Relevant bibliographies by topics / Diffusion-weighted MRI

Contents

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

Academic literature on the topic 'Diffusion-weighted MRI'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Diffusion-weighted MRI"

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Carbonetti, Francesco, and Marco Di Girolamo. "Diffusion-Weighted MRI in Thymomas." Tumori Journal 102, no. 1 (2016): e4-e4. http://dx.doi.org/10.5301/tj.5000410.

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Azhar, M., S. Shakil, A. Greiner, D. Kauzlarić, and J. G. Korvink. "DPD of diffusion-weighted MRI." Computers & Fluids 172 (August 2018): 467–73. http://dx.doi.org/10.1016/j.compfluid.2018.03.067.

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Moseley, Michael E., John Kucharczyk, Haleh S. Asgari, and David Norman. "Anisotropy in diffusion-weighted MRI." Magnetic Resonance in Medicine 19, no. 2 (1991): 321–26. http://dx.doi.org/10.1002/mrm.1910190222.

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Neumann-Haefelin, Tobias, Hans-Jörg Wittsack, Frank Wenserski, et al. "Diffusion- and Perfusion-Weighted MRI." Stroke 30, no. 8 (1999): 1591–97. http://dx.doi.org/10.1161/01.str.30.8.1591.

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Neumann-Haefelin, Tobias, Hans-Jörg Wittsack, Gereon R. Fink, et al. "Diffusion- and Perfusion-Weighted MRI." Stroke 31, no. 6 (2000): 1311–17. http://dx.doi.org/10.1161/01.str.31.6.1311.

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Lavdas, Ioannis, Kevin C. Behan, Annie Papadaki, Donald W. McRobbie, and Eric O. Aboagye. "A phantom for diffusion-weighted MRI (DW-MRI)." Journal of Magnetic Resonance Imaging 38, no. 1 (2013): 173–79. http://dx.doi.org/10.1002/jmri.23950.

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Akçay, Ahmet, and Abdusselim Adil Peker. "Diagnostic Comparison of MRI Sequences for Assessing Myometrial Invasion in Endometrial Cancer: A 1.5T MRI Study." Hitit Medical Journal 7, no. 1 (2025): 29–36. https://doi.org/10.52827/hititmedj.1559453.

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Objective: To compare the diagnostic accuracy of T2-weighted, dynamic contrast-enhanced imaging, and diffusion-weighted imaging in assessing myometrial invasion depth in endometrial cancer and to determine if any sequence performs better. Material and Method: In this retrospective study, 98 patients with histopathologically confirmed endometrial cancer underwent preoperative multiparametric pelvic magnetic resonance imaging scans using a 1.5-T machine between January 2018 and May 2024. The T2-weighted, dynamic contrast-enhanced, and diffusion-weighted imaging sequences were independently revie
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Hall, Matt G., and Thomas R. Barrick. "From diffusion-weighted MRI to anomalous diffusion imaging." Magnetic Resonance in Medicine 59, no. 3 (2008): 447–55. http://dx.doi.org/10.1002/mrm.21453.

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Bhandari, Dr Gaurav, Dr Alka Agrawal, Dr P. S. Tripathi, Dr Neha Nischal, and Dr Vibhu Goel. "Role of Diffusion Weighted MRI in Intra-Axial Brain Tumours." Scholars Journal of Applied Medical Sciences 4, no. 6 (2016): 2111–19. http://dx.doi.org/10.21276/sjams.2016.4.6.49.

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Baehring, Joachim M., and Robert K. Fulbright. "Diffusion-weighted MRI in neuro-oncology." CNS Oncology 1, no. 2 (2012): 155–67. http://dx.doi.org/10.2217/cns.12.28.

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Dissertations / Theses on the topic "Diffusion-weighted MRI"

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McGraw, Tim E. "Denoising, segmentation and visualization of diffusion weighted MRI." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011618.

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Gaige, Terry A. (Terry Alden) 1981. "Complex muscle architecture described with diffusion weighted MRI." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39867.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.<br>Includes bibliographical references (p. 89-98).<br>The complex array of fiber orientations exhibited by muscles such as the tongue, esophagus, and heart, enable function beyond basic pulling. Among other things, the presence of crossing geometry adds the ability to push by bi-directional contraction causing expansion in the orthogonal direction. Diffusion weighted magnetic resonance imaging (DW-MRI), provides a convenient, non-destructive, method for deriving fiber architecture in many tissues. DW-M
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Mosavi, Firas. "Whole-Body MRI including Diffusion-Weighted Imaging in Oncology." Doctoral thesis, Uppsala universitet, Enheten för radiologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-209777.

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Cancer is one of the major causes of worldwide mortality. Imaging plays a vital role in the staging, follow-up, and evaluation of therapeutic response in cancer patients. Whole-body (WB) magnetic resonance imaging (MRI), as a non-ionizing imaging technique, is a promising procedure to assess tumor spreading in a single examination. New MRI technological developments now enable the application of diffusion-weighted imaging (DWI) of the entire body. DWI reflects the random motion of water molecules and provides functional information of body tissues. DWI can be quantified with the use of the app
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Blackledge, Matthew David. "Assessing response in bone metastases using diffusion weighted MRI." Thesis, University of London, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.548045.

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Dhital, Bibek. "Characterizing Brain White Matter with Diffusion-Weighted Magnetic Resonance." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-180140.

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It has been known for almost two decades that the water proton NMR signal of diffusing water molecules in brain white matter undergoes a non-monoexponential decay with increasing diffusion gradient factor b. With the help of numerical simulations and analytical expressions, much effort has been directed to describing the signal decay and to extracting relevant biophysical features of the system under investigation. However, the physical basis of such nonmonoexponential behavior is still not properly understood. The primary difficulty in characterizing this phenomenon is the variation in behav
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Wilm, Bertram Jakob. "Diffusion-weighted MRI : volume selection, field monitoring and image reconstruction /." [S.l.] : [s.n.], 2009. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=18318.

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Schob, Stefan, Jonas Meyer, Matthias Gawlitza, et al. "Diffusion-weighted MRI reflects proliferative activity in primary CNS lymphoma." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-210909.

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Purpose: To investigate if apparent diffusion coefficient (ADC) values within primary central nervous system lymphoma correlate with cellularity and proliferative activity in corresponding histological samples. Materials and Methods: Echo-planar diffusion-weighted magnetic resonance images obtained from 21 patients with primary central nervous system lymphoma were reviewed retrospectively. Regions of interest were drawn on ADC maps corresponding to the contrast enhancing parts of the tumors. Biopsies from all 21 patients were histologically analyzed. Nuclei count, total nuclei area and average
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McHugh, Damien Joseph. "The effect of tumour microstructure on diffusion-weighted MRI measurements." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/the-effect-of-tumour-microstructure-on-diffusionweighted-mri-measurements(9821717e-df69-4dd0-baf7-51cf27a18aa2).html.

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By sensitising the magnetic resonance signal to the diffusion of water molecules in tissue, diffusion-weighted magnetic resonance imaging provides a means of assessing tumour microstructure non-invasively. Such measurements have the potential to provide important information about tumour development and the response of tumours to treatment, but the way in which different tissue properties affect the diffusion-weighted signal remains unclear. Through simulations, in vivo studies and phantom experiments, this thesis investigates the relationship between the diffusion-weighted signal, the pulse s
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Nguyen, Huyen Thanh. "Dynamic Contrast-Enhanced MRI and Diffusion-Weighted MRI for the Diagnosis of Bladder Cancer." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365176629.

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Sahalan, Mariaulpa. "Diffusion-weighted Imaging of Lymph Node Tissue." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20070.

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Purpose: The study investigates the hypothesis of clinically observed decreased apparent diffusion coefficient (ADC) of cancerous lymph nodes can be attributed to increased cellularity. The study characterises the mean diffusivity (MD) of lymph node sub-structures and investigates correlation between MD and cellularity metrics. The study also investigates the theoretical information content of single and multi-biophysical models. Methods:. A 3 mm diameter core sample was extracted from a formalin fixed lymph node tissue post-surgery and imaged using 9.4T and 16.4T Bruker MRI system. Samples
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Books on the topic "Diffusion-weighted MRI"

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Taouli, Bachir, ed. Extra-Cranial Applications of Diffusion-Weighted MRI. Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511778070.

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Taouli, Bachir. Extra-Cranial Applications of Diffusion-Weighted MRI. Cambridge University Press, 2010.

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Extra-Cranial Applications of Diffusion-Weighted MRI. Cambridge University Press, 2010.

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Taouli, Bachir. Extra-Cranial Applications of Diffusion-Weighted MRI. Cambridge University Press, 2010.

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Taouli, Bachir. Extra-Cranial Applications of Diffusion-Weighted Mri. Cambridge University Press, 2010.

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Goel, Mohit, Sanjeev Singhal, and Saurabh Chaudhuri. Role of Diffusion Weighted MR Imaging in Abdomen and Pelvic Lesions: Diffusion Weighted MRI. LAP Lambert Academic Publishing, 2015.

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Özarslan, Evren. Developments in diffusion weighted Magnetic Resonance Imaging (MRI) with applications to neural tissue. 2004.

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Gardner, Andrew, Grant L. Iverson, Paul van Donkelaar, Philip N. Ainslie, and Peter Stanwell. Magnetic Resonance Spectroscopy, Diffusion Tensor Imaging, and Transcranial Doppler Ultrasound Following Sport-Related Concussion. Edited by Ruben Echemendia and Grant L. Iverson. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199896585.013.12.

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Sport-related concussion has been referred to as a functional rather than a structural injury with neurometabolic and microstructural alterations reported in several studies. Accordingly, conventional neuroimaging techniques, such as computed tomography (CT) and structural magnetic resonance imaging (MRI), have limited value beyond ruling out structural injury such as a contusion or hemorrhage. This chapter presents a review of three neuroimaging techniques that offer insight into the connectivity and neurometabolic consequences of concussion. A number of studies have now been published using
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Glockner, James F., Kazuhiro Kitajima, and Akira Kawashima. Magnetic resonance imaging. Edited by Christopher G. Winearls. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199592548.003.0015_update_001.

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Magnetic resonance imaging (MRI) provides excellent anatomic detail and soft tissue contrast for the evaluation of patients with renal disease. MRI needs longer scan time than computed tomography (CT); however, no radiation is involved. Gadolinium-based contrast agents (GBCAs) are used to help provide additional image contrast during MRI. MRI is indicated for characterization of renal mass, staging of malignant renal neoplasms, and determination of vena cava involvement by the renal tumour. Magnetic resonance (MR) angiography is widely accepted as a non-invasive imaging work-up of renal artery
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Das, Raj, Susan Heenan, and Uday Patel. Magnetic resonance imaging in urology. Edited by Michael Weston. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199659579.003.0134.

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Magnetic resonance imaging is essential for urological imaging. It offers excellent soft tissue contrast and resolution, allowing manipulation of tissue contrast with different image weighting and sequences. The multiplanar aspect of MRI allows image acquisition in different planes and degrees of obliquity to best exhibit pathology. The basic physics of MRI is explored initially with explanation of image weighting, sequences, and diffusion-weighted imaging. The chapter is then divided into renal, bladder, and prostate MRI imaging. The paragraphs on renal MRI outline renal mass analysis and inc
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Book chapters on the topic "Diffusion-weighted MRI"

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Filippi, M., and M. A. Rocca. "Diffusion-Weighted MRI." In New Frontiers of MR-based Techniques in Multiple Sclerosis. Springer Milan, 2003. http://dx.doi.org/10.1007/978-88-470-2237-9_4.

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Akin, Oguz, and Yousef Mazaheri. "Diffusion-Weighted Imaging." In Prostate MRI Essentials. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45935-2_5.

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Padhani, Anwar R., and Shilpan M. Patel. "Diffusion-Weighted Imaging." In Clinical MRI of the Abdomen. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-85689-4_29.

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Guadilla, Irene, Daniel Calle, and Pilar López-Larrubia. "Diffusion-Weighted Magnetic Resonance Imaging." In Preclinical MRI. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7531-0_6.

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D’Agata, Federico, Mario Coriasco, and Alberto De Luca. "Introduction to Diffusion Weighted MRI." In A Practical Guide to Advanced Diffusion MRI. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-70337-9_2.

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Chauvin, Laurent, Kuldeep Kumar, Christian Desrosiers, Jacques De Guise, and Matthew Toews. "Diffusion Orientation Histograms (DOH) for Diffusion Weighted Image Analysis." In Computational Diffusion MRI. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73839-0_7.

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Fiebach, J. B., P. D. Schellinger, S. Heiland, and K. Sartor. "Conventional MRI, Diffusion-weighted MRI (DWI) and Apparent Diffusion Coefficient (ADC)." In Stroke MRI. Steinkopff, 2003. http://dx.doi.org/10.1007/978-3-642-57387-3_4.

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Dela Haije, Tom, and Aasa Feragen. "Conceptual Parallels Between Stochastic Geometry and Diffusion-Weighted MRI." In Mathematics and Visualization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_9.

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AbstractDiffusion-weighted magnetic resonance imaging (MRI) is sensitive to ensemble-averaged molecular displacements, which provide valuable information on e.g. structural anisotropy in brain tissue. However, a concrete interpretation of diffusion-weighted MRI data in terms of physiological or structural parameters turns out to be extremely challenging. One of the main reasons for this is the multi-scale nature of the diffusion-weighted signal, as it is sensitive to the microscopic motion of particles averaged over macroscopic volumes. In order to analyze the geometrical patterns that occur i
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Ceranka, Jakub, Mathias Polfliet, Frederic Lecouvet, Nicolas Michoux, Johan de Mey, and Jef Vandemeulebroucke. "Registration Strategies for Whole-Body Diffusion-Weighted MRI Stitching." In Computational Diffusion MRI. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28588-7_17.

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Alcalá, Lidia, Teodoro Martín, and Antonio Luna. "Diffusion-Weighted Imaging of Anorectal Region." In Diffusion MRI Outside the Brain. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21052-5_12.

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Conference papers on the topic "Diffusion-weighted MRI"

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Wu, Yinzhe, Jiahao Huang, Fanwen Wang, et al. "Enhancing Diffusion-Weighted Images (DWI) for Diffusion MRI: Is it Enough without Non-Diffusion-Weighted B=0 Reference?" In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980956.

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Yoon, Jongyeon, Elyssa M. McMaster, Chloe Cho, Kurt G. Schilling, Bennett A. Landman, and Daniel Moyer. "Tractography enhancement in clinically-feasible diffusion MRI using T1-weighted MRI and anatomical context." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047112.

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Su, Michelle, Cemre Ariyurek, Serge Vasylechko, Onur Afacan, and Sila Kurugol. "Optimizing Respiratory Phase Binning for Improved Motion Correction in Abdominal Diffusion-Weighted MRI." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981053.

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Ghorbani, Reza, Jyothi Rikhab Chand, Chu-Yu Lee, Mathews Jacob, and Merry Mani. "Three-Dimensional Diffusion-Weighted Multi-Slab MRI with Slice Profile Compensation Using Deep Energy Model." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981200.

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Sotiropoulos, Stamatios N., Timothy E. Behrens, and Saad Jbabdi. "Modelling fibre fanning in diffusion-weighted MRI." In 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isbi.2012.6235472.

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Özcan, Alpay. "A new model for diffusion weighted MRI: Complete Fourier direct MRI." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5626530.

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Arkesteijn, G. A. M., D. H. J. Poot, M. de Groot, et al. "CSF contamination-invariant statistics in diffusion-weighted MRI." In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015). IEEE, 2015. http://dx.doi.org/10.1109/isbi.2015.7163909.

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Kafali, Sevgi Gokce, Tolga Cukur, and Emine Ulku Saritas. "Simultaneous phase-correction and denoising for diffusion-weighted MRI." In 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7495989.

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Zhukov, Leonid, Ken Museth, David E. Breen, and Ross T. Whitaker. "3D modeling and segmentation of diffusion weighted MRI data." In Medical Imaging 2001, edited by Seong K. Mun. SPIE, 2001. http://dx.doi.org/10.1117/12.428081.

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Serpi, F., S. Gitto, M. Bologna, et al. "Diffusion-weighted MRI Radiomics Classification of Spinal Bone Tumors." In 28th Annual Scientific Meeting of the European Society of Musculoskeletal Radiology (ESSR), Virtual Edition, June 2021. Thieme Medical Publishers, Inc., 2021. http://dx.doi.org/10.1055/s-0041-1731518.

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Reports on the topic "Diffusion-weighted MRI"

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โควาวิสารัช, นงลักษณ์. MRI brain segmentation : โครงการวิจัยโครงการย่อยที่ 4 : รายงานฉบับสมบูรณ์. คณะวิศวกรรมศาสตร์ จุฬาลงกรณ์มหาวิทยาลัย, 2003. https://doi.org/10.58837/chula.res.2003.50.

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ศึกษา ออกแบบเทคนิควิธีการแบ่งส่วนภาพของสมองและพัฒนาโปรแกรมเพื่อแบ่งส่วนภาพของสมองจากชุดภาพถ่าย MRI ของสมองโดยใช้วิธีใช้ค่าขีดแบ่งและวิธี 3D morphological watershed เป็นวิธีแบ่งส่วนแบบอัตโนมัติ โดยวิธีการแบ่งส่วนโดยอัตโนมัติที่ใช้ค่าขีดแบ่งที่พัฒนาขึ้น พัฒนาจากพื้นฐานความรู้ที่ว่าระดับความสว่างของสมองแบบ White matter, gray matter และ CSF ในภาพ T1-weighted MRI ของสมองจะมีความสว่างจากมากไปน้อย ดังนั้นวิธีใช้ค่าขีดแบ่งที่พัฒนานี้จึงให้ผลการแบ่งส่วนสมองที่ดีกับภาพที่มีข้อมูลในลักษณะดังกล่าว ส่วนวิธี 3D morphological watershed เป็นวิธีที่เหมาะจะใช้ภาพที่บริเวณสมองมีความสว่างที่กลมกลืนกันและไม่แตกต่า
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MR (Diffusion-Weighted Imaging (DWI) of the Apparent Diffusion Coefficient (ADC), Clinically Feasible Profile. Chair Michael Boss, Dariya Malyarenko, and Daniel Margolis. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2022. http://dx.doi.org/10.1148/qiba/20221215.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. The Claim (Section 2) describes the biomarker performance and is derived from the body of scientific literature meeting specific requirements, in particular test-retest studies. The Activities (Section 3) contribute to generating the biomarker. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim. Assessment Procedures (Section 4) for evaluating specific requirements are defined as needed to ensure acceptable performance. Diffusion-Weighted
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