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Learning compact q-space representations for multi-shell diffusion-weighted MRI
摘要： Diffusion-weighted MRI measures the direction and scale of the local diffusion process in every voxel through its spectrum in q-space, typically acquired in one or more shells. Recent developments in microstructure imaging and multi-tissue decomposition have sparked renewed attention in the radial b-value dependence of the signal. Applications in motion correction and outlier rejection therefore require a compact linear signal representation that extends over the radial as well as angular domain. Here, we introduce SHARD, a data-driven representation of the q-space signal based on spherical harmonics and a radial decomposition into orthonormal components. This representation provides a complete, orthogonal signal basis, tailored to the spherical geometry of q-space and calibrated to the data at hand. We demonstrate that the rank-reduced decomposition outperforms model-based alternatives in human brain data, whilst faithfully capturing the micro- and meso-structural information in the signal. Furthermore, we validate the potential of joint radial-spherical as compared to single-shell representations. As such, SHARD is optimally suited for applications that require low-rank signal predictions, such as motion correction and outlier rejection. Finally, we illustrate its application for the latter using outlier robust regression.
关键词： Diffusion-weighted imaging,Blind source separation,Multi-shell HARDI,Dimensionality reduction
更新于2025-09-23 15:23:52
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Combined texture analysis of diffusion-weighted imaging with conventional MRI for non-invasive assessment of IDH1 mutation in anaplastic gliomas
摘要： To examine whether texture analysis (TA) of diffusion-weighted imaging (DWI) combined with conventional magnetic resonance imaging (MRI) could non-invasively predict isocitrate dehydrogenase 1 (IDH1) mutational status in anaplastic gliomas.
关键词： anaplastic gliomas,IDH1 mutation,diffusion-weighted imaging,conventional MRI,texture analysis
更新于2025-09-23 15:21:01
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Initial experience of correlating diffusion spectral parameters with histopathologic indexes in murine colorectal tumor homografts
摘要： Purpose: To determine the correlation between continuously distributed diffusion-weighted image (DWI)-derived parameters and histopathologic indexes. Methods: Fifty-four mice bearing HCT-116 colorectal tumors were included for analysis; 12 mice were used for continuous observation, and the other 42 mice were used for break-point observation. All mice were randomly divided into radiotherapy and non-radiotherapy groups. Optical imaging and MRI were performed at different time points according to radiotherapy regimen (baseline, 24 h, 48 h, 72 h, 7 d, 14 d, and 28 d). Continuous observation data were analyzed to show the difference of dynamic changing trends of optical and MR-DWI–derived parameters between radiotherapy and non-radiotherapy groups (photon numbers, D_max, full width half maximum [FWHM], and apparent diffusion coefficient [ADC] value). Break-point observation data were used to analyze the correlation between histopathologic indices and DWI-derived parameters. Results: There was a significant difference in the changing trends of photon numbers, D_max, FWHM, and ADC value between radiotherapy and non-radiotherapy groups, especially at early time points. There was moderate negative correlation between Ki67 and percentage changes of D_max, FWHM, and ADC values (the correlation coefficients were 0.632, 0.449, and 0.586, P,0.001, P=0.008, and P,0.001, respectively). There was moderate negative correlation between survivin and percentage changes of D_max and ADC values (correlation coefficients were 0.496 and 0.473, P=0.004 and P=0.006, respectively). Conclusion: The continuously distributed DWI-derived parameters could reflect histological behavior to some extent and, thus, are potential markers for early noninvasive monitoring of tumor cell apoptosis and proliferation.
关键词： magnetic resonance imaging,colorectal cancer,murine homografts,diffusion-weighted imaging,continuously distributed
更新于2025-09-04 15:30:14