Purpose To investigate motion compensated, self‐supervised, model based deep learning
(MBDL) as a method to reconstruct free breathing, 3D pulmonary UTE acquisitions. Theory …

Purpose To develop a high‐scanning efficiency, motion‐corrected imaging strategy for free‐
breathing pulmonary MRI by combining an iterative motion‐compensation reconstruction …

Object The investigation of three-dimensional radial, zero-echo time (TE) imaging for high-
resolution, free-breathing magnetic resonance (MR) lung imaging using prospective and …

Purpose Three‐dimensional UTE MRI has shown the ability to provide simultaneous
structural and functional lung imaging, but it is limited by respiratory motion and relatively …

Objectives Respiratory binning of free-breathing magnetic resonance imaging data reduces
motion blurring; however, it exacerbates noise and introduces severe artifacts due to …

Purpose To develop an isotropic high‐resolution stack‐of‐spirals UTE sequence for
pulmonary imaging at 0.55 Tesla by leveraging a combination of robust respiratory‐binning …

Purpose To achieve motion robust high resolution 3D free‐breathing pulmonary MRI
utilizing a novel dynamic 3D image navigator derived directly from imaging data. Methods …

Purpose To optimize 3D radial ultrashort echo time MRI for high resolution whole‐lung
imaging. Methods 3D radial ultrashort echo time was implemented on a 3T scanner to …

Background Computed tomography (CT) and spirometry are the current standard methods
for assessing lung anatomy and pulmonary ventilation, respectively. However, CT provides …

Purpose Develop a novel three‐dimensional (3D) generative adversarial network (GAN)‐
based technique for simultaneous image reconstruction and respiratory motion …