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

Deep learning (DL) methods can reconstruct highly accelerated magnetic resonance imaging (MRI) scans, but they rely on application-specific large training datasets and often generalize poorly to out-of-distribution data. Self-supervised deep learning algorithms perform scan-specific reconstructions, but still require complicated hyperparameter tuning based on the acquisition and often offer limited acceleration. This work develops a bilevel-optimized implicit neural representation (INR) approach for scan-specific MRI reconstruction. The method explicitly formulates the undersampled MRI reconstruction problem as a bilevel optimization problem and automatically optimizes the multidimensional hyperparameters of the reconstruction method for a given acquisition protocol, enabling a tailored reconstruction without training data. The proposed algorithm uses Gaussian process regression to optimize INR hyperparameters, accommodating various acquisitions. The INR includes a trainable positional encoder for high-dimensional feature embedding and a small multilayer perceptron for decoding. The bilevel optimization is computationally efficient, requiring only a few minutes per typical 2D Cartesian scan. On the scanner hardware, the subsequent scan-specific reconstruction-using offline-optimized hyperparameters-is completed within seconds, while achieving comparable or improved image quality compared to previous model-based and self-supervised learning methods.