Wu Xue, Eggebrecht Adam T, Ferradal Silvina L, Culver Joseph P, Dehghani Hamid
School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.
Department of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St Louis, MO, 63110, USA.
Biomed Opt Express. 2014 Oct 13;5(11):3882-900. doi: 10.1364/BOE.5.003882. eCollection 2014 Nov 1.
Image recovery in diffuse optical tomography (DOT) of the human brain often relies on accurate models of light propagation within the head. In the absence of subject specific models for image reconstruction, the use of atlas based models are showing strong promise. Although there exists some understanding in the use of some limited rigid model registrations in DOT, there has been a lack of a detailed analysis between errors in geometrical accuracy, light propagation in tissue and subsequent errors in dynamic imaging of recovered focal activations in the brain. In this work 11 different rigid registration algorithms, across 24 simulated subjects, are evaluated for DOT studies in the visual cortex. Although there exists a strong correlation (R(2) = 0.97) between geometrical surface error and internal light propagation errors, the overall variation is minimal when analysing recovered focal activations in the visual cortex. While a subject specific mesh gives the best results with a 1.2 mm average location error, no single algorithm provides errors greater than 4.5 mm. This work demonstrates that the use of rigid algorithms for atlas based imaging is a promising route when subject specific models are not available.
人脑扩散光学层析成像(DOT)中的图像重建通常依赖于头部内光传播的精确模型。在缺乏用于图像重建的个体特异性模型的情况下,基于图谱的模型显示出巨大潜力。尽管在DOT中对一些有限的刚性模型配准的使用已有一定了解,但在几何精度误差、组织中的光传播以及随后大脑中恢复的焦点激活动态成像中的误差之间,仍缺乏详细分析。在这项工作中,针对视觉皮层的DOT研究,对24个模拟对象的11种不同刚性配准算法进行了评估。尽管几何表面误差与内部光传播误差之间存在很强的相关性(R² = 0.97),但在分析视觉皮层中恢复的焦点激活时,总体变化很小。虽然个体特异性网格给出了最佳结果,平均位置误差为1.2毫米,但没有一种算法的误差大于4.5毫米。这项工作表明,在没有个体特异性模型时,使用基于图谱成像的刚性算法是一条很有前景的途径。