Faculty of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran.
Med Biol Eng Comput. 2018 Apr;56(4):599-610. doi: 10.1007/s11517-017-1707-x. Epub 2017 Aug 24.
The problem of simultaneous blood oxygenation level dependent (BOLD) detection and data completion is addressed in this paper. It is assumed that a set of fMRI data with significant number of missing samples is available and the aim is to recover those samples with least possible quality degradation. At the same time, BOLD should be detected. We propose a new cost function comprising both BOLD detection and data reconstruction terms. A solution based on singular value thresholding and sparsity-inducing approach is proposed. Due to the low-rank nature of the fMRI data, it is expected that the related techniques to be very effective for reconstruction. Extensive experiments are conducted on different datasets in noisy conditions. The achieved results, both in terms of data quality and data analysis accuracy, are promising and confirm that the proposed method can be effective for recovery of compressed/incomplete fMRI data. Several state-of-the art image reconstruction techniques are compared with the proposed method. In addition, the results of applying general linear model (GLM) using statistical parameter mapping (SPM) toolbox are compared with those of the proposed method.
本文解决了同时进行血氧水平依赖（BOLD）检测和数据补全的问题。假设存在一组具有大量缺失样本的 fMRI 数据，目标是使用尽可能小的质量降级来恢复这些样本。同时，应该检测 BOLD。我们提出了一个新的代价函数，包括 BOLD 检测和数据重构项。提出了一种基于奇异值阈值和稀疏诱导方法的解决方案。由于 fMRI 数据的低秩性质，预计相关技术对重建非常有效。在不同的数据集和噪声条件下进行了广泛的实验。在数据质量和数据分析准确性方面，所获得的结果都很有前景，证实了所提出的方法可以有效地恢复压缩/不完整的 fMRI 数据。将几种最先进的图像重建技术与所提出的方法进行了比较。此外，还比较了使用统计参数映射（SPM）工具箱的一般线性模型（GLM）的结果与所提出的方法的结果。
