Guided Multicontrast Reconstruction based on the Decomposition of Content and Style

Motivation: Scans within an MR exam share redundant information due to the same underlying structures. One contrast can hence be used to guide the reconstruction of another, thereby requiring less measurements. Goal(s): Multimodal guided reconstruction to reduce scanning times. Approach: Our method exploits AI-based content/style decomposition in an iterative reconstruction algorithm. We explored this concept via numerical simulation and subsequently validated it on in vivo data. Results: Compared to a conventional compressed sensing baseline, our method showed consistent improvement in simulations and produced sharper reconstructions from undersampled in vivo data. By enforcing data consistency, it was also more reliable than blind image translation. Impact: In the clinic, this can potentially enable a reduced MR exam time for a given image quality or improve image quality given a scan time budget. The former can reduce strain on the patient, whereas the latter can improve diagnosis.