Zobrazeno 1 - 10
of 318
pro vyhledávání: '"Koch, Kevin M"'
Background: Deep neural networks have proven to be powerful computational tools for modeling, prediction, and generation. However, the workings of these models have generally been opaque. Recent work has shown that the performance of some models are
Externí odkaz:
http://arxiv.org/abs/2311.10898
Purpose: In multi-spectral imaging (MSI), several fast spin echo volumes with discrete Larmor frequency offsets are acquired in an interleaved fashion with multiple concatenations. Here, a variable resolution (VR) method to nearly halve scan time is
Externí odkaz:
http://arxiv.org/abs/2306.01933
Introduction: Wrist instability remains a common health concern. The potential of dynamic Magnetic Resonance Imaging (MRI) in assessing carpal dynamics associated with this condition is a field of ongoing research. This study contributes to this line
Externí odkaz:
http://arxiv.org/abs/2305.16423
Autor:
Zarenia, Mohammad, Arpinar, Volkan Emre, Nencka, Andrew S., Muftuler, L. Tugan, Koch, Kevin M.
In this preliminary study technical methodology for kinematic tracking and profiling of wrist carpal bones during unconstrained movements is explored. Heavily under-sampled and fat-saturated 3D Cartesian MRI acquisition were used to capture temporal
Externí odkaz:
http://arxiv.org/abs/2111.04582
Purpose: This study evaluates the effectiveness and impact of automated order-based protocol assignment for magnetic resonance imaging (MRI) exams using natural language processing (NLP) and deep learning (DL). Methods: NLP tools were applied to retr
Externí odkaz:
http://arxiv.org/abs/2106.08963
Advanced diffusion MRI models are being explored to study the complex microstructure of the brain with higher accuracy. However, these techniques require long acquisition times. Simultaneous multi-slice (SMS) accelerates data acquisition by exciting
Externí odkaz:
http://arxiv.org/abs/2103.13340
Autor:
Liu, Juan, Koch, Kevin M
Quantitative susceptibility mapping (QSM) utilizes MRI phase information to estimate tissue magnetic susceptibility. The generation of QSM requires solving ill-posed background field removal (BFR) and field-to-source inversion problems. Because curre
Externí odkaz:
http://arxiv.org/abs/2008.06187
Autor:
Nencka, Andrew S., PhD, Arpinar, Volkan E., Bhave, Sampada, Yang, Baolian, Banerjee, Suchandrima, McCrea, Michael, Mickevicius, Nikolai J., Muftuler, L. Tugan, Koch, Kevin M.
Split-slice augmentation for simultaneous multi-slice RAKI networks positively impacts network performance. Hyperparameter tuning of such reconstruction networks can lead to further improvements in unaliasing performance.
Externí odkaz:
http://arxiv.org/abs/2006.01917
Autor:
Liu, Juan, Koch, Kevin M.
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that estimates magnetic susceptibility of tissue from Larmor frequency offset measurements. The generation of QSM requires solving a challenging ill-posed field
Externí odkaz:
http://arxiv.org/abs/2004.06259
Autor:
Liu, Juan, Koch, Kevin M.
Quantitative Susceptibility Mapping (QSM) can estimate the underlying tissue magnetic susceptibility and reveal pathology. Current deep-learning-based approaches to solve the QSM inverse problem are restricted on fixed image resolution. They trained
Externí odkaz:
http://arxiv.org/abs/1908.00206