Xiaoqing Zhou

Spatial-frequency-compression scheme for diffuse optical tomography with dense sampling dataset

Dense sampling of illumination and detection offers an effective way of improving the image-reconstruction performance of near-infrared diffuse optical tomography (DOT) at a cost of lengthy computation times. In this paper, we describe a fast DOT scheme for reconstructing the absorption coefficient image of a slab medium from dense sampling of both illumination and detection in the noncontact DOT. The proposed method is carried out with spatial-frequency encoding in both the source and detection spaces, and involves a spatial-frequency-compression (SFC) strategy for selecting the useful spatial frequency based on the tissue transfer function. The method is expected to considerably reduce the calculation time for reconstruction while improving the quality of the reconstructed images. Results from the simulated data show that the speed for absorption reconstruction with the proposed SFC method is more than 400 times faster than that with the conventional one. A noncontact DOT system for dense sampling of both illumination and detection is developed by using laser raster scanning and CCD-based data acquisition. Experimental measurements on several solid phantoms demonstrate that a high quantitativeness ratio can be obtained from the proposed method thanks to reduction of the ill-posedness of the inverse calculation. It takes less than 20 s for the proposed method to experimentally reconstruct one absorption image from a 256×256-sized dataset, which would take a few hours with the conventional method.

Near-infrared frequency domain system and fast inverse Monte Carlo algorithm for endoscopic measurement of tubular tissue.

A near infrared diffuse reflectance system for endoscopic measurement and an inverse algorithm for extracting optical properties of tubular tissues were developed in this paper. The measurement system worked in the frequency domain mode and a custom probe was employed for endoscopic detection of cervical cancer. Experiments for evaluating the measurement accuracy indicate that the model-to-data mismatch for the AC amplitude and phase lag is less than 3.7% and 6.7%, respectively. To facilitate the extraction of the optical properties in tubular tissues and to minimize the influence of the initial guess on the reconstruction accuracy, a fast inverse Monte Carlo simulation algorithm with cluster analysis method was proposed. Simulation results showed that the relative errors of the absorption coefficient recovered using the proposed inverse algorithm are less than 5.8% and those of the sacttering coefficient are less than 10.2%. Endoscopic measurement on two tubular solid phantoms were also carried out to evaluate the system and the inversion scheme. The results demonstrated that less than 20% relative error can be achieved.

An endoscopic diffuse optical tomographic method based on the effective detection range

Endoscopic diffuse optical tomography (DOT) is a new medical imaging modality with the potential applications in functional imaging of the internal organs. To cut down the measurement time and the computation burden of image reconstruction, in this paper, we developed the image reconstruction algorithm with the partial measurement in the effective detection range (EDR) of a tubular tissue and the corresponding endoscopic imaging system with a novel endoscopic probe for flexibly selecting the detection sites. For a typical inner size and optical properties of the cervix, it is found that EDR is less than half of the inner circumference. Comparing to the traditional method, the adoption of EDR results in a reduction of more than a factor of two in the time cost for a measurement cycle and for the total iteration reconstruction. Images reconstructed from the simulation data demonstrate that the proposed method achieves equivalent image quality to that obtained from the complete data set. The images reconstructed from the EDR measurements on cervix-like solid phantoms show that both the location and size of the targets are reconstructed correctly. The proposed method will be useful to the development of endoscopic DOT technologies for cancer detection in tubular organs including cervix.

Fast Monte Carlo inversion for extracting the optical properties of tubular tissues

Reconstruction of absorption coefficient \mu_{a} and scattering coefficient \mu_{s} is very important for applications of diffuse optical tomography and near infrared spectroscopy. Aiming at the early cancer detection of cervix and stomach, we present a fast inverse Monte-Carlo scheme for extracting \mu_{a} and \mu_{s} of a tubular tissue from the measurement on frequency domain. Results show that the computation time for reconstructing one set of \mu_{a} and \mu_{s} is less than 1 min and the relative errors in reconstruction are less than +-10% for the optical properties of normal cervical tissue and precancerous lesions.

A continuous wave non-contact diffuse optical tomographic measurement system and the corresponding image reconstruction algorithm for dense sampling data

In this paper, we constructed a continuous wave non-contact diffuse optical tomography (DOT) system for the dense sampling of both the illumination and detection by using a laser raster scanning and a CCD-based data acquisition. For dealing with the large size of measurement data obtained from the non-contact system, a fast tomographic image reconstruction scheme for reconstructing the absorption coefficient of a slab is developed. The proposed algorithm is carried out with the spatial-frequency encoding in both the measurement and the image spaces, and involves a strategy for selecting the useful spatial frequency based on the transfer function of tissue. Dense sampling offers an effective way of improving the image reconstruction performances and the developed algorithm is expected to considerably reduce the calculation time for reconstruction whilst retain the quality of the reconstructed images. Reconstructions from the experimental data show that the inversion scheme developed in this paper can get an absorption image within 20s, which has higher quality than those reconstructed in several hours by using the conventional reconstruction method.

Frequency-domain endoscopic diffuse optical tomography reconstruction algorithm based on dual-modulation-frequency and dual-points source diffuse equation

In this paper, frequency-domain endoscopic diffuse optical tomography image reconstruction algorithm based on dual-modulation-frequency and dual-points source diffuse equation is investigated for the reconstruction of the optical parameters including the absorption and reducing scattering coefficients. The forward problem is solved by the finite element method based on the frequency domain diffuse equation (FD-DE) for dual-points source approximation and multi-modulation-frequency. In the image reconstruction, a multi-modulation-frequency Newton-Raphson algorithm is applied to obtain the solution. To further improve the image accuracy and quality, a method based on the region of interest (ROI) is applied on the above procedures. The simulation is performed in the tubular model to verify the validity of the algorithm. Results show that the FD-DE with dual-points source approximate is more accuracy at shorter source-detector separation. The reconstruction with dual-modulation-frequency improves the image accuracy and quality compared to the results with single-modulation-frequency and triple-modulation-frequency method. The peak optical coefficients in ROI (ROI_max) are almost equivalent to the true optical coefficients with the relative error less than 6.67%. The full width at half maximum (FWHM) achieves 82% of the true radius. The contrast-to-noise ratio (CNR) and image coefficient(IC) is 5.678 and 26.962, respectively. Additionally, the results with the method based on ROI show that the ROI_max is equivalent to the true value. The FWHM can improve by 88% of the true radius. The CNR and IC is improved over 7.782 and 45.335, respectively.

Near-infrared optical imaging of human brain based on the semi-3D reconstruction algorithm

In the non-invasive brain imaging with near-infrared light, precise head model is of great significance to the forward model and the image reconstruction. To deal with the individual difference of human head tissues and the problem of the irregular curvature, in this paper, we extracted head structure with Mimics software from the MRI image of a volunteer. This scheme makes it possible to assign the optical parameters to every layer of the head tissues reasonably and solve the diffusion equation with the finite-element analysis. During the solution of the inverse problem, a semi-3D reconstruction algorithm is adopted to trade off the computation cost and accuracy between the full 3-D and the 2-D reconstructions. In this scheme, the changes in the optical properties of the inclusions are assumed either axially invariable or confined to the imaging plane, while the 3-D nature of the photon migration is still retained. This therefore leads to a 2-D inverse issue with the matched 3-D forward model. Simulation results show that comparing to the 3-D reconstruction algorithm, the Semi-3D reconstruction algorithm cut 27% the calculation time consumption.

The reconstruction algorithm for endoscopic diffuse optical tomography based on effective detection area

To reduce the cost of near-infrared endoscopic image equipment and the reconstruction time, a measurement method based on the effective detection area is proposed and the corresponding algorithm which simultaneously reconstructs the absorption coefficient and the reduced scattering coefficient is developed. First, the effective detection area is investigated with the Monte Carlo simulation. Secondly, the image reconstruction algorithm based on the effective detection area is studied. The Jacobin matrix is built by combining the adjoint method with the modified Generalized Pulse Spectrum Technique and calibrated by the maximum of its absolute value. The Generalized Minimal Residual Krylov method is used to obtain the iterative update factor. Finally, the impact of the number of measured points in the effective detection area on the reconstructed results is discussed, and the robustness of the algorithm to noise and cross-talk are verified by the simulated test data. The results show that the reconstructed algorithm based on the effective detection area has equivalent accuracy to the traditional ones. The fidelity of reconstructed absorption and reduced scattering coefficients can be 80%, respectively. The scales and positions of the reconstructed lesions are both correspond to the true and the reconstruction time is reduced by half. The optimal number of sources and detectors is 16 depending on the scale of the simulation model. The detection using the effective detection area and the developed reconstruction algorithm will promote the development of diffuse optical tomography which is applied to cervical and other tubular organs.

Fast calculation of tissue optical properties using MC and the experimental evaluation for diagnosis of cervical cancer

This article aims at the development of the fast inverse Monte Carlo (MC) simulation for the reconstruction of optical properties (absorption coefficient μs and scattering coefficient μs) of cylindrical tissue, such as a cervix, from the measurement of near infrared diffuse light on frequency domain. Frequency domain information (amplitude and phase) is extracted from the time domain MC with a modified method. To shorten the computation time in reconstruction of optical properties, efficient and fast forward MC has to be achieved. To do this, firstly, databases of the frequency-domain information under a range of μa and μs were pre-built by combining MC simulation with Lambert-Beers law. Then, a double polynomial model was adopted to quickly obtain the frequency-domain information in any optical properties. Based on the fast forward MC, the optical properties can be quickly obtained in a nonlinear optimization scheme. Reconstruction resulting from simulated data showed that the developed inverse MC method has the advantages in both the reconstruction accuracy and computation time. The relative errors in reconstruction of the μs and μs are less than ±6% and ±12% respectively, while another coefficient (μs or μs) is in a fixed value. When both μs and μs are unknown, the relative errors in reconstruction of the reduced scattering coefficient and absorption coefficient are mainly less than ±10% in range of 45< μs <80 cm-1 and 0.25< a μ <0.55 cm-1. With the rapid reconstruction strategy developed in this article the computation time for reconstructing one set of the optical properties is less than 0.5 second. Endoscopic measurement on two tubular solid phantoms were also carried out to evaluate the system and the inversion scheme. The results demonstrated that less than 20% relative error can be achieved.

Improvement of the frequency-domain inverse Monte Carlo simulation

This article aims at the optical property (absorption coefficient and scatter coefficient) reconstruction from the frequency-domain (FD) near-infrared diffuse measurement on small tissues, such as a cervix, for which inverse Monte Carlo (MC) simulation is the suitable choice. To achieve the fast and accurate reconstruction based on the inverse Monte Carlo simulation, following techniques were adopted. First, in the forward calculation, a database, which include the frequency-domain information calculated from MC simulation for a series of optical parameters of tissue, were established with fast methods. Then, in the reconstruction procedure, Levenberg-Marquardt (L-M) optimization was adopted and Multiple Polynomial Regression (MPR) method was used to rapidly get the FD information at any optical properties by best fitting the curved surface formed by the above database. At Last, in the reconstruction, to eliminate the influence of the initial guess of optical properties on the reconstruction accuracy, cluster analysis method was introduced into L-M reconstruction algorithm to determine the region of the initial guess. The reconstruction algorithm was demonstrated with simulation data. The results showed that it takes less than 0.5s to reconstruction one set of optical properties. The average relative error from the reconstruction algorithm joined with cluster analysis is 10% lower than that without cluster analysis.