result
PREVIOUS
${prev-page}
NEXT
${next-page}
Subscribe Now
Manage Subscription
FOLLOW US
Contact Us • Cookie Preferences • Privacy Policy • California Privacy PolicyDo Not Sell or Share My Personal Information • Terms & Conditions • Security
© 2024 GE HealthCare. GE is a trademark of General Electric Company. Used under trademark license.
NEWS
ISMRM 2023 presentations
ISMRM 2023 presentations
GE HealthCare is pleased to announce the following abstracts on AIR™ Recon DL, other new deep-learning-based applications and novel MR imaging techniques were accepted for presentation at the 2023 Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) scheduled to be held June 3–8 in Toronto, Ontario, Canada. AIR™ Recon DL is a GE-first, deep-learning MR reconstruction algorithm designed to improve SNR and image sharpness and enable shorter scan times.
AI
Modified Homodyne reconstruction using a high-resolution phase in magnetic resonance images
GE HealthCare
Perturbation loss with carrier image reconstruction: A loss function for optimized point spread functions
GE HealthCare
Body
Addressing Bias and Precision in Low SNR Chemical Shift Encoded MRI Proton Density Fat Fraction Estimation using a Deep Learning Reconstruction
University of Wisconsin - Madison, US
Establishment of SPGR-based MOLLI T1 mapping for the calculation of extracellular volume fraction (ECV) in Gd-EOB-DTPA-enhanced MRI
Fukuoka University, Japan
Feasibility of 3D Quantitative Synthetic MRI for Discriminating Immunohistochemical Status in Invasive Ductal Carcinoma of the Breast
Juntendo University, Japan
Free-Breathing, Confounder-Corrected, 3D T1 Mapping of the Liver through Simultaneous Estimation of T1, PDFF, R2* and B1+
University of Wisconsin - Madison, US
Free-Breathing, Gadoxetic Acid Enhanced, 3D T1w Phase Sensitive Inversion Recovery Hepatobiliary MRI Optimized for 3.0 Tesla
University of Wisconsin - Madison, US
In Vivo Evaluation of a Novel Deep Learning-based
MR Image Reconstruction for Liver Fat Quantification
University of Wisconsin - Madison, US
Radial vs. Spiral – A Comparison of Stack-of-stars and Stack-of-spirals Spatial Encoding Schemes in Multiparametric Body MRI with QTI
IRCCS Stella Maris, Italy
Slice-by-slice Dynamic Shimming Based on a Chemical Shift-Encoded Acquisition to Improve Fat Suppression in DWI
University of Wisconsin - Madison, US
Body, 7T
Quantitative Parameter Mapping in the Abdomen at 7T using Radial QTI Encoding
IRCCS Stella Maris, Italy
Body, AI
Deep Learning Based Reconstruction for Multi-shot DWI of the Breast: A Preliminary Study
National Taiwan University, Taiwan
Deep Learning Reconstruction to Pelvis Multi-Shot DWI Improved Image Quality with Less Image Distortion: A Preliminary Study
University of Hong Kong, Hong Kong
High-resolution PROPELLER T2-weighted imaging of the prostate with deep learning reconstruction: a phantom and clinical preliminary study
Osaka University Graduate School of Medicine, Japan
Improved Image Quality with Deep Learning-Based Image Reconstruction for Multi-shot Diffusion-Weighted Imaging of the Prostate
Stanford University, US
Multi-Shot M1-Nulled Pancreatic Diffusion Weighted Imaging with Deep Learning-Based Denoising
Stanford University, US
Utility of Thin-Slice Fat-Suppressed Single-Shot T2-Weighted MRI with Deep Learning Image Reconstruction for Pancreatic Cancer
Kobe University Hospital, Japan
Body, Lungs
Batch-mode production of hyperpolarised xenon gas with a continuous-flow polariser for preclinical and clinical human lung ventilation images
Aarhus University, Denmark; University of Sheffield, UK
Implementation of dissolved Xe lung MRI with 4-echo 3D radial spectroscopic imaging at 3T: comparing with results at 1.5T in healthy volunteers
University of Sheffield, UK
Mapping transmit and receive B using variable flip angle acquisition on a person-by-person basis for hyperpolarized Carbon-13 and Xenon-129 MRI
The University of Oxford (OCMR), UK
A feasibility study of deep learning cardiac cine comparing image quality and volumetry with the conventional ASSET cine
Keio University, Japan
Evaluation of image quality and global cardiac function for deep learning accelerated cardiac Cine
Stanford University, US; Fairfax Radiological Associates, US; University of Wisconsin - Madison, US
Relative noise variation with Unrolled Neural Networks for Accelerated Cardiac Cine
GE HealthCare
Lung
Self-navigated free-breathing ZTE lung imaging
University of Cambridge, UK; Kings College London, UK
Lung, AI
Improving Xenon-129 Lung Ventilation Image Quality with a Commercial Deep-Learning Based Image Reconstruction
University of Sheffield, UK
MSK
Comparison of UTE-T1p vs MAPSS-T1p Sequences in In-Vivo Knees
Hospital of Special Surgery, US
Deep Learning Reconstruction for 4-fold Accelerated 2D FSE Imaging: optimization of variable density undersampling
GE HealthCare
Deep learning reconstruction of zero echo time imaging: bone erosion detection in axial spondyloarthritis
Inje University Haeundae Paik Hospital, South Korea
Evaluation of an accelerated Deep Learning-reconstructed T2 mapping technique through knee cartilage regional analysis using DOSMA framework
Clinica CEMTRO, Spain; University of California San Francisco, US; Stanford University, US
Improved 3D DESS MR Neurography of the Lumbosacral Plexus with Deep Learning and Geometric Image Combination
Hospital of Special Surgery, US
Intelligent volume rendering of ZTE MR Bone images
GE HealthCare
Quantitative 3D DESS T2 mapping with Deep Learning Reconstruction for Magnetic Resonance Neurography
Hospital of Special Surgery, US
ZTE segmentation of glenohumeral bone structure using deep learning
University of California San Diego, US
Neuro
Application of Deep Learning-based Reconstruction for Diffusion Kurtosis Imaging in Head and Neck Cancer
Memorial Sloan Kettering Cancer Center, US
Comparison of Myelin Water Imaging from Multi-echo T2 Decay Curve and Myelin Content from Synthetic MRI
GE HealthCare
Controlled Modeling of Cerebrospinal Fluid Flow Artifacts with a Simple Digital Spine Phantom
GE HealthCare
Evaluation of deep learning-based reconstruction for qualitative and quantitative DW-MRI in head and neck cancers
Memorial Sloan Kettering Cancer Center, US
Performance Evaluation of Deep Learning-based Image Reconstruction for Head and Neck Imaging Protocol
Memorial Sloan Kettering Cancer Center, US
Repeatability and reproducibility of MRF-based Myelin Water Fraction maps of healthy human brains
IRCCS Stella Maris, Italy
The validation of ASL-aCBV measured by Hadamard encoded ASL imaging evaluating moyamoya disease correlative study with O-H O PET-CBV
Fukui University, Japan
TR effect on Myelin Water Imaging
GE HealthCare
What if every voxel was measured with a different diffusion protocol
NYU Grossman School of Medicine, US
Neuro, 7T
Simultaneous T1 and T2 relaxometry of the human brain at 7T using Quantitative Transient-state Imaging
IRCCS Stella Maris, Italy
Neuro, AI
Multiparameter estimation from DANTE-prepared multi-delay ASL using artificial neural network
Fukui University, Japan
Neuro, MNS
Beyond lactate: using hyperpolarized [1-C] pyruvate to measure human brain pH and amino acid metabolism
University of Cambridge, UK
Spine
Deep Learning based prediction of the planes for automated planning of MRI imaging of cervical neural foramina and lumbar pars interarticularis
GE Research, US
Vascular
Diffusion Weighted-Viscosity Imaging for Atherosclerotic Plaques
Tokushima University, Japan; Kanazawa University, Japan
Evaluation of Biological Metabolic Activity within an Atherosclerotic Plaque using Chemical Exchange Saturation Transfer Imaging
Tokushima University, Japan; Kanazawa University, Japan
Vascular, AI
Breath-hold Whole Heart Coronary MRA with Parallel Imaging, Compressed Sensing and Deep Learning reconstruction
Keio University, Japan
High Resolution Intracranial MR Angiography at 3T and 7T using a Deep Learning based Image Reconstruction
University of Iowa, US; Hirosaki University, Japan
Highly accelerated FLEXA 3D TOF MR Angiography with iterative deep learning reconstruction
Keio University, Japan
For more information on ISMRM 2023, visit:
https://www.ismrm.org/23m/