Speaker
Description
In the MR-zero project [1,2], we approached to reinvent MRI from scratch by treating MR sequence design as a learning-based optimization problem. We formulate an end-to-end differentiable simulation pipeline, given a specific imaging task or even just a target image, the entire chain can be optimized jointly.
In this talk, I will introduce the MR-zero approach and illustrate it with examples: learning MRI from scratch, improving image sharpness in TSE and FLASH MRI, optimizing sequences for imperfect scanner systems, and performing end-to-end quantitative MRI experiments. These results show what becomes possible when we borrow tools from optimization, automatic differentiation, and machine learning and combine it with recent MR physics developments like phase distribution graphs [3].
At the same time, MR-zero is far from an ideal mathematical framework. Many parts of the pipeline and problem formulation are still in progress, and are slow, unstable, approximate, or non-optimal. For this audience, this is exactly the point: the project exposes open problems in inverse problems in MR, differentiable simulation, typical optimization constraints and regularizations, and the translation from continuous models to executable scanner experiments. The talk is both a report and an invitation. I will show what we achieved with out-of-the-box mathematical tools, and where we would benefit from deeper mathematical insight. If you leave thinking, “What are they even doing? This could be done so much better,” then the talk has succeeded.
[1] https://mrsources.github.io/MRzero-Core/
[2] https://doi.org/10.1002/mrm.28727
[3] https://doi.org/10.1002/mrm.30055