Abstract. Intra-voxel models of the diffusion signal are essential for interpreting organization of the tissue environment at micrometer level with data at millimeter resolution. Recent advances in data driven methods have enabled direct compari-son and optimization of methods for in-vivo data with externally validated histological sections with both 2-D and 3-D histology. Yet, all existing methods make limiting assumptions of either (1) model-based linkages between b-values or (2) limited associations with single shell data. We generalize prior deep learning models that used single shell spherical harmonic transforms to integrate the re-cently developed simple harmonic oscillator reconstruction (SHORE) basis. To enable learning on the SHORE manifold, we present an alternative formulation of the fiber orientation distribution (FOD) object using the SHORE basis while rep-resenting the observed diffusion weighted data in the SHORE basis. To ensure consistency of hyper-parameter optimization for SHORE, we present our Deep SHORE approach to learn on a data-optimized manifold. Deep SHORE is evalu-ated with eight-fold cross-validation of a preclinical MRI-histology data with four b-values. Generalizability of in-vivo human data is evaluated on two separate 3T MRI scanners. Specificity in terms of angular correlation (ACC) with the preclinical data improved on single shell: 0.78 relative to 0.73 and 0.73, multi-shell: 0.80 relative to 0.74 (p < 0.001). In the in-vivo human data, Deep SHORE was more consistent across scanners with 0.63 relative to other multi-shell methods 0.39, 0.52 and 0.57 in terms of ACC. In conclusion, Deep SHORE is a promising method to enable data driven learning with DW-MRI under conditions with varying b-values, number of diffusion shells, and gradient directions per shell.
翻译:抽象 。 传播信号的立方体模型对于以毫米分辨率的数据解释组织组织组织组织组织组织而言至关重要。 数据驱动方法的最近进展使得直接比较和优化使用外部验证的2-D和3-D的文体部分进行校正分析的数据。 然而,所有现有方法都对以下两种假设作了限制:(1) 基于模型的b-值之间的联系或(2) 与单一贝壳数据之间的有限关联。 我们推广了先前的深学习模型,这些模型使用了单一贝壳多球体流变异法,以整合重新开发的简单直流眼振荡器重建(SHORE)基础。 为了能够在SHORE中进行直接比较比较和优化,我们用SHORE基础对观察到的传播加权数据分布进行重新校正。 我们用深度的深SHORE方法来学习数据,在数据转换过程中, 深度蒸析了0. 0. 3 和 数值前 的直方数据进行两次对比评估。 Sral- 的S-rmal- dal- dal- dental 数据在S- silvial- dal- dal- dal- dal- dal- dal- dal- disal- disal- disal- disal disal dismald sild.