SIGNA™ Pulse of MR

Spring 2022

Volume 32 – Spring 2022

Issue Archive

result

${prev-page}

${next-page}

Subscribe Now

Manage Subscription

SPOTLIGHT

ISMRM 2022 presentations

GE Healthcare is pleased to announce the following abstracts on AIR™ Recon DL and other new DL-based reconstruction applications that were accepted for presentation at the 2022 Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) scheduled to be held May 7-12 in London, England. AIR™ Recon DL is a GE-first, deep-learning MR reconstruction algorithm designed to improve SNR and image sharpness and enable shorter scan times.

Body

1.5T prostate MRI utilizing deep learning reconstruction in patients with hip prosthesis

Fairfax Radiological Consultants, GE Healthcare

3D T2-weighted rectal cancer imaging using a 3D fast spin echo sequence with deep learning reconstruction

MD Anderson Cancer Center, GE Healthcare

Comparison between image quality of 3D T1 GRE sequence with and without deep learning reconstruction at gadoxetic acid-enhanced liver MRI

Seoul National University Hospital, GE Healthcare

Image quality of deep-learning reconstructed near-isotropic (3D) enhanced MR enterography with LAVA HyperSense in Crohn’s disease patients

Haeundae Paik Hospital, GE Healthcare

Rapid and high resolution pelvic MRI using deep learning reconstruction

Fairfax Radiological Consultants, GE Healthcare

Cardiac

Improved myocardial T1 mapping accuracy with deep learning reconstruction of low flip angle MOLLI series

Hospital Universitario Quirónsalud, Onewelbeck, GE Healthcare

MSK

Deep learning reconstruction-enabled 2D and 3D neurography

Hospital for Special Surgery, GE Healthcare

Evaluation of deep-learning reconstructed high-resolution 3D cervical spine MRI to improve foraminal stenosis evaluation

Hospital for Special Surgery, GE Healthcare

Improving diagnostic confidence using a deep-learning reconstructed fast motion-robust PROPELLER protocol for shoulder imaging

Clínica CEMTRO, Rey Juan Carlos University, GE Healthcare

Quantitative & qualitative evaluation of accelerated T2 mapping technique using deep learning reconstruction in knee cartilage

Clínica CEMTRO, Rey Juan Carlos University, GE Healthcare

Shoulder PROPELLER MRI with deep learning-based reconstruction: image quality and agreement between standard and accelerated sequence

Haeundae Paik Hospital, Inje University College of Medicine, GE Healthcare

Neuro & spine

A clinical protocol for 3D imaging of the locus coeruleus with super-resolution at 3 Tesla

Invicro (Imperial College London), GE Healthcare

Axial T2 weighted MR imaging of the cervical spine using PROPELLER with deep learning reconstruction to improve image quality

Pusan National University Hospital, GE Healthcare

Deep learning based image reconstruction for improved 3D multiparameter mapping and synthetic MR imaging

MD Anderson Cancer Center, GE Healthcare

Deep learning reconstruction enables accelerated acquisitions with consistent volumetric measurements

Cortechs, GE Healthcare

Development of a high-speed MRI protocol with deep learning reconstruction method for brain imaging in a clinical setting

Columbia University, GE Healthcare

Evaluation of the efficacy of a deep learning-based reconstruction in the connectomic deep brain stimulation

Mount Sinai, GE Healthcare

Fast and high-resolution T2* weighted and susceptibility-based MRI using 3D EPI with deep learning reconstruction

Karolinska University Hospital, Karolinska Institutet

Highly accelerated volumetric brain imaging with META and deep learning reconstruction

Juntendo University, GE Healthcare

High resolution diffusion tensor imaging at 7T with multi-band multi-shot EPI acquisition and deep learning reconstruction

University of Minnesota, GE Healthcare

Improving motion-robust structural imaging at 7T with deep learning-based PROPELLER reconstruction

GE Healthcare

K-space based deep learning reconstruction empowers 60-70% acceleration of MR imaging of the spine

Radnet, GE Healthcare

Mitigation of partial-volume artifacts in synthetic T2 FLAIR using separately acquired fast T2 FLAIR contrast information combined with keyhole and deep learning-based image reconstruction

GE Healthcare

Robust diffusion weighted imaging with deep learning-based DW PROPELLER reconstruction

MD Anderson Cancer Center, GE Healthcare

For more information on ISMRM 2022, visit: https://www.ismrm.org/22m/