Model discovery approach enables noninvasive measurement of intra-tumoral fluid transport in dynamic MRI
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportuni...
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Published in | APL bioengineering Vol. 8; no. 2; p. 026106 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
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United States
AIP Publishing LLC
01.06.2024
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Abstract | Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response. |
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AbstractList | Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics
in situ
and
in vivo
. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics and . Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response. |
Author | Esparza, Cora C. Wang, Maosen Brummer, Alexander B. Brown, Christine C. Rockne, Russell C. Stine, Caleb A. Gutova, Margarita Cunningham, Jessica J. Munson, Jennifer M. Woodall, Ryan T. |
Author_xml | – sequence: 1 givenname: Ryan T. surname: Woodall fullname: Woodall, Ryan T. organization: Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center – sequence: 2 givenname: Cora C. surname: Esparza fullname: Esparza, Cora C. organization: Fralin Biomedical Research Institute, Virginia Institute of Technology at Virginia Tech Carilion, Virginia Tech – sequence: 3 givenname: Margarita surname: Gutova fullname: Gutova, Margarita organization: Department of Stem Cell Biology and Regenerative Medicine, Beckman Research Institute, City of Hope National Medical Center – sequence: 4 givenname: Maosen surname: Wang fullname: Wang, Maosen organization: Fralin Biomedical Research Institute, Virginia Institute of Technology at Virginia Tech Carilion, Virginia Tech – sequence: 5 givenname: Jessica J. surname: Cunningham fullname: Cunningham, Jessica J. organization: Fralin Biomedical Research Institute, Virginia Institute of Technology at Virginia Tech Carilion, Virginia Tech – sequence: 6 givenname: Alexander B. surname: Brummer fullname: Brummer, Alexander B. organization: Department of Physics and Astronomy, College of Charleston – sequence: 7 givenname: Caleb A. surname: Stine fullname: Stine, Caleb A. organization: Fralin Biomedical Research Institute, Virginia Institute of Technology at Virginia Tech Carilion, Virginia Tech – sequence: 8 givenname: Christine C. surname: Brown fullname: Brown, Christine C. organization: 6Department of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd., Duarte, California 91010, USA – sequence: 9 givenname: Jennifer M. surname: Munson fullname: Munson, Jennifer M. organization: Fralin Biomedical Research Institute, Virginia Institute of Technology at Virginia Tech Carilion, Virginia Tech – sequence: 10 givenname: Russell C. surname: Rockne fullname: Rockne, Russell C. organization: Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38715647$$D View this record in MEDLINE/PubMed |
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