Real‐time radial reconstruction with domain transform manifold learning for MRI‐guided radiotherapy

Abstract

AbstractBackgroundMRI‐guidance techniques that dynamically adapt radiation beams to follow tumor motion in real time will lead to more accurate cancer treatments and reduced collateral healthy tissue damage. The gold‐standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real‐time adaptation.PurposeOnce trained, neural networks can be used to accurately reconstruct raw MRI data with minimal latency. Here, we test the suitability of deep‐learning‐based image reconstruction for real‐time tracking applications on MRI‐Linacs.MethodsWe use automated transform by manifold approximation (AUTOMAP), a generalized framework that maps raw MR signal to the target image domain, to rapidly reconstruct images from undersampled radial k‐space data. The AUTOMAP neural network was trained to reconstruct images from a golden‐angle radial acquisition, a benchmark for motion‐sensitive imaging, on lung cancer patient data and generic images from ImageNet. Model training was subsequently augmented with motion‐encoded k‐space data derived from videos in the YouTube‐8M dataset to encourage motion robust reconstruction.ResultsAUTOMAP models fine‐tuned on retrospectively acquired lung cancer patient data reconstructed radial k‐space with equivalent accuracy to CS but with much shorter processing times. Validation of motion‐trained models with a virtual dynamic lung tumor phantom showed that the generalized motion properties learned from YouTube lead to improved target tracking accuracy.ConclusionAUTOMAP can achieve real‐time, accurate reconstruction of radial data. These findings imply that neural‐network‐based reconstruction is potentially superior to alternative approaches for real‐time image guidance applications.