1H-MRS and machine learning for predicting voxel-wise histopathology of tumor cells in newly-diagnosed glioma patients

A Deep Learning Neural Network for Quantifying Metabolite Concentrations by Multi-echo MRS

A multi-output deep learning algorithm to improve brain lesion segmentation by enhancing the resistance of variabilities in tissue contrast

Accelerate MR imaging by anatomy-driven deep learning image reconstruction

Accelerated 2D Cardiac MRF Using a Self-Supervised Deep Image Prior Reconstruction

Author:Jesse Hamilton  

Session Type:Oral  

Session Date:Wednesday, 11 May 2022  

Topic:Module 5: Machine Learning/Artificial Intelligence  

Session Name:Deep/Machine Learning-Based Image Analysis  

Program Number:0551  

Room Session:N11 (Breakout B)  

Institution:University of Michigan  

Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks

Accelerating MR Elastography using Deep Learning-Reconstruction of Undersampled Data

Advancing RAKI Parallel Imaging Reconstruction with Virtual Conjugate Coil and Enhanced Non-Linearity

Amide Proton Transfer (APT) Mapping from Undersampled Z-spectra in the Brain Using Deep Learning

Analysing The Role Of Model Uncertainty in Flourine-19 MRI using Markov Chain Monte Carlo methods

Applying advanced denoisers to enhance highly undersampled MRI reconstruction under plug-and-play ADMM framework

Assessment of resolution and noise in MR images reconstructed by data driven approaches

Automated Adipose Tissue Segmentation using 3D Attention-Based Competitive Dense Networks and Volumetric Multi-Contrast MRI

Automated classification of intramedullary spinal cord tumors and inflammatory demyelinating lesions using deep learning

Automated IDH genotype prediction pipeline using multimodal domain adaptive segmentation (MDAS) model

Automated Quantification of Ventilation Defects and Heterogeneity in 3D Isotropic 129Xe MRI

Automated Sequence Design using Neural Architecture Search

AUTOMATIC EVALUATION OF HIP ABDUCTOR MUSCLE QUALITY IN HIP OSTEOARTHRITIS

Benchmarking Accelerated MRI: A Head-to-Head Comparison of Deep Learning Reconstruction and Super-Resolution Techniques

Brain MR image super resolution using simulated data to perform in real-world MRI

Brain MRI acceleration with deep modular networks (BRACELET)

Comparison of machine learning methods for detection of prostate cancer using bpMRI radiomics features

Contrastive Learning of Inter-domain Similarity for Unsupervised Multi-modality Deformable Registration

Convolutional Neuronal Network Inception-v3 detects Partial Volume Artifacts on 2D MR-Images of the Lung for Automated Quality Control

Correlated and specific features fusion based on attention mechanism for grading hepatocellular carcinoma with Contrast-enhanced MR

Data scarcity mitigation approaches in deep learning reconstruction of undersampled low field MR images

DCE-DRONE: Perfusion MRI Parameter Estimation using a DRONE Neural Network

Author:Soudabeh Kargar  Ouri Cohen  Ricardo Otazo  

Session Type:Digital Poster  

Session Date:Thursday, 12 May 2022  

Topic:Module 5: Machine Learning/Artificial Intelligence  

Session Name:Quantitative Imaging I  

Program Number:2529  

Room Session:Exhibition Hall:S8 & S9  

Institution:MSKCC  

Deep generative model for learning tractography streamline embeddings based on a Convolutional Variational Autoencoder

A Deep Learning Approach to Improve 7T MRI Anatomical Image Quality Deterioration Due mainly to B1+ Inhomogeneity

Deep learning framework for accelerating revised-NODDI parameter estimation with tensor-valued diffusion encoding

Deep learning reconstruction enables accelerated acquisitions with consistent volumetric measurements

Deep learning-based brain MRI reconstruction with realistic noise

Author:Quan Dou  Xue Feng  Craig Meyer  

Session Type:Online Gather.town Pitches  

Session Date:Monday, 09 May 2022  

Topic:Module 5: Machine Learning/Artificial Intelligence  

Session Name:Machine Learning & Artificial Intelligence II  

Program Number:3470  

Room Session:5  

Institution:University of Virginia  

Deep Learning-Based Receiver Coil Sensitivity Map Estimation for SENSE Reconstruction using Transfer Learning

Deep CNNs with Physical Constraints for simultaneous Multi-tissue Segmentation and Multi-parameter Quantification (MSMQ-Net) of Knee