Purpose: To develop a high‐scanning efficiency, motion‐corrected imaging strategy for free‐breathing pulmonary MRI by combining an iterative motion‐compensation reconstruction with a ultrashort echo time (UTE) acquisition called iMoCo UTE.Methods: An optimized golden‐angle ordering radial UTE sequence was used to continuously acquire data for 5 minutes. All readouts were grouped to different respiratory motion states based on self‐navigator signals, and then motion‐resolved data was reconstructed by XD golden‐angle radial sparse parallel reconstruction. One state from the motion‐resolved images was selected as a reference, and then motion fields from the other states to the reference were derived via nonrigid registration. Finally, all motion‐resolved data and motion fields were reconstructed by using an iterative motion‐compensation (MoCo) reconstruction with a total generalized variation sparse constraint.Results: The iMoCo UTE strategy was evaluated in volunteers and nonsedated pediatric patient (4‐6 years old) studies. Images reconstructed with iMoCo UTE provided sharper anatomical lung structures and higher apparent SNR and contrast‐to‐noise ratio compared to using other motion‐correction strategies, such as soft‐gating, motion‐resolved reconstruction, and nonrigid MoCo. iMoCo UTE also showed promising results in an infant study.Conclusion: The proposed iMoCo UTE combines self‐navigation, motion modeling, and a compressed sensing reconstruction to increase scan efficiency and SNR and to reduce respiratory motion in lung MRI. This proposed strategy shows improvements in free‐breathing lung MRI scans, especially in very challenging application situations such as pediatric MRI studies.

Deep learning–based cardiac cine sequences performed over three cardiac cycles led to substantially reduced acquisition time, while no differences in image quality and volumetric results compared with a standard clinical sequence could be detected.

Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep learning framework, trained by radiologists’ supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are used to process a stack of 2D image inputs to generate scalars defining the location of a rectangular region of interest (ROI). The attention mechanism is used to make the model focus on a small number of informative slices in a stack. Then, the smallest FOV that makes the neural network predicted ROI free of aliasing is calculated by an algebraic operation derived from MR sampling theory. The framework’s performance is examined quantitatively with intersection over union (IoU) and pixel error on position and qualitatively with a reader study. The proposed model achieves an average IoU of 0.867 and an average ROI position error of 9.06 out of 512 pixels on 80 test cases, significantly better than two baseline models and not significantly different from a radiologist. Finally, the FOV given by the proposed framework achieves an acceptance rate of 92% from an experienced radiologist.