Joint spatial-angular sparse coding for dMRI with separable dictionaries

Autor: Nicolas Charon, Evan Schwab, René Vidal
Rok vydání: 2017
Předmět:
FOS: Computer and information sciences
Computational complexity theory
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Machine Learning (stat.ML)
Health Informatics
computer.software_genre
Regularization (mathematics)
Quantitative Biology - Quantitative Methods
030218 nuclear medicine & medical imaging
03 medical and health sciences
symbols.namesake
0302 clinical medicine
Voxel
Statistics - Machine Learning
Image Processing
Computer-Assisted
Humans
Radiology
Nuclear Medicine and imaging
Representation (mathematics)
Quantitative Methods (q-bio.QM)
Kronecker product
Brain Mapping
Radiological and Ultrasound Technology
business.industry
Pattern recognition
Data Compression
Image Enhancement
Computer Graphics and Computer-Aided Design
Compressed sensing
Diffusion Tensor Imaging
FOS: Biological sciences
symbols
Computer Vision and Pattern Recognition
Artificial intelligence
business
Neural coding
computer
030217 neurology & neurosurgery
Algorithms
Diffusion MRI
Zdroj: Medical image analysis. 48
ISSN: 1361-8423
Popis: Diffusion MRI (dMRI) provides the ability to reconstruct neuronal fibers in the brain, $\textit{in vivo}$, by measuring water diffusion along angular gradient directions in q-space. High angular resolution diffusion imaging (HARDI) can produce better estimates of fiber orientation than the popularly used diffusion tensor imaging, but the high number of samples needed to estimate diffusivity requires longer patient scan times. To accelerate dMRI, compressed sensing (CS) has been utilized by exploiting a sparse dictionary representation of the data, discovered through sparse coding. The sparser the representation, the fewer samples are needed to reconstruct a high resolution signal with limited information loss, and so an important area of research has focused on finding the sparsest possible representation of dMRI. Current reconstruction methods however, rely on an angular representation $\textit{per voxel}$ with added spatial regularization, and so, for non-zero signals, one is required to have at least one non-zero coefficient per voxel. This means that the global level of sparsity must be greater than the number of voxels. In contrast, we propose a joint spatial-angular representation of dMRI that will allow us to achieve levels of global sparsity that are below the number of voxels. A major challenge, however, is the computational complexity of solving a global sparse coding problem over large-scale dMRI. In this work, we present novel adaptations of popular sparse coding algorithms that become better suited for solving large-scale problems by exploiting spatial-angular separability. Our experiments show that our method achieves significantly sparser representations of HARDI than is possible by the state of the art.
Databáze: OpenAIRE
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