Abstract #2921

Greedy Learning for Memory-Efficient Self-Supervised MRI Reconstruction

Arushi Gupta¹, Batu M Ozturkler², Arda Sahiner², Tolga Ergen², Arjun D Desai^2,3, Shreyas Vasanawala³, John M Pauly², Morteza Mardani², and Mert Pilanci²

¹California Institute of Technology, Pasadena, CA, United States, ²Electrical Engineering, Stanford University, Stanford, CA, United States, ³Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Image Reconstruction, Image ReconstructionDeep learning (DL) has recently shown state-of-the-art performance for accelerated MRI reconstruction. However, supervised learning requires fully-sampled training data, and training these networks with end-to-end backpropagation requires significant memory for high-dimensional imaging. These challenges limit the use of DL in high-dimensional settings where access to fully-sampled data is unavailable. Here, we propose self-supervised greedy learning for memory-efficient MRI reconstruction without fully-sampled data. The method divides the end-to-end network into smaller network modules and independently calculates a self-supervised loss for each subnetwork. The proposed method generalizes as well as end-to-end learning without fully-sampled data with at least 7x less memory usage.

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