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Journal articles on the topic 'CT quantification'

Author: Grafiati

Published: 9 March 2023

Last updated: 19 July 2025

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1

Ferrando, Ornella, Alessandro Chimenz, Franca Foppiano, and Andrea Ciarmiello. "SPECT/CT activity quantification in 99mTc-MAA acquisitions." Journal of Diagnostic Imaging in Therapy 5, no. 1 (2018): 32–36. http://dx.doi.org/10.17229/jdit.2018-0624-034.

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Murk, Rehman, and Pertab Rai Dr. "QUANTIFICATION OF PLEURAL EFFUSION ON CT IMAGES BY AUTOMATIC AND MANUAL SEGMENTATION." International Journal of Engineering Technologies and Management Research 6, no. 5 (2019): 95–100. https://doi.org/10.5281/zenodo.3232648.

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The objective of this research is to make reliable estimation of pleural effusion volume in CT imaging using digital image processing algorithms. In order to make reliable estimation we need to do the manual and automatic segmentation of CT images and to perform the comparison of automatic and manual segmentation for the quantification of pleural effusion on CT images which provides help in the diagnosis of the pleural disease. Pleural effusion is the collection of excess fluid in the pleural cavity. Excessive amount of fluid can impair breathing by limiting the expansion of lungs. Heart failure, cancer, cirrhosis, pneumonia, tuberculosis and many other are the causes of pleural effusion. A number of noninvasive imaging techniques such as radiography, ultrasound and computed tomography (CT) can detect the pleural effusion. The problem faced is the quantification of pleural effusion volume for the purpose of diagnosis of the pleural disease. The objective of this research is to make reliable estimation of pleural effusion volume in CT imaging using digital image processing algorithm. In order to make reliable estimation we need to do the manual and automatic segmentation of CT images and to perform the comparison of automatic and manual segmentation for the quantification of pleural effusion on CT images which provides help in diagnosis of the pleural disease. The results obtained by both the aforementioned techniques indicate that the manual segmentation is better because automated technique has less number of pixels.

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Ferrando, Ornella, Franca Foppiano, Tindaro Scolaro, Chiara Gaeta, and Andrea Ciarmiello. "PET/CT images quantification for diagnostics and radiotherapy applications." Journal of Diagnostic Imaging in Therapy 2, no. 1 (2015): 18–29. http://dx.doi.org/10.17229/jdit.2015-0216-013.

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4

Do, Synho, Kristen Salvaggio, Supriya Gupta, Mannudeep Kalra, Nabeel U. Ali, and Homer Pien. "Automated Quantification of Pneumothorax in CT." Computational and Mathematical Methods in Medicine 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/736320.

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An automated, computer-aided diagnosis (CAD) algorithm for the quantification of pneumothoraces from Multidetector Computed Tomography (MDCT) images has been developed. Algorithm performance was evaluated through comparison to manual segmentation by expert radiologists. A combination of two-dimensional and three-dimensional processing techniques was incorporated to reduce required processing time by two-thirds (as compared to similar techniques). Volumetric measurements on relative pneumothorax size were obtained and the overall performance of the automated method shows an average error of just below 1%.

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Morsbach, Fabian, Lotus Desbiolles, André Plass, et al. "Stenosis Quantification in Coronary CT Angiography." Investigative Radiology 48, no. 1 (2013): 32–40. http://dx.doi.org/10.1097/rli.0b013e318274cf82.

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Gutjahr, Ralf, Robbert C. Bakker, Feiko Tiessens, Sebastiaan A. van Nimwegen, Bernhard Schmidt, and Johannes Frank Wilhelmus Nijsen. "Quantitative dual-energy CT material decomposition of holmium microspheres: local concentration determination evaluated in phantoms and a rabbit tumor model." European Radiology 31, no. 1 (2020): 139–48. http://dx.doi.org/10.1007/s00330-020-07092-1.

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Abstract Objectives The purpose of this study was to assess the feasibility of dual-energy CT-based material decomposition using dual-X-ray spectra information to determine local concentrations of holmium microspheres in phantoms and in an animal model. Materials and methods A spectral calibration phantom with a solution containing 10 mg/mL holmium and various tube settings was scanned using a third-generation dual-energy CT scanner to depict an energy-dependent and material-dependent enhancement vectors. A serial dilution of holmium (microspheres) was quantified by spectral material decomposition and compared with known holmium concentrations. Subsequently, the feasibility of the spectral material decomposition was demonstrated in situ in three euthanized rabbits with injected (radioactive) holmium microspheres. Results The measured CT values of the holmium solutions scale linearly to all measured concentrations and tube settings (R2 = 1.00). Material decomposition based on CT acquisitions using the tube voltage combinations of 80/150 Sn kV or 100/150 Sn kV allow the most accurate quantifications for concentrations down to 0.125 mg/mL holmium. Conclusion Dual-energy CT facilitates image-based material decomposition to detect and quantify holmium microspheres in phantoms and rabbits. Key Points • Quantification of holmium concentrations based on dual-energy CT is obtained with good accuracy. • The optimal tube-voltage pairs for quantifying holmium were 80/150 Sn kV and 100/150 Sn kV using a third-generation dual-source CT system. • Quantification of accumulated holmium facilitates the assessment of local dosimetry for radiation therapies.

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Lawal, Ismaheel O., Gbenga O. Popoola, Johncy Mahapane, et al. "[68Ga]Ga-Pentixafor for PET Imaging of Vascular Expression of CXCR-4 as a Marker of Arterial Inflammation in HIV-Infected Patients: A Comparison with 18F[FDG] PET Imaging." Biomolecules 10, no. 12 (2020): 1629. http://dx.doi.org/10.3390/biom10121629.

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People living with human immunodeficiency virus (PLHIV) have excess risk of atherosclerotic cardiovascular disease (ASCVD). Arterial inflammation is the hallmark of atherogenesis and its complications. In this study we aimed to perform a head-to-head comparison of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) and Gallium-68 pentixafor positron emission tomography/computed tomography [68Ga]Ga-pentixafor PET/CT for quantification of arterial inflammation in PLHIV. We prospectively recruited human immunodeficiency virus (HIV)-infected patients to undergo [18F]FDG PET/CT and [68Ga]Ga-pentixafor PET/CT within two weeks of each other. We quantified the levels of arterial tracer uptake on both scans using maximum standardized uptake value (SUVmax) and target–background ratio. We used Bland and Altman plots to measure the level of agreement between tracer quantification parameters obtained on both scans. A total of 12 patients were included with a mean age of 44.67 ± 7.62 years. The mean duration of HIV infection and mean CD+ T-cell count of the study population were 71.08 ± 37 months and 522.17 ± 260.33 cells/µL, respectively. We found a high level of agreement in the quantification variables obtained using [18F]FDG PET and [68Ga]Ga-pentixafor PET. There is a good level of agreement in the arterial tracer quantification variables obtained using [18F]FDG PET/CT and [68Ga]Ga-pentixafor PET/CT in PLHIV. This suggests that [68Ga]Ga-pentixafor may be applied in the place of [18F]FDG PET/CT for the quantification of arterial inflammation.

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8

Erlandsson, K., D. Visvikis, W. A. Waddington, I. D. Cullum, and G. Davies. "39. Absolute quantification with hybrid PET/CT and SPET/CT systems." Nuclear Medicine Communications 24, no. 4 (2003): 456–57. http://dx.doi.org/10.1097/00006231-200304000-00058.

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9

Bovenschulte, H., B. Krug, T. Schneider, et al. "CT coronary angiography: Coronary CT-flow quantification supplements morphological stenosis analysis." European Journal of Radiology 82, no. 4 (2013): 608–16. http://dx.doi.org/10.1016/j.ejrad.2012.08.004.

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10

Im, Won Hyeong, Gong Yong Jin, Young Min Han, and Eun Young Kim. "CT Quantification of Central Airway in Tracheobronchomalacia." Journal of the Korean Society of Radiology 74, no. 5 (2016): 299. http://dx.doi.org/10.3348/jksr.2016.74.5.299.

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11

Williams, Michelle C., and David E. Newby. "CT myocardial perfusion: a step towards quantification." Heart 98, no. 7 (2012): 521–22. http://dx.doi.org/10.1136/heartjnl-2012-301677.

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12

Mawlawi, Osama, S. Kappadath, Tinsu Pan, Eric Rohren, and Homer Macapinlac. "Factors Affecting Quantification in PET/CT Imaging." Current Medical Imaging Reviews 4, no. 1 (2008): 34–45. http://dx.doi.org/10.2174/157340508783502778.

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13

Wang, Zhimin, Suicheng Gu, Joseph K. Leader, et al. "Optimal threshold in CT quantification of emphysema." European Radiology 23, no. 4 (2012): 975–84. http://dx.doi.org/10.1007/s00330-012-2683-z.

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14

Antunovic, Lidija, Marcello Rodari, Pietro Rossi, and Arturo Chiti. "Standardization and Quantification in PET/CT Imaging." PET Clinics 9, no. 3 (2014): 259–66. http://dx.doi.org/10.1016/j.cpet.2014.03.002.

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15

Bai, Zhe, Abdelilah Essiari, Talita Perciano, and Kristofer E. Bouchard. "AutoCT: Automated CT registration, segmentation, and quantification." SoftwareX 26 (May 2024): 101673. http://dx.doi.org/10.1016/j.softx.2024.101673.

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16

Almasi Nokiani, Alireza. "Making the best use of CT Quantification Scores in Management of COVID-19 Patients." Clinical Research and Clinical Trials 5, no. 5 (2022): 01–03. http://dx.doi.org/10.31579/2693-4779/091.

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Because of the primary involvement of the respiratory system, chest computed tomography (CT) is strongly recommended in suspected COVID-19 cases, for both initial evaluation and follow-up [1]. At least seven scoring systems using chest CT have been proposed to quantify lung involvement in COVID-19 which are summarized in table 1 [1-10] and we use the term CT severity score (CTSS) to refer to them with numbers 1-7 to refer to a specific scoring system.

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17

Molwitz, Isabel, Miriam Leiderer, Cansu Özden, and Jin Yamamura. "Dual-Energy Computed Tomography for Fat Quantification in the Liver and Bone Marrow: A Literature Review." RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 192, no. 12 (2020): 1137–53. http://dx.doi.org/10.1055/a-1212-6017.

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Background With dual-energy computed tomography (DECT) it is possible to quantify certain elements and tissues by their specific attenuation, which is dependent on the X-ray spectrum. This systematic review provides an overview of the suitability of DECT for fat quantification in clinical diagnostics compared to established methods, such as histology, magnetic resonance imaging (MRI) and single-energy computed tomography (SECT). Method Following a systematic literature search, studies which validated DECT fat quantification by other modalities were included. The methodological heterogeneity of all included studies was processed. The study results are presented and discussed according to the target organ and specifically for each modality of comparison. Results Heterogeneity of the study methodology was high. The DECT data was generated by sequential CT scans, fast-kVp-switching DECT, or dual-source DECT. All included studies focused on the suitability of DECT for the diagnosis of hepatic steatosis and for the determination of the bone marrow fat percentage and the influence of bone marrow fat on the measurement of bone mineral density. Fat quantification in the liver and bone marrow by DECT showed valid results compared to histology, MRI chemical shift relaxometry, magnetic resonance spectroscopy, and SECT. For determination of hepatic steatosis in contrast-enhanced CT images, DECT was clearly superior to SECT. The measurement of bone marrow fat percentage via DECT enabled the bone mineral density quantification more reliably. Conclusion DECT is an overall valid method for fat quantification in the liver and bone marrow. In contrast to SECT, it is especially advantageous to diagnose hepatic steatosis in contrast-enhanced CT examinations. In the bone marrow DECT fat quantification allows more valid quantification of bone mineral density than conventional methods. Complementary studies concerning DECT fat quantification by split-filter DECT or dual-layer spectral CT and further studies on other organ systems should be conducted. Key points: Citation Format

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Hagen, Florian, Antonia Mair, Michael Bitzer, Hans Bösmüller, and Marius Horger. "Fully automated whole-liver volume quantification on CT-image data: Comparison with manual volumetry using enhanced and unenhanced images as well as two different radiation dose levels and two reconstruction kernels." PLOS ONE 16, no. 8 (2021): e0255374. http://dx.doi.org/10.1371/journal.pone.0255374.

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Objectives To evaluate the accuracy of fully automated liver volume quantification vs. manual quantification using unenhanced as well as enhanced CT-image data as well as two different radiation dose levels and also two image reconstruction kernels. Material and methods The local ethics board gave its approval for retrospective data analysis. Automated liver volume quantification in 300 consecutive livers in 164 male and 103 female oncologic patients (64±12y) performed at our institution (between January 2020 and May 2020) using two different dual-energy helicals: portal-venous phase enhanced, ref. tube current 300mAs (CARE Dose4D) for tube A (100 kV) and ref. 232mAs tube current for tube B (Sn140kV), slice collimation 0.6mm, reconstruction kernel I30f/1, recon. thickness of 0.6mm and 5mm, 80–100 mL iodine contrast agent 350 mg/mL, (flow 2mL/s) and unenhanced ref. tube current 100mAs (CARE Dose4D) for tube A (100 kV) and ref. 77mAs tube current for tube B (Sn140kV), slice collimation 0.6mm (kernel Q40f) were analyzed. The post-processing tool (syngo.CT Liver Analysis) is already FDA-approved. Two resident radiologists with no and 1-year CT-experience performed both the automated measurements independently from each other. Results were compared with those of manual liver volume quantification using the same software which was supervised by a senior radiologist with 30-year CT-experience (ground truth). Results In total, a correlation of 98% was obtained for liver volumetry based on enhanced and unenhanced data sets compared to the manual liver quantification. Radiologist #1 and #2 achieved an inter-reader agreement of 99.8% for manual liver segmentation (p<0.0001). Automated liver volumetry resulted in an overestimation (>5% deviation) of 3.7% for unenhanced CT-image data and 4.0% for contrast-enhanced CT-images. Underestimation (<5%) of liver volume was 2.0% for unenhanced CT-image data and 1.3% for enhanced images after automated liver volumetry. Number and distribution of erroneous volume measurements using either thin or thick slice reconstructions was exactly the same, both for the enhanced as well for the unenhanced image data sets (p> 0.05). Conclusion Results of fully automated liver volume quantification are accurate and comparable with those of manual liver volume quantification and the technique seems to be confident even if unenhanced lower-dose CT image data is used.

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Chaganti, Shikha, Philippe Grenier, Abishek Balachandran, et al. "Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT." Radiology: Artificial Intelligence 2, no. 4 (2020): e200048. http://dx.doi.org/10.1148/ryai.2020200048.

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20

Narawong, Taratip, and Kanyalak Wiyaporn. "Comparison between the use of one and two CT scans for attenuation correction of rest-stress myocardial perfusion SPECT with Tc-99m sestamibi." ASEAN Journal of Radiology 25, no. 2 (2024): 116–43. http://dx.doi.org/10.46475/asean-jr.v25i2.895.

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Background: The standard protocol is to use separate computed tomography (CT) scans acquired during rest and stress for attenuation correction (AC) of myocardial perfusion (MP) single photon emission computed tomography (SPECT) imaging. Recently, there have been attempts to reduce the radiation dose by using one CT instead of two CTs. Objective: To compare between the use of one and two CTs for AC of rest-stress MP SPECT with Tc-99m sestamibi in quantification of MP and left ventricle (LV) function. Materials and Methods: Gated rest-stress MP SPECT images of 107 patients were reprocessed using 3 different AC methods: 1) rest CT for AC of rest SPECT and stress CT for AC of stress SPECT (2CT); 2) rest CT for AC of both rest and stress SPECT (1CT-rest); and 3) stress CT for AC of both rest and stress SPECT (1CT-stress). SPECT images obtained from 2CT and 1CT were used for quantification of MP values and LV function values. The values from 2CT and 1CT were compared. Results: The MP values of 2CT and 1CT showed a strong correlation (r≥0.712) and they did not differ significantly (p=0.106 to 0.931). In contrast, the LV function values of 2CT and 1CT exhibited a very strong correlation (r≥0.960), but they differ significantly (p=<0.001 to 0.004). Conclusions: The use of one and two CTs for AC in rest-stress MP SPECT with Tc-99m sestamibi can be interchanged for the quantification of MP, but not for the quantification of LV function.

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Lin, Shenghuang, Yu Zhang, Li’an Luo, et al. "Visualization and quantification of coconut using advanced computed tomography postprocessing technology." PLOS ONE 18, no. 2 (2023): e0282182. http://dx.doi.org/10.1371/journal.pone.0282182.

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Introduction Computed tomography (CT) is a non-invasive examination tool that is widely used in medicine. In this study, we explored its value in visualizing and quantifying coconut. Materials and methods Twelve coconuts were scanned using CT for three months. Axial CT images of the coconuts were obtained using a dual-source CT scanner. In postprocessing process, various three-dimensional models were created by volume rendering (VR), and the plane sections of different angles were obtained through multiplanar reformation (MPR). The morphological parameters and the CT values of the exocarp, mesocarp, endocarp, embryo, bud, solid endosperm, liquid endosperm, and coconut apple were measured. The analysis of variances was used for temporal repeated measures and linear and non-linear regressions were used to analyze the relationship between the data. Results The MPR images and VR models provide excellent visualization of the different structures of the coconut. The statistical results showed that the weight of coconut and liquid endosperm volume decreased significantly during the three months, while the CT value of coconut apple decreased slightly. We observed a complete germination of a coconut, its data showed a significant negative correlation between the CT value of the bud and the liquid endosperm volume (y = −2.6955x + 244.91; R2 = 0.9859), and a strong positive correlation between the height and CT value of the bud (y = 1.9576 ln(x) −2.1655; R2 = 0.9691). Conclusion CT technology can be used for visualization and quantitative analysis of the internal structure of the coconut, and some morphological changes and composition changes of the coconut during the germination process were observed during the three-month experiment. Therefore, CT is a potential tool for analyzing coconuts.

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Boers, A. M., I. A. Zijlstra, C. S. Gathier, et al. "Automatic Quantification of Subarachnoid Hemorrhage on Noncontrast CT." American Journal of Neuroradiology 35, no. 12 (2014): 2279–86. http://dx.doi.org/10.3174/ajnr.a4042.

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23

Huynh, T. J., B. Murphy, J. A. Pettersen, et al. "CT Perfusion Quantification of Small-Vessel Ischemic Severity." American Journal of Neuroradiology 29, no. 10 (2008): 1831–36. http://dx.doi.org/10.3174/ajnr.a1238.

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24

Coppini, Giuseppe. "Quantification of Epicardial Fat by Cardiac CT Imaging." Open Medical Informatics Journal 4, no. 1 (2010): 126–35. http://dx.doi.org/10.2174/1874431101004010126.

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McWilliam, A. "SP-0034 CT-based quantification of existing biomarkers." Radiotherapy and Oncology 161 (August 2021): S11—S12. http://dx.doi.org/10.1016/s0167-8140(21)08477-2.

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26

Bowen, Spencer L., Andrea Ferrero, and Ramsey D. Badawi. "Quantification with a dedicated breast PET/CT scanner." Medical Physics 39, no. 5 (2012): 2694–707. http://dx.doi.org/10.1118/1.3703593.

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27

Reich, Jerome M., and Jong S. Kim. "Quantification and consequences of lung cancer CT overdiagnosis." Lung Cancer 87, no. 2 (2015): 96–97. http://dx.doi.org/10.1016/j.lungcan.2014.12.002.

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28

Tseng, Philip H., Songshou Mao, David Z. Chow, et al. "Accuracy in Quantification of Coronary Calcification with CT." Academic Radiology 17, no. 10 (2010): 1249–53. http://dx.doi.org/10.1016/j.acra.2010.05.013.

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Guerrero, Thomas, Kevin Sanders, Josue Noyola-Martinez, et al. "Quantification of regional ventilation from treatment planning CT." International Journal of Radiation Oncology*Biology*Physics 62, no. 3 (2005): 630–34. http://dx.doi.org/10.1016/j.ijrobp.2005.03.023.

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30

Rehman, Murk, and Pertab Rai. "QUANTIFICATION OF PLEURAL EFFUSION ON CT IMAGES BY AUTOMATIC AND MANUAL SEGMENTATION." International Journal of Engineering Technologies and Management Research 6, no. 5 (2020): 95–100. http://dx.doi.org/10.29121/ijetmr.v6.i5.2019.375.

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The objective of this research is to make reliable estimation of pleural effusion volume in CT imaging using digital image processing algorithms. In order to make reliable estimation we need to do the manual and automatic segmentation of CT images and to perform the comparison of automatic and manual segmentation for the quantification of pleural effusion on CT images which provides help in the diagnosis of the pleural disease. Pleural effusion is the collection of excess fluid in the pleural cavity. Excessive amount of fluid can impair breathing by limiting the expansion of lungs. Heart failure, cancer, cirrhosis, pneumonia, tuberculosis and many other are the causes of pleural effusion. A number of noninvasive imaging techniques such as radiography, ultrasound and computed tomography (CT) can detect the pleural effusion. The problem faced is the quantification of pleural effusion volume for the purpose of diagnosis of the pleural disease. The objective of this research is to make reliable estimation of pleural effusion volume in CT imaging using digital image processing algorithm. In order to make reliable estimation we need to do the manual and automatic segmentation of CT images and to perform the comparison of automatic and manual segmentation for the quantification of pleural effusion on CT images which provides help in diagnosis of the pleural disease. The results obtained by both the aforementioned techniques indicate that the manual segmentation is better because automated technique has less number of pixels.

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31

Borges, Ana P., Célia Antunes, and Filipe Caseiro-Alves. "Spectral CT: Current Liver Applications." Diagnostics 13, no. 10 (2023): 1673. http://dx.doi.org/10.3390/diagnostics13101673.

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Using two different energy levels, dual-energy computed tomography (DECT) allows for material differentiation, improves image quality and iodine conspicuity, and allows researchers the opportunity to determine iodine contrast and radiation dose reduction. Several commercialized platforms with different acquisition techniques are constantly being improved. Furthermore, DECT clinical applications and advantages are continually being reported in a wide range of diseases. We aimed to review the current applications of and challenges in using DECT in the treatment of liver diseases. The greater contrast provided by low-energy reconstructed images and the capability of iodine quantification have been mostly valuable for lesion detection and characterization, accurate staging, treatment response assessment, and thrombi characterization. Material decomposition techniques allow for the non-invasive quantification of fat/iron deposition and fibrosis. Reduced image quality with larger body sizes, cross-vendor and scanner variability, and long reconstruction time are among the limitations of DECT. Promising techniques for improving image quality with lower radiation dose include the deep learning imaging reconstruction method and novel spectral photon-counting computed tomography.

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Ko, Hoon, Jimi Huh, Kyung Won Kim, et al. "A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation." Journal of Medical Internet Research 24, no. 1 (2022): e34415. http://dx.doi.org/10.2196/34415.

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Background Detection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients. In an emergency department, automatic detection and quantification of ascites will be beneficial. Objective We aimed to develop an artificial intelligence (AI) algorithm for the automatic detection and quantification of ascites simultaneously using a single deep learning model (DLM). Methods We developed 2D DLMs based on deep residual U-Net, U-Net, bidirectional U-Net, and recurrent residual U-Net (R2U-Net) algorithms to segment areas of ascites on abdominopelvic CT images. Based on segmentation results, the DLMs detected ascites by classifying CT images into ascites images and nonascites images. The AI algorithms were trained using 6337 CT images from 160 subjects (80 with ascites and 80 without ascites) and tested using 1635 CT images from 40 subjects (20 with ascites and 20 without ascites). The performance of the AI algorithms was evaluated for diagnostic accuracy of ascites detection and for segmentation accuracy of ascites areas. Of these DLMs, we proposed an AI algorithm with the best performance. Results The segmentation accuracy was the highest for the deep residual U-Net model with a mean intersection over union (mIoU) value of 0.87, followed by U-Net, bidirectional U-Net, and R2U-Net models (mIoU values of 0.80, 0.77, and 0.67, respectively). The detection accuracy was the highest for the deep residual U-Net model (0.96), followed by U-Net, bidirectional U-Net, and R2U-Net models (0.90, 0.88, and 0.82, respectively). The deep residual U-Net model also achieved high sensitivity (0.96) and high specificity (0.96). Conclusions We propose a deep residual U-Net–based AI algorithm for automatic detection and quantification of ascites on abdominopelvic CT scans, which provides excellent performance.

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Morioka, Tsubasa, Shingo Kato, Ayano Onoma, et al. "Improvement of Quantification of Myocardial Synthetic ECV with Second-Generation Deep Learning Reconstruction." Journal of Cardiovascular Development and Disease 11, no. 10 (2024): 304. http://dx.doi.org/10.3390/jcdd11100304.

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Background: The utility of synthetic ECV, which does not require hematocrit values, has been reported; however, high-quality CT images are essential for accurate quantification. Second-generation Deep Learning Reconstruction (DLR) enables low-noise and high-resolution cardiac CT images. The aim of this study is to compare the differences among four reconstruction methods (hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and second-generation DLR) in the quantification of synthetic ECV. Methods: We retrospectively analyzed 80 patients who underwent cardiac CT scans, including late contrast-enhanced CT (derivation cohort: n = 40, age 71 ± 12 years, 24 males; validation cohort: n = 40, age 67 ± 11 years, 25 males). In the derivation cohort, a linear regression analysis was performed between the hematocrit values from blood tests and the CT values of the right atrial blood pool on non-contrast CT. In the validation cohort, synthetic hematocrit values were calculated using the linear regression equation and the right atrial CT values from non-contrast CT. The correlation and mean difference between synthetic ECV and laboratory ECV calculated from actual blood tests were assessed. Results: Synthetic ECV and laboratory ECV showed a high correlation across all four reconstruction methods (R ≥ 0.95, p < 0.001). The bias and limit of agreement (LOA) in the Bland–Altman plot were lowest with the second-generation DLR (hybrid IR: bias = −0.21, LOA: 3.16; MBIR: bias = −0.79, LOA: 2.81; DLR: bias = −1.87, LOA: 2.90; second-generation DLR: bias = −0.20, LOA: 2.35). Conclusions: Synthetic ECV using second-generation DLR demonstrated the lowest bias and LOA compared to laboratory ECV among the four reconstruction methods, suggesting that second-generation DLR enables more accurate quantification.

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Lanzafame, Ludovica R. M., Giuseppe M. Bucolo, Giuseppe Muscogiuri, et al. "Artificial Intelligence in Cardiovascular CT and MR Imaging." Life 13, no. 2 (2023): 507. http://dx.doi.org/10.3390/life13020507.

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The technological development of Artificial Intelligence (AI) has grown rapidly in recent years. The applications of AI to cardiovascular imaging are various and could improve the radiologists’ workflow, speeding up acquisition and post-processing time, increasing image quality and diagnostic accuracy. Several studies have already proved AI applications in Coronary Computed Tomography Angiography and Cardiac Magnetic Resonance, including automatic evaluation of calcium score, quantification of coronary stenosis and plaque analysis, or the automatic quantification of heart volumes and myocardial tissue characterization. The aim of this review is to summarize the latest advances in the field of AI applied to cardiovascular CT and MR imaging.

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35

Bussink, J. "Quantification of tumour hypoxia." Nuklearmedizin 49, S 01 (2010): S37—S40. http://dx.doi.org/10.1055/s-0038-1626532.

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SummaryTumor cell hypoxia is considered one of the important causes for radiation resistance. The introduction of IMRT (intensity modulated radiotherapy) allows specific boosting of tumor subvolumes that may harbour these radioresistant tumour cells. PET imaging of these subvolumes can be incorporated into treatment planning.However, at this moment microenvironmental changes visualized and quantified by means of PET-imaging need to be validated by highresolution microscopic techniques. This will allow interpretation of imaging techniques with intermediate resolution (such as PET/CT) in relation to complex cellular signaling in response to anti-cancer treatments.

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Moneke, Isabelle, Christine von Nida, Oemer Senbaklavaci, et al. "SPECT/CT Accurately Predicts Postoperative Lung Function in Patients with Limited Pulmonary Reserve Undergoing Resection for Lung Cancer." Journal of Clinical Medicine 13, no. 20 (2024): 6111. http://dx.doi.org/10.3390/jcm13206111.

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Background: Preoperative prediction of postoperative pulmonary function after anatomical resection for lung cancer is essential to prevent long-term morbidity and mortality. Here, we compared the accuracy of hybrid single-photon emission computed tomography/computed tomography (SPECT/CT) with traditional anatomical and planar scintigraphy approaches in predicting postoperative pulmonary function in patients with impaired lung function. Methods: We analyzed the predicted postoperative pulmonary function in patients undergoing major anatomical lung resection, applying a segment counting approach, planar perfusion scintigraphy (PPS), and SPECT/CT-based lung function quantification. Results: In total, 120 patients were evaluated, of whom 82 were included in the study. Postoperative lung function tests were obtained in 21 of 82 patients. The preoperative SPECT/CT-based quantification yielded very accurate results compared to the actual postoperative FEV1 and DLCO values. The linear regression analysis showed that the SPECT/CT-based analysis predicted postoperative FEV1 (%) and DLCO values more accurately than the segment counting approach or PPS. Accordingly, 58/82 patients would qualify for anatomical lung resection according to the SPECT-based quantification, 56/82 qualified according to the PPS (Mende), and only 47/82 qualified according to the segment counting method. Moreover, we noted that the SPECT-predicted FEV1 values were very close to the actual postoperative values in emphysema patients, and selected patients even showed improved lung function after surgery. Conclusions: Anatomically driven methods such as SPECT/CT yielded a very accurate prediction of the postoperative pulmonary function. Accordingly, applying SPECT/CT revealed more patients who would formally qualify for lung resection. We suggest SPECT/CT as the preferred method to evaluate eligibility for lung surgery in selected patients with impaired pulmonary reserve.

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MATERNE, Roland, Bernard E. VAN BEERS, Anne M. SMITH, et al. "Non-invasive quantification of liver perfusion with dynamic computed tomography and a dual-input one-compartmental model." Clinical Science 99, no. 6 (2000): 517–25. http://dx.doi.org/10.1042/cs0990517.

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Various liver diseases lead to significant alterations of the hepatic microcirculation. Therefore, quantification of hepatic perfusion has the potential to improve the assessment and management of liver diseases. Most methods used to quantify liver perfusion are invasive or controversial. This paper describes and validates a non-invasive method for the quantification of liver perfusion using computed tomography (CT). Dynamic single-section CT of the liver was performed after intravenous bolus administration of a low-molecular-mass iodinated contrast agent. Hepatic, aortic and portal-venous time—density curves were fitted with a dual-input one-compartmental model to calculate liver perfusion. Validation studies consisted of simultaneous measurements of hepatic perfusion with CT and with radiolabelled microspheres in rabbits at rest and after adenosine infusion. The feasibility and reproducibility of the CT method in humans was assessed by three observers in 10 patients without liver disease. In rabbits, significant correlations were observed between perfusion measurements obtained with CT and with microspheres (r = 0.92 for total liver perfusion, r = 0.81 for arterial perfusion and r = 0.85 for portal perfusion). In patients, total liver plasma perfusion measured with CT was 112±28 ml·min-1·100 ml-1, arterial plasma perfusion was 18±12 ml·min-1·100 ml-1 and portal plasma perfusion was 93±31 ml·min-1·100 ml-1. The measurements obtained by the three observers were not significantly different from each other (P > 0.1). Our results indicate that dynamic CT combined with a dual-input one-compartmental model provides a valid and reliable method for the non-invasive quantification of perfusion in the normal liver.

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Mohymen, Ahmed Abdel, Hamed Ibrahim Farag, Sameh M. Reda, Ahmed Soltan Monem, and Said A. Ali. "Investigating the Impact of Voxel Size and Postfiltering on Quantitative Analysis of Positron Emission Tomography/Computed Tomography: A Phantom Study." Journal of Medical Physics 49, no. 4 (2024): 597–607. https://doi.org/10.4103/jmp.jmp_123_24.

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Aim: This study aims to investigate the influence of voxel size and postfiltering on the quantification of standardized uptake value (SUV) in positron emission tomography/computed tomography (PET/CT) images. Materials and Methods: National Electrical Manufacturers Association phantom with the spheres of different sizes were utilized to simulate the lesions. The phantom was scanned using a PET/CT scanner, and the acquired images were reconstructed using two different matrix sizes, (192 × 192) and (256 × 256), and a wide range of postfiltering values. Results: The findings demonstrated that postfiltering significantly affected SUV measurements. The changes in postfiltering values can result in overestimation or underestimation of SUV values, highlighting the importance of carefully selecting appropriate filters. Increasing the matrix size improved SUVmax and SUVmean values, particularly for small-sized spheres. Smaller voxel reconstructions slightly reduced partial volume effects and partially enhanced SUV quantification. Conclusions: Careful consideration of postfiltering values and matrix size selection can lead to better SUV quantification. These findings emphasize the need to optimize the reconstruction parameters to enhance the clinical utility of PET/CT in detecting and evaluating malignant lesions.

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Yasuda, Naofumi, Tae Iwasawa, Tomohisa Baba, et al. "Evaluation of Progressive Architectural Distortion in Idiopathic Pulmonary Fibrosis Using Deformable Registration of Sequential CT Images." Diagnostics 14, no. 15 (2024): 1650. http://dx.doi.org/10.3390/diagnostics14151650.

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Background: Monitoring the progression of idiopathic pulmonary fibrosis (IPF) using CT primarily focuses on assessing the extent of fibrotic lesions, without considering the distortion of lung architecture. Objectives: To evaluate three-dimensional average displacement (3D-AD) quantification of lung structures using deformable registration of serial CT images as a parameter of local lung architectural distortion and predictor of IPF prognosis. Materials and Methods: Patients with IPF evaluated between January 2016 and March 2017 who had undergone CT at least twice were retrospectively included (n = 114). The 3D-AD was obtained by deformable registration of baseline and follow-up CT images. A computer-aided quantification software measured the fibrotic lesion volume. Cox regression analysis evaluated these variables to predict mortality. Results: The 3D-AD and the fibrotic lesion volume change were significantly larger in the subpleural lung region (5.2 mm (interquartile range (IQR): 3.6–7.1 mm) and 0.70% (IQR: 0.22–1.60%), respectively) than those in the inner region (4.7 mm (IQR: 3.0–6.4 mm) and 0.21% (IQR: 0.004–1.12%), respectively). Multivariable logistic analysis revealed that subpleural region 3D-AD and fibrotic lesion volume change were independent predictors of mortality (hazard ratio: 1.12 and 1.23; 95% confidence interval: 1.02–1.22 and 1.10–1.38; p = 0.01 and p < 0.001, respectively). Conclusions: The 3D-AD quantification derived from deformable registration of serial CT images serves as a marker of lung architectural distortion and a prognostic predictor in patients with IPF.

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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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Stanford, William, Brad H. Thompson, Trudy L. Burns, Scot D. Heery, and Mary C. Burr. "Coronary Artery Calcium Quantification at Multi–Detector Row Helical CT versus Electron-Beam CT." Radiology 230, no. 2 (2004): 397–402. http://dx.doi.org/10.1148/radiol.2302020901.

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Stanford, W., B. H. Thompson, and T. L. Burns. "Coronary artery calcium quantification at multi-detector row helical CT versus electron-beam CT." ACC Current Journal Review 13, no. 5 (2004): 44. http://dx.doi.org/10.1016/j.accreview.2004.04.022.

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Pelgrim, G. J., A. Handayani, H. Dijkstra, et al. "Quantitative Myocardial Perfusion with Dynamic Contrast-Enhanced Imaging in MRI and CT: Theoretical Models and Current Implementation." BioMed Research International 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/1734190.

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Technological advances in magnetic resonance imaging (MRI) and computed tomography (CT), including higher spatial and temporal resolution, have made the prospect of performing absolute myocardial perfusion quantification possible, previously only achievable with positron emission tomography (PET). This could facilitate integration of myocardial perfusion biomarkers into the current workup for coronary artery disease (CAD), as MRI and CT systems are more widely available than PET scanners. Cardiac PET scanning remains expensive and is restricted by the requirement of a nearby cyclotron. Clinical evidence is needed to demonstrate that MRI and CT have similar accuracy for myocardial perfusion quantification as PET. However, lack of standardization of acquisition protocols and tracer kinetic model selection complicates comparison between different studies and modalities. The aim of this overview is to provide insight into the different tracer kinetic models for quantitative myocardial perfusion analysis and to address typical implementation issues in MRI and CT. We compare different models based on their theoretical derivations and present the respective consequences for MRI and CT acquisition parameters, highlighting the interplay between tracer kinetic modeling and acquisition settings.

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Hazlinger, Martin, Filip Ctvrtlik, Katerina Langova, and Miroslav Herman. "Quantification of pleural effusion on CT by simple measurement." Biomedical Papers 158, no. 1 (2014): 107–11. http://dx.doi.org/10.5507/bp.2012.042.

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Ferraioli, Giovanna, and Richard G. Barr. "Quantification of Liver Steatosis: Is CT Equivalent to PDFF?" American Journal of Roentgenology 216, no. 4 (2021): W14. http://dx.doi.org/10.2214/ajr.20.25069.

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De Schepper, Stijn, Gopinath Gnanasegaran, John C. Dickson, and Tim Van den Wyngaert. "Absolute Quantification in Diagnostic SPECT/CT: The Phantom Premise." Diagnostics 11, no. 12 (2021): 2333. http://dx.doi.org/10.3390/diagnostics11122333.

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The application of absolute quantification in SPECT/CT has seen increased interest in the context of radionuclide therapies where patient-specific dosimetry is a requirement within the European Union (EU) legislation. However, the translation of this technique to diagnostic nuclear medicine outside this setting is rather slow. Clinical research has, in some examples, already shown an association between imaging metrics and clinical diagnosis, but the applications, in general, lack proper validation because of the absence of a ground truth measurement. Meanwhile, additive manufacturing or 3D printing has seen rapid improvements, increasing its uptake in medical imaging. Three-dimensional printed phantoms have already made a significant impact on quantitative imaging, a trend that is likely to increase in the future. In this review, we summarize the data of recent literature to underpin our premise that the validation of diagnostic applications in nuclear medicine using application-specific phantoms is within reach given the current state-of-the-art in additive manufacturing or 3D printing.

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Kerut, Edmund K., Filip To, Michael Turner, James McKinnie, and Thomas Giles. "A mathematical algorithm for quantification of CT image noise." Echocardiography 34, no. 1 (2016): 116–18. http://dx.doi.org/10.1111/echo.13389.

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Challande, Pascal, and Marie Christine Plainfosse. "Reliability of Coronary Calcium Quantification with Electron Beam CT." Radiology 193, no. 1 (1994): 282. http://dx.doi.org/10.1148/radiology.193.1.282-a.

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Hackx, Maxime, Alexander A. Bankier, and Pierre Alain Gevenois. "Chronic Obstructive Pulmonary Disease: CT Quantification of Airways Disease." Radiology 265, no. 1 (2012): 34–48. http://dx.doi.org/10.1148/radiol.12111270.

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Le Pennec, Gilles, Sophie Campana, Erwan Jolivet, Jean-Marc Vital, Xavier Barreau, and Wafa Skalli. "CT-based semi-automatic quantification of vertebral fracture restoration." Computer Methods in Biomechanics and Biomedical Engineering 17, no. 10 (2012): 1086–95. http://dx.doi.org/10.1080/10255842.2012.736968.

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