MRI-guided radiation therapy: an emerging paradigm in adaptive radiation oncology

R Otazo, P Lambin, JP Pignol, ME Ladd… - Radiology, 2021 - pubs.rsna.org
Radiation therapy (RT) continues to be one of the mainstays of cancer treatment.
Considerable efforts have been recently devoted to integrating MRI into clinical RT planning …

Compressed sensing for body MRI

L Feng, T Benkert, KT Block… - Journal of Magnetic …, 2017 - Wiley Online Library
The introduction of compressed sensing for increasing imaging speed in magnetic
resonance imaging (MRI) has raised significant interest among researchers and clinicians …

Deep generative adversarial neural networks for compressive sensing MRI

M Mardani, E Gong, JY Cheng… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed
linear inverse task. The time and resource intensive computations require tradeoffs between …

Compressed sensing: From research to clinical practice with deep neural networks: Shortening scan times for magnetic resonance imaging

CM Sandino, JY Cheng, F Chen… - IEEE signal …, 2020 - ieeexplore.ieee.org
Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying
signals to recover high-resolution images from highly undersampled measurements. When …

T2 shuffling: Sharp, multicontrast, volumetric fast spin‐echo imaging

JI Tamir, M Uecker, W Chen, P Lai… - Magnetic resonance …, 2017 - Wiley Online Library
Purpose A new acquisition and reconstruction method called T2 Shuffling is presented for
volumetric fast spin‐echo (three‐dimensional [3D] FSE) imaging. T2 Shuffling reduces …

Recent advances in parallel imaging for MRI

J Hamilton, D Franson, N Seiberlich - Progress in nuclear magnetic …, 2017 - Elsevier
Abstract Magnetic Resonance Imaging (MRI) is an essential technology in modern
medicine. However, one of its main drawbacks is the long scan time needed to localize the …

Spectral compressed sensing via structured matrix completion

Y Chen, Y Chi - International conference on machine …, 2013 - proceedings.mlr.press
The paper studies the problem of recovering a spectrally sparse object from a small number
of time domain samples. Specifically, the object of interest with ambient dimension n is …

High‐dimensionality undersampled patch‐based reconstruction (HD‐PROST) for accelerated multi‐contrast MRI

A Bustin, G Lima da Cruz, O Jaubert… - Magnetic resonance …, 2019 - Wiley Online Library
Purpose To develop a new high‐dimensionality undersampled patch‐based reconstruction
(HD‐PROST) for highly accelerated 2D and 3D multi‐contrast MRI. Methods HD‐PROST …

Comprehensive MRI simulation methodology using a dedicated MRI scanner in radiation oncology for external beam radiation treatment planning

ES Paulson, B Erickson, C Schultz, X Allen Li - Medical physics, 2015 - Wiley Online Library
Purpose: The use of magnetic resonance imaging (MRI) in radiation oncology is expanding
rapidly, and more clinics are integrating MRI into their radiation therapy workflows. However …

Unsupervised MRI reconstruction with generative adversarial networks

EK Cole, JM Pauly, SS Vasanawala, F Ong - arxiv preprint arxiv …, 2020 - arxiv.org
Deep learning-based image reconstruction methods have achieved promising results
across multiple MRI applications. However, most approaches require large-scale fully …