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Projection Method for Deconvolution-Based CT Brain Perfusion
Programming and Computer Software ( IF 0.7 ) Pub Date : 2020-05-31 , DOI: 10.1134/s0361768820030056 D. A. Lyukov , A. S. Krylov , V. A. Lukshin , D. Yu. Usachev
中文翻译:
基于反卷积的CT脑灌注的投影方法
更新日期:2020-05-31
Programming and Computer Software ( IF 0.7 ) Pub Date : 2020-05-31 , DOI: 10.1134/s0361768820030056 D. A. Lyukov , A. S. Krylov , V. A. Lukshin , D. Yu. Usachev
Abstract
The quantitative analysis of the blood flow in the brain tissue is one of the important problems in neurosurgery. It arises when diagnosing acute ischemic stroke. This problem can be solved using computed tomography (CT) perfusion imaging. There are various methods for extracting quantitative characteristics of cerebral blood flow from CT perfusion data, which differ in degrees of their noise resistance. More noise-resistant methods enable the reduction in radiation doses when conducting the examination of the patient. Hence, the development of noise-resistant methods is an important problem. This paper presents an algorithm for evaluating the quantitative characteristics of cerebral blood flow, based on the regularization using the projection onto a set of monotonic functions while minimizing the functional of total generalized variation (TGV). The proposed approach is tested on synthetic and real-world data. It yields better results than the singular value decomposition (SVD) method with Tikhonov regularization and methods of total variation (TV) minimization and TGV minimization.中文翻译:
基于反卷积的CT脑灌注的投影方法