Movienet: Deep space-time-coil reconstruction network without k-space data consistency for fast motion-resolved 4D MRI.

Purpose: To develop a novel deep learning approach for 4D-MRI reconstruction, named Movienet, which exploits space-time-coil correlations and motion preservation instead of k-space data consistency, to accelerate the acquisition of golden-angle radial data and enable subsecond reconstruction times in dynamic MRI.

Methods: Movienet uses a U-net architecture with modified residual learning blocks that operate entirely in the image domain to remove aliasing artifacts and reconstruct an unaliased motion-resolved 4D image. Motion preservation is enforced by sorting the input image and reference for training in a linear motion order from expiration to inspiration.

Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system.

. To develop real-time 4D MRI using MR signature matching (MRSIGMA) for volumetric motion imaging in patients with pancreatic cancer on a 1.5T MR-Linac system.

A flexible MRI coil based on a cable conductor and applied to knee imaging.

Flexible radiofrequency coils for magnetic resonance imaging (MRI) have garnered attention in research and industrial communities because they provide improved accessibility and performance and can accommodate a range of anatomic postures. Most recent flexible coil developments involve customized conductors or substrate materials and/or target applications at 3 T or above. In contrast, we set out to design a flexible coil based on an off-the-shelf conductor that is suitable for operation at 0.

An Inexpensive Earth Field NMR Spectrometer with Improved Shimming.

A simple, cost-effective, do-it-yourself earth field nuclear magnetic resonance spectrometer with shimming is demonstrated. A spectrometer for understanding magnetic resonance physics in an academic environment is presented here along with its coil and system component design. This system was designed with inexpensive and readily available electronic components costing less than US $130.