کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
466326 697823 2016 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Low-dose CT statistical iterative reconstruction via modified MRF regularization
ترجمه فارسی عنوان
بازسازی تکراری آماری CT با دوز کم با استفاده از روش MRF اصلاح شده
کلمات کلیدی
CT؛ بازسازی تکراری آماری؛ میدان تصادفی مارکوف؛ تنوع کلی عمومی؛ منظم سازی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی


• Proposed a novel regularization scheme MMRF for CT reconstruction.
• Present a new objective function and deduce its iterative equation.
• Optimize the objective function by a modified alternative iterative algorithm.
• Design experiments on different phantoms to validate the validity of the approach.
• Analyze the results by visual evaluation and quantitative evaluation.

It is desirable to reduce the excessive radiation exposure to patients in repeated medical CT applications. One of the most effective ways is to reduce the X-ray tube current (mAs) or tube voltage (kVp). However, it is difficult to achieve accurate reconstruction from the noisy measurements. Compared with the conventional filtered back-projection (FBP) algorithm leading to the excessive noise in the reconstructed images, the approaches using statistical iterative reconstruction (SIR) with low mAs show greater image quality. To eliminate the undesired artifacts and improve reconstruction quality, we proposed, in this work, an improved SIR algorithm for low-dose CT reconstruction, constrained by a modified Markov random field (MRF) regularization. Specifically, the edge-preserving total generalized variation (TGV), which is a generalization of total variation (TV) and can measure image characteristics up to a certain degree of differentiation, was introduced to modify the MRF regularization. In addition, a modified alternating iterative algorithm was utilized to optimize the cost function. Experimental results demonstrated that images reconstructed by the proposed method could not only generate high accuracy and resolution properties, but also ensure a higher peak signal-to-noise ratio (PSNR) in comparison with those using existing methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Methods and Programs in Biomedicine - Volume 123, January 2016, Pages 129–141
نویسندگان
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