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Journal articles on the topic 'CT quantification'

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Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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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 (June 24, 2018): 32–36. http://dx.doi.org/10.17229/jdit.2018-0624-034.

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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 (February 16, 2015): 18–29. http://dx.doi.org/10.17229/jdit.2015-0216-013.

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3

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, Sebastian Leschka, Bernhard Schmidt, Volkmar Falk, Hatem Alkadhi, and Paul Stolzmann. "Stenosis Quantification in Coronary CT Angiography." Investigative Radiology 48, no. 1 (January 2013): 32–40. http://dx.doi.org/10.1097/rli.0b013e318274cf82.

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5

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 (April 2003): 456–57. http://dx.doi.org/10.1097/00006231-200304000-00058.

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6

Bovenschulte, H., B. Krug, T. Schneider, H. Schwabe, C. Kabbasch, C. Bangard, M. Hellmich, G. Michels, D. Maintz, and K. Lackner. "CT coronary angiography: Coronary CT-flow quantification supplements morphological stenosis analysis." European Journal of Radiology 82, no. 4 (April 2013): 608–16. http://dx.doi.org/10.1016/j.ejrad.2012.08.004.

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7

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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8

Williams, Michelle C., and David E. Newby. "CT myocardial perfusion: a step towards quantification." Heart 98, no. 7 (March 15, 2012): 521–22. http://dx.doi.org/10.1136/heartjnl-2012-301677.

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9

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 (February 1, 2008): 34–45. http://dx.doi.org/10.2174/157340508783502778.

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10

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

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Wang, Zhimin, Suicheng Gu, Joseph K. Leader, Shinjini Kundu, John R. Tedrow, Frank C. Sciurba, David Gur, Jill M. Siegfried, and Jiantao Pu. "Optimal threshold in CT quantification of emphysema." European Radiology 23, no. 4 (November 1, 2012): 975–84. http://dx.doi.org/10.1007/s00330-012-2683-z.

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12

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 (August 7, 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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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 (April 27, 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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Lawal, Ismaheel O., Gbenga O. Popoola, Johncy Mahapane, Jens Kaufmann, Cindy Davis, Honest Ndlovu, Letjie C. Maserumule, 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 (December 3, 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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Chaganti, Shikha, Philippe Grenier, Abishek Balachandran, Guillaume Chabin, Stuart Cohen, Thomas Flohr, Bogdan Georgescu, et al. "Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT." Radiology: Artificial Intelligence 2, no. 4 (July 1, 2020): e200048. http://dx.doi.org/10.1148/ryai.2020200048.

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Boers, A. M., I. A. Zijlstra, C. S. Gathier, R. van den Berg, C. H. Slump, H. A. Marquering, and C. B. Majoie. "Automatic Quantification of Subarachnoid Hemorrhage on Noncontrast CT." American Journal of Neuroradiology 35, no. 12 (August 7, 2014): 2279–86. http://dx.doi.org/10.3174/ajnr.a4042.

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Huynh, T. J., B. Murphy, J. A. Pettersen, H. Tu, D. J. Sahlas, L. Zhang, S. P. Symons, S. Black, T. Y. Lee, and R. I. Aviv. "CT Perfusion Quantification of Small-Vessel Ischemic Severity." American Journal of Neuroradiology 29, no. 10 (September 3, 2008): 1831–36. http://dx.doi.org/10.3174/ajnr.a1238.

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18

Coppini, Giuseppe. "Quantification of Epicardial Fat by Cardiac CT Imaging." Open Medical Informatics Journal 4, no. 1 (July 27, 2010): 126–35. http://dx.doi.org/10.2174/1874431101004010126.

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19

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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Bowen, Spencer L., Andrea Ferrero, and Ramsey D. Badawi. "Quantification with a dedicated breast PET/CT scanner." Medical Physics 39, no. 5 (April 23, 2012): 2694–707. http://dx.doi.org/10.1118/1.3703593.

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21

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

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22

Tseng, Philip H., Songshou Mao, David Z. Chow, Yanlin Gao, William W. Chang, Michael M. Schiff, Katherine Y. Kim, Jessica Y. Kwan, and Matthew J. Budoff. "Accuracy in Quantification of Coronary Calcification with CT." Academic Radiology 17, no. 10 (October 2010): 1249–53. http://dx.doi.org/10.1016/j.acra.2010.05.013.

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23

Guerrero, Thomas, Kevin Sanders, Josue Noyola-Martinez, Edward Castillo, Yin Zhang, Richard Tapia, Rudy Guerra, Yerko Borghero, and Ritsuko Komaki. "Quantification of regional ventilation from treatment planning CT." International Journal of Radiation Oncology*Biology*Physics 62, no. 3 (July 2005): 630–34. http://dx.doi.org/10.1016/j.ijrobp.2005.03.023.

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24

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 (September 10, 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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25

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 (August 2, 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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Lin, Shenghuang, Yu Zhang, Li’an Luo, Mengxing Huang, Hongxing Cao, Jinyue Hu, Chengxu Sun, and Jing Chen. "Visualization and quantification of coconut using advanced computed tomography postprocessing technology." PLOS ONE 18, no. 2 (February 24, 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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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 (March 25, 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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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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Lanzafame, Ludovica R. M., Giuseppe M. Bucolo, Giuseppe Muscogiuri, Sandro Sironi, Michele Gaeta, Giorgio Ascenti, Christian Booz, et al. "Artificial Intelligence in Cardiovascular CT and MR Imaging." Life 13, no. 2 (February 11, 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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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 (February 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 (May 2004): 44. http://dx.doi.org/10.1016/j.accreview.2004.04.022.

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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 (April 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 (April 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 (December 11, 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 (September 28, 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 (October 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 (October 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 (October 31, 2012): 1086–95. http://dx.doi.org/10.1080/10255842.2012.736968.

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MÖHLENKAMP, STEFAN, LILACH O. LERMAN, ŽELJKO BAJZER, PATRICIA E. LUND, and ERIK L. RITMAN. "Quantification of Myocardial Microcirculatory Function with X-ray CT." Annals of the New York Academy of Sciences 972, no. 1 (October 2002): 307–16. http://dx.doi.org/10.1111/j.1749-6632.2002.tb04589.x.

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Tu, S., T. Chao, and C. Lee. "SU-FF-I-117: System Quantification for Micro CT." Medical Physics 34, no. 6Part4 (June 2007): 2364–65. http://dx.doi.org/10.1118/1.2760493.

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Di Martino, E. S., I. Verdinelli, E. Votta, and D. Schwartzman. "Quantification of regional cardiovascular mechanics from dynamic-CT data." Journal of Biomechanics 39 (January 2006): S292. http://dx.doi.org/10.1016/s0021-9290(06)84131-x.

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Judex, S., Y. K. Luu, E. Ozcivici, B. Adler, S. Lublinsky, and C. T. Rubin. "Quantification of adiposity in small rodents using micro-CT." Methods 50, no. 1 (January 2010): 14–19. http://dx.doi.org/10.1016/j.ymeth.2009.05.017.

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Dattaram, U., ST Binoj, P. Dhar, OV Sudheer, G. Unnikrishnan, R. Menon, D. Balakrishnan, and S. Sudhindran. "44 HEPATIC STEATOSIS-QUANTIFICATION BY NON-ENHANCED CT SCAN." Journal of Clinical and Experimental Hepatology 1, no. 2 (September 2011): 152. http://dx.doi.org/10.1016/s0973-6883(11)60181-3.

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Cao, Xianxian, Chenwang Jin, Tao Tan, and Youmin Guo. "Optimal threshold in low-dose CT quantification of emphysema." European Journal of Radiology 129 (August 2020): 109094. http://dx.doi.org/10.1016/j.ejrad.2020.109094.

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Kurata, Akira, Anoeshka Dharampal, Admir Dedic, Pim J. de Feyter, Gabriel P. Krestin, Marcel L. Dijkshoorn, and Koen Nieman. "Impact of iterative reconstruction on CT coronary calcium quantification." European Radiology 23, no. 12 (September 22, 2013): 3246–52. http://dx.doi.org/10.1007/s00330-013-3022-8.

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van Assen, Marly, Carlo N. De Cecco, Pooyan Sahbaee, Marwen H. Eid, L. Parkwood Griffith, Maximilian J. Bauer, Rock H. Savage, et al. "Feasibility of extracellular volume quantification using dual-energy CT." Journal of Cardiovascular Computed Tomography 13, no. 1 (January 2019): 81–84. http://dx.doi.org/10.1016/j.jcct.2018.10.011.

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Boogers, Mark J., Joanne D. Schuijf, Pieter H. Kitslaar, Jacob M. van Werkhoven, Fleur R. de Graaf, Eric Boersma, Joëlla E. van Velzen, et al. "Automated Quantification of Stenosis Severity on 64-Slice CT." JACC: Cardiovascular Imaging 3, no. 7 (July 2010): 699–709. http://dx.doi.org/10.1016/j.jcmg.2010.01.010.

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Ko, Hoon, Jimi Huh, Kyung Won Kim, Heewon Chung, Yousun Ko, Jai Keun Kim, Jei Hee Lee, and Jinseok Lee. "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 (January 3, 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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MATERNE, Roland, Bernard E. VAN BEERS, Anne M. SMITH, Isabelle LECONTE, Jacques JAMART, Jean-Paul DEHOUX, André KEYEUX, and Yves HORSMANS. "Non-invasive quantification of liver perfusion with dynamic computed tomography and a dual-input one-compartmental model." Clinical Science 99, no. 6 (November 15, 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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Yeom, Jeong-A., Ki-Uk Kim, Minhee Hwang, Ji-Won Lee, Kun-Il Kim, You-Seon Song, In-Sook Lee, and Yeon-Joo Jeong. "Emphysema Quantification Using Ultra-Low-Dose Chest CT: Efficacy of Deep Learning-Based Image Reconstruction." Medicina 58, no. 7 (July 15, 2022): 939. http://dx.doi.org/10.3390/medicina58070939.

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Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective image quality enhancement strategies are crucial to compensate for the degradation that is caused by a dose reduction. We performed this prospective study to quantify emphysema on ultra-low-dose CT images that were reconstructed using deep learning-based image reconstruction (DLIR) algorithms, and compared and evaluated the accuracies of DLIR algorithms versus standard-dose CT. Materials and Methods: A total of 32 patients were prospectively enrolled, and all underwent standard-dose and ultra-low-dose (120 kVp; CTDIvol < 0.7 mGy) chest CT scans at the same time in a single examination. A total of six image datasets (filtered back projection (FBP) for standard-dose CT, and FBP, adaptive statistical iterative reconstruction (ASIR-V) 50%, DLIR-low, DLIR-medium, DLIR-high for ultra-low-dose CT) were reconstructed for each patient. Image noise values, emphysema indices, total lung volumes, and mean lung attenuations were measured in the six image datasets and compared (one-way repeated measures ANOVA). Results: The mean effective doses for standard-dose and ultra-low-dose CT scans were 3.43 ± 0.57 mSv and 0.39 ± 0.03 mSv, respectively (p < 0.001). The total lung volume and mean lung attenuation of five image datasets of ultra-low-dose CT scans, emphysema indices of ultra-low-dose CT scans reconstructed using ASIR-V 50 or DLIR-low, and the image noise of ultra-low-dose CT scans that were reconstructed using DLIR-low were not different from those of standard-dose CT scans. Conclusions: Ultra-low-dose CT images that were reconstructed using DLIR-low were found to be useful for emphysema quantification at a radiation dose of only 11% of that required for standard-dose CT.

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