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Journal articles on the topic 'CT quantification'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 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 failu

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3

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 jus

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5

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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6

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 decomposi

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7

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 t

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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

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18

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,

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19

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

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21

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, mes

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22

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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25

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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29

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 failu

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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 con

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32

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 (

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33

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 cardia

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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 myocardia

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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 relatio

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36

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

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37

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 tim

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38

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 post

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39

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

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40

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 subj

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41

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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43

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. Clinica

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44

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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46

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 pr

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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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48

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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50

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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