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Learning-based computational MRI reconstruction without big data: from linear interpolation and structured low-rank matrices to recurrent neural networks
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Paper Abstract

We present a brief overview of computational image reconstruction methods that assume that Magnetic Resonance Imaging (MRI) data possesses shift-invariant autoregressive characteristics, where the unique autoregressive structure of each dataset is learned from a small amount of scan-specific calibration data. Our discussion focuses particular attention on a method we recently introduced named LORAKI. LORAKI is a learning-based image reconstruction method that relies on scan-specific nonlinear autoregressive modeling using a recurrent convolutional neural network, and has demonstrated better performance than previous approaches. As a novel contribution, we also describe and evaluate an extension of LORAKI that makes simultaneous use of support, phase, parallel imaging, and sparsity constraints, where the balance between these different constraints is automatically determined through the training procedure. Results with real data demonstrate that this modification leads to further performance improvements.

Paper Details

Date Published: 9 September 2019
PDF: 7 pages
Proc. SPIE 11138, Wavelets and Sparsity XVIII, 1113817 (9 September 2019); doi: 10.1117/12.2527584
Show Author Affiliations
Tae Hyung Kim, The Univ. of Southern California (United States)
Justin P. Haldar, The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 11138:
Wavelets and Sparsity XVIII
Dimitri Van De Ville; Manos Papadakis; Yue M. Lu, Editor(s)

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