Accounting for B0 field-inhomogeneity-gradient induced dephasing in Cartesian and in time-resolved sequences.

Author:Gilad Liberman  Kawin Setsompop  

Institution:Massachusetts General Hospital  

Session Type:Oral  

Session Live Q&A Date:Tuesday, 11 August 2020  

Topic:System Imperfections, Artifacts, and More  

Session Name:Mitigating Sample-Induced Artifacts  

Program Number:0665  

Room Live Q&A Session:Tuesday Parallel 3  

Alleviate motion artifacts in magnetic resonance imaging images using deep learning and compressed sensing

Artificial neural networks for numerical differentiation with application to magnetic resonance elastography

B0 field estimation using Ultrashort echo time/Dixon imaging with a 4-class tissue segmentation

Concurrent Field Monitoring in HCP dMRI at 7T: Correction for Eddy Current Induced Signal Blurring and Geometric Distortion.

Consistency in human and machine-learning based scan-planes for clinical knee MRI planning

Convolutional Neural Network for Slice Encoding for Metal Artifact Correction (SEMAC) MRI

DeepRespi: Retrospective correction for respiration-induced B0 fluctuation artifacts using deep learning

Disentangling time series between gray matter and non-gray matter tissue using deep neural network improves resting state fMRI data quality

Echo-train radial SSFP with golden angle

Edge-preserving B0 inhomogeneity distortion correction for high-resolution multi-echo ex vivo MRI at 7T

Highly 3D accelerated Bloch Siegert B1+ Mapping at 7T

Highly Accelerated MPRAGE Imaging of the Brain Incorporating Deep Learning Priors with Subject-Specific Novel Features

High-Performance Rapid Quantitative Imaging with Model-Based Deep Adversarial Learning

An improved spiral technique for imaging gamma knife subject with metal frame

Investigating the robustness of convolutional neural network based B1+ prediction from localizer scans for SAR efficient 7T FLAIR imaging

Joint 3D parameter mapping and motion correction using a kernel low rank method with offline training

Joint Parallel Imaging reconstruction with Deep Learning for Multi-Contrast Synthetic MRI

Local Perturbation Responses: A tool for understanding the characteristics of advanced nonlinear MR reconstruction algorithms

Multiple phase unwrapping of 4D-flow MRI in cardiovascular valves and vessels

PhysiCal: A rapid calibration scan for B0, B1+, coil sensitivity and Eddy current mapping.

Reduction of vibration-induced signal loss by matching mechanical vibrational states: application in high b-value diffusion weighted MRS

Robust Coil Combination for bSSFP Elliptical Signal Model

A simple method to estimate gradient delay for MRF

Author:Koji Fujimoto  Martijn Cloos  Tomohisa Okada  

Institution:Kyoto University  New York University School of Medicine  

Session Type:Oral  

Session Live Q&A Date:Tuesday, 11 August 2020  

Topic:System Imperfections, Artifacts, and More  

Session Name:Measuring & Correcting System Imperfections  

Program Number:0657  

Room Live Q&A Session:Tuesday Parallel 3  

Spiral real-time cardiac MR imaging using a GSTF-based pre-emphasis

Trajectory calculation for spiral imaging based on concurrent reading of the gradient amplifiers’ internal current sensors

Author:Jürgen Rahmer  Ingo Schmale  Peter Mazurkewitz  Peter Börnert  

Institution:Philips Research  

Session Type:Oral  

Session Live Q&A Date:Tuesday, 11 August 2020  

Topic:System Imperfections, Artifacts, and More  

Session Name:Measuring & Correcting System Imperfections  

Program Number:0655  

Room Live Q&A Session:Tuesday Parallel 3  

An Unsupervised Deep Learning Method for Correcting the Susceptibility Artifacts in Reversed Phase-encoding EPIs

Author:Soan Duong  Sui Paul Ang  Mark Schira  

Institution:University of Wollongong  

Session Type:Oral  

Session Live Q&A Date:Tuesday, 11 August 2020  

Topic:System Imperfections, Artifacts, and More  

Session Name:Mitigating Sample-Induced Artifacts  

Program Number:0670  

Room Live Q&A Session:Tuesday Parallel 3  

Unsupervised Image Reconstruction using Deep Generative Adversarial Networks

Visualizing and utilizing latent features of MR vessel wall images using weakly supervised deep learning analysis workflow