Estrogen depletion and drug treatment alter the microstructure of type I collagen in bone

Autor: Meagan A. Cauble, Matthew J. Muckley, Ming Fang, Jeffrey A. Fessler, Kathleen Welch, Edward D. Rothman, Bradford G. Orr, Le T. Duong, Mark M. Banaszak Holl

Publikováno v: Bone Reports, Vol 5, Iss , Pp 243-251 (2016)

The impact of estrogen depletion and drug treatment on type I collagen fibril nanomorphology and collagen fibril packing (microstructure) was evaluated by atomic force microscopy (AFM) using an ovariectomized (OVX) rabbit model of estrogen deficiency

Externí odkaz: https://doaj.org/article/55178759ab6e4897b395d421f7282d87

Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study

Autor: Matthew J. Muckley, Jure Zbontar, William R. Walter, Mohammad Samim, Tullie Murrell, Aaron Defazio, Nafissa Yakubova, Leon Rybak, Gina A. Ciavarra, C. Lawrence Zitnick, Zhengnan Huang, Dana J Lin, Florian Knoll, Mitchell J Kline, Erin F. Alaia, Michael G. Rabbat, Ruben Stern, Anuroop Sriram, Michael P. Recht, Yvonne W. Lui, Patricia M. Johnson, Daniel K. Sodickson

Publikováno v: AJR Am J Roentgenol

OBJECTIVE: Deep learning (DL) image reconstruction has the potential to disrupt the current state of MRI by significantly decreasing the time required for MRI examinations. Our goal was to use DL to accelerate MRI to allow a 5-minute comprehensive ex

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8dc9fad9586ea435b5fe7176fc5aa6c8
https://doi.org/10.2214/ajr.20.23313

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

Autor: Yohan Jun, Simon Arberet, Jean-Luc Starck, Dominik Nickel, Alireza Radmanesh, Yvonne W. Lui, Sunwoo Kim, Matthew J. Muckley, Mahmoud Mostapha, Jonas Teuwen, Zhengnan Huang, Nafissa Yakubova, Dosik Hwang, Geunu Jeong, Zaccharie Ramzi, Florian Knoll, Anuroop Sriram, Philippe Ciuciu, Chaoping Zhang, Bruno Riemenschneider, Hyungseob Shin, Jingyu Ko, Dimitrios Karkalousos

Publikováno v: IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, In press, ⟨10.1109/TMI.2021.3075856⟩
IEEE transactions on medical imaging
IEEE Transactions on Medical Imaging, In press, ⟨10.1109/TMI.2021.3075856⟩

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided partici

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ada6cd85033aac2da6c4388d81c59f5a
https://hal.archives-ouvertes.fr/hal-03066150v2/document

Reducing Uncertainty in Undersampled MRI Reconstruction With Active Acquisition

Autor: Adriana Romero, Pascal Vincent, Lin Yang, Zizhao Zhang, Matthew J. Muckley, Michal Drozdzal

Publikováno v: CVPR

The goal of MRI reconstruction is to restore a high fidelity image from partially observed measurements. This partial view naturally induces reconstruction uncertainty that can only be reduced by acquiring additional measurements. In this paper, we p

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e499913b3ba26bc91d045e0b8e871625
https://doi.org/10.1109/cvpr.2019.00215

Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions

Autor: Florian Knoll, Kerstin Hammernik, Mary Bruno, Matthew J. Muckley, Patricia M. Johnson, Erich Kobler, Thomas Pock

Publikováno v: Machine Learning for Medical Image Reconstruction ISBN: 9783030338428
MLMIR@MICCAI

Magnetic resonance imaging is a leading image modality for many clinical applications; however, a significant drawback is the lengthy data acquisition. This motivates the development of methods for reconstruction of sparsely sampled image data. One s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2236e0b518a260c4fb1f91262d4eb848
https://doi.org/10.1007/978-3-030-33843-5_7