Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, Respiratory-resolved 4D MRI, Deep Learning reconstruction, radiation therapy planning
Motivation: To contribute to the clinical evidence generation for 4D MRI in radiation therapy planning.
Goal(s): Emphasize the impact of a DL reconstruction with amplitude and phase binning on respiratory motion characterization.
Approach: 4D MRI data of 10 healthy volunteers and 8 patients were acquired using a free-breathing T1-weighted stack-of-stars sequence at 1.5T or 3T.
Results: Independent of the binning strategy, DL reconstruction consistently improves image quality and conspicuity of small anatomical details with the potential to shorten scan times. Differences of binning strategies become prominent for irregular breathers, where amplitude binning reveals larger motion ranges than phase binning.
Impact: To foster the ultimate goal of clinical adoption of 4D MRI for radiotherapy planning, we present an enhanced 4D MRI application supporting multiple binning strategies and an embedded DL reconstruction.
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