Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging
CoRR(2024)
摘要
Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the
noninvasive investigation of tissue microstructural properties and structural
connectivity in the in vivo human brain. However, to effectively
capture the intricate characteristics of water diffusion at various directions
and scales, it is important to employ comprehensive q-space sampling.
Unfortunately, this requirement leads to long scan times, limiting the clinical
applicability of dMRI. To address this challenge, we propose SSOR, a
Simultaneous q-Space sampling Optimization and Reconstruction framework. We
jointly optimize a subset of q-space samples using a continuous representation
of spherical harmonic functions and a reconstruction network. Additionally, we
integrate the unique properties of diffusion magnetic resonance imaging (dMRI)
in both the q-space and image domains by applying l1-norm and total-variation
regularization. The experiments conducted on HCP data demonstrate that SSOR has
promising strengths both quantitatively and qualitatively and exhibits
robustness to noise.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要