论文标题
弥漫性光学断层扫描的半分析重建方法
A semi-analytic reconstruction method for Diffuse Optical Tomography
论文作者
论文摘要
近年来,在生物医学成像和疾病检测领域,弥漫性光学断层扫描(DOT)受到了很大的关注。但是,通过高扩散介质进行成像是一个挑战,稳定性始终是一个问题,这是由于反向问题。这里讨论了一种非线性连续波(CW)半分析重建方法,该方法使用弯曲光束路径进行层析成像,而无需纳入包含,与迭代方法不同。非线性Rosenbrock的函数用于近似路径,近似于多数光子作为曲面。以拟议的不同形式的改性啤酒 - 兰伯法(MBLL)用于计算所有可用光子路径的吸收系数。计算值沿着这些通道进行了重新投影,并作为图像重建的基础,而无需求解逆问题。对于三维(3-D)成像,进行了三个不同深度的测量,覆盖了整个幻影深度。将这些切片堆叠在一起,然后进行插值,以形成幻影的体积图像,以实现其真正意义上的层析成像。数值模拟,具有不同几何形状和对比度的蜡幻像实验以令人满意的结果进行。这种半分析的重建方法是简单有效的,适用于重新吸收图像重建的实时应用。将该方法与贪婪算法进行比较,以进一步验证。同样,估计不同的性能评估矩阵以评估重建图像的准确性,结果相当令人满意。
The Diffuse Optical Tomography (DOT) has received considerable attention in the recent years in the field of biomedical imaging and disease detection. However, imaging through highly diffusive medium is a challenge and stability is always an issue due to the inverse problem. Here a non-linear continous wave (CW) semi-analytic reconstruction method is discussed that used curved-beam paths for tomographic imaging with no assumption on inclusion, unlike iterative methods.The non-linear Rosenbrock's function is used to approximate the paths followed by majority photons as curved ones. The modified Beer-Lambert Law (MBLL) in proposed differntial form is used to calculate the absorption coefficient of all the avilable photon paths. The computed values are back-projected along these channels and serve as the basis for image reconstruction without solving the inverse problem. For three-dimensional (3-D) imaging, measurements at three different depths covering the entire depth of the phantom are taken. These slices are stacked together followed by interpolation to form the volumetric image of the phantom to complete the tomographic imaging in its true sense. Numerical simulations, wax phantom experiments with different geometries and contrast are carried out with satisfactory results. This semi-analytic reconstruction method is simple and efficient and is suitable for real-time applications that reqiure fast absorption image reconstruction. The method is compared with the Greedy algorithms for further validation. Also, different performance evaluation matrices are estimated to assess the accuracy of the reconstructed images and the results are rather satisfactory.