Learning Task-Specific Strategies for Accelerated MRI
- URL: http://arxiv.org/abs/2304.12507v2
- Date: Wed, 6 Dec 2023 01:10:27 GMT
- Title: Learning Task-Specific Strategies for Accelerated MRI
- Authors: Zihui Wu, Tianwei Yin, Yu Sun, Robert Frost, Andre van der Kouwe,
Adrian V. Dalca, Katherine L. Bouman
- Abstract summary: We propose TACKLE as a unified co-design framework for jointly optimizing subsampling, reconstruction, and prediction strategies for the performance on downstream tasks.
We develop a training procedure where a backbone architecture is first trained for a generic pre-training task, and then fine-tuned for different downstream tasks with a prediction head.
Experimental results on multiple public MRI datasets show that TACKLE achieves an improved performance on various tasks over traditional CS-MRI methods.
- Score: 14.937601057648111
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Compressed sensing magnetic resonance imaging (CS-MRI) seeks to recover
visual information from subsampled measurements for diagnostic tasks.
Traditional CS-MRI methods often separately address measurement subsampling,
image reconstruction, and task prediction, resulting in a suboptimal end-to-end
performance. In this work, we propose TACKLE as a unified co-design framework
for jointly optimizing subsampling, reconstruction, and prediction strategies
for the performance on downstream tasks. The na\"ive approach of simply
appending a task prediction module and training with a task-specific loss leads
to suboptimal downstream performance. Instead, we develop a training procedure
where a backbone architecture is first trained for a generic pre-training task
(image reconstruction in our case), and then fine-tuned for different
downstream tasks with a prediction head. Experimental results on multiple
public MRI datasets show that TACKLE achieves an improved performance on
various tasks over traditional CS-MRI methods. We also demonstrate that TACKLE
is robust to distribution shifts by showing that it generalizes to a new
dataset we experimentally collected using different acquisition setups from the
training data. Without additional fine-tuning, TACKLE leads to both numerical
and visual improvements compared to existing baselines. We have further
implemented a learned 4$\times$-accelerated sequence on a Siemens 3T MRI Skyra
scanner. Compared to the fully-sampling scan that takes 335 seconds, our
optimized sequence only takes 84 seconds, achieving a four-fold time reduction
as desired, while maintaining high performance.
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