Relevant bibliographies by topics / CT abdominal image

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'CT abdominal image'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "CT abdominal image"

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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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Dissertations / Theses on the topic "CT abdominal image"

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Golodetz, Stuart Michael. "Zipping and unzipping : the use of image partition forests in the analysis of abdominal CT scans." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558768.

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This thesis focuses on how to allow a computer to identify features (such as major organs) in abdominal computerised tomography (CT) scans in an automatic way, whilst still facilitating user interaction with the results. Identifying such features is important if they are to be visualised in 3D for the purposes of diagnosis or surgical planning, or if their volumes are to be calculated when assessing a patient's response to therapy, but manual identification is time-consuming and error-prone. Some degree of computerised automation is therefore highly desirable, and indeed a small number of existing approaches have even attempted to fully automate the simultaneous identification of multiple abdominal organs. However, no existing method is capable of achieving results that are completely accurate in all cases, and due to the difficulties even of specifying when a result is correct, the development of such a method seems unlikely in the near future. It is thus important that medics retain the ability to correct the results when automated methods fail. My research proposes a way of facilitating both automatic feature identification and intuitive editing of the results by representing CT images as a hierarchy of partitions, or image partition forest (IPF). This data structure has appeared extensively in existing literature, but its potential uses for editing have hitherto received little attention. This thesis shows how it can be used for this purpose, by presenting a systematic set of algorithms that allow the user to modify the IPF, and select and identify features therein, via an intuitive graphical user interface. It further shows how such an IPF can be initially constructed from a set of CT images using morphological techniques, before presenting a series of novel methods for automatic feature identification in both 2D axial CT slices and 3D CT volumes.
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Mali, Shruti Atul. "Multi-Modal Learning for Abdominal Organ Segmentation." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285866.

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Deep Learning techniques are widely used across various medical imaging applications. However, they are often fine-tuned for a specific modality and are not generalizable when it comes to new modalities or datasets. One of the main reasons for this is large data variations for e.g., the dynamic range of intensity values is large across multi-modal images. The goal of the project is to develop a method to address multi-modal learning that aims at segmenting liver from Computed Tomography (CT) images and abdominal organs from Magnetic Resonance (MR) images using deep learning techniques. In this project, a self-supervised approach is adapted to attain domain adaptation across images while retaining important 3D information from medical images using a simple 3D-UNet with a few auxiliary tasks. The method comprises of two main steps: representation learning via self-supervised learning (pre-training) and fully supervised learning (fine-tuning). Pre-training is done using a 3D-UNet as a base model along with some auxiliary data augmentation tasks to learn representation through texture, geometry and appearances. The second step is fine-tuning the same network, without the auxiliary tasks, to perform the segmentation tasks on CT and MR images. The annotations of all organs are not available in both modalities. Thus the first step is used to learn general representation from both image modalities; while the second step helps to fine-tune the representations to the available annotations of each modality. Results obtained for each modality were submitted online, and one of the evaluations obtained was in the form of DICE score. The results acquired showed that the highest DICE score of 0.966 was obtained for CT liver prediction and highest DICE score of 0.7 for MRI abdominal segmentation. This project shows the potential to achieve desired results by combining both self and fully-supervised approaches.
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Alrowily, Maily J. "A comparison of fixed tube current (FTC) and automatic tube current modulation (ATCM) CT methods for abdominal scanning : implications on radiation dose and image quality." Thesis, University of Salford, 2018. http://usir.salford.ac.uk/46737/.

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PURPOSE: There has been a huge increase in the use of abdominal CT scanning in recent years. This has contributed to an increase in radiation dose administered to patients. Abdominal CT scans generally require higher exposure factors when compared to other anatomical regions. This drives a need for urgent optimisation of the radiation dose and image quality for abdominal CT examinations. The aim of this thesis is to evaluate Fixed Tube Current (FTC) and Automatic Tube Current Modulation (ATCM) on image quality and radiation dose during abdominal CT examinations across a range of scanning parameters. MATERIALS AND METHODS: Using a Toshiba Aquilion 16 CT scanner (120 kVp, 0.5 seconds tube rotation), an adult ATOM dosimetry and abdominal anthropomorphic phantom were exposed to a series of FTC and ATCM CT protocols with variations in tube current as follows: FTC - 100, 200, 250, 300 and 400mA; ATCM - low dose+, low dose, standard, quality and high quality. The pitch factors evaluated included were 0.688, 0.938 & 1.438 and the detector configurations included were 0.5×16 mm, 1.0×16 mm and 2.0×16 mm. Radiation doses for nine abdominal organs were directly measured using the Metal Oxide Semiconductor Field Effect Transistors (MOSFET). Effective dose (ED) was measured and estimation comprised of three methods: mathematical modelling with k-factors and dose length product DLP, direct with MOSFET and indirectly with Monte Carlo simulation (ImPACT). Effective risk (ER) was estimated using MOSFET data and Brenner’s equations/BEIR VII 2006 report. The raw data for ATCM radiation dose was corrected using an equivalence equation. The ATCM corrected and uncorrected data were compared against FTC. Image quality was assessed using SNR (five abdominal organs) and a relative visual grading analysis (VGA) method (five different axial images). Image quality evaluation was performed by the researcher after testing agreement between against five different observers. RESULTS: There were no significant differences in the mean radiation doses between FTC and corrected ATCM across a range of acquisition protocols (P > 0.05). This was with the exception of the 300mA/quality protocols, and for a fast pitch factor with 0.5×16mm detector configurations. These had significantly lower doses for FTC (P < 0.05). These differences were up to 13% for the mean abdominal organ doses, effective doses and the effective risk. In addition, for all acquisition parameters, the mean radiation dose was significantly higher (P < 0.05; 17%-23%) for uncorrected ATCM when compared to FTC. In terms of image quality, there were no differences in SNR values between FTC and ATCM for the majority of acquisition protocols, excepting the higher mean SNR value (P < 0.05) for the FTC at 100mA/low dose + and 200 mA/ low dose (pancreas, left and right kidneys). Conversely, the mean SNR values were significantly higher (P < 0.05) for the ATCM scans for 300mA/quality and fast pitch factor (1.438) (liver, spleen and pancreas) than FTC. Finally, relative VGA scores for both FTC and ATCM demonstrated no significant difference, except for ‘quality’ ATCM scans (image # 1, image # 2) and a fast pitch factor (1.438) for image #2 and #3. CONCLUSION: FTC and corrected ATCM were generally similar in terms of radiation dose and image quality except for some acquisition parameters; 300mA/quality tube current and fast (1.483) pitch factor FTC was lower than the corrected ATCM. However, the uncorrected ATCM produced higher radiation dose when compared with FTC techniques. In addition, FTC and ATCM generally produced similar SNR, again with the exception of some protocols. The SNR was higher for FTC than ATCM at lower tube current (pancreas, left and right kidneys), at 300mA/quality and fast pitch factor (1.438) SNR values for ATCM higher than FTC (liver and spleen). However, the ATCM technique is able to produce higher mean relative VGA scores for upper and middle abdominal organs. Further investigation of image quality and radiation dose difference between FTC and ATCM is required.
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Hansson, Olof. "Interactive segmentation of abdominal organs from 3D CT and MRI images." Thesis, Linköpings universitet, Institutionen för teknik och naturvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93510.

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Within the medical field, image segmentation is an important tool which can be used by radiologists and surgeons who want quantitative measurements of a lesion or organ. To be clinically useful, the tool has to be fast and easy to use. This work comprises implementation of the image foresting transform for segmentation using the Dijkstra algorithm and compares computation time between the implemented algorithm and a previous implemented algorithm, Bellman-Ford. These algorithms solve the shortest path with minimum cost problem. For a given cost function, similar results both in computation time and visual results were obtained with the two algorithms. Changing the cost functions, on the other hand, yielded very different segmentation results. The volume of liver and kidney was compared with manually delineated organs regarding seed planting and execution time. A graphical user interface has also been implemented.
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Nakamura, Yoshihiko. "Blood vessel Segmentation from 3-D Abdominal CT Images for Laparoscopic surgery." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2005. http://hdl.handle.net/2237/10395.

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Ma, Qixiang. "Deep learning based segmentation and detection of aorta structures in CT images involving fully and weakly supervised learning." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS029.

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La réparation endovasculaire des anévrismes aortiques abdominaux (EVAR) et l’implantation valvulaire aortique transcathéter (TAVI) sont des interventions endovasculaires pour lesquelles l’analyse des images CT préopératoires est une étape préalable au planning et au guidage de navigation. Dans le cas de la procédure EVAR, les travaux se concentrent spécifiquement sur la question difficile de la segmentation de l’aorte dans l’imagerie CT acquise sans produit de contraste (NCCT), non encore résolue. Dans le cas de la procédure TAVI, ils abordent la détection des repères anatomiques permettant de prédire le risque de complications et de choisir la bioprothèse. Pour relever ces défis, nous proposons des méthodes automatiques basées sur l’apprentissage profond (DL). Un modèle entièrement supervisé basé sur la fusion de caractéristiques 2D-3D est d’abord proposé pour la segmentation vasculaire dans les NCCT. Un cadre faiblement supervisé basé sur des pseudo-labels gaussiens est ensuite envisagé pour réduire et faciliter l’annotation manuelle dans la phase d’apprentissage. Des méthodes hybrides faiblement et entièrement supervisées sont finalement proposées pour étendre la segmentation à des structures vasculaires plus complexes, au-delà de l’aorte abdominale. Pour la valve aortique dans les CT cardiaques, une méthode DL de détection en deux étapes des points de repère d’intérêt et entièrement supervisée est proposée. Les résultats obtenus contribuent à l’augmentation de l’image préopératoire et du modèle numérique du patient pour les interventions endovasculaires assistées par ordinateur<br>Endovascular aneurysm repair (EVAR) and transcatheter aortic valve implantation (TAVI) are endovascular interventions where preoperative CT image analysis is a prerequisite for planning and navigation guidance. In the case of EVAR procedures, the focus is specifically on the challenging issue of aortic segmentation in non-contrast-enhanced CT (NCCT) imaging, which remains unresolved. For TAVI procedures, attention is directed toward detecting anatomical landmarks to predict the risk of complications and select the bioprosthesis. To address these challenges, we propose automatic methods based on deep learning (DL). Firstly, a fully-supervised model based on 2D-3D features fusion is proposed for vascular segmentation in NCCTs. Subsequently, a weakly-supervised framework based on Gaussian pseudo labels is considered to reduce and facilitate manual annotation during the training phase. Finally, hybrid weakly- and fully-supervised methods are proposed to extend segmentation to more complex vascular structures beyond the abdominal aorta. When it comes to aortic valve in cardiac CT scans, a two-stage fully-supervised DL method is proposed for landmarks detection. The results contribute to enhancing preoperative imaging and the patient's digital model for computer-assisted endovascular interventions
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Liu, Min-Shen, and 劉明賢. "Registration of CT and MR Abdominal Images." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/01128879751210887199.

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碩士<br>國立成功大學<br>電機工程研究所<br>84<br>Image registration is the process of determining a point-to- point correspondence between two images of the same object. This is a crucial first step for fusing two image data sets to obtain an integrated image display. Feature base methods are often used in image registration, but they have trouble in registering abdominal images, because it is difficult to extract geometrical invariance features from abdominal images, since these images are too complex and usually distorted. Registration using correlation technique needn't image features, so we tried to use correlation technique to register abdominal images. For using correlation tech- niques, we transform CT and MR images to classified images, which are two similar images. Since calculating correlation is very time-consuming, we use a more specific method to match CT and MR images first. We get scale and rough translation between those two images by calculating body area and central point from extracted body contour. Then, K- mean clustering method was used to classify tis- sues in CT image. And fuzzy inference method was used to classify tissues in MR image. The relation table con- taining the relations between classes in the two classi- fied images was established for counting correlation. Then we count the correlation between two classified images to find which translation and rotation degree derives the highest correlation.
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Lee, Chien-Cheng, and 李建誠. "Automatic Recognition and Identification of Abdominal Organs in CT Images." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/23437123075367212397.

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碩士<br>國立成功大學<br>電機工程研究所<br>84<br>In radiotherapy treatment planning, CT imaging is one of the most widely-used radiographic techniques. After a patient undergoes a CT scan, a sequence of two-dimensional (2D) image slices are generated, each slice representing one cross- section image of the three-dimensional human body. Constructing this sequence of 2D images to form a 3D structure, some useful information for clinical diagnosis, such as volumes of organs, locations of organs, structures of organs and indices of diseases, can be obtained. These information provides doctors valuable reference in disease diagnosis. The extraction of organs from CT images is the first step in the construction of 3D structure. The goal of our work is to develop an automatic organ extraction method for CT images. The method will automatically recognize and identify the abdominal organs such as spine, kidney, spleen and liver from a sequence of CT images. In our proposal, we utilize the image segmentation techniques, knowledge of the anatomy and statistical methods for the goal of automatic organs recognition. In our recognition process, the spine was firstly identified, and considered as a landmark. We use this landmark to identify the spatial relations of the organs. Based on the spatial relations and the knowledge of the anatomy, we locate the slice consisting of the most reliable and maximal organ area in the sequence of CT images. Then, starting from this slice, up-searching and down-searching were conducted to find the organ in other slices. In our experiments 158 images from 12 patients were used for test. Within these experiments all the critical organs were successfully identified and their corresponding contour were outlined, except for some of the poor quality images.
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Lei, Chung-Chih, and 雷仲箎. "An Efficient Method for Kidney Segmentation on Abdominal CT Images." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/03848723397723866382.

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碩士<br>中華大學<br>資訊工程學系碩士班<br>91<br>In this thesis, we have proposed an effective method for kidney segmentation on abdominal CT images. We expect this segmentation system to be able to help the physicians on clinical diagnosis and educational training. The proposed method is divided into two main process. First, we extract the ROIs according to the geometric location of kidney based on the abdomen and statistical information. Besides, we remove the noise by calculating the mean value of ROI and then use conditional median filter to perform primary pixel aggregation. The second stage is applying the adaptive region-growing method to the result gained from first stage, then apply an effeccient filling operation to compensate the problems of "concaves". Furthermore, we used labeling and mathematical morphology operation such as erosion and dilation to smooth the object boundary and to eliminate thin spots. In addition, in order to provide di®erent view for physicians, we carry out edge detection by the second-order neighborhood and show renal segmentation system automatically.
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Peng, Cong-Qi, and 彭琮棋. "Texture-Learning Based System for Three-Dimensional Segmentation of Renal Parenchyma in Abdominal CT Images." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/62717669733398963923.

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碩士<br>中原大學<br>資訊工程研究所<br>96<br>Abdominal CT images are commonly used for the diagnosis of kidney diseases. With the advances of CT technology, processing of CT images has become a challenging task mainly because of the large number of CT images being studied. This paper presents a texture-learning based system for the three-dimensional (3D) segmentation of renal parenchyma in abdominal CT images. The system is designed to automatically delineate renal parenchyma and is based on the texture-learning and the region-homogeneity-based approaches. The first approach is achieved with the texture analysis using the gray-level co-occurrence matrix (GLCM) features and an artificial neural network (ANN) to determine if a pixel in the CT image is likely to fall within the renal parenchyma. The second approach incorporates a two-dimensional (2D) region growing to segment renal parenchyma in single CT image slice and a 3D region growing to propagate the segmentation results to neighboring CT image slices. The criterion for the region growing is a test of region-homogeneity which is defined by examining the ANN outputs. In system evaluation, 10 abdominal CT image sets were used. Automatic segmentation results were compared with manually segmentation results using the Dice similarity coefficient. Among the 10 CT image sets, our system has achieved an average Dice similarity coefficient of 0.87 that clearly shows a high correlation between the two segmentation results. Ultimately, our system could be incorporated in applications for the delineation of renal parenchyma or as a preprocessing in a CAD system of kidney diseases.
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Books on the topic "CT abdominal image"

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McDonald, Elizabeth S., Azadeh Elmi, and David A. Mankoff. Breast Cancer Metastatic Imaging. Edited by Christoph I. Lee, Constance D. Lehman, and Lawrence W. Bassett. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190270261.003.0010.

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This chapter reviews appropriate indications for advanced imaging, such as PET/CT, in the evaluation of breast cancer. An informed imaging approach is based on primary tumor size and pathological characteristics, as well as patient symptoms that may indicate a higher likelihood of metastatic disease. When evaluation for metastatic disease is indicated, survey imaging with CT, bone scintigraphy, abdominal MRI, brain MRI, and/or PET/CT can be used to establish disease burden, and to identify a biopsy target for pathological confirmation. We emphasize the evolving role of FDG PET/CT in this chapter, including basic principles of PET imaging, followed by a short section on image interpretation. Finally, the concept of using imaging as a response biomarker is introduced.
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Lerman, Imanuel R., Joseph Hung, Dmitri Souzdalnitski, Bruce Vrooman, and Mihir Kamdar. Celiac Plexus Blockade and Neurolysis: Fluoroscopy. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199908004.003.0032.

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Image-guided celiac plexus neurolysis can provide substantial and long-lasting pain relief in patients suffering from malignant pain from upper abdominal viscera. When performed by experienced hands, celiac plexus neurolysis also appears to be a relatively safe procedure with a limited side effect profile. Multiple imaging modalities are available for this procedure, though no single approach has systematically been proven superior in terms of efficacy or side effect profile. Each imaging guidance modality has advantages and disadvantages. Given the ability to visualize soft-tissue structures, CT guidance is recommended over fluoroscopy when intentionally transgressing into the retroperitoneum for celiac plexus neurolysis. It is also recommended in those patients with complicated anatomy, where anatomic distortion may complicate successful celiac blockade. However, in the patient without significant tumor burden involving the celiac axis and/or pancreatic body/tail, the fluoroscopy-guided retrocrural approach has been demonstrated to be efficacious, and complications are exceedingly rare.
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Book chapters on the topic "CT abdominal image"

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Zhuang, Yan, Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee, Boah Kim, and Ronald M. Summers. "Semantic Image Synthesis for Abdominal CT." In Deep Generative Models. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53767-7_21.

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Hu, Xuebin, Akinobu Shimizu, Hidefumi Kobatake, and Shigeru Nawano. "Independent Component Analysis of Four-Phase Abdominal CT Images." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30136-3_111.

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Zhou, Yuyin, Lingxi Xie, Elliot K. Fishman, and Alan L. Yuille. "Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans." In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66179-7_26.

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Suzuki, Miyuki, Marius George Linguraru, and Kazunori Okada. "Multi-Organ Segmentation with Missing Organs in Abdominal CT Images." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33454-2_52.

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Zhou, Yuyin, Lingxi Xie, Wei Shen, Yan Wang, Elliot K. Fishman, and Alan L. Yuille. "A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans." In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66182-7_79.

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Wolz, Robin, Chengwen Chu, Kazunari Misawa, Kensaku Mori, and Daniel Rueckert. "Multi-organ Abdominal CT Segmentation Using Hierarchically Weighted Subject-Specific Atlases." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33415-3_2.

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Camara, Oscar, Gaspar Delso, and Isabelle Bloch. "Free Form Deformations Guided by Gradient Vector Flow: A Surface Registration Method in Thoracic and Abdominal PET-CT Applications." In Biomedical Image Registration. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39701-4_24.

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Sreeja, P., and S. Hariharan. "Image Analysis for the Detection and Diagnosis of Hepatocellular Carcinoma from Abdominal CT Images." In Intelligent Communication and Computational Technologies. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5523-2_11.

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Zhang, Fan, Meihuan Wang, and Hua Yang. "Self-training with Selective Re-training Improves Abdominal Organ Segmentation in CT Image." In Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23911-3_1.

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Li, Pan, Jun Feng, QiRong Bu, Feihong Liu, and HongYu Wang. "Multi-object Segmentation for Abdominal CT Image Based on Visual Patch Classification." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48570-5_13.

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Conference papers on the topic "CT abdominal image"

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Wang, Xu, Dinglun He, Baoming Zhang, Yao Hao, Deshan Yang, and Ye Duan. "Conditional Diffusion Model for Abdominal CT Image Synthesis." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980773.

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Jeong, Jin Gyo, Ji Seung Ryu, Jhii-Hyun Ahn, and Sejung Yang. "Pancreatic Anomaly Detection in Abdominal CT Images using Latent Diffusion Models." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822025.

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Prakash, R. Meena, M. Vimala, V. Srilekha, P. Krishnaleela, and S. Thayammal. "UNet with Attention Mechanism for Segmentation of Liver from Abdominal CT Images." In 2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceeict61591.2024.10718577.

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Hashempour, Vahideh, Ramin Sahba, Amin Sahba, and Farshid Sahba. "AI-Driven Automated Measurement of Abdominal Aorta Diameter in CT Scan Images." In 2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2024. http://dx.doi.org/10.1109/uemcon62879.2024.10754698.

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Rajmohan, R., Mayank Verma, A. Harish, S. Saran Raj, Syed Fiaz A S, and R. Divya. "Rupture Prediction of Abdominal Aortic Aneursym from CT Images using Integrated Learning Models." In 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC). IEEE, 2024. https://doi.org/10.1109/icesic61777.2024.10846050.

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Campadelli, Paola, Elena Casiraghi, and Gabriele Lombardi. "Automatic liver segmentation from abdominal CT scans." In 14th International Conference on Image Analysis and Processing (ICIAP 2007). IEEE, 2007. http://dx.doi.org/10.1109/iciap.2007.4362863.

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Pan, Shengxue, Dehui Xiang, Yun Bian, Jianping Lu, Hui Jiang, and Jianming Zheng. "Automatic pancreas segmentation in abdominal CT image with contrast enhancement block." In Image Processing, edited by Bennett A. Landman and Ivana Išgum. SPIE, 2021. http://dx.doi.org/10.1117/12.2581040.

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Hattori, Chihiro, Daisuke Furukawa, Fukashi Yamazaki, Yasuko Fujisawa, and Takuya Sakaguchi. "Centerline detection and estimation of pancreatic duct from abdominal CT images." In Image Processing, edited by Ivana Išgum and Olivier Colliot. SPIE, 2022. http://dx.doi.org/10.1117/12.2603445.

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Luo, Xiongbiao, Wankang Zeng, Wenkang Fan, et al. "Towards cascaded V-Net for automatic accurate kidney segmentation from abdominal CT images." In Image Processing, edited by Bennett A. Landman and Ivana Išgum. SPIE, 2021. http://dx.doi.org/10.1117/12.2581932.

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Yu, Xin, Yucheng Tang, Qi Yang, et al. "Longitudinal variability analysis on low-dose abdominal CT with deep learning-based segmentation." In Image Processing, edited by Ivana Išgum and Olivier Colliot. SPIE, 2023. http://dx.doi.org/10.1117/12.2653762.

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Reports on the topic "CT abdominal image"

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Saleem, Athary, Saqer Alenezi, Nimer Al-Shadidi, and Khaleel Mohammad. Pyogenic Hepatic Abscess Formation after Roux-En-Y Gastric Bypass: A Case Report and Literature Review of an Infrequently Encountered Postoperative Complication. Science Repository, 2024. http://dx.doi.org/10.31487/j.ajscr.2024.01.03.

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Introduction and Importance: Pyogenic liver abscess (PLA) is an uncommon postoperative complication of Roux-en-Y gastric bypass (RYGB). Radiological investigations such as abdominal ultrasonography (USG) and computed tomography (CT) are crucial to evaluate and diagnose intra-abdominal abscesses, especially hepatic collections. Case Presentation: A 66-year-old female patient with multiple comorbidities, including urticaria requiring monoclonal antibody therapy (humera). She underwent an uneventful RYGB to treat her weight regain and reflux after a prior sleeve gastrectomy and presented with diffuse abdominal pain. This occurred on postoperative day 23 after the patient was discharged home. Patient evaluation was initiated by physical examination, laboratory investigations, and radiological diagnostic tools. Chest and abdominal X-rays together with abdominal ultrasonography were unremarkable. Then, abdominal computed tomography (CT) scans with IV contrast were done, and a liver abscess was detected. Image-guided percutaneous transhepatic liver abscess drainage through pigtail drain placement was performed. The patient’s response was evaluated by serial abdominal CT scans. The liver abscess was successfully treated by percutaneous drainage for 5 weeks and IV antibiotic therapy. Clinical Discussion: PLA is a rare entity that might occur after gastro-intestinal surgery such as Roux-en-Y gastric bypass. Patients with a history of immunosuppressive therapy may be at increased risk of this complication. This life-threatening complication can be prevented by treating liver abscesses early on by utilizing imaging-guided drainage and intravenous antibiotics. Conclusion: Due to the unusual etiologic origin of hepatic abscess post-RYGB, we report the case of a 66-year-old female with diffuse abdominal pain, which was found to be caused by PLA.
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