title: Diffeomorphic metric mapping and probabilistic atlas generation of hybrid diffusion imaging based on BFOR signal basis.
abstract: We first propose a large deformation diffeomorphic metric mapping algorithm to align multiple b-value diffusion weighted imaging (mDWI) data, specifically acquired via hybrid diffusion imaging (HYDI). We denote this algorithm as LDDMM-HYDI. We then propose a Bayesian probabilistic model for estimating the white matter atlas from HYDIs. We adopt the work given in Hosseinbor et al. (2013) and represent the q-space diffusion signal with the Bessel Fourier orientation reconstruction (BFOR) signal basis. The BFOR framework provides the representation of mDWI in the q-space and the analytic form of the emsemble average propagator (EAP) reconstruction, as well as reduces memory requirement. In addition, since the BFOR signal basis is orthonormal, the L(2) norm that quantifies the differences in the q-space signals of any two mDWI datasets can be easily computed as the sum of the squared differences in the BFOR expansion coefficients. In this work, we show that the reorientation of the q-space signal due to spatial transformation can be easily defined on the BFOR signal basis. We incorporate the BFOR signal basis into the LDDMM framework and derive the gradient descent algorithm for LDDMM-HYDI with explicit orientation optimization. Additionally, we extend the previous Bayesian atlas estimation framework for scalar-valued images to HYDIs and derive the expectation-maximization algorithm for solving the HYDI atlas estimation problem. Using real HYDI datasets, we show that the Bayesian model generates the white matter atlas with anatomical details. Moreover, we show that it is important to consider the variation of mDWI reorientation due to a small change in diffeomorphic transformation in the LDDMM-HYDI optimization and to incorporate the full information of HYDI for aligning mDWI. Finally, we show that the LDDMM-HYDI outperforms the LDDMM algorithm with diffusion tensors generated from each shell of HYDI.
authors: Department of Biomedical Engineering, National University of Singapore, Singapore.
authors: Jia
authors: J
authors: Du
authors: Department of Medical Physics, University of Wisconsin-Madison, USA; Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, USA.
authors: A Pasha
authors: AP
authors: Hosseinbor
authors: Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, USA; Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA.
authors: Moo K
authors: MK
authors: Chung
authors: Department of Medicine, University of Wisconsin-Madison, USA.
authors: Barbara B
authors: BB
authors: Bendlin
authors: Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, USA.
authors: Gaurav
authors: G
authors: Suryawanshi
authors: Department of Medical Physics, University of Wisconsin-Madison, USA; Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, USA.
authors: Andrew L
authors: AL
authors: Alexander
authors: Department of Biomedical Engineering, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore; Clinical Imaging Research Center, National University of Singapore, Singapore. Electronic address: bieqa@nus.edu.sg.
authors: Anqi
authors: A
authors: Qiu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D015233:Models, Statistical
keywords: Bayesian estimation; Bessel Fourier orientation reconstruction (BFOR) signal basis; Hybrid diffusion imaging (HYDI); Large deformation diffeomorphic metric mapping (LDDMM); The white matter atlas