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Chapter and Conference Paper
Neural Pre-processing: A Learning Framework for End-to-End Brain MRI Pre-processing
Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space. In this paper, we propose an end-to-end weakly supervised learning approa...
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Chapter and Conference Paper
HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks
Reconstructing under-sampled k-space measurements in Compressed Sensing MRI (CS-MRI) is classically solved by minimizing a regularized least-squares cost function. In the absence of fully-sampled training data, t...
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Chapter and Conference Paper
Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy
Compressed sensing fluorescence microscopy (CS-FM) proposes a scheme whereby less measurements are collected during sensing and reconstruction is performed to recover the image. Much work has gone into optimiz...
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Chapter and Conference Paper
Neural Network-Based Reconstruction in Compressed Sensing MRI Without Fully-Sampled Training Data
Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing under-sampled MR images, offering the potential to reduce scan times. Classical techniques minimize a regularized least-squares cost function...