Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction

Autor: Bo Zhou, Jo Schlemper, Neel Dey, Seyed Sadegh Mohseni Salehi, Kevin Sheth, Chi Liu, James S. Duncan, Michal Sofka

While enabling accelerated acquisition and improved reconstruction accuracy, current deep MRI reconstruction networks are typically supervised, require fully sampled data, and are limited to Cartesian sampling patterns. These factors limit their prac

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83cda975a5aa57fc6ea2f99d9dd44dc1

Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination

Autor: Chen Qin, Daniel Rueckert, Ronald M. Summers, Kerstin Hammernik, Jinming Duan, Jo Schlemper

Publikováno v: Magnetic Resonance in Medicine. 86:1859-1872

PURPOSE To systematically investigate the influence of various data consistency layers and regularization networks with respect to variations in the training and test data domain, for sensitivity-encoded accelerated parallel MR image reconstruction.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76993fee0175a9d1a7bf6cd8183d8fe6
https://doi.org/10.1002/mrm.28827

Point-of-Care MRI with Artificial Intelligence to Measure Midline Shift in Acute Stroke Follow-Up

Autor: Prantik Kundu, Seyed Sadegh Mohseni Salehi, Bradley A. Cahn, Mercy H. Mazurek, Matthew M. Yuen, E. Brian Welch, Barbara S. Gordon-Kundu, Jo Schlemper, Gordon Sze, W. Taylor Kimberly, Jonathan M. Rothberg, Michal Sofka, Kevin N. Sheth

Background and PurposeIn stroke, timely treatment is vital for preserving neurologic function. However, decision-making in neurocritical care is hindered by limited accessibility of neuroimaging and radiological interpretation. We evaluated an artifi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9bc7884985b6b20d4e22fba1e58fe7b4
https://doi.org/10.1101/2022.01.22.22269697

Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training

Autor: Chen Qin, Jo Schlemper, Loic Le Folgoc, Benjamin Hou, Daniel Rueckert, Huaqi Qiu

Publikováno v: Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges ISBN: 9783030390730
STACOM@MICCAI

Deep learning based registration methods have emerged as alternatives to traditional registration methods, with competitive accuracy and significantly less runtime. Two different strategies have been proposed to train such deep learning registration

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::20ae9918eac014b0731886341f0fc32c
https://doi.org/10.1007/978-3-030-39074-7_20

Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging

Autor: Veronika A. Zimmer, Matthew Sinclair, Daniel Rueckert, Alberto Gomez, James Housden, Jo Schlemper, Ozan Oktay, Jacqueline Matthew, Bernhard Kainz, Benjamin Hou, Julia A. Schnabel, Nicolas Toussaint, Martin Rajchl, Qingjie Meng

Publikováno v: Meng, Q, Sinclair, M, Zimmer, V, Hou, B, Rajchl, M, Toussaint, N, Oktay, O, Schlemper, J, Gomez, A, Housden, J, Matthew, J, Rueckert, D, Schnabel, J A & Kainz, B 2019, ' Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging ', IEEE transactions on medical imaging, vol. 38, no. 12, 8698843, pp. 2755-2767 . https://doi.org/10.1109/TMI.2019.2913311

Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provid

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::512a6b1ee72845511912a59c30300847
https://pubmed.ncbi.nlm.nih.gov/31021795