کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1806555 | 1025215 | 2013 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Non-convex algorithm for sparse and low-rank recovery: Application to dynamic MRI reconstruction
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
فیزیک ماده چگال
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
In this work we exploit two assumed properties of dynamic MRI in order to reconstruct the images from under-sampled K-space samples. The first property assumes the signal is sparse in the x-f space and the second property assumes the signal is rank-deficient in the x-t space. These assumptions lead to an optimization problem that requires minimizing a combined lp-norm and Schatten-p norm. We propose a novel FOCUSS based approach to solve the optimization problem. Our proposed method is compared with state-of-the-art techniques in dynamic MRI reconstruction. Experimental evaluation carried out on three real datasets shows that for all these datasets, our method yields better reconstruction both in quantitative and qualitative evaluation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Magnetic Resonance Imaging - Volume 31, Issue 3, April 2013, Pages 448–455
Journal: Magnetic Resonance Imaging - Volume 31, Issue 3, April 2013, Pages 448–455
نویسندگان
Angshul Majumdar, Rabab K. Ward, Tyseer Aboulnasr,