Abstract #0843

Systematic Standardization of Deep Learning Based Accelerated MRI Reconstruction Pipelines

Beliz Gunel^1,2, Arjun Divyang Desai^1,2, Shreyas Vasanawala³, Akshay Chaudhari^3,4, and John Pauly¹

¹Electrical Engineering, Stanford University, Stanford, CA, United States, ²Equal contribution, Stanford University, Stanford, CA, United States, ³Radiology, Stanford University, Stanford, CA, United States, ⁴Biomedical Data Science, Stanford University, Stanford, CA, United States

Synopsis

Deep learning based accelerated MRI reconstruction pipelines have potential to enable higher acceleration factors compared to traditional methods with fast reconstruction times and improved image quality. Although there have been studies regarding model architecture, loss function, and k-space undersampling patterns; the effect of scanner parameters, variations in sensitivity map estimation, training data requirement, and engineering decisions during model optimization and evaluation on the reconstruction performance remain largely unexplored. We systematically study the impact of such and show that such data extent, re-processing, and metric computation impact performance to the same or at larger extents than new architectures and loss functions.

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