MR-double-zero - Proof-of-concept for a framework to autonomously discover MRI contrasts

A framework for supervised design of MR sequences for any given target contrast is proposed, based on fully automatic acquisition and reconstruction of MR data on a real MR scanner. The proposed method does not require any modeling of MR physics and thus allows even unknown contrast mechanisms to be addressed.

A derivative-free optimization algorithm is set up to repeatedly update and execute a parametrized sequence on the MR scanner to acquire data. In each iteration, the acquired data are mapped to a given target contrast by linear regression.

It is shown that with the proposed framework it is possible to find an MR sequence that yields a predefined target contrast. In the present case, as a proof-of principle, a sequence mapping absolute creatine concentration, which cannot be extracted from T1 or T2-weighted scans directly, is discovered. The sequence was designed in a comparatively short time and with no human interaction.

New MR contrasts for mapping a given target can be discovered by derivative-free optimization of parametrized sequences that are directly executed on a real MRI scanner. This is demonstrated by 're-discovery' of a chemical exchange weighted sequence. The proposed method is considered to be a paradigm shift towards autonomous, model-free and target-driven sequence design.