Shixin Jiang

Novel l 2,1-norm optimization method for fluorescence molecular tomography reconstruction.

Fluorescence molecular tomography (FMT) is a promising tomographic method in preclinical research, which enables noninvasive real-time three-dimensional (3-D) visualization for in vivo studies. The ill-posedness of the FMT reconstruction problem is one of the many challenges in the studies of FMT. In this paper, we propose a l 2,1-norm optimization method using a priori information, mainly the structured sparsity of the fluorescent regions for FMT reconstruction. Compared to standard sparsity methods, the structured sparsity methods are often superior in reconstruction accuracy since the structured sparsity utilizes correlations or structures of the reconstructed image. To solve the problem effectively, the Nesterovs method was used to accelerate the computation. To evaluate the performance of the proposed l 2,1-norm method, numerical phantom experiments and in vivo mouse experiments are conducted. The results show that the proposed method not only achieves accurate and desirable fluorescent source reconstruction, but also demonstrates enhanced robustness to noise.

Meshless reconstruction method for fluorescence molecular tomography based on compactly supported radial basis function.

Abstract. Fluorescence molecular tomography (FMT) is a promising tool in the study of cancer, drug discovery, and disease diagnosis, enabling noninvasive and quantitative imaging of the biodistribution of fluorophores in deep tissues via image reconstruction techniques. Conventional reconstruction methods based on the finite-element method (FEM) have achieved acceptable stability and efficiency. However, some inherent shortcomings in FEM meshes, such as time consumption in mesh generation and a large discretization error, limit further biomedical application. In this paper, we propose a meshless method for reconstruction of FMT (MM-FMT) using compactly supported radial basis functions (CSRBFs). With CSRBFs, the image domain can be accurately expressed by continuous CSRBFs, avoiding the discretization error to a certain degree. After direct collocation with CSRBFs, the conventional optimization techniques, including Tikhonov, L1-norm iteration shrinkage (L1-IS), and sparsity adaptive matching pursuit, were adopted to solve the meshless reconstruction. To evaluate the performance of the proposed MM-FMT, we performed numerical heterogeneous mouse experiments and in vivo bead-implanted mouse experiments. The results suggest that the proposed MM-FMT method can reduce the position error of the reconstruction result to smaller than 0.4 mm for the double-source case, which is a significant improvement for FMT.

Precise integrin-targeting near-infrared imaging-guided surgical method increases surgical qualification of peritoneal carcinomatosis from gastric cancer in mice

Peritoneal carcinomatosis from gastric cancer represents a common recurrent gastric cancer that seriously affects the survival, prognosis, and quality of life of patients at its advanced stage. In recent years, complete cytoreduction surgery in combination with hyperthermic intraperitoneal chemotherapy has been demonstrated to improve the survival and prognosis of patients with malignant tumors including peritoneal carcinomatosis from gastric cancer. Establishing viable methods of accurately assessing the tumor burden in patients with peritoneal carcinoma and correctly selecting suitable patients in order to improve cytoreduction surgical outcomes and reduce the risk of postoperative complications has become a challenge in the field of peritoneal carcinoma research. Here, we investigated peritoneal carcinomatosis from gastric cancer in a mouse model by using our self-developed surgical navigation system that combines optical molecular imaging with an integrin-targeting Arg-Gly-Asp-indocyanine green (RGD-ICG) molecular probe. The results showed that our diagnostic method could achieve a sensitivity and specificity of up to 93.93% and 100%, respectively, with a diagnostic index (DI) of 193.93% and diagnostic accuracy rate of 93.93%.Furthermore, the minimum tumor diameter measured during the surgery was 1.8 mm and the operative time was shortened by 3.26-fold when compared with the conventionally-treated control group. Therefore, our surgical navigation system that combines optical molecular imaging with an RGD-ICG molecular probe, could improve the diagnostic accuracy rate for peritoneal carcinomatosis from gastric cancer, shorten the operative time, and improve the quality of the cytoreduction surgery for peritoneal carcinomatosis from gastric cancer, thus providing a solid foundation for its future clinical development and application.

Compactly supported radial basis function-based meshless method for photon propagation model of fluorescence molecular tomography

Fluorescence Molecular Tomography (FMT) is a powerful imaging modality for the research of cancer diagnosis, disease treatment and drug discovery. Via three-dimensional (3-D) imaging reconstruction, it can quantitatively and noninvasively obtain the distribution of fluorescent probes in biological tissues. Currently, photon propagation of FMT is conventionally described by the Finite Element Method (FEM), and it can obtain acceptable image quality. However, there are still some inherent inadequacies in FEM, such as time consuming, discretization error and inflexibility in mesh generation, which partly limit its imaging accuracy. To further improve the solving accuracy of photon propagation model (PPM), we propose a novel compactly supported radial basis functions (CSRBFs)-based meshless method (MM) to implement the PPM of FMT. We introduced a series of independent nodes and continuous CSRBFs to interpolate the PPM, which can avoid complicated mesh generation. To analyze the performance of the proposed MM, we carried out numerical heterogeneous mouse simulation to validate the simulated surface fluorescent measurement. Then we performed an in vivo experiment to observe the tomographic reconstruction. The experimental results confirmed that our proposed MM could obtain more similar surface fluorescence measurement with the golden standard (Monte-Carlo method), and more accurate reconstruction result was achieved via MM in in vivo application.

Sparse Reconstruction of Fluorescence Molecular Tomography Using Variable Splitting and Alternating Direction Scheme

PurposeFluorescence molecular tomography (FMT) is a novel imaging modality for three-dimensional preclinical research and has many potential applications for drug therapy evaluation and tumor diagnosis. However, FMT presents an ill-conditioned and ill-posed inverse problem, which is a challenge for its tomography reconstruction. Due to the importance of FMT reconstruction, it is valuable and necessary to develop further practical reconstruction methods for FMT.ProceduresIn this study, an efficient method using variable splitting strategy as well as alternating direction strategy (VSAD) was proposed for FMT reconstruction. In this method, the variable splitting strategy and the augmented Lagrangian function were first introduced to obtain an equivalent optimization formulation of the original problem. Then, the alternating direction scheme was used to solve the optimization problem and to accelerate its convergence. To examine the property of the VSAD method, three numerical simulation experiments (accuracy assessment experiment, robustness assessment experiment, and reconstruction speed assessment experiment) were performed and analyzed.ResultsThe results indicated that the reconstruction accuracy, the reconstruction robustness, and the reconstruction speed of FMT were satisfactory by using the proposed VSAD method. Two in vivo studies, which were conducted by using two nude mouse models, further confirmed the advantages of the proposed method.ConclusionsThe results indicated that the proposed VSAD algorithm is effective for FMT reconstruction. It was accurate, robust, and efficient for FMT imaging and was feasibly applied for in vivo FMT applications.

Novel trace norm regularization method for fluorescence molecular tomography reconstruction

Fluorescence molecular tomography (FMT) is developing rapidly in the field of molecular imaging. FMT has been used in surgical navigation for tumor resection and has many potential applications at the physiological, metabolic, and molecular levels in tissues. Due to the ill-posed nature of the problem, many regularized methods are generally adopted. In this paper, we propose a region reconstruction method for FMT in which the trace norm regularization. The trace norm penalty was defined as the sum of the singular values of the matrix. The proposed method adopts a priori information which is the structured sparsity of the fluorescent regions for FMT reconstruction. In order to improve the solution efficiency, the accelerated proximal gradient algorithms was used to accelerate the computation. The numerical phantom experiment was conducted to evaluate the performance of the proposed trace norm regularization method. The simulation study shows that the proposed method achieves accurate and is able to reconstruct image effectively.

A novel method for image denoising of fluorescence molecular imaging based on fuzzy C-Means clustering

As an important molecular imaging modality, fluorescence molecular imaging (FMI) has the advantages of high sensitivity, low cost and ease of use. By labeling the regions of interest with fluorophore, FMI can noninvasively obtain the distribution of fluorophore in-vivo. However, due to the fact that the spectrum of fluorescence is in the section of the visible light range, there are mass of autofluorescence on the surface of the bio-tissues, which is a major disturbing factor in FMI. Meanwhile, the high-level of dark current for charge-coupled device (CCD) camera and other influencing factor can also produce a lot of background noise. In this paper, a novel method for image denoising of FMI based on fuzzy C-Means clustering (FCM) is proposed, because the fluorescent signal is the major component of the fluorescence images, and the intensity of autofluorescence and other background signals is relatively lower than the fluorescence signal. First, the fluorescence image is smoothed by sliding-neighborhood operations to initially eliminate the noise. Then, the wavelet transform (WLT) is performed on the fluorescence images to obtain the major component of the fluorescent signals. After that, the FCM method is adopt to separate the major component and background of the fluorescence images. Finally, the proposed method was validated using the original data obtained by in vivo implanted fluorophore experiment, and the results show that our proposed method can effectively obtain the fluorescence signal while eliminate the background noise, which could increase the quality of fluorescence images.

High sensitivity optical molecular imaging system

Optical Molecular Imaging (OMI) has the advantages of high sensitivity, low cost and ease of use. By labeling the regions of interest with fluorescent or bioluminescence probes, OMI can noninvasively obtain the distribution of the probes in vivo, which play the key role in cancer research, pharmacokinetics and other biological studies. In preclinical and clinical application, the image depth, resolution and sensitivity are the key factors for researchers to use OMI. In this paper, we report a high sensitivity optical molecular imaging system developed by our group, which can improve the imaging depth in phantom to nearly 5cm, high resolution at 2cm depth, and high image sensitivity. To validate the performance of the system, special designed phantom experiments and weak light detection experiment were implemented. The results shows that cooperated with high performance electron-multiplying charge coupled device (EMCCD) camera, precision design of light path system and high efficient image techniques, our OMI system can simultaneously collect the light-emitted signals generated by fluorescence molecular imaging, bioluminescence imaging, Cherenkov luminance and other optical imaging modality, and observe the internal distribution of light-emitting agents fast and accurately.

Novel regularized sparse model for fluorescence molecular tomography reconstruction

Fluorescence molecular tomography (FMT) is an imaging modality that exploits the specificity of fluorescent biomarkers to enable 3D visualization of molecular targets and pathways in small animals. FMT has been used in surgical navigation for tumor resection and has many potential applications at the physiological, metabolic, and molecular levels in tissues. The hybrid system combined FMT and X-ray computed tomography (XCT) was pursued for accurate detection. However, the result is usually over-smoothed and over-shrunk. In this paper, we propose a region reconstruction method for FMT in which the elastic net (E-net) regularization is used to combine L1-norm and L2-norm. The E-net penalty corresponds to adding the L1-norm penalty and a L2-norm penalty. Elastic net combines the advantages of L1-norm regularization and L2-norm regularization. It could achieve the balance between the sparsity and smooth by simultaneously employing the L1-norm and the L2-norm. To solve the problem effectively, the proximal gradient algorithms was used to accelerate the computation. To evaluate the performance of the proposed E-net method, numerical phantom experiments are conducted. The simulation study shows that the proposed method achieves accurate and is able to reconstruct image effectively.

A novel wireless wearable fluorescence image-guided surgery system

Segmentectomy using indocyanine green (ICG) has become a primary treatment option to achieve a complete resection and preserve lung function in early-stage lung cancer. However, owing to a lack of appropriate intraoperative imaging systems, it is a huge challenge for surgeons to identify the intersegmental plane during the operation, leading to poor prognosis. Thus, we developed a novel wireless wearable fluorescence image-guided surgery system (LIGHTEN) for fast and accurate identification of intersegmental planes in human patients. The system consists of a handle, light source, Google glass and laptop. Application software is written to capture clear real-time images and Google glass is adopted to display with augmented reality. Twelve in vivo studies of pulmonary segmentectomy in swine by intravenous injection of ICG were conducted to test the performance of the system. A distinct black-and-white transition zone image was observed and displayed simultaneously on the Google glass in all swine. The results demonstrated that surgeons using LIGHTEN can effortlessly and quickly discern intersegmental planes during the operation. Our system has enormous potential in helping surgeons to precisely identify intersegmental planes with mobility and high-sensitivity.Segmentectomy using indocyanine green (ICG) has become a primary treatment option to achieve a complete resection and preserve lung function in early-stage lung cancer. However, owing to a lack of appropriate intraoperative imaging systems, it is a huge challenge for surgeons to identify the intersegmental plane during the operation, leading to poor prognosis. Thus, we developed a novel wireless wearable fluorescence image-guided surgery system (LIGHTEN) for fast and accurate identification of intersegmental planes in human patients. The system consists of a handle, light source, Google glass and laptop. Application software is written to capture clear real-time images and Google glass is adopted to display with augmented reality. Twelve in vivo studies of pulmonary segmentectomy in swine by intravenous injection of ICG were conducted to test the performance of the system. A distinct black-and-white transition zone image was observed and displayed simultaneously on the Google glass in all swine. The results demonstrated that surgeons using LIGHTEN can effortlessly and quickly discern intersegmental planes during the operation. Our system has enormous potential in helping surgeons to precisely identify intersegmental planes with mobility and high-sensitivity.