Incremental Undersampling MRI Acquisition With Neural Self Assessment

Abstract

Accelerated MRI acquisition is widely adopted and basically consists in undersampling the current slice at the cost of a quality degradation. What samples to skip is determined by an encoder, while the quality loss is partially compensated by the use of a decoder. The hypothesis behind accelerated MRI acquisition is that to higher acceleration factors always correspond lower reconstruction qualities with an undersampling pattern that is usually fixed at design time, neglecting adaptability on the slice acquired at inference time. This paper proposes a novel accelerated MRI acquisition method that enables single-slice adaptation by dividing the acquisition into incremental batches and estimating the reconstruction quality at the end of each batch. The acquisition terminates as soon as the target quality is reached. We demonstrate the efficacy of our novel method using a state-of-the-art neural model capable of jointly optimizing the encoder and decoder. To estimate the current quality of the slice we reconstruct and propose a neural quality predictor. We demonstrate the advantages of our novel acquisition method compared to classic acquisition for two different datasets and for both line-constrained and unconstrained Cartesian sampling strategies (theoretically implementable via 2D and 3D imaging respectively).