*Result*: Accelerated Diffusion Basis Spectrum Imaging With Tensor Computations.
Title:
Accelerated Diffusion Basis Spectrum Imaging With Tensor Computations.
Authors:
Utt KL; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA., Blum JS; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA., Rim D; Department of Mathematics, Washington University in St. Louis, St. Louis, Missouri, USA., Song SK; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA.
Source:
Human brain mapping [Hum Brain Mapp] 2026 Feb 01; Vol. 47 (2), pp. e70460.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: United States NLM ID: 9419065 Publication Model: Print Cited Medium: Internet ISSN: 1097-0193 (Electronic) Linking ISSN: 10659471 NLM ISO Abbreviation: Hum Brain Mapp Subsets: MEDLINE
Imprint Name(s):
Publication: New York : Wiley
Original Publication: New York : Wiley-Liss, c1993-
Original Publication: New York : Wiley-Liss, c1993-
MeSH Terms:
References:
Afzali, M., T. Pieciak, S. Newman, et al. 2021. “The Sensitivity of Diffusion MRI to Microstructural Properties and Experimental Factors.” Journal of Neuroscience Methods 347: 108951.
Aja‐Fernández, S., and G. Vegas‐Sánchez‐Ferrero. 2016. Statistical Analysis of Noise in MRI. Springer International Publishing.
Basser, P. J., J. Mattiello, and D. LeBihan. 1994. “MR Diffusion Tensor Spectroscopy and Imaging.” Biophysical Journal 66: 259–267.
Basser, P. J., and S. Pajevic. 2000. “Statistical Artifacts in Diffusion Tensor MRI (DT‐MRI) Caused by Background Noise.” Magnetic Resonance in Medicine 44: 41–50.
Batchelor, P. G., D. Atkinson, D. L. G. Hill, F. Calamante, and A. Connelly. 2003. “Anisotropic Noise Propagation in Diffusion Tensor MRI Sampling Schemes.” Magnetic Resonance in Medicine 49: 1143–1151.
Berman, J. I., M. R. Lanza, L. Blaskey, J. C. Edgar, and T. P. L. Roberts. 2013. “High Angular Resolution Diffusion Imaging Probabilistic Tractography of the Auditory Radiation.” American Journal of Neuroradiology 34: 1573–1578.
Bottou, L., F. E. Curtis, and J. Nocedal. 2018. “Optimization Methods for Large‐Scale Machine Learning.” SIAM Review 60: 223–311.
Brunet, D., E. R. Vrscay, and Z. Wang. 2012. “On the Mathematical Properties of the Structural Similarity Index.” IEEE Transactions on Image Processing 21: 1488–1499.
Canales‐Rodríguez, E. J., A. Daducci, S. N. Sotiropoulos, et al. 2015. “Spherical Deconvolution of Multichannel Diffusion MRI Data With Non‐Gaussian Noise Models and Spatial Regularization.” PLoS One 10: e0138910.
Coelho, S., S. H. Baete, G. Lemberskiy, et al. 2022. “Reproducibility of the Standard Model of Diffusion in White Matter on Clinical MRI Systems.” NeuroImage 257: 119290.
Coelho, S., J. M. Pozo, S. N. Jespersen, D. K. Jones, and A. F. Frangi. 2019. “Resolving Degeneracy in Diffusion MRI Biophysical Model Parameter Estimation Using Double Diffusion Encoding.” Magnetic Resonance in Medicine 82: 395–410.
Crombe, A., V. Planche, G. Raffard, et al. 2018. “Deciphering the Microstructure of Hippocampal Subfields With in Vivo DTI and NODDI: Applications to Experimental Multiple Sclerosis.” NeuroImage 172: 357–368.
Cross, A. H., and S.‐K. Song. 2017. “A New Imaging Modality to Non‐Invasively Assess Multiple Sclerosis Pathology.” Journal of Neuroimmunology 304: 81–85.
De Santis, S., T. Granberg, R. Ouellette, et al. 2019. “Evidence of Early Microstructural White Matter Abnormalities in Multiple Sclerosis From Multi‐Shell Diffusion MRI.” NeuroImage: Clinical 22: 101699.
Diao, Y., and I. Jelescu. 2023. “Parameter Estimation for WMTI‐Watson Model of White Matter Using Encoder–Decoder Recurrent Neural Network.” Magnetic Resonance in Medicine 89: 1193–1206.
Diao, Y., F. Li, and Z. Li. 2023. “Joint Learning‐Based Feature Reconstruction and Enhanced Network for Incomplete Multi‐Modal Brain Tumor Segmentation.” Computers in Biology and Medicine 163: 107234.
Dosselmann, R., and X. D. Yang. 2009. “A Comprehensive Assessment of the Structural Similarity Index.” Signal, Image and Video Processing 5: 81–91.
Fieremans, E., A. Benitez, J. H. Jensen, et al. 2013. “Novel White Matter Tract Integrity Metrics Sensitive to Alzheimer Disease Progression.” American Journal of Neuroradiology 34: 2105–2112.
Garyfallidis, E., M. Brett, B. Amirbekian, et al. 2014. “Dipy, a Library for the Analysis of Diffusion MRI Data.” Frontiers in Neuroinformatics 8: 8.
Hagler, D. J., S. N. Hatton, M. D. Cornejo, et al. 2019. “Image Processing and Analysis Methods for the Adolescent Brain Cognitive Development Study.” NeuroImage 202: 116091.
Han, R. H., T. M. Johanns, K. F. Roberts, et al. 2023. “Diffusion Basis Spectrum Imaging as an Adjunct to Conventional MRI Leads to Earlier Diagnosis of High‐Grade Glioma Tumor Progression Versus Treatment Effect.” Neuro‐Oncology Advances 5: vdad050.
Henkelman, R. M. 1985. “Measurement of Signal Intensities in the Presence of Noise in MR Images.” Medical Physics 12: 232–233.
Hoy, A. R., C. G. Koay, S. R. Kecskemeti, and A. L. Alexander. 2014. “Optimization of a Free Water Elimination Two‐Compartment Model for Diffusion Tensor Imaging.” NeuroImage 103: 323–333.
Hu, Y.‐C., L.‐F. Yan, Q. Sun, et al. 2016. “Comparison Between Ultra‐High and Conventional Mono b‐Value DWI for Preoperative Glioma Grading.” Oncotarget 8: 37884–37895.
Huang, X., X. Xu, Y. Sun, et al. 2021. “Ultra‐High b‐Value DWI in Distinguishing Fresh Gray Matter Ischemic Lesions From White Matter Ones: A Comparative Study With Routine and High b‐Value DWI.” Quantitative Imaging in Medicine and Surgery 11: 4583–4593.
Hutchinson, E. B., A. V. Avram, M. O. Irfanoglu, et al. 2017. “Analysis of the Effects of Noise, DWI Sampling, and Value of Assumed Parameters in Diffusion MRI Models.” Magnetic Resonance in Medicine 78: 1767–1780.
Jain, P., and P. Kar. 2017. “Non‐Convex Optimization for Machine Learning.” Foundations and Trends in Machine Learning 10: 142–336.
Jelescu, I. O., and M. D. Budde. 2017. “Design and Validation of Diffusion MRI Models of White Matter.” Frontiers in Physics 5: 61.
Jelescu, I. O., J. Veraart, E. Fieremans, and D. S. Novikov. 2015. “Degeneracy in Model Parameter Estimation for Multi‐Compartmental Diffusion in Neuronal Tissue.” NMR in Biomedicine 29: 33–47.
Jensen, J. H., J. A. Helpern, A. Ramani, H. Lu, and K. Kaczynski. 2005. “Diffusional Kurtosis Imaging: The Quantification of Non‐Gaussian Water Diffusion by Means of Magnetic Resonance Imaging.” Magnetic Resonance in Medicine 53: 1432–1440.
Jespersen, S. N., J. L. Olesen, B. Hansen, and N. Shemesh. 2018. “Diffusion Time Dependence of Microstructural Parameters in Fixed Spinal Cord.” NeuroImage 182: 329–342.
Jiang, R., Z. Wang, J. Liu, et al. 2024. “High b‐Value and Ultra‐High b‐Value Diffusion Weighted MRI in Stroke.” Journal of Magnetic Resonance Imaging 61: 1459–1469.
Jones, D. K. 2004. “The Effect of Gradient Sampling Schemes on Measures Derived From Diffusion Tensor MRI: A Monte Carlo Study.” Magnetic Resonance in Medicine 51: 807–815.
Jones, D. K. 2010. Diffusion MRI. Oxford University Press.
Jones, D. K., and A. Leemans. 2010. Methods in Molecular Biology, 127–144. Humana Press.
Kingma, D. P., and J. Ba. 2014. “Adam: A Method for Stochastic Optimization.”.
Konieczny, M. J., A. Dewenter, A. ter Telgte, et al. 2021. “Multi‐Shell Diffusion MRI Models for White Matter Characterization in Cerebral Small Vessel Disease.” Neurology 96: e698–e708.
Lampinen, B., F. Szczepankiewicz, M. Novén, et al. 2019. “Searching for the Neurite Density With Diffusion MRI: Challenges for Biophysical Modeling.” Human Brain Mapping 40: 2529–2545.
Lavadi, R. S., K. L. Utt, S. N. Housley, et al. 2025. “Diffusion Basis Spectrum Imaging Detects Axonal Injury in the Optic Nerve Following Traumatic Brain Injury.” Magnetic Resonance Imaging 122: 110451.
Lawson, C. L., and R. J. Hanson. 1995. Solving Least Squares Problems. Society for Industrial and Applied Mathematics.
Le Bihan, D. 1991. “Molecular Diffusion Nuclear Magnetic Resonance Imaging.” Magnetic Resonance Quarterly 7: 1–30.
Le Bihan, D., J.‐F. Mangin, C. Poupon, et al. 2001. “Diffusion Tensor Imaging: Concepts and Applications.” Journal of Magnetic Resonance Imaging 13: 534–546.
Lim, C. S., J. Abreu‐Gomez, R. Thornhill, et al. 2021. “Utility of Machine Learning of Apparent Diffusion Coefficient (ADC) and T2‐Weighted (T2W) Radiomic Features in PI‐RADS Version 2.1 Category 3 Lesions to Predict Prostate Cancer Diagnosis.” Abdominal Radiology 46: 5647–5658.
Lin, T.‐H., J. Zhan, C. Song, et al. 2021. “Diffusion Basis Spectrum Imaging Detects Axonal Loss After Transient Dexamethasone Treatment in Optic Neuritis Mice.” Frontiers in Neuroscience 14: 592063.
Manzano Patron, J. P., S. Moeller, J. L. Andersson, K. Ugurbil, E. Yacoub, and S. N. Sotiropoulos. 2024. “Denoising Diffusion MRI: Considerations and Implications for Analysis.” Imaging Neuroscience 2: 1–29.
Meyer, J. R., A. Gutierrez, B. Mock, et al. 2000. “High‐b‐Value Diffusion‐Weighted MR Imaging of Suspected Brain Infarction.” AJNR. American Journal of Neuroradiology 21: 1821–1829.
Mohammadian, M., T. Roine, J. Hirvonen, et al. 2017. “High Angular Resolution Diffusion‐Weighted Imaging in Mild Traumatic Brain Injury.” NeuroImage: Clinical 13: 174–180.
Montalt‐Tordera, J., V. Muthurangu, A. Hauptmann, and J. A. Steeden. 2021. “Machine Learning in Magnetic Resonance Imaging: Image Reconstruction.” Physica Medica 83: 79–87.
Moradi, E., A. Pepe, C. Gaser, H. Huttunen, and J. Tohka. 2015. “Machine Learning Framework for Early MRI‐Based Alzheimer's Conversion Prediction in MCI Subjects.” NeuroImage 104: 398–412.
Mozumder, M., J. M. Pozo, S. Coelho, and A. F. Frangi. 2019. “Population‐Based Bayesian Regularization for Microstructural Diffusion MRI With NODDIDA.” Magnetic Resonance in Medicine 82: 1553–1565.
Mudeng, V., M. Kim, and S.‐w. Choe. 2022. “Prospects of Structural Similarity Index for Medical Image Analysis.” Applied Sciences 12: 3754.
Novikov, D. S., E. Fieremans, S. N. Jespersen, and V. G. Kiselev. 2019. “Quantifying Brain Microstructure With Diffusion MRI: Theory and Parameter Estimation.” NMR in Biomedicine 32: e3998.
Novikov, D. S., V. G. Kiselev, and S. N. Jespersen. 2018. “On Modeling.” Magnetic Resonance in Medicine 79: 3172–3193.
O'Donnell, L. J., and C.‐F. Westin. 2011. “An Introduction to Diffusion Tensor Image Analysis.” Neurosurgery Clinics of North America 22: 185–196.
Paszke, A. 2019. PyTorch: An Imperative Style, High‐Performance Deep Learning Library.
Pines, A. R., M. Cieslak, B. Larsen, et al. 2020. “Leveraging Multi‐Shell Diffusion for Studies of Brain Development in Youth and Young Adulthood.” Developmental Cognitive Neuroscience 43: 100788.
Polzehl, J., and K. Tabelow. 2016. “Low SNR in Diffusion MRI Models.” Journal of the American Statistical Association 111: 1480–1490.
Rafipoor, H., Y.‐Q. Zheng, L. Griffanti, S. Jbabdi, and M. Cottaar. 2022. “Identifying Microstructural Changes in Diffusion MRI: How to Circumvent Parameter Degeneracy.” NeuroImage 260: 119452.
Ramirez‐Manzanares, A., M. Rivera, B. C. Vemuri, P. Carney, and T. Mareci. 2007. “Diffusion Basis Functions Decomposition for Estimating White Matter Intravoxel Fiber Geometry.” IEEE Transactions on Medical Imaging 26: 1091–1102.
Reig, B., L. Heacock, K. J. Geras, and L. Moy. 2019. “Machine Learning in Breast MRI.” Journal of Magnetic Resonance Imaging 52: 998–1018.
Sun, S., Z. Cao, H. Zhu, and J. Zhao. 2020. “A Survey of Optimization Methods From a Machine Learning Perspective.” IEEE Transactions on Cybernetics 50: 3668–3681.
Tabesh, A., J. H. Jensen, B. A. Ardekani, and J. A. Helpern. 2010. “Estimation of Tensors and Tensor‐Derived Measures in Diffusional Kurtosis Imaging.” Magnetic Resonance in Medicine 65: 823–836.
Tax, C. M., M. Bastiani, J. Veraart, E. Garyfallidis, and M. Okan Irfanoglu. 2022. “What's New and What's Next in Diffusion MRI Preprocessing.” NeuroImage 249: 118830.
Tuch, D. S., T. G. Reese, M. R. Wiegell, N. Makris, J. W. Belliveau, and V. J. Wedeen. 2002. “High Angular Resolution Diffusion Imaging Reveals Intravoxel White Matter Fiber Heterogeneity.” Magnetic Resonance in Medicine 48: 577–582.
VanRossum, G., and F. L. Drake. 2010. The Python Language Reference. Python Software Foundation.
Voorter, P. H. M., J. F. A. Jansen, M. M. van der Thiel, et al. 2024. “Diffusion‐Derived Intravoxel‐Incoherent Motion Anisotropy Relates to CSF and Blood Flow.” Magnetic Resonance in Medicine 93: 930–941.
Wang, Y., Q. Wang, J. P. Haldar, et al. 2011. “Quantification of Increased Cellularity During Inflammatory Demyelination.” Brain 134: 3590–3601.
Wang, Z., A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. “Image Quality Assessment: From Error Visibility to Structural Similarity.” IEEE Transactions on Image Processing 13: 600–612.
Yang, R., and T. Jernigan. 2021. “Adolescent Brain Cognitive Development Study (ABCD)—Annual Release 4.0.”.
Ye, Z., S. E. Gary, P. Sun, et al. 2020. “The Impact of Edema and Fiber Crossing on Diffusion MRI Metrics Assessed in an Ex Vivo Nerve Phantom: Multi‐Tensor Model vs. Diffusion Orientation Distribution Function.” NMR in Biomedicine 34: e4414.
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Aja‐Fernández, S., and G. Vegas‐Sánchez‐Ferrero. 2016. Statistical Analysis of Noise in MRI. Springer International Publishing.
Basser, P. J., J. Mattiello, and D. LeBihan. 1994. “MR Diffusion Tensor Spectroscopy and Imaging.” Biophysical Journal 66: 259–267.
Basser, P. J., and S. Pajevic. 2000. “Statistical Artifacts in Diffusion Tensor MRI (DT‐MRI) Caused by Background Noise.” Magnetic Resonance in Medicine 44: 41–50.
Batchelor, P. G., D. Atkinson, D. L. G. Hill, F. Calamante, and A. Connelly. 2003. “Anisotropic Noise Propagation in Diffusion Tensor MRI Sampling Schemes.” Magnetic Resonance in Medicine 49: 1143–1151.
Berman, J. I., M. R. Lanza, L. Blaskey, J. C. Edgar, and T. P. L. Roberts. 2013. “High Angular Resolution Diffusion Imaging Probabilistic Tractography of the Auditory Radiation.” American Journal of Neuroradiology 34: 1573–1578.
Bottou, L., F. E. Curtis, and J. Nocedal. 2018. “Optimization Methods for Large‐Scale Machine Learning.” SIAM Review 60: 223–311.
Brunet, D., E. R. Vrscay, and Z. Wang. 2012. “On the Mathematical Properties of the Structural Similarity Index.” IEEE Transactions on Image Processing 21: 1488–1499.
Canales‐Rodríguez, E. J., A. Daducci, S. N. Sotiropoulos, et al. 2015. “Spherical Deconvolution of Multichannel Diffusion MRI Data With Non‐Gaussian Noise Models and Spatial Regularization.” PLoS One 10: e0138910.
Coelho, S., S. H. Baete, G. Lemberskiy, et al. 2022. “Reproducibility of the Standard Model of Diffusion in White Matter on Clinical MRI Systems.” NeuroImage 257: 119290.
Coelho, S., J. M. Pozo, S. N. Jespersen, D. K. Jones, and A. F. Frangi. 2019. “Resolving Degeneracy in Diffusion MRI Biophysical Model Parameter Estimation Using Double Diffusion Encoding.” Magnetic Resonance in Medicine 82: 395–410.
Crombe, A., V. Planche, G. Raffard, et al. 2018. “Deciphering the Microstructure of Hippocampal Subfields With in Vivo DTI and NODDI: Applications to Experimental Multiple Sclerosis.” NeuroImage 172: 357–368.
Cross, A. H., and S.‐K. Song. 2017. “A New Imaging Modality to Non‐Invasively Assess Multiple Sclerosis Pathology.” Journal of Neuroimmunology 304: 81–85.
De Santis, S., T. Granberg, R. Ouellette, et al. 2019. “Evidence of Early Microstructural White Matter Abnormalities in Multiple Sclerosis From Multi‐Shell Diffusion MRI.” NeuroImage: Clinical 22: 101699.
Diao, Y., and I. Jelescu. 2023. “Parameter Estimation for WMTI‐Watson Model of White Matter Using Encoder–Decoder Recurrent Neural Network.” Magnetic Resonance in Medicine 89: 1193–1206.
Diao, Y., F. Li, and Z. Li. 2023. “Joint Learning‐Based Feature Reconstruction and Enhanced Network for Incomplete Multi‐Modal Brain Tumor Segmentation.” Computers in Biology and Medicine 163: 107234.
Dosselmann, R., and X. D. Yang. 2009. “A Comprehensive Assessment of the Structural Similarity Index.” Signal, Image and Video Processing 5: 81–91.
Fieremans, E., A. Benitez, J. H. Jensen, et al. 2013. “Novel White Matter Tract Integrity Metrics Sensitive to Alzheimer Disease Progression.” American Journal of Neuroradiology 34: 2105–2112.
Garyfallidis, E., M. Brett, B. Amirbekian, et al. 2014. “Dipy, a Library for the Analysis of Diffusion MRI Data.” Frontiers in Neuroinformatics 8: 8.
Hagler, D. J., S. N. Hatton, M. D. Cornejo, et al. 2019. “Image Processing and Analysis Methods for the Adolescent Brain Cognitive Development Study.” NeuroImage 202: 116091.
Han, R. H., T. M. Johanns, K. F. Roberts, et al. 2023. “Diffusion Basis Spectrum Imaging as an Adjunct to Conventional MRI Leads to Earlier Diagnosis of High‐Grade Glioma Tumor Progression Versus Treatment Effect.” Neuro‐Oncology Advances 5: vdad050.
Henkelman, R. M. 1985. “Measurement of Signal Intensities in the Presence of Noise in MR Images.” Medical Physics 12: 232–233.
Hoy, A. R., C. G. Koay, S. R. Kecskemeti, and A. L. Alexander. 2014. “Optimization of a Free Water Elimination Two‐Compartment Model for Diffusion Tensor Imaging.” NeuroImage 103: 323–333.
Hu, Y.‐C., L.‐F. Yan, Q. Sun, et al. 2016. “Comparison Between Ultra‐High and Conventional Mono b‐Value DWI for Preoperative Glioma Grading.” Oncotarget 8: 37884–37895.
Huang, X., X. Xu, Y. Sun, et al. 2021. “Ultra‐High b‐Value DWI in Distinguishing Fresh Gray Matter Ischemic Lesions From White Matter Ones: A Comparative Study With Routine and High b‐Value DWI.” Quantitative Imaging in Medicine and Surgery 11: 4583–4593.
Hutchinson, E. B., A. V. Avram, M. O. Irfanoglu, et al. 2017. “Analysis of the Effects of Noise, DWI Sampling, and Value of Assumed Parameters in Diffusion MRI Models.” Magnetic Resonance in Medicine 78: 1767–1780.
Jain, P., and P. Kar. 2017. “Non‐Convex Optimization for Machine Learning.” Foundations and Trends in Machine Learning 10: 142–336.
Jelescu, I. O., and M. D. Budde. 2017. “Design and Validation of Diffusion MRI Models of White Matter.” Frontiers in Physics 5: 61.
Jelescu, I. O., J. Veraart, E. Fieremans, and D. S. Novikov. 2015. “Degeneracy in Model Parameter Estimation for Multi‐Compartmental Diffusion in Neuronal Tissue.” NMR in Biomedicine 29: 33–47.
Jensen, J. H., J. A. Helpern, A. Ramani, H. Lu, and K. Kaczynski. 2005. “Diffusional Kurtosis Imaging: The Quantification of Non‐Gaussian Water Diffusion by Means of Magnetic Resonance Imaging.” Magnetic Resonance in Medicine 53: 1432–1440.
Jespersen, S. N., J. L. Olesen, B. Hansen, and N. Shemesh. 2018. “Diffusion Time Dependence of Microstructural Parameters in Fixed Spinal Cord.” NeuroImage 182: 329–342.
Jiang, R., Z. Wang, J. Liu, et al. 2024. “High b‐Value and Ultra‐High b‐Value Diffusion Weighted MRI in Stroke.” Journal of Magnetic Resonance Imaging 61: 1459–1469.
Jones, D. K. 2004. “The Effect of Gradient Sampling Schemes on Measures Derived From Diffusion Tensor MRI: A Monte Carlo Study.” Magnetic Resonance in Medicine 51: 807–815.
Jones, D. K. 2010. Diffusion MRI. Oxford University Press.
Jones, D. K., and A. Leemans. 2010. Methods in Molecular Biology, 127–144. Humana Press.
Kingma, D. P., and J. Ba. 2014. “Adam: A Method for Stochastic Optimization.”.
Konieczny, M. J., A. Dewenter, A. ter Telgte, et al. 2021. “Multi‐Shell Diffusion MRI Models for White Matter Characterization in Cerebral Small Vessel Disease.” Neurology 96: e698–e708.
Lampinen, B., F. Szczepankiewicz, M. Novén, et al. 2019. “Searching for the Neurite Density With Diffusion MRI: Challenges for Biophysical Modeling.” Human Brain Mapping 40: 2529–2545.
Lavadi, R. S., K. L. Utt, S. N. Housley, et al. 2025. “Diffusion Basis Spectrum Imaging Detects Axonal Injury in the Optic Nerve Following Traumatic Brain Injury.” Magnetic Resonance Imaging 122: 110451.
Lawson, C. L., and R. J. Hanson. 1995. Solving Least Squares Problems. Society for Industrial and Applied Mathematics.
Le Bihan, D. 1991. “Molecular Diffusion Nuclear Magnetic Resonance Imaging.” Magnetic Resonance Quarterly 7: 1–30.
Le Bihan, D., J.‐F. Mangin, C. Poupon, et al. 2001. “Diffusion Tensor Imaging: Concepts and Applications.” Journal of Magnetic Resonance Imaging 13: 534–546.
Lim, C. S., J. Abreu‐Gomez, R. Thornhill, et al. 2021. “Utility of Machine Learning of Apparent Diffusion Coefficient (ADC) and T2‐Weighted (T2W) Radiomic Features in PI‐RADS Version 2.1 Category 3 Lesions to Predict Prostate Cancer Diagnosis.” Abdominal Radiology 46: 5647–5658.
Lin, T.‐H., J. Zhan, C. Song, et al. 2021. “Diffusion Basis Spectrum Imaging Detects Axonal Loss After Transient Dexamethasone Treatment in Optic Neuritis Mice.” Frontiers in Neuroscience 14: 592063.
Manzano Patron, J. P., S. Moeller, J. L. Andersson, K. Ugurbil, E. Yacoub, and S. N. Sotiropoulos. 2024. “Denoising Diffusion MRI: Considerations and Implications for Analysis.” Imaging Neuroscience 2: 1–29.
Meyer, J. R., A. Gutierrez, B. Mock, et al. 2000. “High‐b‐Value Diffusion‐Weighted MR Imaging of Suspected Brain Infarction.” AJNR. American Journal of Neuroradiology 21: 1821–1829.
Mohammadian, M., T. Roine, J. Hirvonen, et al. 2017. “High Angular Resolution Diffusion‐Weighted Imaging in Mild Traumatic Brain Injury.” NeuroImage: Clinical 13: 174–180.
Montalt‐Tordera, J., V. Muthurangu, A. Hauptmann, and J. A. Steeden. 2021. “Machine Learning in Magnetic Resonance Imaging: Image Reconstruction.” Physica Medica 83: 79–87.
Moradi, E., A. Pepe, C. Gaser, H. Huttunen, and J. Tohka. 2015. “Machine Learning Framework for Early MRI‐Based Alzheimer's Conversion Prediction in MCI Subjects.” NeuroImage 104: 398–412.
Mozumder, M., J. M. Pozo, S. Coelho, and A. F. Frangi. 2019. “Population‐Based Bayesian Regularization for Microstructural Diffusion MRI With NODDIDA.” Magnetic Resonance in Medicine 82: 1553–1565.
Mudeng, V., M. Kim, and S.‐w. Choe. 2022. “Prospects of Structural Similarity Index for Medical Image Analysis.” Applied Sciences 12: 3754.
Novikov, D. S., E. Fieremans, S. N. Jespersen, and V. G. Kiselev. 2019. “Quantifying Brain Microstructure With Diffusion MRI: Theory and Parameter Estimation.” NMR in Biomedicine 32: e3998.
Novikov, D. S., V. G. Kiselev, and S. N. Jespersen. 2018. “On Modeling.” Magnetic Resonance in Medicine 79: 3172–3193.
O'Donnell, L. J., and C.‐F. Westin. 2011. “An Introduction to Diffusion Tensor Image Analysis.” Neurosurgery Clinics of North America 22: 185–196.
Paszke, A. 2019. PyTorch: An Imperative Style, High‐Performance Deep Learning Library.
Pines, A. R., M. Cieslak, B. Larsen, et al. 2020. “Leveraging Multi‐Shell Diffusion for Studies of Brain Development in Youth and Young Adulthood.” Developmental Cognitive Neuroscience 43: 100788.
Polzehl, J., and K. Tabelow. 2016. “Low SNR in Diffusion MRI Models.” Journal of the American Statistical Association 111: 1480–1490.
Rafipoor, H., Y.‐Q. Zheng, L. Griffanti, S. Jbabdi, and M. Cottaar. 2022. “Identifying Microstructural Changes in Diffusion MRI: How to Circumvent Parameter Degeneracy.” NeuroImage 260: 119452.
Ramirez‐Manzanares, A., M. Rivera, B. C. Vemuri, P. Carney, and T. Mareci. 2007. “Diffusion Basis Functions Decomposition for Estimating White Matter Intravoxel Fiber Geometry.” IEEE Transactions on Medical Imaging 26: 1091–1102.
Reig, B., L. Heacock, K. J. Geras, and L. Moy. 2019. “Machine Learning in Breast MRI.” Journal of Magnetic Resonance Imaging 52: 998–1018.
Sun, S., Z. Cao, H. Zhu, and J. Zhao. 2020. “A Survey of Optimization Methods From a Machine Learning Perspective.” IEEE Transactions on Cybernetics 50: 3668–3681.
Tabesh, A., J. H. Jensen, B. A. Ardekani, and J. A. Helpern. 2010. “Estimation of Tensors and Tensor‐Derived Measures in Diffusional Kurtosis Imaging.” Magnetic Resonance in Medicine 65: 823–836.
Tax, C. M., M. Bastiani, J. Veraart, E. Garyfallidis, and M. Okan Irfanoglu. 2022. “What's New and What's Next in Diffusion MRI Preprocessing.” NeuroImage 249: 118830.
Tuch, D. S., T. G. Reese, M. R. Wiegell, N. Makris, J. W. Belliveau, and V. J. Wedeen. 2002. “High Angular Resolution Diffusion Imaging Reveals Intravoxel White Matter Fiber Heterogeneity.” Magnetic Resonance in Medicine 48: 577–582.
VanRossum, G., and F. L. Drake. 2010. The Python Language Reference. Python Software Foundation.
Voorter, P. H. M., J. F. A. Jansen, M. M. van der Thiel, et al. 2024. “Diffusion‐Derived Intravoxel‐Incoherent Motion Anisotropy Relates to CSF and Blood Flow.” Magnetic Resonance in Medicine 93: 930–941.
Wang, Y., Q. Wang, J. P. Haldar, et al. 2011. “Quantification of Increased Cellularity During Inflammatory Demyelination.” Brain 134: 3590–3601.
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Grant Information:
R01-NS116091 United States NH NIH HHS; R01-CA258690 United States NH NIH HHS; RO1-CA282022 United States NH NIH HHS
Contributed Indexing:
Keywords: diffusion MRI; multitensor estimation; self‐diffusion; signal processing
Entry Date(s):
Date Created: 20260202 Date Completed: 20260202 Latest Revision: 20260209
Update Code:
20260209
PubMed Central ID:
PMC12862189
DOI:
10.1002/hbm.70460
PMID:
41622723
Database:
MEDLINE
*Further Information*
*This paper introduces an advanced framework for accelerated processing of diffusion-weighted imaging (DWI) data that utilizes an entire-image modeling approach to optimize the estimation of diffusion parameters from DWIs by mapping input diffusion data to predicted signals and estimating parameter values via a stochastic gradient descent optimizer (Adam). To validate this approach, we applied this framework to diffusion basis spectrum imaging (DBSI) and analyzed in vivo human brain and ex vivo mouse brain DWIs. Results demonstrate significant improvements to computational speed and signal-to-noise ratio (SNR) in estimated parameter maps compared to standard DBSI. Our approach is applicable to any diffusion signal representation and enables rapid and reliable signal partitioning in complex microstructural environments, demonstrating the potential of this framework for future neuroimaging research.
(© 2026 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)*