文献类型：期刊文献

英文题名：Patch-based local learning method for cerebral blood flow quantification with arterial spin-labeling MRI

作者：Zhu, Hancan[1];He, Guanghua[2];Wang, Ze[3,4]

机构：[1]Shaoxing Univ, Sch Math Phys & Informat, Shaoxing 312000, Peoples R China;[2]Zhejiang Yuexiu Univ Foreign Languages, Sch Int Business, Shaoxing 312000, Peoples R China;[3]Hangzhou Normal Univ, Inst Psychol Sci, Ctr Cognit & Brain Disorders, Hangzhou 310010, Zhejiang, Peoples R China;[4]Hangzhou Normal Univ, Affiliated Hosp, 126 Wenzhou Rd,Bldg 7, Hangzhou 310015, Zhejiang, Peoples R China

年份：2018

卷号：56

期号：6

起止页码：951

外文期刊名：MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING

收录：SCI-EXPANDED(收录号：WOS:000433045400002)、、EI(收录号：20174604394545)、Scopus(收录号：2-s2.0-85033479773)、WOS

基金：This study was supported by the National Natural Science Foundation of China (No. 61602307, 61671198), Natural Science Foundation of Zhejiang Province Grant LZ15H180001, the Youth 1000 Talent Program of China, Hangzhou Qianjiang Endowed Professor Program, and Hangzhou Innovation Seed Fund.

语种：英文

外文关键词：Arterial spin labeling; Cerebral blood flow; Patch-wise denoising; Support vector machine

外文摘要：Arterial spin-labeling (ASL) perfusion MRI is a non-invasive method for quantifying cerebral blood flow (CBF). Standard ASL CBF calibration mainly relies on pair-wise subtraction of the spin-labeled images and controls images at each voxel separately, ignoring the abundant spatial correlations in ASL data. To address this issue, we previously proposed a multivariate support vector machine (SVM) learning-based algorithm for ASL CBF quantification (SVMASLQ). But the original SVMASLQ was designed to do CBF quantification for all image voxels simultaneously, which is not ideal for considering local signal and noise variations. To fix this problem, we here in this paper extended SVMASLQ into a patch-wise method by using a patch-wise classification kernel. At each voxel, an image patch centered at that voxel was extracted from both the control images and labeled images, which was then input into SVMASLQ to find the corresponding patch of the surrogate perfusion map using a non-linear SVM classifier. Those patches were eventually combined into the final perfusion map. Method evaluations were performed using ASL data from 30 young healthy subjects. The results showed that the patch-wise SVMASLQ increased perfusion map SNR by 6.6% compared to the non-patch-wise SVMASLQ.