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Journal articles on the topic 'Cardiac diffusion imaging'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Scott, Andrew D., Pedro F. A. D. C. Ferreira, Sonia Nielles-Vallespin, et al. "Optimal diffusion weighting for in vivo cardiac diffusion tensor imaging." Magnetic Resonance in Medicine 74, no. 2 (2014): 420–30. http://dx.doi.org/10.1002/mrm.25418.

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2

Lau, Angus Z., Elizabeth M. Tunnicliffe, Robert Frost, Peter J. Koopmans, Damian J. Tyler, and Matthew D. Robson. "Accelerated human cardiac diffusion tensor imaging using simultaneous multislice imaging." Magnetic Resonance in Medicine 73, no. 3 (2014): 995–1004. http://dx.doi.org/10.1002/mrm.25200.

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3

Pop, Mihaela, and Nicoleta Stefu. "Diffusion Magnetic Resonance Imaging with Applications to Cardiac Muscle: Short Review." Annals of West University of Timisoara - Physics 62, no. 1 (2020): 108–19. http://dx.doi.org/10.2478/awutp-2020-0007.

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AbstractThis review describes in brief recent magnetic resonance imaging (MRI) methods for assessing cardiac structure in healthy and pathologic state using diffusion-weighted (DW) and diffusion tensor imaging (DTI) approaches. A background on the theory and MR pulse sequences employed in DW/DT imaging is given, along with the calculation of diffusion tensor (D), apparent diffusion coefficient (ADC) and fractional anisotropy (FA). Parametric maps derived from DW/DT images can quantify microstructure alterations due to fibrotic collagen deposition, along with associated changes in cardiac muscl

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4

Oda, Seitaro, Kosuke Morita, Tomoyuki Okuaki, Tetsuo Ogino, and Yasuyuki Yamashita. "Cardiac diffusion-weighted magnetic resonance imaging for assessment of cardiac metastasis." European Heart Journal - Cardiovascular Imaging 19, no. 6 (2018): 683. http://dx.doi.org/10.1093/ehjci/jey039.

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5

Axel, Leon. "Faster Diffusion-weighted MR Imaging of Cardiac Microstructure." Radiology 282, no. 3 (2017): 622–26. http://dx.doi.org/10.1148/radiol.2016162269.

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6

Delattre, Benedicte M. A., Magalie Viallon, Hongjiang Wei, et al. "In Vivo Cardiac Diffusion-Weighted Magnetic Resonance Imaging." Investigative Radiology 47, no. 11 (2012): 662–70. http://dx.doi.org/10.1097/rli.0b013e31826ef901.

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7

Luyt, Charles-Edouard, Damien Galanaud, Vincent Perlbarg, et al. "Diffusion Tensor Imaging to Predict Long-term Outcome after Cardiac Arrest." Anesthesiology 117, no. 6 (2012): 1311–21. http://dx.doi.org/10.1097/aln.0b013e318275148c.

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Background Prognostication in comatose survivors of cardiac arrest is a major clinical challenge. The authors' objective was to determine whether an assessment with diffusion tensor imaging, a brain magnetic resonance imaging sequence, increases the accuracy of 1 yr functional outcome prediction in cardiac arrest survivors. Methods Prospective, observational study in two intensive care units. Fifty-seven comatose survivors of cardiac arrest underwent brain magnetic resonance imaging. Fractional anisotropy (FA), a diffusion tensor imaging value, was measured in predefined white matter regions,

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8

Stoeck, Christian T., Aleksandra Kalinowska, Constantin von Deuster, et al. "Dual-Phase Cardiac Diffusion Tensor Imaging with Strain Correction." PLoS ONE 9, no. 9 (2014): e107159. http://dx.doi.org/10.1371/journal.pone.0107159.

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9

Moulin, Kevin, Tess E. Wallace, Jennifer Rodriguez, et al. "Fully automated analysis of cardiac magnetic resonance diffusion imaging." Journal of Cardiovascular Magnetic Resonance 27 (2025): 101380. https://doi.org/10.1016/j.jocmr.2024.101380.

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10

Hannum, Ariel J., Michael Loecher, Qingping Chen, et al. "Towards open-source spin-echo cardiac diffusion tensor imaging." Journal of Cardiovascular Magnetic Resonance 27 (2025): 101389. https://doi.org/10.1016/j.jocmr.2024.101389.

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11

Rapacchi, Stanislas, Han Wen, Magalie Viallon, et al. "Low b-Value Diffusion-Weighted Cardiac Magnetic Resonance Imaging." Investigative Radiology 46, no. 12 (2011): 751–58. http://dx.doi.org/10.1097/rli.0b013e31822438e8.

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12

Ihab ELAFF. "Effect of the material properties on modeling of the excitation propagation of the human heart." World Journal of Biology Pharmacy and Health Sciences 22, no. 3 (2025): 088–94. https://doi.org/10.30574/wjbphs.2025.22.3.0564.

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Accurate modeling of cardiac excitation propagation requires detailed representation of the heart’s anisotropic properties and tissue heterogeneity. Traditional imaging modalities such as CT and MRI fail to capture the fiber orientation essential for modeling the myocardium. This study employs the Monodomain reaction-diffusion equation and integrates Diffusion Tensor Imaging (DTI) data to enhance modeling fidelity by accounting for both anisotropic properties and non-uniform conductivity distributions. The proposed method modifies the conductivity tensor using diffusion volume as a proxy for i

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13

Teh, Irvin, Feng-Lei Zhou, Penny L. Hubbard Cristinacce, Geoffrey J. M. Parker, and Jürgen E. Schneider. "Biomimetic phantom for cardiac diffusion MRI." Journal of Magnetic Resonance Imaging 43, no. 3 (2016): spcone. http://dx.doi.org/10.1002/jmri.25197.

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14

Nielles‐Vallespin, Sonia, Andrew Scott, Pedro Ferreira, Zohya Khalique, Dudley Pennell, and David Firmin. "Cardiac Diffusion: Technique and Practical Applications." Journal of Magnetic Resonance Imaging 52, no. 2 (2019): 348–68. http://dx.doi.org/10.1002/jmri.26912.

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15

Qin, Xulei, Silun Wang, Ming Shen, et al. "Simulating cardiac ultrasound image based on MR diffusion tensor imaging." Medical Physics 42, no. 9 (2015): 5144–56. http://dx.doi.org/10.1118/1.4927788.

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16

Dorman, T. "Prognostic Value of Brain Diffusion-Weighted Imaging after Cardiac Arrest." Yearbook of Critical Care Medicine 2010 (January 2010): 337–39. http://dx.doi.org/10.1016/s0734-3299(09)79198-1.

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17

Wijman, Christine A. C., Michael Mlynash, Anna Finley Caulfield, et al. "Prognostic value of brain diffusion-weighted imaging after cardiac arrest." Annals of Neurology 65, no. 4 (2009): 394–402. http://dx.doi.org/10.1002/ana.21632.

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18

Shiraga, Kazuhiro, Kojiro Ono, Ryo Inuzuka, et al. "Intravoxel incoherent motion imaging has the possibility to detect liver abnormalities in young Fontan patients with good hemodynamics." Cardiology in the Young 29, no. 7 (2019): 898–903. http://dx.doi.org/10.1017/s1047951119001070.

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AbstractIntroduction:Liver fibrosis and cirrhosis are one of the critical complications in Fontan patients. However, there are no well-established non-invasive and quantitative techniques for evaluating liver abnormalities in Fontan patients. Intravoxel incoherent motion diffusion-weighted imaging with MRI is a non-invasive and quantitative method to evaluate capillary network perfusion and molecular diffusion. The objective of this study is to assess the feasibility of intravoxel incoherent motion imaging in evaluating liver abnormalities in Fontan children.Materials and Methods:Five consecut

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19

Moulin, Kevin, Thomas Troalen, Pierre Croisille, and Magalie Viallon. "Distortion-FBee Cardiac Diffusion Tensor Imaging Using Multi-shot Echo-planar Imaging at 3T." Journal of Cardiovascular Magnetic Resonance 26 (2024): 100919. http://dx.doi.org/10.1016/j.jocmr.2024.100919.

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20

Stoeck, Christian T., Andrew D. Scott, Pedro F. Ferreira, et al. "Motion‐Induced Signal Loss in In Vivo Cardiac Diffusion‐Weighted Imaging." Journal of Magnetic Resonance Imaging 51, no. 1 (2019): 319–20. http://dx.doi.org/10.1002/jmri.26767.

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21

Hannum, Ariel, Tyler Cork, Kawin Setsompop, and Daniel Ennis. "Evaluation of Voxel Volume and Shape for Cardiac Diffusion Tensor Imaging." Journal of Cardiovascular Magnetic Resonance 26 (2024): 100254. http://dx.doi.org/10.1016/j.jocmr.2024.100254.

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22

Park, Clara, Yiling Fan, Gregor Hager, et al. "An organosynthetic dynamic heart model with enhanced biomimicry guided by cardiac diffusion tensor imaging." Science Robotics 5, no. 38 (2020): eaay9106. http://dx.doi.org/10.1126/scirobotics.aay9106.

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The complex motion of the beating heart is accomplished by the spatial arrangement of contracting cardiomyocytes with varying orientation across the transmural layers, which is difficult to imitate in organic or synthetic models. High-fidelity testing of intracardiac devices requires anthropomorphic, dynamic cardiac models that represent this complex motion while maintaining the intricate anatomical structures inside the heart. In this work, we introduce a biorobotic hybrid heart that preserves organic intracardiac structures and mimics cardiac motion by replicating the cardiac myofiber archit

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23

Wang, Lihui, Yao Hong, Yong-Bin Qin, et al. "Connecting macroscopic diffusion metrics of cardiac diffusion tensor imaging and microscopic myocardial structures based on simulation." Medical Image Analysis 77 (April 2022): 102325. http://dx.doi.org/10.1016/j.media.2021.102325.

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24

Aliotta, Eric, Kévin Moulin, Patrick Magrath, and Daniel B. Ennis. "Quantifying precision in cardiac diffusion tensor imaging with second-order motion-compensated convex optimized diffusion encoding." Magnetic Resonance in Medicine 80, no. 3 (2018): 1074–87. http://dx.doi.org/10.1002/mrm.27107.

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25

Ma, Sen, Christopher T. Nguyen, Anthony G. Christodoulou, et al. "Accelerated Cardiac Diffusion Tensor Imaging Using Joint Low-Rank and Sparsity Constraints." IEEE Transactions on Biomedical Engineering 65, no. 10 (2018): 2219–30. http://dx.doi.org/10.1109/tbme.2017.2787111.

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26

Youn, Chun Song, Kyu Nam Park, Jee Young Kim, et al. "Repeated diffusion weighted imaging in comatose cardiac arrest patients with therapeutic hypothermia." Resuscitation 96 (November 2015): 1–8. http://dx.doi.org/10.1016/j.resuscitation.2015.06.029.

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27

Kim, Sungheon, Stephen Pickup, and Harish Poptani. "Effects of cardiac pulsation in diffusion tensor imaging of the rat brain." Journal of Neuroscience Methods 194, no. 1 (2010): 116–21. http://dx.doi.org/10.1016/j.jneumeth.2010.10.003.

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28

Grieve, S., P. Kuchel, B. Chapman, and G. Figtree. "Tracking Cardiac Myofibrils by Magnetic Resonance Diffusion Tensor Imaging at High Field." Heart, Lung and Circulation 19 (January 2010): S201. http://dx.doi.org/10.1016/j.hlc.2010.06.486.

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29

Hirsch, K. G., M. Mlynash, I. Eyngorn, et al. "Multi-Center Study of Diffusion-Weighted Imaging in Coma After Cardiac Arrest." Neurocritical Care 24, no. 1 (2015): 82–89. http://dx.doi.org/10.1007/s12028-015-0179-9.

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30

Hamaguchi, Hiroyuki, Khin Khin Tha, Hiroyuki Sugimori, et al. "Effect of respiratory and cardiac gating on the major diffusion-imaging metrics." Neuroradiology Journal 29, no. 4 (2016): 254–59. http://dx.doi.org/10.1177/1971400916643337.

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31

Helm, Patrick A., Hsiang-Jer Tseng, Laurent Younes, Elliot R. McVeigh, and Raimond L. Winslow. "Ex vivo 3D diffusion tensor imaging and quantification of cardiac laminar structure." Magnetic Resonance in Medicine 54, no. 4 (2005): 850–59. http://dx.doi.org/10.1002/mrm.20622.

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32

Le Bars, Anne-Lise, Kevin Moulin, Daniel B. Ennis, Jacques Felblinger, Bailiang Chen, and Freddy Odille. "In Vivo Super-Resolution Cardiac Diffusion Tensor MRI: A Feasibility Study." Diagnostics 12, no. 4 (2022): 877. http://dx.doi.org/10.3390/diagnostics12040877.

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A super-resolution (SR) technique is proposed for imaging myocardial fiber architecture with cardiac magnetic resonance. Images were acquired with a motion-compensated cardiac diffusion tensor imaging (cDTI) sequence. The heart left ventricle was covered with three stacks of thick slices, in short axis, horizontal and vertical long axes orientations, respectively. The three low-resolution stacks (2 × 2 × 8 mm3) were combined into an isotropic volume (2 × 2 × 2 mm3) by a super-resolution reconstruction. For in vivo measurements, each slice was acquired during a breath-hold period. Bulk motion w

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33

Tseng, Wen-Yih I., Timothy G. Reese, Robert M. Weisskoff, and Van J. Wedeen. "Cardiac diffusion tensor MRI in vivo without strain correction." Magnetic Resonance in Medicine 42, no. 2 (1999): 393–403. http://dx.doi.org/10.1002/(sici)1522-2594(199908)42:2<393::aid-mrm22>3.0.co;2-f.

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34

Rahman, Tanjib, Kévin Moulin, and Luigi E. Perotti. "Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data." Applied Sciences 12, no. 7 (2022): 3512. http://dx.doi.org/10.3390/app12073512.

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In vivo cardiac diffusion tensor imaging (cDTI) data were acquired in swine subjects six to ten weeks post-myocardial infarction (MI) to identify microstructural-based biomarkers of MI. Diffusion tensor invariants, diffusion tensor eigenvalues, and radial diffusivity (RD) are evaluated in the infarct, border, and remote myocardium, and compared with extracellular volume fraction (ECV) and native T1 values. Additionally, to aid the interpretation of the experimental results, the diffusion of water molecules was numerically simulated as a function of ECV. Finally, findings based on in vivo measu

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35

Kotadia, Irum, John Whitaker, Caroline Roney, et al. "Anisotropic Cardiac Conduction." Arrhythmia & Electrophysiology Review 9, no. 4 (2020): 202–10. http://dx.doi.org/10.15420/aer.2020.04.

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Anisotropy is the property of directional dependence. In cardiac tissue, conduction velocity is anisotropic and its orientation is determined by myocyte direction. Cell shape and size, excitability, myocardial fibrosis, gap junction distribution and function are all considered to contribute to anisotropic conduction. In disease states, anisotropic conduction may be enhanced, and is implicated, in the genesis of pathological arrhythmias. The principal mechanism responsible for enhanced anisotropy in disease remains uncertain. Possible contributors include changes in cellular excitability, chang

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36

Du, Hongbo, Nannan Yuan, and Lihui Wang. "Node2Node: Self-Supervised Cardiac Diffusion Tensor Image Denoising Method." Applied Sciences 13, no. 19 (2023): 10829. http://dx.doi.org/10.3390/app131910829.

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Although the existing cardiac diffusion tensor imaging (DTI) denoising methods have achieved promising results, most of them are dependent on the number of diffusion gradient directions, noise distributions, and noise levels. To address these issues, we propose a novel self-supervised cardiac DTI denoising network, Node2Node, which firstly expresses the diffusion-weighted (DW) image volumes along different directions as a graph, then the graph framelet transform (GFT) is implemented to map the DW signals into the GFT coefficients at different spectral bands, allowing us to accurately match the

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37

Jiang, Yi, Julius M. Guccione, Mark B. Ratcliffe, and Edward W. Hsu. "Transmural heterogeneity of diffusion anisotropy in the sheep myocardium characterized by MR diffusion tensor imaging." American Journal of Physiology-Heart and Circulatory Physiology 293, no. 4 (2007): H2377—H2384. http://dx.doi.org/10.1152/ajpheart.00337.2007.

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The orientation of MRI-measured diffusion tensor in the myocardium has been directly correlated to the tissue fiber direction and widely characterized. However, the scalar anisotropy indexes have mostly been assumed to be uniform throughout the myocardial wall. The present study examines the fractional anisotropy (FA) as a function of transmural depth and circumferential and longitudinal locations in the normal sheep cardiac left ventricle. Results indicate that FA remains relatively constant from the epicardium to the midwall and then decreases (25.7%) steadily toward the endocardium. The dec

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38

Kirschen, Matthew, Jeffrey Berman, Madeline Winters, et al. "728: Diffusion Tensor MR Imaging Is Associated With Outcome After Pediatric Cardiac Arrest." Critical Care Medicine 49, no. 1 (2020): 360. http://dx.doi.org/10.1097/01.ccm.0000728800.75886.f0.

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39

Devine, Danielle, Neil Munjal, Vincent Schmithorst, et al. "729: Whole Brain Diffusion Tensor Imaging Analysis and Outcomes Following Pediatric Cardiac Arrest." Critical Care Medicine 49, no. 1 (2020): 360. http://dx.doi.org/10.1097/01.ccm.0000728804.84156.c2.

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40

Agger, Peter, Thomas Lass, Morten Smerup, Jesper Frandsen, and Michael Pedersen. "Optimal preservation of porcine cardiac tissue prior to diffusion tensor magnetic resonance imaging." Journal of Anatomy 227, no. 5 (2015): 695–701. http://dx.doi.org/10.1111/joa.12377.

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41

Welsh, Christopher L., Edward V. R. DiBella, and Edward W. Hsu. "Higher-Order Motion-Compensation for In Vivo Cardiac Diffusion Tensor Imaging in Rats." IEEE Transactions on Medical Imaging 34, no. 9 (2015): 1843–53. http://dx.doi.org/10.1109/tmi.2015.2411571.

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42

Ryoo, Seung Mok, Sang-Beom Jeon, Chang Hwan Sohn, et al. "Predicting Outcome With Diffusion-Weighted Imaging in Cardiac Arrest Patients Receiving Hypothermia Therapy." Critical Care Medicine 43, no. 11 (2015): 2370–77. http://dx.doi.org/10.1097/ccm.0000000000001263.

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43

HELM, PATRICK, MIRZA FAISAL BEG, MICHAEL I. MILLER, and RAIMOND L. WINSLOW. "Measuring and Mapping Cardiac Fiber and Laminar Architecture Using Diffusion Tensor MR Imaging." Annals of the New York Academy of Sciences 1047, no. 1 (2005): 296–307. http://dx.doi.org/10.1196/annals.1341.026.

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44

Liu, Sizhuo, Shen Zhao, Xitong Wang, Quan Chen, and Michael Salerno. "Plug-and-play reconstruction for accelerated cardiac perfusion imaging via denoising diffusion models." Journal of Cardiovascular Magnetic Resonance 27 (2025): 101500. https://doi.org/10.1016/j.jocmr.2024.101500.

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45

Hannum, Ariel J., Tyler E. Cork, Luigi Perotti, and Daniel B. Ennis. "Regional evaluation of left ventricular cardiac diffusion tensor imaging metrics in healthy subjects." Journal of Cardiovascular Magnetic Resonance 27 (2025): 101616. https://doi.org/10.1016/j.jocmr.2024.101616.

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46

Wong, Oi Lei, Gladys Goh Lo, Raymond Lee, et al. "The Effect of Respiratory and Cardiac Motion in Liver Diffusion Tensor Imaging (DTI)." Journal of Computer Assisted Tomography 38, no. 3 (2014): 352–59. http://dx.doi.org/10.1097/rct.0000000000000064.

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47

Moana-Filho, E., I. Tchivileva, and R. Gracely. "White matter microstructural integrity assessment in fibromyalgia using cardiac-gated diffusion tensor imaging." Journal of Pain 14, no. 4 (2013): S52. http://dx.doi.org/10.1016/j.jpain.2013.01.545.

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48

Wu, Ona, A. Gregory Sorensen, Thomas Benner, Aneesh B. Singhal, Karen L. Furie, and David M. Greer. "Comatose Patients with Cardiac Arrest: Predicting Clinical Outcome with Diffusion-weighted MR Imaging." Radiology 252, no. 1 (2009): 173–81. http://dx.doi.org/10.1148/radiol.2521081232.

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49

Okayama, Satoshi, Shiro Uemura, and Yoshihiko Saito. "Detection of infarct-related myocardial edema using cardiac diffusion-weighted magnetic resonance imaging." International Journal of Cardiology 133, no. 1 (2009): e20-e21. http://dx.doi.org/10.1016/j.ijcard.2007.08.096.

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50

Mazumder, Ria, Bradley D. Clymer, Xiaokui Mo, Richard D. White, and Arunark Kolipaka. "Adaptive anisotropic gaussian filtering to reduce acquisition time in cardiac diffusion tensor imaging." International Journal of Cardiovascular Imaging 32, no. 6 (2016): 921–34. http://dx.doi.org/10.1007/s10554-016-0848-6.

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