Min-Cheng Pan

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

In this study, we first propose the use of edge-preserving regularization in optimizing an ill-conditioned problem in the reconstruction procedure for diffuse optical tomography to prevent unwanted edge smoothing, which usually degrades the attributes of images for distinguishing tumors from background tissues when using Tikhonov regularization. In the edge-preserving regularization method presented here, a potential function with edge-preserving properties is introduced as a regularized term in an objective function. With the minimization of this proposed objective function, an iterative method to solve this optimization problem is presented in which half-quadratic regularization is introduced to simplify the minimization task. Both numerical and experimental data are employed to justify the proposed technique. The reconstruction results indicate that edge-preserving regularization provides a superior performance over Tikhonov regularization.

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

In this paper, a flexible edge-preserving regularization algorithm based on the finite element method is proposed to reconstruct the optical-property images of near-infrared diffuse optical tomography. This regularization algorithm can easily incorporate with varied weighting functions, such as a generalized Lorentzian function, an exponential function, or a generalized total variation function. To evaluate the performance, results obtained from Tikhonov or edge-preserving regularization are compared with each other. As found, the edge-preserving regularization with the generalized Lorentzian function is more attractive than that with other functions for the estimation of absorption-coefficient images concerning functional tomographic images to discover functional information of tested phantoms/tissues.

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

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.

Rapid convergence to the inverse solution regularized with Lorentzian distributed function for near-infrared continuous wave diffuse optical tomography

A promising method to achieve rapid convergence for image reconstruction is introduced for the continuous-wave near-infrared (NIR) diffuse optical tomography (DOT). Tomographic techniques are usually implemented off line and are time consuming to realize image reconstruction, especially for NIR DOT. Therefore, it is essential to both speed up reconstruction and achieve stable and convergent solutions. We propose an approach using a constraint based on a Lorentzian distributed function incorporated into Tikhonov regularization, thereby rapidly converging a stable solution. It is found in the study that using the proposed method with around five or six iterations leads to a stable solution. The result is compared to the primary method usually converging in approximately 25 iterations. Our algorithm rapidly converges to stable solution in the case of noisy (>20 dB) detected intensities.

Design and implementation of three-dimensional ring-scanning equipment for optimized measurements of near-infrared diffuse optical breast imaging

Abstract. We propose and implement three-dimensional (3-D) ring-scanning equipment for near-infrared (NIR) diffuse optical imaging to screen breast tumors under prostrating examination. This equipment has the function of the radial, circular, and vertical motion without compression of breast tissue, thereby achieving 3-D scanning; furthermore, a flexible combination of illumination and detection can be configured for the required resolution. Especially, a rotation-sliding-and-moving mechanism was designed for the guidance of source- and detection-channel motion. Prior to machining and construction of the system, a synthesized image reconstruction was simulated to show the feasibility of this 3-D NIR ring-scanning equipment; finally, this equipment is verified by performing phantom experiments. Rather than the fixed configuration, this addressed screening/diagnosing equipment has the flexibilities of optical-channel expansion for spatial resolution and the dimensional freedom for scanning in reconstructing optical-property images.

Design for source-and-detector configuration of a ring-scanning-based near-infrared optical imaging system

Abstract. The design scheme of the source-and-detector arrangement of a ring-scanning-based near-infrared optical imaging system prior to the mechanical and optical construction is demonstrated. In terms of the effectiveness and efficiency of design, through the computation of image reconstruction for varied imaging configurations, the influences of the source-and-detector arrangement on the resulting images are evaluated and a formula to estimate the scanning time is provided. The basic idea of our design is to divide circular scanning into several zones, each of which includes n sources and l detectors; i.e., m zones and n sources along with l detectors per zone are defined in the design. Comparison is made among different imaging configurations where their contrast-to-noise ratio measures are evaluated and contrast-and-size detail resolution curves are depicted. Results show that the 2Z3S or 3Z3S configuration is the optimal design in terms of the time consumption of a complete scanning and the resolution of reconstructed optical-property images.

Breast Tumor Detection Instrument Through Mammography Based NIR DOT

X-ray mammography has been used in the early detection of breast tumors for decades. Related techniques to enhance preventive screenings are still demanding. Since the 1990s, near infrared diffuse optical tomography (NIR DOT), a functional medical imaging modality, is being exploited and developed to reconstruct optical-coefficient images of tissues. Much endeavor to improve the spatial resolution and contrast of DOT images has been exerted for clinic applications in the diagnosis of breast tumors. The study aims at the design, implementation, and verification of a mammography based NIR DOT. This multimodality imaging method is able to provide information that neither X-ray nor diffuse optical tomography can give alone. To this end, a device with multiple-channel switching of NIR sources and translational scanning of out-emitted intensity constructed on a commercialized mammography system is being designed and built up. We employ the mammography image as structure information that is used as an initial guess for the image reconstruction of optical properties of tissues. Preliminary numerical trials are performed using heterogeneous phantoms made of high-scattering Intralipid. Promising results are obtained with various spatial resolutions due to partial NIR detected intensity.

Frequency-Domain Diffuse Optical Tomography Implemented with Edge-Preserving Regularization

To overcome the unwanted edge smoothing occurred in DOT, the use of edge-preserving regularization as a priori information in the reconstruction procedure is presented and verified by a variety of test cases in frequency domain.

Approach to accurately estimating low‐contrast inclusion with a priori guidance in near‐infrared diffuse optical tomography

Abstract In this paper, the slight variation in low‐contrast optical properties of an inclusion (tumor) is estimated and investigated by near infra‐red diffusive optical tomography (NIR DOT). We show that absorption‐image reconstruction of test phantoms is based on the continuous wave domain with the aid of structural a priori knowledge obtained from other imaging modalities as well as empirical updating information that is proportional to the off‐boundary distance, but not the size of inclusion against the background. Numerical simulation is demonstrated using various sizes, locations and contrasts of inclusion, where a stable and accurate reconstruction of absorption‐coefficient images can be achieved using our method with structural knowledge and updating information.

Inverse Solution Regularized with the Edge-Preserving Constraint for NIR DOT

To remedy the low spatial resolution of diffuse optical tomography, an iterative solution to the optimization problem is developed using Tikhonov regularization with the edge-preserving constraint as a prior knowledge into the objective function. Article not available.