Zobrazeno 1 - 10
of 256
pro vyhledávání: '"Uecker-Martin A"'
Magnetic resonance imaging (MRI) is a widely used non-invasive imaging modality. However, a persistent challenge lies in balancing image quality with imaging speed. This trade-off is primarily constrained by k-space measurements, which traverse speci
Externí odkaz:
http://arxiv.org/abs/2405.14327
Open Science is a recurrent topic in scientific discussion, and there is a current effort to make research more accessible to a broader audience. A focus on delivering research findings that are reproducible, or even re-usable has been proposed as on
Externí odkaz:
http://arxiv.org/abs/2402.09475
Autor:
Blumenthal, Moritz, Fantinato, Chiara, Unterberg-Buchwald, Christina, Haltmeier, Markus, Wang, Xiaoqing, Uecker, Martin
Purpose: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods: NLINV-Net is a model-based neural network architecture that directly estimates image
Externí odkaz:
http://arxiv.org/abs/2402.06550
Autor:
Wang, Xiaoqing, Scholand, Nick, Tan, Zhengguo, Mackner, Daniel, Telezki, Vitali, Blumenthal, Moritz, Schaten, Philip, Uecker, Martin
Purpose: To develop a model-based nonlinear reconstruction for simultaneous water-specific $T_{1}$, $R_{2}^{*}$, $B_{0}$ field and/or fat fraction (FF) mapping using single-shot inversion-recovery (IR) multi-echo radial FLASH. Methods: The proposed m
Externí odkaz:
http://arxiv.org/abs/2402.05366
Autor:
Scholand, Nick, Schaten, Philip, Graf, Christina, Mackner, Daniel, Holme, H. Christian M., Blumenthal, Moritz, Mao, Andrew, Assländer, Jakob, Uecker, Martin
Purpose: To develop a generic radial sampling scheme that combines the advantages of golden ratio sampling with simplicity of equidistant angular patterns. The irrational angle between consecutive spokes in golden ratio based sampling schemes enables
Externí odkaz:
http://arxiv.org/abs/2401.02892
Publikováno v:
Scientific Reports 2024;14:3754
In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed
Externí odkaz:
http://arxiv.org/abs/2311.14049
Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase Augmentation
Autor:
Luo, Guanxiong, Wang, Xiaoqing, Blumenthal, Mortiz, Schilling, Martin, Rauf, Erik Hans Ulrich, Kotikalapudi, Raviteja, Focke, Niels, Uecker, Martin
Purpose: In this work, we present a workflow to construct generic and robust generative image priors from magnitude-only images. The priors can then be used for regularization in reconstruction to improve image quality. Methods: The workflow begins w
Externí odkaz:
http://arxiv.org/abs/2308.02340
Publikováno v:
Magn Reson Med 2023;90:520-538
Purpose: Development of a generic model-based reconstruction framework for multi-parametric quantitative MRI that can be used with data from different pulse sequences. Methods: Generic nonlinear model-based reconstruction for quantitative MRI estimat
Externí odkaz:
http://arxiv.org/abs/2209.08027
Autor:
Thu A. Doan, Tadg S. Forward, Johnathon B. Schafer, Erin D. Lucas, Ira Fleming, Aspen Uecker-Martin, Edgardo Ayala, Jenna J. Guthmiller, Jay R. Hesselberth, Thomas E. Morrison, Beth A. Jirón Tamburini
Publikováno v:
npj Vaccines, Vol 9, Iss 1, Pp 1-17 (2024)
Abstract Antigens from viruses or immunizations can persist or are archived in lymph node stromal cells such as lymphatic endothelial cells (LEC) and fibroblastic reticular cells (FRC). Here, we find that, during the time frame of antigen archiving,
Externí odkaz:
https://doaj.org/article/c51ada010dc34878b794df452efbd18b
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, 2022, 16: 2056-2073
Stent intervention is a recommended therapy to reduce the pressure gradient and restore blood flow for patients with coarctation of the aorta (CoA). In this work, we developed a framework for personalized stent intervention in CoA using in silico mod
Externí odkaz:
http://arxiv.org/abs/2205.04518