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Proceedings Paper

Task-based optimization of flip angle for texture analysis in MRI
Author(s): Jonathan F. Brand; Lars R. Furenlid; Maria I. Altbach; Jean-Phillippe Galons; Achyut Bhattacharyya; Puneet Sharma; Tulshi Bhattacharyya; Ali Bilgin; Diego R. Martin
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Paper Abstract

Chronic liver disease is a worldwide health problem, and hepatic fibrosis (HF) is one of the hallmarks of the disease. The current reference standard for diagnosing HF is biopsy followed by pathologist examination, however this is limited by sampling error and carries risk of complications. Pathology diagnosis of HF is based on textural change in the liver as a lobular collagen network that develops within portal triads. The scale of collagen lobules is characteristically on order of 1-5 mm, which approximates the resolution limit of in vivo gadolinium-enhanced magnetic resonance imaging in the delayed phase. We have shown that MRI of formalin fixed human ex vivo liver samples mimic the textural contrast of in vivo Gd-MRI and can be used as MRI phantoms. We have developed local texture analysis that is applied to phantom images, and the results are used to train model observers. The performance of the observer is assessed with the area-under-the-receiveroperator- characteristic curve (AUROC) as the figure of merit. To optimize the MRI pulse sequence, phantoms are scanned with multiple times at a range of flip angles. The flip angle that associated with the highest AUROC is chosen as optimal based on the task of detecting HF.

Paper Details

Date Published: 24 March 2016
PDF: 13 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 97870B (24 March 2016); doi: 10.1117/12.2214564
Show Author Affiliations
Jonathan F. Brand, College of Optical Sciences, The Univ. of Arizona (United States)
Lars R. Furenlid, College of Optical Sciences, The Univ. of Arizona (United States)
College of Medicine, The Univ. of Arizona (United States)
Maria I. Altbach, College of Medicine, The Univ. of Arizona (United States)
Jean-Phillippe Galons, College of Medicine, The Univ. of Arizona (United States)
Achyut Bhattacharyya, College of Medicine, The Univ. of Arizona (United States)
Puneet Sharma, College of Medicine, The Univ. of Arizona (United States)
Tulshi Bhattacharyya, College of Medicine, The Univ. of Arizona (United States)
Ali Bilgin, College of Medicine, The Univ. of Arizona (United States)
Diego R. Martin, College of Medicine, The Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 9787:
Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Matthew A. Kupinski, Editor(s)

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