约束非负矩阵分解增强提取心脏小鼠FDG PET成像血液和组织时间-活性曲线。

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
Otman Sarrhini, Pedro D'Orléans-Juste, Jacques A Rousseau, Jean-François Beaudoin, Roger Lecomte
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引用次数: 2

摘要

我们提出了一种增强的方法,可以准确地从小鼠动态2-脱氧-2-[18F]氟-d -葡萄糖([18F]FDG)正电子发射断层扫描(PET)心脏图像中检索血液和组织的时间-活性曲线(tac)。该方法是非侵入性的,使用约束非负矩阵分解算法(CNMF)应用于包含左心室(LV) PET图像体素强度值的矩阵(a)。CNMF通过最小化扩展成本函数,将A分解为非负矩阵H和W,分别表示生理因素(血液和组织)及其相关权重。我们在32只C57BL/6小鼠身上验证了我们的方法,其中14只患有急性心肌梗死(AMI)。利用CNMF,我们可以将小鼠左室分解成心肌和血池图像。将它们对应的tac用于动力学建模,以方便地确定计算心肌葡萄糖代谢率所需的[18F]FDG内流常数(Ki)。使用CNMF计算的对照小鼠心脏Ki值与文献中发表的值一致。使用CNMF发现对照组和AMI小鼠心脏的Ki值有显著差异。W元素值与左冠状动脉结扎引起的左室结构改变吻合较好。将CNMF与最近发表的基于感兴趣区域(RUDUR)的动态序列鲁棒解混方法进行了比较。与RUDUR方法相比,CNMF方法明显改善了信号分离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhanced Extraction of Blood and Tissue Time-Activity Curves in Cardiac Mouse FDG PET Imaging by Means of Constrained Nonnegative Matrix Factorization.

Enhanced Extraction of Blood and Tissue Time-Activity Curves in Cardiac Mouse FDG PET Imaging by Means of Constrained Nonnegative Matrix Factorization.

Enhanced Extraction of Blood and Tissue Time-Activity Curves in Cardiac Mouse FDG PET Imaging by Means of Constrained Nonnegative Matrix Factorization.

Enhanced Extraction of Blood and Tissue Time-Activity Curves in Cardiac Mouse FDG PET Imaging by Means of Constrained Nonnegative Matrix Factorization.

We propose an enhanced method to accurately retrieve time-activity curves (TACs) of blood and tissue from dynamic 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET) cardiac images of mice. The method is noninvasive and consists of using a constrained nonnegative matrix factorization algorithm (CNMF) applied to the matrix (A) containing the intensity values of the voxels of the left ventricle (LV) PET image. CNMF factorizes A into nonnegative matrices H and W, respectively, representing the physiological factors (blood and tissue) and their associated weights, by minimizing an extended cost function. We verified our method on 32 C57BL/6 mice, 14 of them with acute myocardial infarction (AMI). With CNMF, we could break down the mouse LV into myocardial and blood pool images. Their corresponding TACs were used in kinetic modeling to readily determine the [18F]FDG influx constant (Ki) required to compute the myocardial metabolic rate of glucose. The calculated Ki values using CNMF for the heart of control mice were in good agreement with those published in the literature. Significant differences in Ki values for the heart of control and AMI mice were found using CNMF. The values of the elements of W agreed well with the LV structural changes induced by ligation of the left coronary artery. CNMF was compared with the recently published method based on robust unmixing of dynamic sequences using regions of interest (RUDUR). A clear improvement of signal separation was observed with CNMF compared to the RUDUR method.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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