William Kovacs
National Institutes of Health
13 Papers
28 Citations
William Kovacs is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Medicine & Respiratory muscle. The author has an hindex of 6, co-authored 13 publications.
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Papers
Skeletal muscle magnetic resonance biomarkers in GNE myopathy
Chia-Ying Liu,Jianhua Yao,William Kovacs,Joseph A. Shrader,Galen O. Joe,Ronald Ouwerkerk,Ami Mankodi,William A. Gahl,Ronald M. Summers,Nuria Carrillo +9 more
TL;DR: 1H-MRS showed a significant decrease in intramyocellular lipid and trimethylamines between stage I and II, suggesting altered muscle metabolism at early stages of GNE myopathy.
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Technical and Clinical Factors Affecting Success Rate of a Deep Learning Method for Pancreas Segmentation on CT.
Mohammad Hadi Bagheri,Holger R. Roth,William Kovacs,Jianhua Yao,Faraz Farhadi,Xiaobai Li,Ronald M. Summers +6 more
TL;DR: Increased visceral abdominal fat and accumulation of fat within or around the pancreas are major factors associated with more accurate segmentation of the panceas.
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Identification of muscle and subcutaneous and intermuscular adipose tissue on thigh MRI of muscular dystrophy
William Kovacs,Chia-Ying Liu,RonaldM. Summers,Jianhua Yao +3 more
- 13 Apr 2016
TL;DR: This work proposes a method that utilizes muscle location if available, but also identifies the fascia lata to serve as the boundary between SAT and IMAT, and achieves DICE coefficients of 0.93, 0.88, and 0.68 for muscle, SAT, and IMat in mild cases.
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Holistic segmentation of the lung in cine MRI.
William Kovacs,Nathan Hsieh,Holger R. Roth,Chioma Nnamdi-Emeratom,W. Patricia Bandettini,Andrew Arai,Ami Mankodi,Ronald M. Summers,Jianhua Yao +8 more
TL;DR: A deep learning approach that utilizes sequence-specific prior information to assist the segmentation of lung in cine MRI is deployed and suggests that the deep learning-based method reliably and accurately segments the lung across the breathing cycle.
Cumulative Radiation Exposures from CT Screening and Surveillance Strategies for von Hippel-Lindau-associated Solid Pancreatic Tumors.
Amit Tirosh,Amit Tirosh,Neige M. Y. Journy,Les R. Folio,Choonsik Lee,Christiane Leite,Jianhua Yao,William Kovacs,W. Marston Linehan,Ashkan A. Malayeri,Electron Kebebew,Amy Berrington de Gonzalez +11 more
TL;DR: CT screening of patients with von Hippel-Lindau syndrome can lead to substantial radiation exposures, even with dual-energy virtual noncontrast CT, and a genome and tumor diameter-based algorithm for pancreatic neuroendocrine tumor surveillance may potentially reduce lifetime radiation exposure.
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