Adaptive model-based Magnetic Resonance

PurposeConventional sequences are static in nature, fixing measurement parameters in advance in anticipation of a wide range of expected tissue parameter values. We set out to design and benchmark a new, personalized approach-termed adaptive MR-in which incoming subject data is used to update and fine-tune the pulse sequence parameters in real time. MethodsWe implemented an adaptive, real-time multi-echo (MTE) experiment for estimating T(2)s. Our approach combined a Bayesian framework with model-based reconstruction. It maintained and continuously updated a prior distribution of the desired tissue parameters, including T-2, which was used to guide the selection of sequence parameters in real time. ResultsComputer simulations predicted accelerations between 1.7- and 3.3-fold for adaptive multi-echo sequences relative to static ones. These predictions were corroborated in phantom experiments. In healthy volunteers, our adaptive framework accelerated the measurement of T-2 for n-acetyl-aspartate by a factor of 2.5. ConclusionAdaptive pulse sequences that alter their excitations in real time could provide substantial reductions in acquisition times. Given the generality of our proposed framework, our results motivate further research into other adaptive model-based approaches to MRI and MRS.

Automated summary

The study aimed to design and test a personalized adaptive MR approach in which pulse sequence parameters are updated and fine-tuned in real-time based on incoming subject data. By combining a Bayesian framework with model-based reconstruction, an adaptive real-time multi-echo (MTE) experiment was implemented, resulting in significant reduction in acquisition times.