Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images

Abstract

We attempt to develop a systematic scheme through adopting high-pass filtering (HPF) to well resolve value-preserved images such as medical images. Our approach is derived from the Poisson maximum a posteriori superresolution algorithm employing the HP filters, where four filters are considered such as two low-pass-filter-combination based filters, wavelet filter, and negative-oriented Laplacian HP filter. The proposed approach is incorporated into the procedure of finite-element-method (FEM)-based image reconstruction for diffuse optical tomography in the direct current domain, posterior to each iteration without altering the original FEM modeling. This approach is justified with various HPF for different cases that breast-like phantoms embedded with two or three inclusions that imitate tumors are employed to examine the resolution performances under certain extreme conditions. The proposed approach to enhancing image resolution is evaluated for all tested cases. A qualitative investigation of reconstruction performance for each case is presented. Following this, we define a set of measures on the quantitative evaluation for a range of resolutions including separation, size, contrast, and location, thereby providing a comparable evaluation to the visual quality. The most satisfactory result is obtained by using the wavelet HP filter, and it successfully justifies our proposed scheme.

Flexible near-infrared diffuse optical tomography with varied weighting functions of edge-preserving regularization

“…To obtain further quantitative information about the reconstructed images in these simulations, two measures [25] (contrast resolution and size resolution) were used over the region of interest. To define the contrast and size resolution in the onedimensional (1D) and two-dimensional (2D) domains, the idea originates from precision and density/ saturation, respectively, of which the advantage is easy to be implemented.…”

Reconstruction and Localization of Tumors in Breast Optical Imaging via Convolution Neural Network Based on Batch Normalization Layers

“…In radiance data (X), we have two kinds of data in the different domains for human tissue the reduced scattering coefficients are much higher than the absorption coefficients. In the current study, we employed an in-house coded program [23,24] to generate a dataset by the TR method to simulate photon propagation inside circular phantoms with a diameter of the range that varies from 60−150 mm; the absorption and scattering coefficients of the phantom are in the range 0.005−0.03 mm −1 and 0.5-3 mm −1 , respectively. The phantom includes one or two inclusions associated with varying radii.…”

Implementation of edge-preserving regularization for frequency-domain diffuse optical tomography

“…To provide a quantitative assessment for these reconstructed images with using TR or EPR, two measures (contrast resolution and size resolution) defined in [30] were determined over the region of interest (ROI). The contrast resolution is defined to evaluate the resolution on the contrast of opticalproperty values of the inclusions relative to that of the background region, and the size resolution is designed to evaluate the resolution on the size over all inclusions.…”

“…On the other hand, apart from the use of a novel hardware design to obtain a forward measurement, quite a few algorithms [18][19][20][21][22][23][24][25][26][27][28][29][30] were investigated to explore the solution of NIR DOT, which were implemented in various aspects. The forward problem in NIR DOT describes the photon propagation in tissue, and the inverse problem involves estimating the absorption and scattering coefficients of tissue from light measurements around the surface.…”

“…A normalization technique [27][28][29] was investigated, thereby evaluating the effect on the intercoefficient cross-talk issue or the wavelength dependency. A systematic scheme [30] was proposed with adopting value-preserved high-pass filtering in the interprocessing to well resolve optical-property images, which can be derived from the Poisson maximum a posteriori superresolution algorithm.…”