关键词：扩散成像 超高梯度成像 微观结构成像 神经科学
ISMRM 2025 Abstract #4783
Denoising diffusion MRI data: principal components meet non-local block-matching
Authors
Vinicius P. Campos1,2, Diego Szczupak1, Tales Santini3, Afonso C. Silva1,3, Alessandro Foi4, Marcelo A. C. Vieira2, and Corey Baron5,6
1Department of Neurobiology, University of PIttsburgh, Pittsburgh, PA, United States,
2Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, Brazil,
3Department of Bioengineering, University of PIttsburgh, Pittsburgh, PA, United States,
4Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland,
5Department of Medical Biophysics, Western University, London, ON, Canada,
6Center for Functional and Metabolic Mapping, Western University, London, ON, Canada
The innovative BM4PC reduces noise in diffusion-weighted images, enabling diagnostic capabilities and sensitivity to tissue microstructure and degeneration. It can be applied across diverse acquisition types and datasets (in vivo and ex vivo), from Cartesian encoding to advanced spiral techniques.
FA and neurite density index (NDI) maps of Dataset 1c – (0.9mm res.).
Note the detail preservation provided by BM4PC, highlighted by the yellow arrow.
The boxplot graphs show FA values calculated in two ROIs. BM4PC achieves the lowest mean and standard deviation (std) for the CSF region.
All methods achieve similar mean values for the CC region, with BM4PC having the smallest std.
(CSF – left; midbody of CC – right)
DWIs (with spherical tensor encoding) and Kurtosis metrics for Dataset 2.
BM4PC achieves the most striking result.
We reinforce that Kiso is highly sensitive to noise, with considerably large increases in the values when no denoising is used. Ktotal and Kaniso retain their exquisite white/grey matter contrast and high-resolution features for all denoising approaches, but BM4PC results in the lowest propagation of noise.
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