Motion compensated self supervised deep learning for highly accelerated 3D ultrashort Echo time pulmonary MRI

Abstract

PurposeTo investigate motion compensated, self‐supervised, model based deep learning (MBDL) as a method to reconstruct free breathing, 3D pulmonary UTE acquisitions.Theory and MethodsA self‐supervised eXtra dimension MBDL architecture (XD‐MBDL) was developed that combined respiratory states to reconstruct a single high‐quality 3D image. Non‐rigid motion fields were incorporated into this architecture by estimating motion fields from a lower resolution motion resolved (XD‐GRASP) reconstruction. Motion compensated XD‐MBDL was evaluated on lung UTE datasets with and without contrast and compared to constrained reconstructions and variants of self‐supervised MBDL that do not account for dynamic respiratory states or leverage motion correction.ResultsImages reconstructed using XD‐MBDL demonstrate improved image quality as measured by apparent SNR (aSNR), contrast to noise ratio (CNR), and visual assessment relative to self‐supervised MBDL approaches that do not account for dynamic respiratory states, XD‐GRASP and a recently proposed motion compensated iterative reconstruction strategy (iMoCo). Additionally, XD‐MBDL reduced reconstruction time relative to both XD‐GRASP and iMoCo.ConclusionA method was developed to allow self‐supervised MBDL to combine multiple respiratory states to reconstruct a single image. This method was combined with graphics processing unit (GPU)‐based image registration to further improve reconstruction quality. This approach showed promising results reconstructing a user‐selected respiratory phase from free breathing 3D pulmonary UTE acquisitions.