). Methods: Phantom images of children and adults were obtained for kV, radiation dose, and image reconstruction methods. The CT emits a fan beam to the opposite detector, and the geometry of the detector was symmetrical. Phantom images of children and adults were acquired at a volume CT dose index (CTDIvol) from 0.5 to 10.0 mGy for tube voltages at 80, 100, and 120 kV. A DLR was used to reconstruct the phantom image, and filtered back projection (FBP) and iterative reconstruction (IR) were also performed for comparison with the DLR. Image quality was evaluated by measuring the contrast-to-noise ratio (CNR) and noise. Results: Under the same imaging conditions, the DLR images of pediatric and adult phantoms generally provided improved CNR and noise compared with the FBP and IR images. At a similar CNR and noise, the FBP, IR, and DLR of the pediatric images showed a dose reduction compared with the FBP, IR, and DLR of the adult images, respectively. In terms of the effect of tube voltage, the CNR of the 100 kV DLR images was higher than that of the 120 kV DLR images. Conclusion: According to the results, since pediatric CT images maintain the same image quality at lower doses compared with adult CT images, DLR can improve image quality while reducing the radiation dose in children’s abdominal CT scans.
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Yang, Shaodi, Yuqian Zhao, Miao Liao, and Fan Zhang. "An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images." Sensors 21, no. 18 (2021): 6254. http://dx.doi.org/10.3390/s21186254.
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Medical image registration is an essential technique to achieve spatial consistency geometric positions of different medical images obtained from single- or multi-sensor, such as computed tomography (CT), magnetic resonance (MR), and ultrasound (US) images. In this paper, an improved unsupervised learning-based framework is proposed for multi-organ registration on 3D abdominal CT images. First, the explored coarse-to-fine recursive cascaded network (RCN) modules are embedded into a basic U-net framework to achieve more accurate multi-organ registration results from 3D abdominal CT images. Then, a topology-preserving loss is added in the total loss function to avoid a distortion of the predicted transformation field. Four public databases are selected to validate the registration performances of the proposed method. The experimental results show that the proposed method is superior to some existing traditional and deep learning-based methods and is promising to meet the real-time and high-precision clinical registration requirements of 3D abdominal CT images.
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Kim, Yu Jang, Eun Young Choi, Sang Heon Jeong, Heon Jung Jung, and Yong Sik Bang. "Effectiveness of Tube Voltage Selection of 70 kV in Abdominal Dual Energy CT Protocol." Korean Society of Computed Tomographic Technology 24, no. 1 (2022): 15–21. http://dx.doi.org/10.31320/jksct.2022.24.1.15.
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Recently, CT equipment has a dose modulation function that adjusts the dose to the patient. But, the dose is proportional to the tube voltage and changes with the square of the tube current. Therefore, reducing tube voltage rather than tube current has a greater effect on reducing radiation exposure. High tube voltage is used to improve image noise and contrast resolution. When high tube voltage is used, the surface dose is reduced, but the overall exposure dose is increased. So, the dose should be appropriately adjusted to the patient. Accordingly, research and attempts to obtain high-quality images with a small dose are continuously being conducted, the development of detectors and software performance are improving. Due to the improvement of CT equipment, 70 kV can be used for dual scan. In this study, the 70 kV, and 80 kV, 90 kV, 100 kV used in the conventional CT examination were compared and evaluated at the same dose. The study was conducted using ACR Phantom and KAGAKU Abdomen Phantom. CT number(HU), Noise, SNR, and CNR of each image were compared by quantitative evaluation, and CT number and noise level were evaluated by qualitative evaluation. As a result of comparison, using 70 kV obtained equivalent or better noise, CT number, SNR, and CNR than 80 kV, 90 kV, and 100 kV with the same CTDIvol. In conclusion, it is considered that diagnostically excellent image can be obtained with a small exposure dose by dual scan using 70 kV.
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Kwon, Gi-Jin, Soo-Yeong Lee, Seul-Bi Lee, et al. "Comparative Evaluation of CT Angiography Images Applied with CE-BOOST." Korean Society of Computed Tomographic Technology 24, no. 2 (2022): 43–47. http://dx.doi.org/10.31320/jksct.2022.24.2.43.
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With the rapid development of image processing technology using Deep Learning Reconstruction (DLR), Contrast Enhancement Boost Technique (CE-Boost) was developed using a subtraction technique for contrast-enhanced images. In this study, CE-Boost was applied to CT angiography (CTA) to conduct quantitative evaluation. Lower extremity, neck and abdominal angiography images from 60 people quantitatively analyzed the difference between conventional enhanced image and CE-Boost image quality by measuring signal to noise ratio (SNR), noise and contrast to noise ratio (CNR) values. When CE-Boost technology was applied to CTA, Noise was reduced and SNR and CNR values were increased by more than 50% compared to conventional images. CE-Boost reduces noise, increases SNR and CNR values in CTA, and helps improve overall image quality.
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Takizawa, Hotaka, Takenobu Suzuki, Hiroyuki Kudo, and Toshiyuki Okada. "Interactive Segmentation of Pancreases in Abdominal Computed Tomography Images and Its Evaluation Based on Segmentation Accuracy and Interaction Costs." BioMed Research International 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/5094592.
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The present paper proposed an interactive segmentation method of pancreases in abdominal computed tomography (CT) images based on the anatomical knowledge of medical doctors and the statistical information of pancreas shapes. This segmentation method consisted of two phases: training and testing. In the training phase, pancreas regions were manually extracted from sample CT images for training, and then a probabilistic atlas (PA) was constructed from the extracted regions. In the testing phase, a medical doctor selected seed voxels for a pancreas and background in a CT image for testing by use of our graphical user interface system. The homography transformation was used to fit the PA to the seeds. The graph cut technique whose data term was weighted by the transformed PA was applied to the test image. The seed selection, the atlas transformation, and the graph cut were executed iteratively. This doctor-in-the-loop segmentation method was applied to actual abdominal CT images of fifteen cases. The experimental results demonstrated that the proposed method was more accurate and effective than the conventional graph cut.
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Yang, Shao-Di, Yu-Qian Zhao, Fan Zhang, et al. "An Abdominal Registration Technology for Integration of Nanomaterial Imaging-Aided Diagnosis and Treatment." Journal of Biomedical Nanotechnology 17, no. 5 (2021): 952–59. http://dx.doi.org/10.1166/jbn.2021.3076.
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Image registration technology is a key technology used in the process of nanomaterial imaging-aided diagnosis and targeted therapy effect monitoring for abdominal diseases. Recently, the deep-learning based methods have been increasingly used for large-scale medical image registration, because their iteration is much less than those of traditional ones. In this paper, a coarse-to-fine unsupervised learning-based three-dimensional (3D) abdominal CT image registration method is presented. Firstly, an affine transformation was used as an initial step to deal with large deformation between two images. Secondly, an unsupervised total loss function containing similarity, smoothness, and topology preservation measures was proposed to achieve better registration performances during convolutional neural network (CNN) training and testing. The experimental results demonstrated that the proposed method severally obtains the average MSE, PSNR, and SSIM values of 0.0055, 22.7950, and 0.8241, which outperformed some existing traditional and unsupervised learning-based methods. Moreover, our method can register 3D abdominal CT images with shortest time and is expected to become a real-time method for clinical application.
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Siri, Sangeeta K., and Mrityunjaya V. Latte. "A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method." Journal of Intelligent Systems 28, no. 4 (2019): 517–32. http://dx.doi.org/10.1515/jisys-2017-0144.
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Abstract Liver segmentation from abdominal computed tomography (CT) scan images is a complicated and challenging task. Due to the haziness in the liver pixel range, the neighboring organs of the liver have the same intensity level and existence of noise. Segmentation is necessary in the detection, identification, analysis, and measurement of objects in CT scan images. A novel approach is proposed to meet the challenges in extracting liver images from abdominal CT scan images. The proposed approach consists of three phases: (1) preprocessing, (2) CT scan image transformation to neutrosophic set, and (3) postprocessing. In preprocessing, noise in the CT scan is reduced by median filter. A “new structure” is introduced to transform a CT scan image into a neutrosophic domain, which is expressed using three membership subsets: true subset (T), false subset (F), and indeterminacy subset (I). This transform approximately extracts the liver structure. In the postprocessing phase, morphological operation is performed on the indeterminacy subset (I). A novel algorithm is designed to identify the start points within the liver section automatically. The fast marching method is applied at start points that grow outwardly to detect the accurate liver boundary. The evaluation of the proposed segmentation algorithm is concluded using area- and distance-based metrics.
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Othman, Ahmed E., Malte Niklas Bongers, Dominik Zinsser, et al. "Evaluation of reduced-dose CT for acute non-traumatic abdominal pain: evaluation of diagnostic accuracy in comparison to standard-dose CT." Acta Radiologica 59, no. 1 (2017): 4–12. http://dx.doi.org/10.1177/0284185117703152.
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Background Patients with acute non-traumatic abdominal pain often undergo abdominal computed tomography (CT). However, abdominal CT is associated with high radiation exposure. Purpose To evaluate diagnostic performance of a reduced-dose 100 kVp CT protocol with advanced modeled iterative reconstruction as compared to a linearly blended 120 kVp protocol for assessment of acute, non-traumatic abdominal pain. Material and Methods Two radiologists assessed 100 kVp and linearly blended 120 kVp series of 112 consecutive patients with acute non-traumatic pain (onset < 48 h) regarding image quality, noise, and artifacts on a five-point Likert scale. Both radiologists assessed both series for abdominal pathologies and for diagnostic confidence. Both 100 kVp and linearly blended 120 kVp series were quantitatively evaluated regarding radiation dose and image noise. Comparative statistics and diagnostic accuracy was calculated using receiver operating curve (ROC) statistics, with final clinical diagnosis/clinical follow-up as reference standard. Results Image quality was high for both series without detectable significant differences ( P = 0.157). Image noise and artifacts were rated low for both series but significantly higher for 100 kVp ( P ≤ 0.021). Diagnostic accuracy was high for both series (120 kVp: area under the curve [AUC] = 0.950, sensitivity = 0.958, specificity = 0.941; 100 kVp: AUC ≥ 0.910, sensitivity ≥ 0.937, specificity = 0.882; P ≥ 0.516) with almost perfect inter-rater agreement (Kappa = 0.939). Diagnostic confidence was high for both dose levels without significant differences (100 kVp 5, range 4–5; 120 kVp 5, range 3–5; P = 0.134). The 100 kVp series yielded 26.1% lower radiation dose compared with the 120 kVp series (5.72 ± 2.23 mSv versus 7.75 ± 3.02 mSv, P < 0.001). Image noise was significantly higher in reduced-dose CT (13.3 ± 2.4 HU versus 10.6 ± 2.1 HU; P < 0.001). Conclusion Reduced-dose abdominal CT using 100 kVp yields excellent image quality and high diagnostic accuracy for the assessment of acute non-traumatic abdominal pain.
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Asari, Toru, Kanichiro Wada, Eiji Sasaki, Gentaro Kumagai, Sunao Tanaka, and Yasuyuki Ishibashi. "Abdominal Arterial Translation in Lower Lumbar Spine Level Due to Positional Change: A Clinical Survey Using Intraoperative Computed Tomography." Journal of Clinical Medicine 13, no. 7 (2024): 1897. http://dx.doi.org/10.3390/jcm13071897.
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Background: Abdominal vascular injury, a fatal complication of lumbar disc surgery, should concern spine surgeons. This study aimed to compare the position of the abdominal arteries in the supine and prone positions and the factors involved. Thirty patients who underwent lumbar surgery by posterior approach were included. Methods: All patients underwent computed tomography (CT) preoperatively in the supine position and intraoperatively in the prone position. In the CT axial image, at the L4, L4/5 disc, L5, and L5/S1 disc level, we measured the shortest distance between the abdominal arteries and the vertebral body (SDA: shortest distance to the aorta), and the amount of abdominal arterial translation, defined as “SDA on intraoperative CT” minus “SDA on preoperative CT”. Additionally, the preoperative CT axial images were evaluated for the presence of aortic calcification. Results: No significant difference in SDA values based on patients’ positions was observed at each level. In males, the supine position brought the abdominal artery significantly closer to the spine at the left side of the L5/S level (p = 0.037), and, in cases of calcification of the abdominal artery, the abdominal artery was found to be closer to the spine at the left side of the L4/5 level (p = 0.026). Conclusions: It is important to confirm preoperative images correctly to prevent great vessel injuries in lumbar spine surgery using a posterior approach.
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Grosser, Oliver S., Juri Ruf, Dennis Kupitz, et al. "Iterative CT reconstruction in abdominal low-dose CT used for hybrid SPECT-CT applications: effect on image quality, image noise, detectability, and reader’s confidence." Acta Radiologica Open 8, no. 6 (2019): 205846011985626. http://dx.doi.org/10.1177/2058460119856266.
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Background Iterative computed tomography (CT) image reconstruction shows high potential for the preservation of image quality in diagnostic CT while reducing patients’ exposure; it has become available for low-dose CT (LD-CT) in high-end hybrid imaging systems (e.g. single-photon emission computed tomography [SPECT]-CT). Purpose To examine the effect of an iterative CT reconstruction algorithm on image quality, image noise, detectability, and the reader’s confidence for LD-CT data by a subjective assessment. Material and Methods The LD-CT data were validated for 40 patients examined by an abdominal hybrid SPECT-CT (U = 120 kV, I = 40 mA, pitch = 1.375). LD-CT was reconstructed using either filtered back projection (FBP) or an iterative image reconstruction algorithm (Adaptive Statistical Iterative Reconstruction [ASIR]®) with different parameters (ASIR levels 50% and 100%). The data were validated by two independent blinded readers using a scoring system for image quality, image noise, detectability, and reader confidence, for a predefined set of 16 anatomic substructures. Results The image quality was significantly improved by iterative reconstruction of the LD-CT data compared with FBP ( P ≤ 0.0001). While detectability increased in only 2/16 structures ( P ≤ 0.03), the reader’s confidence increased significantly due to iterative reconstruction ( P ≤ 0.002). Meanwhile, at the ASIR level of 100%, the detectability in bone structure was highly reduced ( P = 0.003). Conclusion An ASIR level of 50% represents a good compromise in abdominal LD-CT image reconstruction. The specific ASIR level improved image quality (reduced image noise) and reader confidence, while preserving detectability of bone structure.
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Yoon, Jong-Tae, Deuk-Yong Kim, and Ki Baek Lee. "Establishment of Radiation Dose Reduction Criteria for Thoracic and Abdominal CT via Systematic Spatial Resolution Evaluation." Korean Society of Computed Tomographic Technology 25, no. 2 (2023): 5–14. http://dx.doi.org/10.31320/jksct.2023.25.2.5.
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With the development of various reconstruction algorithms for CT imaging, it has become possible to perform low-dose CT examinations without compromising image quality. However, quantitative evaluation such as image noise, signal-to-noise ratio (SNR), and contrast-tonoise ratio (CNR) have been mainly used yet, while the spatial resolution and overall image quality rely on subjective assessment. Therefore, this study aimed to propose a quantitative method for evaluating spatial resolution in chest and abdominal CT scans, in order to facilitate dose reduction. For this study, a phantom was fabricated using 3D printing and scanned under chest and abdominal imaging conditions, with additional scans performed under low-dose conditions. The peak, bottom, FWHM, DFWHM, and peak-bottom were compared and analyzed. The results showed no statistically significant differences in spatial resolution parameters between all chest and abdominal protocol, with FWHM and DFWHM indicating the same distance. Even if the dose is reduced to 30 mAs in the chest CT condition and to 50 mAs in the abdominal CT condition, there will not be a big problem in terms of spatial resolution. Lastly, the indices from this study demonstrate potential as objective measures of spatial resolution when applying low-dose CT protocols.
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Yasaka, Koichiro, Toshihiro Furuta, Takatoshi Kubo, et al. "Full and hybrid iterative reconstruction to reduce artifacts in abdominal CT for patients scanned without arm elevation." Acta Radiologica 58, no. 9 (2017): 1085–93. http://dx.doi.org/10.1177/0284185116684675.
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Background Abdominal computed tomography (CT) without arm elevation is associated with degraded image quality due to streak artifacts. Purpose To compare the degree of streak artifacts in abdominal CT images without arm elevation between full iterative reconstruction (IR), hybrid IR, and filtered back projection (FBP) using two commercially available scanners. Material and Methods First, a phantom study simulating CT examination without arm elevation was performed. Second, unenhanced axial images of 33 patients (17 and 16 patients for each vendor) who underwent CT without arm elevation were reconstructed with full IR, hybrid IR and FBP. A radiologist placed 50 parallel lines with lengths of 50 pixels vertical to the streaks and quantitatively evaluated the images for streak artifacts in the phantom study. Two radiologists evaluated the images of patients for streak artifacts (on the liver and the kidney) and diagnostic acceptability using a four-point scale. Results The phantom study indicated that full IR algorithms were more effective than FBP in reducing streak artifacts. In the clinical patient study, streak artifacts were significantly more reduced with full IR compared with FBP in both the liver and kidney ( P < 0.012). Streak artifact reduction was limited with hybrid IR. Model-based iterative reconstruction (MBIR) (one of the full IR algorithms) provided diagnostically more acceptable image quality ( P < 0.016) compared with FBP. Conclusion In abdominal CT without arm elevation, full IR enabled a more efficient streak artifact reduction compared with FBP and MBIR was associated with diagnostically more acceptable images.
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Hsu, Li-Yueh, Zara Ali, Hadi Bagheri, Fahimul Huda, Bernadette A. Redd, and Elizabeth C. Jones. "Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework." Tomography 9, no. 3 (2023): 1041–51. http://dx.doi.org/10.3390/tomography9030085.
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Purpose: Reliable and objective measures of abdominal fat distribution across imaging modalities are essential for various clinical and research scenarios, such as assessing cardiometabolic disease risk due to obesity. We aimed to compare quantitative measures of subcutaneous (SAT) and visceral (VAT) adipose tissues in the abdomen between computed tomography (CT) and Dixon-based magnetic resonance (MR) images using a unified computer-assisted software framework. Materials and Methods: This study included 21 subjects who underwent abdominal CT and Dixon MR imaging on the same day. For each subject, two matched axial CT and fat-only MR images at the L2-L3 and the L4-L5 intervertebral levels were selected for fat quantification. For each image, an outer and an inner abdominal wall regions as well as SAT and VAT pixel masks were automatically generated by our software. The computer-generated results were then inspected and corrected by an expert reader. Results: There were excellent agreements for both abdominal wall segmentation and adipose tissue quantification between matched CT and MR images. Pearson coefficients were 0.97 for both outer and inner region segmentation, 0.99 for SAT, and 0.97 for VAT quantification. Bland–Altman analyses indicated minimum biases in all comparisons. Conclusion: We showed that abdominal adipose tissue can be reliably quantified from both CT and Dixon MR images using a unified computer-assisted software framework. This flexible framework has a simple-to-use workflow to measure SAT and VAT from both modalities to support various clinical research applications.
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Balogová, Zdenka, and Lucie Súkupová. "OPTIMISATION OF ABDOMINAL CT EXAM PROTOCOLS IN OBESE PATIENTS." Radiation Protection Dosimetry 198, no. 9-11 (2022): 560–65. http://dx.doi.org/10.1093/rpd/ncac099.
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Abstract Obesity is marked as a global epidemic, and the number of obese people is steadily increasing. This disease affects various aspects of health care, which also includes radiodiagnostic imaging modalities. CT exams of bariatric patients are becoming common in radiological practice and bring problems of both technical and physical nature. Obesity affects the quality of CT images, therefore, optimisation of the used CT protocols is important, which is difficult to be carried out on patients, because of the principles of radiation protection and ethical point of view. This study evaluates and compares three available CT protocols for examination of the abdomen in terms of image quality, radiation dose and scan time on two CT scanners of the manufacturer Siemens. For optimisation, an anthropomorphic phantom of the abdomen was used, which, by its size and attenuation of the used materials, suitably simulated an obese patient.
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Niu, Zhongfeng, Xia Qiu, Hong Ren, Yangyang Jiang, Feidan Yu, and Hongjie Hu. "Optimizing twin-beam dual-energy CT reconstruction: Quantitative consistency and stability assessment in reference to 120 kV: An observational study." Medicine 103, no. 25 (2024): e38276. http://dx.doi.org/10.1097/md.0000000000038276.
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The split filter CT can filter X-ray beam. Theoretically, the split filter CT not only provides a good low-energy beam, but also provides a more robust CT value. The aim of this study was to compare conventional single-energy computed tomography (SECT) and twin-beam dual-energy (TBDE) CT regarding the quantitative consistency and stabilities of HU measurements at different abdominal organs. Forty-four patients were prospectively enrolled to randomly receive SECT and TBDE protocols at either body part of a thorax-abdominal examination. Their overlapping scan coverage was subjected to further image analysis. For TBDE scans, composed images(c-images) and virtual monoenergetic images (VMIs) at 60, 70, 80, and 90 kiloelectron volt (keV) were reconstructed. The attenuations were measured at 5 abdominal organs and compared between SECT and TBDE to characterize quantitative consistency by intraclass correlation coefficients (ICCs), whereas their standard deviations were used to assess the Hounsfield Unit (HU) stability. The c-images, 70 keV and 80 keV VMIs from TBDE provided consistent HU values (all ICCs > 0.8) with the SECT measurements; moreover, these TBDE images had superior HU stability over SECT images in all abdominal measurements except for fat tissue. The best HU stability can be achieved in 80 keV VMIs with the lowest noise level. The c-images and VMIs derived from TBDE can produce consistent values as SECT. The 80 keV images displayed better HU stability and a lower noise level across various abdominal organs.
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Sasirekha, N., R. Anitha, Vanathi T, and Umarani Balakrishnan. "Automatic liver tumor segmentation from CT images using random forest algorithm." Scientific Temper 14, no. 03 (2023): 696–702. http://dx.doi.org/10.58414/scientifictemper.2023.14.3.19.
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Automatic liver segmentation is challenging, and the tumor segmenting process adds more complexity. Based on the grey levels and shape, separating the liver and tumor from abdominal CT images is critical. In our paper suggests a more effective approach by using Gabor features (GF) to segment liver tumors from CT images and three alternative neural network algorithms to address these problems: RF, CNN and ANN. This thesis uses the same collection of classifiers and GF to first segment a variety of Gabor liver images. The organ (liver) is then extracted from an abdominal CT image using liver segmentation, which is done by three classifiers: ANN, CNN, RF trained on Gabor filter and the tumor segmentation is done by the human visual system (HVS). For pixel-wise segmentation, reliable and accurate ML techniques were used. For the liver segmentation, the classification accuracy was 99.55, 97.88 and 98.13% for RF, CNN and ANN, respectively. From the extracted image of liver, the classification accuracy for tumor was 99.52, 98.07 and 98.45% for RF, CNN and ANN, respectively.
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BELGHERBI, AICHA, ISMAHEN HADJIDJ, and ABDELHAFID BESSAID. "MORPHOLOGICAL SEGMENTATION OF THE KIDNEYS FROM ABDOMINAL CT IMAGES." Journal of Mechanics in Medicine and Biology 14, no. 05 (2014): 1450073. http://dx.doi.org/10.1142/s0219519414500730.
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The phase of segmentation is an important step in the processing and interpretation of medical images. In this paper, we focus on the segmentation of kidneys from the abdomen computed tomography (CT) images. The importance of our study comes from the fact that the segmentation of kidneys from CT images is usually a difficult task. This difficulty is the gray's level which is similar to the spine level. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to remove the spine by applying morphological filters. This first step makes the extraction of interest regions easier. This step is fulfilled by using various transformations such as the geodesic reconstruction. In the second step, we apply the watershed algorithm controlled by marker for kidney segmentation. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm.
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Simanjuntak, Josepa, Martua Damanik, Indra Yudha Prasetya, and Irfan Agung Maulana. "ANALISIS AUDIT DOSIS SEBAGAI UPAYA OPTIMISASI PADA PEMERIKSAAN CT ABDOMEN INTRAVENOUS (IV) DI RUMAH SAKIT UMUM PUSAT ADAM MALIK MEDAN." Prosiding Seminar Si-INTAN 3, no. 1 (2023): 40–44. http://dx.doi.org/10.53862/ssi.v3.092023.007.
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Audits are conducted on CT scan examinations using dose metrics to assess the need for optimization in computed tomography procedures. Diagnostic reference levels (DRL) provide an indicator that helps optimize patient medical exposure by adhering to the recommendations of the International Commission on Radiological Protection (ICRP). In the radiology unit at Adam Malik Hospital, efforts were made to reduce the radiation dose given to patients during CT abdominal IV examinations by using the lowest possible radiation dose without compromising image quality. The CT scans were carried out on 1526 patients using a Philips Ingenuity brand CT Scan, type MRC880, serial number 168014, and a built-in phantom as a patient representation. The radiation output was represented by the volumetric computed tomography dose index (CTDIvol) and dose length product (DLP). The physical parameters of tube voltage (kV) and pitch were evaluated to optimize the CT abdominal IV dose. At the same time, the Signal Difference to Noise Ratio (SDNR) was used to analyze image quality for each change in physical parameters. The results of dose audit data for CT abdominal IV patients, when using parameters of 120 kV and 1.1-1.2 pitch, obtained a Q2 value of 1452 mGy.cm higher than I-DRL of 1360 mGy.cm. Data from measurements with a phantom at 100 kV and 1.40-1.48 pitch showed a lower dose than the previous parameters. CT abdominal IV patient dose data with 100 kV and 1.40 -1.48 pitch showed that the DLP value in Q2 was 755 mGy.cm, lower than the previous measurement of 1452 mGy.cm. This means that there was a 52% decrease in dose after changing the parameters of 100 kV and 1.40 -1.48 pitch with image quality remaining optimal. The highest SDNR value of 12 was obtained at 100 kV and 1.48 pitch. The results indicate that it is necessary to establish a new protocol for CT abdominal IV with parameters of 100 kV and 1.40 -1.48 pitch for adult patients over 15 years of age. Keywords: optimization, radiation dose, image quality, CT abdomen
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Chapagain, KM, M. Humagain, B. Lohani, SL Shrestha, and N. Thapa. "Evaluation of arterial phase images with 90vp in multiphase abdominal CT scan." Journal of Institute of Medicine Nepal 39, no. 1 (2017): 86–93. http://dx.doi.org/10.59779/jiomnepal.743.
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Introduction: CT scan of abdomen is usually performed in 120-140 kVp and such high ranges of kilovoltage in all phases will increases the radiation dose many fold. The purpose of the study was to qualitatively and quantitatively assess image quality with low kVp in arterial phase of examination of multiphasic abdominal CT study. Methods: A prospective cross-sectional study was done in 206 participants of age 18 to 88 years who were undergoing multiphase CT studies of the abdomen in Neusoft 16 detector MDCT. After performing non contrast scan, arterial phase study of limited region of abdomen (diaphragm to infrarenal margins) was obtained with 90 kVp. The portovenous phase scan with standard protocol was obtained (120kVp). All other scanning parameters were kept same for two phases. Images were rated on 5 point scale (1-worst, 2-Suboptimal, 3-adequate,4-very good,5-excellent) based on visualization of boundaries, anatomical details of the organs and visualization of noise and artifact by two radiologists. Patient weight, abdominal circumference (AC), height and BMI were recorded and correlated with the image quality score. Statistical analysis was done with Wilcoxon’s signed ranks test k test and descriptive analysis. Results: Overall the image quality of portovenous phase was significantly better (p<0.005) than low kVp arterial phase. Image quality score correlated best with abdominal circumference in standard dose technique (r=0.54) and patient weight in reduced dose technique (r=0.44). Arterial phase scanning had acceptable image quality score for patient weight of <60 kg, AC <80cm and BMI<25 kg/m2. The CTDIvol was 7.71 with reduced kVp protocol and 20.02 with standard resulting significant reduction in radiation dose of about 61% Conclusions: The image quality of arterial phase images with 90kVp tube potential is acceptable in thin and average built patients. Hence reduction in radiation dose is possible if arterial phase scanning is done with reduced kVp except in patients with large anthropometric parameters.
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Kakarwal, Sangeeta, and Pradip Paithane. "Automatic pancreas segmentation using ResNet-18 deep learning approach." System research and information technologies, no. 2 (August 30, 2022): 104–16. http://dx.doi.org/10.20535/srit.2308-8893.2022.2.08.
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The accurate pancreas segmentation process is essential in the early detection of pancreatic cancer. The pancreas is situated in the abdominal cavity of the human body. The abdominal cavity contains the pancreas, liver, spleen, kidney, and adrenal glands. Sharp and smooth detection of the pancreas from this abdominal cavity is a challenging and tedious job in medical image investigation. Top-down approaches like Novel Modified K-means Fuzzy clustering algorithm (NMKFCM), Scale Invariant Feature Transform (SIFT), Kernel Density Estimator (KDE) algorithms were applied for pancreas segmentation in the early days. Recently, Bottom-up method has become popular for pancreas segmentation in medical image analysis and cancer diagnosis. LevelSet algorithm is used to detect the pancreas from the abdominal cavity. The deep learning, bottom-up approach performance is better than another. Deep Residual Network (ResNet-18) deep learning, bottom-up approach is used to detect accurate and sharp pancreas from CT scan medical images. 18 layers are used in the architecture of ResNet-18. The automatic pancreas and kidney segmentation is accurately extracted from CT scan images. The proposed method is applied to the medical CT scan images dataset of 82 patients. 699 images and 150 images with different angles are used for training and testing purposes, respectively. ResNet-18 attains a dice similarity index value up to 98.29±0.63, Jaccard Index value up to 96.63±01.25, Bfscore value up to 84.65±03.96. The validation accuracy of the proposed method is 97.01%, and the loss rate value achieves up to 0.0010. The class imbalance problem is solved by class weight and data augmentation.
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Knott, Danielle R. "Appropriate abdominal imaging for the emergency department patient." Proceedings of the Nova Scotian Institute of Science (NSIS) 51, no. 1 (2021): 169. http://dx.doi.org/10.15273/pnsis.v51i1.10738.
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Abdominal x-ray series (AXR) and abdominal CT scans (ACT) are commonly performed to aid in the diagnosis for patients who present to the emergency room with abdominal pain. Patients commonly receive both an AXR and ACT, due to a lack of knowledge regarding imaging appropriateness among healthcare professionals who order these exams. A primary simple retrospective data-analysis was performed to understand the prevalence of how often both exams were ordered in three Nova Scotia emergency departments. A literature review was also conducted to compare the diagnostic accuracy of each diagnostic imaging modality. Several articles showed that patients who have an AXR also have an ACT that demonstrates an abnormal finding. Emergency department physicians are not reassured when abdominal x-rays are negative and do not show abnormal findings, and as a result, a CT scan is also performed. Radiation dose must be considered when ordering multiple diagnostic imaging exams. A low-dose CT (LDCT) can be used to reduce the radiation exposure to the patient, while maintaining high diagnostic quality images. Image quality can be enhanced at a reduced radiation dose by using an image reconstruction technique such as adaptive statistical iterative reconstruction (ASIR). Understanding the most appropriate abdominal imaging modality for emergency department patients allows for fewer examinations being ordered and a reduction of radiation dose to the patient. When the most appropriate imaging is performed, a definitive diagnosis can be made and the best treatment can be provided to patients. This information can help to create an imaging appropriateness protocol for emergency departments.Additional research can help determine the cost differences between the two exams and the influence a protocol change could have on the emergency and diagnostic imaging departments.Keywords: AXR – Abdominal x-ray series, ACT – Abdominal computed tomography scan, CT – Computed tomography, SDCT – Standard-dose CT, LDCT – Low-dose CT, ASIR – Adaptative statistical iterative reconstruction FBP – Filtered back projection, CTDIvol – Volume computed tomography dose index
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Shin, Hyo-Jung, Sang-Ook Kim, and Yung-Kyoon Kim. "Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Low dose CT Examination." Korean Society of Computed Tomographic Technology 26, no. 1 (2024): 49–55. http://dx.doi.org/10.31320/jksct.2024.26.1.49.
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As the obese population increases, computed tomography (CT) tests that can quantitatively measure abdominal obesity have been applied in various studies. To reduce the burden of radiation exposure in CT examinations, various image reconstruction methods such as filtered back projection (FBP) and iterative reconstruction (IR) have been developed, and deep learning iterative reconstruction (DLIR) techniques combined with artificial intelligence have recently emerged. We applied FBP, Adaptive Statistical Iterative Reconstruction-V (ASIR-V) at 30%, DLIR Low, Medium, and High - to low-dose CT images using only 20% of the standard abdominal examination protocols. We compared the measured fat area, total amount, and muscle mass, and evaluated them based on the images applying ASIR-V at 30% to the standard protocols. As a result of the study, the fat area and the amount of fat in the test group decreased by approximately 3~4% in VFA and VFV, and up to about 8% decreased in SFA and SFV in low-dose protocol images applying FBP and ASIR-V 30%. In addition, muscle mass also decreased by 3~6% in image reconstruction images, excluding DLIR. In the case of DLIR, there was either no difference in all items or a slight difference of less than 2%. Image reconstruction methods aimed at reducing noise may impact the results of follow up fat measurement tests. Therefore, the same inspection protocol and reconstruction methods should be applied for accurate evaluation.
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Anthon, R., S. H. Intifadhah, and E. R. Putri. "Radiation Dose and Image Quality of Bladder Cancer Patients Analysis on Abdominal CT-Scan Examinations." Atom Indonesia 51, no. 1 (2025): 27–33. https://doi.org/10.55981/aij.2025.1526.
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The bladder is a subperitoneal, hollow muscular organ that acts as areservoir for urine and located in the lower abdomen. Bladder cancer is one of health issues that can affect many people each year. Bladder cancer ranks as the 10th most common cancer worldwide. Early management includes cancer screening using abdominal CT-Scan. The objective of this study was to analyze the radiation dose received by patients and the image quality of patients underwent abdominal CT scans based on the Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) values obtained. Data analysis management, specifically using quantitative analysis techniques, involved observing 20 bladder cancer patients with a total of 2,653 images. The IndoseCT software was used for analyzing the radiation dose to patients, while the IndoQCT software was used for analyzing image quality in CT-Abdomen examinations based on Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) values. The results showed that the radiation dose received by patients during CT-Abdomen examinations was higher than the dose output by the device. The maximum dose output by the device (CTDIvol) was 50.10 mGy, and the minimum was 6.70 mGy, while the maximum dose received by patients (SSDE) was 53.34 mGy, and the minimum was 9.34 mGy. The image quality results for CT-Abdomen examinations based on SNR and CNR values indicated that the image quality obtained was adequate for diagnostic purposes.
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Ruf, Juri, Dennis Kupitz, Damian Czuczwara, et al. "Image Quality Assessment for Low-Dose-CT in Hybrid SPECT/CT Imaging." Nuklearmedizin 57, no. 04 (2018): 153–59. http://dx.doi.org/10.3413/nukmed-0953-17-12.
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Summary Objectives: Low-dose-computed tomography (LD-CT) is used in nuclear medicine hybrid imaging (e.g., SPECT/CT) for attenuation correction of emission data and anatomical correlation of findings. However, there are currently no standards for image quality (e. g., detectability) comparable to those for diagnostic CT. Therefore, the aim of this explorative study was to evaluate retrospective LDCT data in terms of CT image quality and detectability of anatomical structures. Methods: Two readers blindly scored abdominal LD-CT images (n = 40 patients) in terms of detectability (n = 20 structures/patient), image quality, and readers’ confidence in scoring the image quality for a clinically hybrid imaging protocol. Results were analysed by ANOVA to identify factors (e. g., anatomical structures) that influenced performance scores. The inter-rater agreement was evaluated by determining the chance-corrected Cohen’s Kappa coefficient. Results: Image noise was acceptable for anatomical correlation in 96.1 % of the readings with an almost perfect inter-rater agreement (KBP = 0.85). A detectability of at least 80 % was observed in 13/20 (KBP ≥ 0.7) and 90 % in 9/20 (KBP ≥ 0.85) of the structures analysed by both readers. The confidence of both readers in scoring image quality was at least sufficient in 98.8 % of the examined patients (KBP = 0.95). Conclusion: Although LD-CT protocols commonly used in hybrid imaging have a poor image quality not suitable for primary CT diagnostics, they enable detection of a variety of anatomical structures. LDCT can therefore also be referenced in the associated reports for anatomical correlation of findings from SPECT imaging.
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Ungania, Sara, Francesco Maria Solivetti, Marco D’Arienzo, et al. "New-Generation ASiR-V for Dose Reduction While Maintaining Image Quality in CT: A Phantom Study." Applied Sciences 13, no. 9 (2023): 5639. http://dx.doi.org/10.3390/app13095639.
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Over the last few decades, the need to reduce and optimize patient medical radiation exposure has prompted the introduction of novel reconstruction algorithms in computed tomography (CT). Against this backdrop, the present study aimed to assess whether reduced radiation dose CT images reconstructed with the new-generation adaptive statistical iterative reconstruction (ASiR-V) maintain the same image quality as that of routine image reconstruction. In addition, the optimization of image quality parameters for the ASiR-V algorithm (e.g., an optimal combination of blending percentage and noise index (NI)) was investigated. An abdominal reference phantom was imaged using the routine clinical protocol (fixed noise index of 18 and 40% ASiR reconstruction). Reduced radiation dose CT scans were performed with varying NI (22, 24, and 30) and using the ASiR-V reconstruction algorithm. Quantitative and qualitative analyses of image noise, contrast, and resolution were performed against NI and reconstruction blending percentages. Our results confirm the ability of the ASiR-V algorithm to provide images of high diagnostic quality while reducing the patient dose. All the parameters were improved in ASiR-V images as compared to ASiR. Both quantitative and qualitative analyses showed that the best agreement was obtained for the images reconstructed using ASiR-V with NI24 and a high percentage of blending (70–100%). This preliminary study results show that ASiR-V allows for a significant reduction in patient dose (about 40%) while maintaining a good overall image quality when appropriate NI (i.e., 24) is used.
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Zhu, Xiqi, Jian Jiang, Jian Wang, Yue Tang, and Xiaoming Ge. "Image Mosaic Algorithm-Based Analysis of Pathological Characteristics of Gastric Polyp Patients Using Computed Tomography Images." Journal of Healthcare Engineering 2021 (November 9, 2021): 1–9. http://dx.doi.org/10.1155/2021/6086106.
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The application value of image mosaic algorithm (IMA) based CT imaging technology in the analysis of pathological characteristics of gastric polyp (GP) patients was explored in this work. 588 cases of GP patients in the hospital were selected as the research objects, and CT images based on IMA were adopted for examination. The patient’s basic information, image performance, and gastroscopy results were recorded. The results showed that the absolute mean bright error (AMBE) index and information entropy of the IMA are 0.0625 and 7.0385, respectively. The clinical symptoms of patients were mostly abdominal pain (21.4%), abdominal distension (15.6%), and sour regurgitation (17.8%). The common size of GP was no more than 0.5 cm, and the common type was Yamada type II. There were notable differences between single and multiple GPs of different pathological types ( P < 0.05 ). Proliferative polyps were mostly found in the stomach and antrum, while fundus gland polyps were mostly in the stomach and fundus. There was significant difference between the growth location of the hyperplastic polyp and basal gland polyp ( P < 0.05 ). In summary, the CT images of IMA proposed in this paper can not only realize image splicing effectively but also were superior to the traditional SIFT method in the quality of splicing image and were conducive to the analysis of the pathological characteristics of GP patients, which had significant clinical promotion value.
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Nan, Yunguang, Zuyan Zhang, Jianbo Zhang, Bo Jiang, Yuxi Zhu, and Li Zhang. "Role of CT Images in the Diagnosis of Common Acute Abdominal Diseases in General Surgery." Journal of Healthcare Engineering 2022 (March 23, 2022): 1–13. http://dx.doi.org/10.1155/2022/5732357.
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Acute abdomen is a clinical emergency disease with acute abdominal pain as the main prominent feature. Through severe disease changes in intra-abdominal, extrapelvic, and retroperitoneal tissues and organs, symptoms and clinical signs led by abdominal pain are formed. This article mainly explores the role of CT imaging diagnosis in common acute abdominal diseases in general surgery. In this paper, the use of computer-aided CT scan imaging technology in pulmonary nodules was firstly investigated, and the image segmentation algorithms based on CT images were given, including the spatial domain fuzzy C-mean clustering separation algorithm and the spatial domain fuzzy clustering level set semiautomatic separation algorithm, then the treatment of acute abdomen under the concept of ERAS was explored, and the treatment of ERAS under CT images of the acute abdomen was analyzed and studied. The empirical research results show that the ERAS's concept is guided by the undergoing national nutritional support with the traditional perioperative management. Compared to 12.9% of complications in traditional CPM groups, the recall rate of complications after ERAS group was only 6.01%, the improvement was obvious and the results were statistically significant ( P < 0.05 ). Postoperative hospitalization time was also 4.62 days from 7.93 days, thus controlling the clinical risks of perioperative periods, providing a benefit to patient life.
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W, Lydia Purna, Rini Indrati, and Arieyanti Biyono. "ADAPTIVE STATISTICAL ITERATIVE RECONSTRUCTION FOR OPTIMIZATION IMAGE QUALITY OF CT SCAN ABDOMEN." Jurnal Riset Kesehatan 9, no. 1 (2020): 61–64. http://dx.doi.org/10.31983/jrk.v9i1.5716.
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Adaptive Statistical Iterative Reconstruction is software used to reduce noise. In several hospital uses the ASIR application with varying percentages between radiographers. The purpose of this study was to determine differences in noise and anatomical image information on variations in the percentage of ASIR and ASIR values that reveal optimal CT scan anatomic image information. This type of research is experimental, data are taken from 30 samples of reconstructive CT scan of the abdomen by giving four variations of ASIR (0%, 40%, 60%, and 80%). Noise measurement is done by placing the ROI size of 105.61 mm2 at three points, namely superior liver, inferior liver and middle of the aorta on the axial section. while the assessment of anatomical image information by observation of the results of variations in the value of ASIR by two radiologists. Data analysis uses the One way Anova test to determine differences in noise, Friedman test to determine differences in anatomical image information with a confidence level of 95%. The results showed that there were differences in the abdominal CT scan image noise on variations in the percentage of ASIR with p -alues 0.001. Noise decreased with increasing percentage ASIR. The highest noise value is 15.34 at ASIR 0% while the lowest noise is 8.57 at ASIR 80%. There are differences in anatomical image information on the variation of ASIR with p-values 0.001. The percentage ASIR of 40% is the optimal ASIR value for displaying CT images of abdominal with mean rank of 3.46.
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Mrs., K. Ramanandhini. "Diagnosis of Abdominal Diseases Affecting Major Organs Using CT Image and YOLOV8." International Journal of Preventive Medicine and Health (IJPMH) 5, no. 2 (2025): 17–19. https://doi.org/10.54105/ijpmh.B1050.05020125.
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<strong>Abstract:</strong> This study investigates the YOLOv8 method, a popular object detection model, to detect abnormalities in abdominal CT scans. Our study leverages the sophisticated architecture and point-of- care detection capabilities of YOLOv8 to show that the model improves diagnostic accuracy and helps radiologists quickly identify potential panic cases.