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Abstract #0389

Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction

Hongze Yu1, Jeffrey A. Fessler1,2,3, and Yun Jiang2,3
1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States, 2Department of Radiology, University of Michigan, Ann Arbor, MI, United States, 3Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Implicit Neural Representation

Motivation: Supervised deep learning methods require application-specific training datasets and perform poorly with out-of-distribution data. Scan-specific methods do not require training data, but need careful hyperparameter tuning.

Goal(s): To propose an automatically hyperparameter-optimized, scan-specific deep learning method that reconstructs various highly accelerated MRI acquisitions.

Approach: We developed a self-supervised, bilevel-optimized, implicit neural representation (INR) network. It splits undersampled data into training and validation sets and applies Bayesian optimization for hyperparameter tuning. A multiresolution trainable parametric encoder reconstructs accelerated MRI scans.

Results: Our method achieved performance comparable to oracle-optimized reconstructions, demonstrating the benefits of automatic hyperparameter optimization, and outperformed existing model-based and self-supervised methods.

Impact: By automatically optimizing hyperparameters for scan-specific deep learning, our method reconstructs accelerated MRI scans across diverse protocols with superior image quality. It avoids reliance on training data and complicated task-dependent tuning, enhancing the clinical applicability of deep learning in MRI.

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Keywords