Relevant bibliographies by topics / Diffusion Weighted MR Imaging

Academic literature on the topic 'Diffusion Weighted MR Imaging'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Diffusion Weighted MR Imaging"

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Ramsing, B., and P. Corr. "Diffusion weighted MR imaging." South African Journal of Radiology 3, no. 3 (August 31, 1998): 4–6. http://dx.doi.org/10.4102/sajr.v3i3.1570.

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Diffusion weighted imaging (DWI) allows the measurement of molecular motion in tissue. This technique has significant clinical applications. Recent technological developments in fast MR imaging have brought diffusion imaging into clinical practice. This review will explain the physical principles, and current and future potential applications of diffusion imaging in medicine.
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Youn, Byung Jae, Jin Wook Chung, Kyu Ri Son, Hyo-Cheol Kim, Hwan Jun Jae, Jeong Min Lee, In Chan Song, In-One Kim, and Jae Hyung Park. "Diffusion-Weighted MR." Academic Radiology 15, no. 5 (May 2008): 593–600. http://dx.doi.org/10.1016/j.acra.2007.10.022.

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Goyal, Mayank, Aravind Ganesh, Michael Tymianski, Michael D. Hill, and Johanna Maria Ospel. "Iatrogenic Diffusion-Weighted Imaging Lesions." Stroke 52, no. 5 (May 2021): 1929–36. http://dx.doi.org/10.1161/strokeaha.120.033984.

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Infarct volume in acute ischemic stroke is closely linked with clinical outcome, with larger infarct volumes being associated with a worse prognosis. Small iatrogenic infarcts, which can occur as a result of surgical or endovascular procedures, are often only seen on diffusion-weighted MR imaging. They often do not lead to any overtly appreciable clinical deficits, hence the term covert or silent infarcts. There is relative paucity of data on the clinical impact of periprocedural hyperintense diffusion-weighted MR imaging lesions, partly because they commonly remain undiagnosed. Clearly, a better understanding of iatrogenic periprocedural diffusion-weighted MR imaging lesions and their clinical significance is needed. In this article, we describe the current limitations of our understanding of the significance of iatrogenic diffusion-weighted MR imaging lesions using exemplary data from the ENACT trial (Safety and Efficacy of NA-1 in Patients With Iatrogenic Stroke After Endovascular Aneurysm Repair) and outline a framework for how to investigate their clinical impact.
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Tsuchiya, K., S. Katase, A. Yoshino, and J. Hachiya. "Diffusion-weighted MR imaging of encephalitis." American Journal of Roentgenology 173, no. 4 (October 1999): 1097–99. http://dx.doi.org/10.2214/ajr.173.4.10511186.

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Chan, J. H. M., E. Y. K. Tsui, S. H. Luk, S. L. Fung, Y. K. Cheung, M. S. M. Chan, M. K. Yuen, S. F. Mak, and K. P. C. Wong. "MR diffusion-weighted imaging of kidney." Clinical Imaging 25, no. 2 (March 2001): 110–13. http://dx.doi.org/10.1016/s0899-7071(01)00246-7.

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Kovoor, J. M. E., P. N. Jayakumar, A. S. Guruprasad, S. K. Shankar, and B. Anandh. "Diffusion Weighted MR Imaging in Glioma." Rivista di Neuroradiologia 16, no. 6 (December 2003): 1065–67. http://dx.doi.org/10.1177/197140090301600606.

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Patay, Zoltan. "Diffusion-weighted MR imaging in leukodystrophies." European Radiology 15, no. 11 (July 15, 2005): 2284–303. http://dx.doi.org/10.1007/s00330-005-2846-2.

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SİVRİOĞLU, Ali Kemal, Kemal KARA, Ersin ÖZTÜRK, and Erol KILIÇ. "Diffusion weighted imaging of the chest." Tuberkuloz ve Toraks 63, no. 4 (December 29, 2015): 296–97. http://dx.doi.org/10.5578/tt.8912.

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Motoshima, Shigenobu, Hiroyuki Irie, Takahiko Nakazono, Toshiharu Kamura, and Sho Kudo. "Diffusion-weighted MR imaging in gynecologic cancers." Journal of Gynecologic Oncology 22, no. 4 (2011): 275. http://dx.doi.org/10.3802/jgo.2011.22.4.275.

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Pezzullo, John A., Glenn A. Tung, Sanjay Mudigonda, and Jeffrey M. Rogg. "Diffusion-Weighted MR Imaging of Pyogenic Ventriculitis." American Journal of Roentgenology 180, no. 1 (January 2003): 71–75. http://dx.doi.org/10.2214/ajr.180.1.1800071.

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Dissertations / Theses on the topic "Diffusion Weighted MR Imaging"

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Candrák, Matúš. "Zpracování difuzně vážených obrazů pořízených MR tomografem." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220983.

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The semester thesis describes the basic principles of MRI, methods for measuring diffusion coefficients and creating DWI and DTI images. As a result a practical implementation of program was implemented in Matlab, based on theoretical knowledge of the problem.
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MacGillivray, Cathy Carleton University Dissertation Physics. "Diffusion-weighted MR imaging of moving structures using a three echo navigator imaging technique." Ottawa, 1996.

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Coope, David John. "Use of [11C]-methionine PET and diffusion-/perfusion-weighted MR imaging in gliomas." Thesis, University of Manchester, 2010. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:207525.

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Introduction: Low-grade gliomas are a sub-group of primary brain tumours that typically affect young adults and which present specific challenges to conventional diagnostic imaging. They demonstrate a pattern of growth whereby tumour cells infiltrate healthy brain tissue without distortion of the surrounding brain or blood-brain barrier integrity. These features limit the capacity of conventional neuro-imaging strategies to effectively delineate the tumour extent or characterise the degree of 'malignancy'. One solution is to apply multiple imaging modalities to image different aspects of the tumour behaviour, analogous to histological classification based upon changes in mitotic activity, cellular atypia, microvascular proliferation and necrosis. Published information regarding how imaging techniques that address these parameters correlate within the tumour volume is limited. This reflects the technical challenges in acquiring and processing data at an adequate spatial resolution to characterise small but heterogenous tumours. In this thesis, following a series of experiments seeking to optimise the sensitivity and reproducibility of PET analysis in gliomas, a prospective multi-modal neuro-imaging study is presented addressing this need. Methods: Retrospective [11C]-methionine PET (MET PET) data made available through a collaboration with the Max-Planck Institute for Neurological Research in Cologne was carried out first to address the optimal method of analysis of PET data in gliomas. A normal methionine uptake map was created and its use in the analysis of patient scans validated against a conventional approach. Automated methods for delineating the extent of abnormal methionine uptake and identifying the region of peak uptake were developed and evaluated to optimise the reproducibility of the approach. High-resolution MET PET and a comprehensive MRI brain tumour protocol were then acquired prospectively in 20 subjects in Manchester. Detailed analysis of the peak uptake and extent of abnormal tissue defined using PET and MRI modalities including structural, diffusion- and perfusion-weighted techniques was performed. Results: Evaluation of methionine uptake with respect to population normal data, the 'RatioMap' technique, yielded peak uptake measurements that correlated closely with a conventional approach (r = 0.97) but with improved reproducibility. The constrained 3D region-growing algorithm designed to delineate the abnormal region was shown to be reproducible and to generate volumes that correlated with tumour grade. High-resolution multi-modal data in suspected low-grade gliomas demonstrated consistent correlation between peak methionine uptake ratio and peak regional cerebral blood volume (r = 0.85) but with disparity between the location of the maximal uptake regions (mean distance = 11.2mm). Significant correlation was seen between multi-modal MRI and PET ‘tumour’ volumes (r = 0.91) but with substantially larger MRI defined abnormal volumes (ratio = 2.0) including small regions identified as abnormal by multiple MRI parameters but normal on PET imaging. Conclusion: A novel method to enhance the reproducibility of analysis of MET PET images in gliomas has been presented and validated but there remains no single imaging modality capable of fully characterising glioma extent and 'malignancy' non-invasively. Considerable correlation between PET and MRI tumour biomarkers has been demonstrated but there are significant differences between the regions identified as the 'most malignant' for biopsy targeting and the extent of potentially tumour bearing tissue. Combined use of diffusion- and perfusion-weighted MRI parameters can provide results very closely correlated to the PET findings but cannot yet completely replace the use of nuclear medicine techniques. The use of multi-modal approaches to tumour characterisation as demonstrated in this study provides the most effective currently available approach to fully characterise a suspected glioma.
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Tamai, Ken. "The utility of diffusion-weighted MR imaging in the diagnosis of uterine malignancy." Kyoto University, 2008. http://hdl.handle.net/2433/135802.

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Kanao, Shotaro. "Differentiating benign and malignant inflammatory breast lesions: Value of T2 weighted and diffusion weighted MR images." Kyoto University, 2019. http://hdl.handle.net/2433/236592.

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Kerttula, L. (Liisa). "Magnetic resonance imaging of the intervertebral disc:post-traumatic findings and the value of diffusion-weighted MR imaging." Doctoral thesis, University of Oulu, 2001. http://urn.fi/urn:isbn:9514264711.

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Abstract Magnetic resonance imaging (MRI) provides important information about structural and biochemical changes in organs. MRI is also an effective imaging method for the evaluation of spinal disorders. However, many of its potential applications - particularly diffusion imaging - have not yet been thoroughly explored. The purpose of this study was to determine the MRI-detectable changes in the intervertebral disc after trauma and to test the feasibility of diffusion-weighted MR imaging of the intervertebral discs. A minipig model was used in the experimental study to determine the MRI changes in the intervertebral disc after peripheral annular lesions in different time frames. Three of eight discs with experimental annular lesions had a normal annular appearance in MRI. Annular lesions, when detectable, were manifested as a bulging of the disc or as a high-intensity zone (HIZ) inside the annulus. Either the signal intensity or the area of bright signal intensity in the nucleus had nearly always decreased after one month, but they were still detectable even in cases where no signs of annular trauma could be seen in the MR images. The histology of HIZ is presented for the first time: clusters of nuclear cells and disorganized granulation tissue with capillaries were detected in the HIZ area. Fourteen patients 8 to 21 years of age with histories of vertebral fracture at least one year previously and 14 asymptomatic healthy control subjects 8 to 22 years of age were studied by MRI. In these young people a vertebral fracture, especially with end-plate injury, proved to be a notable risk factor for initiating disc degeneration. The apparent diffusion coefficients (ADCs) of the thoracolumbar intervertebral discs were determined in three orthogonal directions in 18 healthy young volunteers aged 8-22 years. The ADCs were also determined in 10 young patients with previous vertebral fractures, and clear decreases were found in the ADCx and ADCy directions, but in the ADCz direction values had not changed significantly as compared to the values in the controls. The most marked changes were observed in the degenerated discs, followed by those in the discs with a normal signal intensity adjacent to the primary trauma area. Diffusion-weighted MR imaging affords a useful tool for evaluating disc diseases in the early phases. Additionally, 37 adult volunteers without back symptoms were studied by MRI and by magnetic resonance angiography (MRA) and it was found that the status of the lumbar arteries significantly explained the diffusion values in the lumbar intervertebral discs. The correlation between disc degeneration and diffusion was mostly linear, but not significant.
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Wang, Yanxin. "Hypoxic-ischemic injury in the neonatal rat model prediction of irreversible infarction size by Diffusion Weighted MR Imaging /." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B35757577.

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Iima, Mami. "Apparent Diffusion Coefficient as an MR Imaging Biomarker of Low-Risk Ductal Carcinoma in Situ: A Pilot Study." Kyoto University, 2014. http://hdl.handle.net/2433/188640.

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Stahle, Jessica Anne. "Diffusion Weighted MR Imaging in the Differentiation between Metastatic and Benign Lymph Nodes in Canine Patients with Head and Neck Disease." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/86612.

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In dogs with large primary tumors, regional lymph node involvement or evidence of distant metastasis can have worse prognoses and significantly decreased survival. Lymph node size alone has been shown to be insufficient as a predictor for the accurate clinical staging of some canine neoplasia, including oral malignant melanoma. However, regional lymph nodes of the oral cavity, such as the medial retropharyngeal lymph nodes, are difficult to access for routine sampling. Diffusion weighted magnetic resonance imaging (DWI) has demonstrated the ability to differentiate metastatic from inflammatory/benign lymph nodes in clinical studies with human cancer patients through the calculation of quantitative values of diffusion termed apparent diffusion coefficients (ADC). The objective of this exploratory study was to evaluate DWI and ADC as potential future methods for detecting malignant lymph nodes in dogs with naturally occurring disease. We hypothesized that DWI would identify significantly different ADC values between benign and metastatic lymph nodes in a group of canine patients with head or neck disease. Our results demonstrated that two of four observers identified a significant difference between the mean ADC values of the benign and metastatic lymph nodes. When data from all four observers were pooled, the difference between the mean ADC values of the benign and metastatic lymph nodes approached but did not reach significance (P-value: 0.0566). Therefore, our hypothesis was not supported. However, DWI does show promise in its ability to differentiate benign from metastatic lymph nodes, and further studies with increased patient numbers are warranted
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Purvis, Nina Louise. "Classification of breast malignancy using optimised advanced diffusion-weighted imaging, and, Surgical planning for breast tumour resection using MR-guided focused ultrasound." Thesis, University of Hull, 2016. http://hydra.hull.ac.uk/resources/hull:15193.

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Intravoxel Incoherent Motion Imaging (IVIM) is a non-invasive MR-imaging technique that enables the measurement of cellularity and vascularity using diffusion-weighted (DW)-imaging. IVIM has been applied to various cancer types including breast cancer, and is becoming more popular but lacks standardisation. The quantitative parameters; diffusion, D, perfusion fraction, f, and pseudo micro capillary diffusion, D* are thought to be correlated with tumour physiognomies such as proliferation, angiogenesis and heterogeneity. In Part 1 of this thesis, an optimised clinical b-value protocol is produced using a robust statistical method. This optimised protocol and various fitting methodologies are investigated in healthy volunteers, and then the most precise approach is applied in a clinical trial in patients following diagnosis of breast cancer, before treatment, to correlate IVIM parameters with breast cancer grade, histological type and molecular subtype with statistically significant results supporting IVIM’s potential as a non-invasive biomarker for malignancy. Monte Carlo simulations support this clinical application, where real data mean squared errors due to SNR limitations lie within simulated errors. A computed DW-imaging program is also presented to produce better quality images than acquired high b-value images as an adjunct to the optimised IVIM protocol. In Part 2 of this thesis, MR-guided Focused Ultrasound (MRgFUS) is explored as a means to create a pre-surgical template of thermally induced palpable markers to enable a surgeon to resect occult lesions and potentially reduce positive tumour margin status and local recurrence after breast conserving surgery. A surrogate animal model with pseudo lesion is presented, as well as a clinical tool to plan spot markers around a lesion as seen on MRI.
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Books on the topic "Diffusion Weighted MR Imaging"

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Koh, D. M., and H. C. Thoeny, eds. Diffusion-Weighted MR Imaging. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-78576-7.

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Moritani, Toshio, Sven Ekholm, and Per-Lennart Westesson. Diffusion-Weighted MR Imaging of the Brain. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-78785-3.

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Moritani, Toshio, and Aristides A. Capizzano, eds. Diffusion-Weighted MR Imaging of the Brain, Head and Neck, and Spine. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62120-9.

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Matos, Celso, and Nickolas Papanikolaou, eds. Diffusion Weighted Imaging of the Hepatobiliary System. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-62977-3.

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Gourtsoyianni, Sofia, and Nikolaos Papanikolaou, eds. Diffusion Weighted Imaging of the Gastrointestinal Tract. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92819-7.

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Akata, Deniz, and Nikolaos Papanikolaou, eds. Diffusion Weighted Imaging of the Genitourinary System. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69575-4.

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Clinical MR neuroimaging: Physiological and functional techniques. 2nd ed. New York: Cambridge University Press, 2010.

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Moritani, T., S. Ekholm, and P. L. Westesson. Diffusion-Weighted MR Imaging of the Brain. Springer, 2005.

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Moritani, Toshio, Sven Ekholm, and Per-Lennart A. Westesson. Diffusion-Weighted MR Imaging of the Brain. Springer, 2010.

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P. -L Westesson,T. Moritani,S. Ekholm. Diffusion-Weighted MR Imaging of the Brain. Springer, 2009.

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Book chapters on the topic "Diffusion Weighted MR Imaging"

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Tam, Henry H., and Dow-Mu Koh. "Diffusion-Weighted MR Imaging." In Functional Imaging in Oncology, 307–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40412-2_14.

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Runge, Val M., and Johannes T. Heverhagen. "Diffusion-Weighted Imaging." In The Physics of Clinical MR Taught Through Images, 142–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85413-3_66.

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Karaosmanoğlu, Ali Devrim, Musturay Karcaaltıncaba, Mustafa N. Özmen, and Deniz Akata. "MR Imaging of Ovarian Masses." In Diffusion Weighted Imaging of the Genitourinary System, 105–23. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69575-4_5.

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Martin-Fiori, Ernst, and Thierry A. G. M. Huisman. "Diffusion-Weighted, Perfusion-Weighted, and Functional MR Imaging." In Pediatric Neuroradiology, 1073–114. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-26398-5_24.

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Soto, Jorge A., German A. Castrillon, Stephan Anderson, and Nagaraj Holalkere. "Diffusion-Weighted MR Imaging of the Pancreas." In Diffusion MRI Outside the Brain, 99–122. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21052-5_6.

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Hiwatashi, A., and J. Zhong. "Pitfalls and Artifacts of DW Imaging." In Diffusion-Weighted MR Imaging of the Brain, 23–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-78785-3_3.

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Zhong, J. "Basics of Diffusion Measurements by MRI." In Diffusion-Weighted MR Imaging of the Brain, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-78785-3_1.

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Kademian, Jack. "Scalp and Skull Lesions." In Diffusion-Weighted MR Imaging of the Brain, 341–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-78785-3_15.

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Hiwatashi, A. "How to Use This Book." In Diffusion-Weighted MR Imaging of the Brain, 371–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-78785-3_16.

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Ohgiya, Y., and R. de Guzman. "Infarction." In Diffusion-Weighted MR Imaging of the Brain, 55–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-78785-3_5.

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Conference papers on the topic "Diffusion Weighted MR Imaging"

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Li, Wu, and Jie Tian. "Automatic segmentation of brain infarction in diffusion-weighted MR images." In Medical Imaging 2003, edited by Milan Sonka and J. Michael Fitzpatrick. SPIE, 2003. http://dx.doi.org/10.1117/12.481350.

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Ceranka, Jakub, Mathias Polfliet, Frederic Lecouvet, Nicolas Michoux, and Jef Vandemeulebroucke. "Whole-body diffusion-weighted MR image stitching and alignment to anatomical MRI." In SPIE Medical Imaging, edited by Martin A. Styner and Elsa D. Angelini. SPIE, 2017. http://dx.doi.org/10.1117/12.2253838.

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Jha, Abhinav K., Matthew A. Kupinski, Jeffrey J. Rodríguez, Renu M. Stephen, and Alison T. Stopeck. "Evaluating segmentation algorithms for diffusion-weighted MR images: a task-based approach." In SPIE Medical Imaging, edited by David J. Manning and Craig K. Abbey. SPIE, 2010. http://dx.doi.org/10.1117/12.845515.

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Peng, Yahui, Yulei Jiang, Tatjana Antic, Maryellen L. Giger, Scott Eggener, and Aytekin Oto. "A study of T2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer." In SPIE Medical Imaging, edited by Carol L. Novak and Stephen Aylward. SPIE, 2013. http://dx.doi.org/10.1117/12.2007979.

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Zhang, Hao, Yunmei Chen, Eduardo Pasiliao, and Feng Huang. "Joint multi-shot multi-channel image reconstruction in compressive diffusion weighted MR imaging." In SPIE Medical Imaging, edited by Sébastien Ourselin and Martin A. Styner. SPIE, 2015. http://dx.doi.org/10.1117/12.2082104.

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Peng, Yahui, Yulei Jiang, Fatma N. Soylu, Mark Tomek, William Sensakovic, and Aytekin Oto. "Registration of T2-weighted and diffusion-weighted MR images of the prostate: comparison between manual and landmark-based methods." In SPIE Medical Imaging, edited by Craig K. Abbey and Claudia R. Mello-Thoms. SPIE, 2012. http://dx.doi.org/10.1117/12.911637.

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Toselli, Benedetta, Cristina Franchin, Paola Scifo, and Giovanna Rizzo. "Improved spherical deconvolution to solve fiber crossing in diffusion-weighted MR Imaging." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7318385.

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Jafar, Maysam, Sharon Giles, Veronica Morgan, Maria Schmidt, Martin O. Leach, and Nandita M. de Souza. "Evaluation of distortion correction of diffusion-weighted MR images of human cervix." In 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isbi.2012.6235598.

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Jian, Bing, Baba C. Vemuri, Evren Ozarslan, Paul Carney, and Thomas Mareci. "A CONTINUOUS MIXTURE OF TENSORS MODEL FOR DIFFUSION-WEIGHTED MR SIGNAL RECONSTRUCTION." In 2007 4th IEEE International Symposium on Biomedical Imaging: Macro to Nano. IEEE, 2007. http://dx.doi.org/10.1109/isbi.2007.356966.

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Isik, Esin Ozturk, and Sarah J. Nelson. "The differences of MR spectroscopic imaging and MR diffusion weighted imaging parameters in different subtypes of grade 3 gliomas." In 2010 15th National Biomedical Engineering Meeting (BIYOMUT 2010). IEEE, 2010. http://dx.doi.org/10.1109/biyomut.2010.5479775.

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Reports on the topic "Diffusion Weighted MR Imaging"

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Gareau, Paul, and Brian K. Rutt. Prediction of Malignancy in Breast Tumors Using Diffusion Weighted Magnetic Resonance Imaging. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada390993.

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Bodanapally, Uttam, Andrew Choi, and Robert Shin. Diffusion-Weighted Imaging of Traumatic Optic Neuropathy: Diagnosis and Predicting the Prognosis. Fort Belvoir, VA: Defense Technical Information Center, January 2014. http://dx.doi.org/10.21236/ada601984.

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Thomas, Michael A. A Novel Multivoxel-Based Quantitation of Metabolites and Lipids Noninvasively Combined with Diffusion-Weighted Imaging in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada570966.

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Thomas, Michael A. A Novel Multivoxel-Based Quantitation of Metabolites and Lipids Noninvasively Combined with Diffusion-Weighted Imaging in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada601642.

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Thomas, Michael A. A Novel Multi-voxel Based Quantitation of Metabolites and Lipids Non-invasively Combined with Diffusion Weighted Imaging in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada555478.

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