کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10693081 | 1019757 | 2005 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Comparative evaluation of strain-based and model-based modulus elastography
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
آکوستیک و فرا صوت
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چکیده انگلیسی
Elastography based on strain imaging currently endures mechanical artefacts and limited contrast transfer efficiency. Solving the inverse elasticity problem (IEP) should obviate these difficulties; however, this approach to elastography is often fraught with problems because of the ill-posed nature of the IEP. The aim of the present study was to determine how the quality of modulus elastograms computed by solving the IEP compared with those produced using standard strain imaging methodology. Strain-based modulus elastograms (i.e., modulus elastograms computed by simply inverting strain elastograms based on the assumption of stress uniformity) and model-based modulus elastograms (i.e., modulus elastograms computed by solving the IEP) were computed from a common cohort of simulated and gelatin-based phantoms that contained inclusions of varying size and modulus contrast. The ensuing elastograms were evaluated by employing the contrast-to-noise ratio (CNRe) and the contrast transfer efficiency (CTEe) performance metrics. The results demonstrated that, at a fixed spatial resolution, the CNRe of strain-based modulus elastograms was statistically equivalent to those computed by solving the IEP. At low modulus contrast, the CTEe of both elastographic imaging approaches was comparable; however, at high modulus, the CTEe of model-based modulus elastograms was superior. (E-mail: marvin.m.doyley@dartmouth.edu)
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ultrasound in Medicine & Biology - Volume 31, Issue 6, June 2005, Pages 787-802
Journal: Ultrasound in Medicine & Biology - Volume 31, Issue 6, June 2005, Pages 787-802
نویسندگان
Marvin M. Doyley, Seshadri Srinivasan, Sarah A. Pendergrass, Ziji Wu, Jonathan Ophir,