Relevant bibliographies by topics / MRI quantification

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'MRI quantification'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "MRI quantification"

1

Murali, P., P. Rodrigo, K. Ranga, M. M., and G. S. "MRI quantification." Neurology 41, no. 6 (1991): 954. http://dx.doi.org/10.1212/wnl.41.6.954.

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Dyverfeldt, Petter, Roland Gårdhagen, Andreas Sigfridsson, Matts Karlsson, and Tino Ebbers. "On MRI turbulence quantification." Magnetic Resonance Imaging 27, no. 7 (2009): 913–22. http://dx.doi.org/10.1016/j.mri.2009.05.004.

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Banerjee, A. K. "MRI quantification of obesity." Clinical Radiology 64, no. 8 (2009): 845–47. http://dx.doi.org/10.1016/j.crad.2009.03.008.

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van der Geest, Rob J., and Johan H. C. Reiber. "Quantification in cardiac MRI." Journal of Magnetic Resonance Imaging 10, no. 5 (1999): 602–8. http://dx.doi.org/10.1002/(sici)1522-2586(199911)10:5<602::aid-jmri3>3.0.co;2-c.

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Jerosch-Herold, Michael, and Ravi V. Shah. "Quantification of Myocardial Perfusion: MRI." Current Cardiovascular Imaging Reports 5, no. 3 (2012): 158–66. http://dx.doi.org/10.1007/s12410-012-9135-7.

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Kholmovski, Eugene G., Alan K. Morris, and Mihail G. Chelu. "Cardiac MRI and Fibrosis Quantification." Cardiac Electrophysiology Clinics 11, no. 3 (2019): 537–49. http://dx.doi.org/10.1016/j.ccep.2019.04.005.

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Deshmukh, Anjali R., John E. Desmond, Edith V. Sullivan, et al. "Quantification of cerebellar structures with MRI." Psychiatry Research: Neuroimaging 75, no. 3 (1997): 159–71. http://dx.doi.org/10.1016/s0925-4927(97)00051-6.

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Globits, S., H. Mayr, H. Frank, A. Neuhold, and D. Glogar. "Quantification of regurgitant lesions by MRI." International Journal of Cardiac Imaging 6, no. 2 (1990): 109–16. http://dx.doi.org/10.1007/bf02398894.

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Edupuganti, Vineet, Morteza Mardani, Shreyas Vasanawala, and John Pauly. "Uncertainty Quantification in Deep MRI Reconstruction." IEEE Transactions on Medical Imaging 40, no. 1 (2021): 239–50. http://dx.doi.org/10.1109/tmi.2020.3025065.

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Rocca, Maria A., Marco Battaglini, Ralph H. B. Benedict, et al. "Brain MRI atrophy quantification in MS." Neurology 88, no. 4 (2016): 403–13. http://dx.doi.org/10.1212/wnl.0000000000003542.

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Patients with the main clinical phenotypes of multiple sclerosis (MS) manifest varying degrees of brain atrophy beyond that of normal aging. Assessment of atrophy helps to distinguish clinically and cognitively deteriorating patients and predicts those who will have a less-favorable clinical outcome over the long term. Atrophy can be measured from brain MRI scans, and many technological improvements have been made over the last few years. Several software tools, with differing requirements on technical ability and levels of operator intervention, are currently available and have already been a
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Dissertations / Theses on the topic "MRI quantification"

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Peterson, Erika. "Synthetic MRI for visualization of quantitative MRI." Thesis, Linköpings universitet, Avdelningen för radiologiska vetenskaper, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-102651.

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Magnetic resonance imaging (MRI) is an imaging technique that is used in hospitals worldwide. The images are acquired through the use of an MRI scanner and the clinical information is provided through the image contrast, which is based on the magnetic properties in biological tissue. By altering the scanner settings, images with different contrast properties can be obtained. Conventional MRI is a qualitative imaging technique and no absolute measurements are performed. At Center for Medical Imaging and Visualization (CMIV) researchers are developing a new MRI technique named synthetic MRI (SyM
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Dyverfeldt, Petter. "Extending MRI to the Quantification of Turbulence Intensity." Doctoral thesis, Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-52561.

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In cardiovascular medicine, the assessment of blood flow is fundamental to the understanding and detection of disease. Many pharmaceutical, interventional, and surgical treatments impact the flow. The primary purpose of the cardiovascular system is to drive, control and maintain blood flow to all parts of the body. In the normal cardiovascular system, fluid transport is maintained at high efficiency and the blood flow is essentially laminar. Disturbed and turbulent blood flow, on the other hand, appears to be present in many cardiovascular diseases and may contribute to their initiation and pr
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Ingrisch, Michael. "Quantification of cerebral hemodynamics with dynamic contrast-enhanced MRI." Diss., lmu, 2012. http://nbn-resolving.de/urn:nbn:de:bvb:19-149513.

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Bustamante, Mariana. "Detection and Quantification of Small Changes in MRI Volumes." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-219487.

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The focus of this research is to attempt to solve the problem of comparing two MRI brain volumes of the same subject taken at different times, and detect the location and size of the differences between them, especially when such differences are too small to be perceived with the naked eye. The research focuses on a combination of registration and morphometry techniques in order to create two different possible solutions: A voxel-based method and a tensor-based method. The first method uses Affine or B-Spline registration combined with voxel-by-voxel subtraction of the volumes; the second meth
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Lin, Hung-Yu. "REAL-TIME FLOW QUANTIFICATION TECHNIQUES IN CARDIOVASCULAR MRI APPLICATIONS." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1238594589.

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Vitouš, Jiří. "MRI potkanů - kvantifikace T1 myokardu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442490.

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This thesis focuses on cardiac imaging and quantification of T1 relaxation time in rat hearts. Its main focus is to investigate available methods for such quantification and their application in the development of quantification tools. The large impact is given to methods of acquisition synchronization, mainly with respect to cardiac motion and breathing using retrospective gating, where the navigator signal is obtained solely from the acquired data, so without any external equipment such as the ECG or respiratory sensors. This paper takes into account situations where steady-state has been re
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Gibert, Guillaume. "Quantification of the Cerebral Perfusion with the Arterial Spin Labelling 3D-MRI method. Quantification of the Cerebral Perfusion with the Arterial Spin Labelling 3D-MRI method." Thesis, KTH, Skolan för teknik och hälsa (STH), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-148020.

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The Arterial Spin Labelling (ASL) method is a Magnetic Resonance technique used toquantify the cerebral perfusion. It has the big advantage to be non-invasive so doesn’tneed the injection of any contrast agent. But due to a relatively low Signal-to-NoiseRatio (SNR) of the signal acquired (only approximately 1% of the image intensity), ithas been hampered to be widely used in a clinical setting so far.The primary objective of this project is to make the method more robust by improvingthe quality of the images, the SNR, and by reducing the acquisition time. DifferentASL protocols with different
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Kadir, Kushsairy Abdul. "Automatic edema segmentation and quantification from cardiac MRI with 3D visualization." Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=25810.

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The extent of myocardial edema delineates the ischemic area-at-risk (AAR) after myocardial infarction (MI). Since AAR can be used to estimate the amount of salvageable myocardial post-MI, edema imaging has potential clinical utility in the management of acute MI patients. T2 weighted Cardiac Magnetic Resonance (CMR) imaging is widely used to investigate the extent of edema with recent acute MI patient. This thesis describes new approaches and methods of automatic edema segmentation and quantification with 3D visualization. An integrated approach has been developed, including the localization o
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Zananiri, F. V. "The quantification of dynamic processes measured by magnetic resonance imaging." Thesis, University of Bristol, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246264.

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Wallin, Ashley Kay. "Renal Arterial Blood Flow Quantification by Breath-held Phase-velocity Encoded MRI." Thesis, Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4982.

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Autosomal dominant polycystic disease (ADPKD) is the most common hereditary renal disease and is characterized by renal cyst growth and enlargement. Hypertension occurs early when renal function is normal and is characterized by decreased renal blood flow. Accordingly, the measurement of blood flow in the renal arteries can be a valuable tool in evaluating disease progression. In studies performed in conjunction with this work, blood flow was measured through the renal arteries using magnetic resonance imaging (MRI). In order to validate these in vivo measurements, a vascular phantom was c
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Books on the topic "MRI quantification"

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Société mathématique de France. Journées. Feuilletages riemanniens, quantification géométrique et mécanique: Journées lyonnaises de la Société mathématique de France, 26-30 mai 1986, dédiées à A. Lichnerowicz. Hermann, 1988.

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Chappell, Michael, Bradley MacIntosh, and Thomas Okell. Introduction to Perfusion Quantification using Arterial Spin Labelling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793816.001.0001.

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Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is unique in being a completely non-invasive method for imaging perfusion in the brain. Relying upon a blood-borne tracer that is created by the MRI scanner itself, ASL is becoming a popular tool to study cerebral perfusion, as well as how this perfusion changes in response to neuronal activity or in disease. This primer provides an introduction to perfusion quantification using ASL MRI, focusing both on the methods needed to extract perfusion-weighted images and on how to quantify perfusion and other hemodynamic parameters. Startin
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Chappell, Michael, Bradley MacIntosh, and Thomas Okell. Partial Volume Effects. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793816.003.0006.

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Partial volume effects are present in all medical imaging methods, and they play a specific role in arterial spin labeling (ASL) MRI measurements of perfusion. This chapter demonstrates how differences in the perfusion properties of gray matter, white matter, and cerebrospinal fluid give rise to the distinctive visual appearance of cerebral perfusion images. The implications of this for quantification of perfusion in gray matter are discussed and solutions to correct for partial volume effects presented.
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Chappell, Michael, Bradley MacIntosh, and Thomas Okell. Kinetic Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793816.003.0004.

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The quantification of perfusion from arterial spin labeling (ASL) perfusion MRI data relies upon the principles of tracer kinetics. This chapter first outlines the simplest form of a tracer kinetic model that can be applied to ASL data, before exploring variations on this model that can be applied to extract other hemodynamic information such as arterial transit time. Finally, the chapter examines how tracer kinetic models are used with data to estimate perfusion parameters, including the use of model fitting and Bayesian inference.
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Morrison, Alan R., Joseph C. Wu, and Mehran M. Sadeghi. Cardiovascular Molecular Imaging. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0029.

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Cardiovascular molecular imaging is a relatively young but rapidly expanding discipline that consists of a biologically-targeted approach to the assessment of physiologic and pathologic processes in vivo. This novel approach to imaging involves the integration of multiple disciplines such as cell and molecular biology, chemistry, and imaging sciences. The ultimate goal is quantitative assessment of cardiovascular processes at the cellular and molecular level, moving beyond traditional diagnostic information, in order to guide individually tailored therapy. In fact, it is likely that specific a
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Lancellotti, Patrizio, Julien Magne, Kim O’Connor, and Luc A. Pierard. Mitral valve disease. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199599639.003.0015.

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Native mitral valve disease is the second valvular heart disease after aortic valve disease. For the last few decades, two-dimensional Doppler echocardiography was the cornerstone technique for evaluating patients with mitral valve disease. Besides aetiological information, echocardiography allows the description of valve anatomy, the assessment of disease severity, and the description of the associated lesions.This chapter will address the echocardiographic evaluation of mitral regurgitation (MR) and mitral stenosis (MS).In MR, the following findings should be assessed: 1. Aetiology. 2. Type
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France, Societe mathematique de. Feuilletages riemanniens, quantification geometrique et mecanique: Journees lyonnaises de la Societe mathematique de France, 26-30 mai 1986, dediees a ... (Seminaire sud-rhodanien de geometrie). Hermann, 1988.

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Book chapters on the topic "MRI quantification"

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Kunth, Martin, and Leif Schröder. "CEST MRI." In Quantification of Biophysical Parameters in Medical Imaging. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65924-4_10.

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Haacke, E. Mark, Karthik Prabhakaran, Ilaya Raja Elangovan, Zhen Wu, and Jaladhar Neelavalli. "Oxygen Saturation: Quantification." In Susceptibility Weighted Imaging in MRI. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470905203.ch27.

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Chiribiri, Amedeo. "Cardiac Perfusion MRI." In Quantification of Biophysical Parameters in Medical Imaging. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65924-4_22.

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Schmitter, Sebastian, and Susanne Schnell. "4D Flow MRI." In Quantification of Biophysical Parameters in Medical Imaging. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65924-4_9.

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Holm, Andreas Nugaard, Aasa Feragen, Tom Dela Haije, and Sune Darkner. "Deterministic Group Tractography with Local Uncertainty Quantification." In Computational Diffusion MRI. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05831-9_30.

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Sánchez-González, Javier, and Antonio Luna. "Quantification and Postprocessing of DWI." In Diffusion MRI Outside the Brain. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21052-5_3.

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Jung, Bernd, and Michael Markl. "Phase-Contrast MRI and Flow Quantification." In Magnetic Resonance Angiography. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-1686-0_3.

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Francois, Christopher. "MR Flow and Quantification." In Protocols and Methodologies in Basic Science and Clinical Cardiac MRI. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53001-7_10.

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Tummala, Sudhakar, Erik B. Dam, and Mads Nielsen. "Automatic Quantification of Congruity from Knee MRI." In Computational Biomechanics for Medicine. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3172-5_7.

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Kolbitsch, Christoph, and Tobias Schaeffter. "Acceleration Strategies for Data Sampling in MRI." In Quantification of Biophysical Parameters in Medical Imaging. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65924-4_8.

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Conference papers on the topic "MRI quantification"

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Antony, Joseph, Kevin McGuinness, Neil Welch, et al. "Fat quantification in MRI-defined lumbar muscles." In 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2014. http://dx.doi.org/10.1109/ipta.2014.7001983.

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Farias, Fabiano Ricardo, Pedro Costa Klein, Ricardo Bernardi Soder, Jefferson Becker, and Marcio Sarroglia Pinho. "MRI Interpolation for Multiple Sclerosis Lesion Quantification." In 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2016. http://dx.doi.org/10.1109/compsac.2016.159.

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Figueiredo, Patricia, and Jodo Sanches. "Sampling strategy for perfusion quantification using PASL-MRI." In 2008 5th IEEE International Symposium on Biomedical Imaging (ISBI 2008). IEEE, 2008. http://dx.doi.org/10.1109/isbi.2008.4541061.

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Sacha, Jaroslaw P., Michael D. Cockman, Thomas E. Dufresne, and Darren Trokhan. "Quantification of regional fat volume in rat MRI." In Medical Imaging 2003, edited by Anne V. Clough and Amir A. Amini. SPIE, 2003. http://dx.doi.org/10.1117/12.480405.

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Guo, Zhipeng, Yi Xin, Shuai Liu, Xiaodan Lv, and Shuai Li. "Comparisons of fat quantification methods based on MRI segmentation." In 2014 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2014. http://dx.doi.org/10.1109/icma.2014.6885977.

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Valindria, Vanya V., Marion Angue, Nicolas Vignon, Paul M. Walker, Alexandre Cochet, and Alain Lalande. "Automatic Quantification of Myocardial Infarction from Delayed Enhancement MRI." In Internet-Based Systems (SITIS 2011). IEEE, 2011. http://dx.doi.org/10.1109/sitis.2011.83.

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Beattie, E. E., J. H. Yoder, S. M. Moon, E. J. Vresilovic, D. M. Elliott, and A. C. Wright. "Quantification of intervertebral disc cartilaginous endplate morphology using MRI." In 2012 38th Annual Northeast Bioengineering Conference (NEBEC). IEEE, 2012. http://dx.doi.org/10.1109/nebc.2012.6206983.

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Layton, Kelvin, Mark Morelande, Leigh A. Johnston, Peter M. Farrell, and Bill Moran. "Improved quantification of MRI relaxation rates using Bayesian estimation." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495694.

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TRIPOLITI, EVANTHIA E., DIMITRIOS I. FOTIADIS, and MARIA ARGYROPOULOU. "AUTOMATED DIAGNOSIS AND QUANTIFICATION OF RHEUMATOID ARTHRITIS USING MRI." In Proceedings of the Sixth International Workshop. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702593_0062.

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Carminati, M. Chiara, Cinzia Boniotti, Mauro Pepi, and Enrico G. Caiani. "Quantification of myocardial viability in late Gadolinium enhancement Cardiac MRI." In 2015 Computing in Cardiology Conference (CinC). IEEE, 2015. http://dx.doi.org/10.1109/cic.2015.7408595.

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Reports on the topic "MRI quantification"

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Shafiiha, Roshanak. Combined MR and Optical Imaging System for Noninvasive Tumor Characterization and Quantification of Oxygenation Gain Factor in a Breast Cancer Animal Model. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada472342.

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