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Accurate parameter estimation using scan-specific unsupervised deep learning for relaxometry and MR fingerprinting

Bayesian sensitivity encoding enables parameter-free, highly accelerated joint multi-contrast reconstruction

BUDA-SAGE with Slider encoding and self-supervised denoising enables fast, distortion-free, high-resolution T2 and T2* mapping

Chi-sepnet: Susceptibility source separation using deep neural network

Diffusion MRI data analysis using brain segmentation from anatomical images synthesized from diffusion data by deep learning(DeepAnat)

EPI with Parallel Imaging (eπ2) self-calibrates the image distortion due to B0 field inhomogeneity

Feasibility of Dynamic Contrast-free Vascular Magnetic Resonance Fingerprinting

High-fidelity submillimeter-isotropic-resolution diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction

Improving the accessibility of deep learning-based denoising for MRI using transfer learning and self-supervised learning

QALAS + QSM: Efficient Multi-parameter Mapping Allows Disentangling Para- and Dia-magnetic Contributions in Brain Tissue

Quantifying the uncertainty of neural networks using Monte Carlo dropout
for safer and more accurate deep learning based quantitative MRI

Rapid, high-spatial resolution in vivo diffusion MRI with joint subsampling and reconstruction in k-,q- and RF-space

Semi-supervised learning for fast multi-compartment relaxometry myelin water imaging (MCR-MWI)

Spatial encoding with a 48-ch Transcranial Magnetic Stimulation coil array: Application to diffusion imaging

Value of Multicontrast Techniques (Neuro)

Author:Berkin Bilgic  

Session Type:Weekday Course  

Session Date:Wednesday, 11 May 2022  

Topic:

Session Name:Added Value of Sophisticated Multicontrast Techniques  

Program Number:

Room Session:ICC Capital Suite 10-11  

Institution:MGH, Martinos Center for Biomedical Imaging