Abstract
We propose an original spatio-temporal deconvolution approach for perfusion-weighted MRI applied to cerebral ischemia. The regularization of the underlying inverse problem is achieved with spatio-temporal priors and the resulting optimization problem is solved by half-quadratic minimization. Our approach offers strong convergence guarantees, including when the spatial priors are non-convex. Moreover, experiments on synthetic data and on real data collected from subjects with ischemic stroke show significant performance improvements over the standard approaches-namely, temporal deconvolution based on either truncated singular-value decomposition or ℓ2-regularization-in terms of various performance measures.
Original language | English |
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Pages (from-to) | 144-160 |
Number of pages | 17 |
Journal | Medical Image Analysis |
Volume | 18 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2014 Jan |
Externally published | Yes |
Keywords
- Acute stroke
- Deconvolution
- Perfusion weighted MRI
- Spatio-temporal model
- Tissue outcome prediction
ASJC Scopus subject areas
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging
- Computer Vision and Pattern Recognition
- Health Informatics
- Computer Graphics and Computer-Aided Design