Patient-Adaptive and Learned MRI Data Undersampling Using Neighborhood Clustering Article Swipe

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Siddhant Gautam , Angqi Li , Saiprasad Ravishankar ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2312.08507

There has been much recent interest in adapting undersampled trajectories in MRI based on training data. In this work, we propose a novel patient-adaptive MRI sampling algorithm based on grouping scans within a training set. Scan-adaptive sampling patterns are optimized together with an image reconstruction network for the training scans. The training optimization alternates between determining the best sampling pattern for each scan (based on a greedy search or iterative coordinate descent (ICD)) and training a reconstructor across the dataset. The eventual scan-adaptive sampling patterns on the training set are used as labels to predict sampling design using nearest neighbor search at test time. The proposed algorithm is applied to the fastMRI knee multicoil dataset and demonstrates improved performance over several baselines.

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Undersampling Computer science Sampling (signal processing) Adaptive sampling Artificial intelligence Cluster analysis Set (abstract data type) Data set Compressed sensing Pattern recognition (psychology) Data mining Machine learning Computer vision Monte Carlo method Mathematics Statistics Filter (signal processing) Programming language

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2312.08507
PDF: https://arxiv.org/pdf/2312.08507
OA Status: green
References: 26
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2312.08507

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Title: Patient-Adaptive and Learned MRI Data Undersampling Using Neighborhood Clustering

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Language: en

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Publication year: 2023

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Publication date: 2023-12-13

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Authors: Siddhant Gautam, Angqi Li, Saiprasad Ravishankar

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Landing page: https://arxiv.org/abs/2312.08507

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Concepts: Undersampling, Computer science, Sampling (signal processing), Adaptive sampling, Artificial intelligence, Cluster analysis, Set (abstract data type), Data set, Compressed sensing, Pattern recognition (psychology), Data mining, Machine learning, Computer vision, Monte Carlo method, Mathematics, Statistics, Filter (signal processing), Programming language

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abstract_inverted_index.interest	5
abstract_inverted_index.neighbor	99
abstract_inverted_index.patterns	37, 84
abstract_inverted_index.proposed	105
abstract_inverted_index.sampling	25, 36, 58, 83, 95
abstract_inverted_index.together	40
abstract_inverted_index.training	14, 33, 48, 51, 74, 87
abstract_inverted_index.algorithm	26, 106
abstract_inverted_index.iterative	69
abstract_inverted_index.multicoil	113
abstract_inverted_index.optimized	39
abstract_inverted_index.alternates	53
abstract_inverted_index.baselines.	121
abstract_inverted_index.coordinate	70
abstract_inverted_index.determining	55
abstract_inverted_index.performance	118
abstract_inverted_index.demonstrates	116
abstract_inverted_index.optimization	52
abstract_inverted_index.trajectories	9
abstract_inverted_index.undersampled	8
abstract_inverted_index.Scan-adaptive	35
abstract_inverted_index.reconstructor	76
abstract_inverted_index.scan-adaptive	82
abstract_inverted_index.reconstruction	44
abstract_inverted_index.patient-adaptive	23
cited_by_percentile_year
countries_distinct_count	1
institutions_distinct_count	3
citation_normalized_percentile.value	0.3967334
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False