An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Sin...An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.展开更多
This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The mos...This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These segmented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radiologist.展开更多
Ultra-wideband （UWB） microwave imaging is a promising method for breast cancer detection based on the large contrast of electric parameters between the malignant tumor and its surrounded normal breast organisms. In ...Ultra-wideband （UWB） microwave imaging is a promising method for breast cancer detection based on the large contrast of electric parameters between the malignant tumor and its surrounded normal breast organisms. In the case of multiple tumors being present, the conventional imaging approaches may be ineffective to detect all the tumors clearly. In this paper, a progressive processing method is proposed for detecting more than one tumor. The method is divided into three stages： primary detection, refocusing and image optimization. To test the feasibility of the approach, a numerical breast model is developed based on the realistic magnetic resonance image （MRI）. Two tumors are assumed embedded in different positions. Successful detection of a 3.6 mm-diameter tumor at a depth of 42 mm is achieved. The correct information of both tumors is shown in the reconstructed image, suggesting that the progressive processing method is promising for multi-tumor detection.展开更多
Objective: Cell structural changes are one of the main features observed during the development of amyotrophic lateral sclerosis(ALS). In this work, we propose the use of diffusion tensor imaging(DTI) metrics to asses...Objective: Cell structural changes are one of the main features observed during the development of amyotrophic lateral sclerosis(ALS). In this work, we propose the use of diffusion tensor imaging(DTI) metrics to assess specific ultrastructural changes in the central nervous system during the early neurodegenerative stages of ALS.Methods: Ultra-high field MRI and DTI data at 17.6 T were obtained from fixed, excised mouse brains, and spinal cords from ALS(G93 A-SOD1) mice.Results: Changes in fractional anisotropy(FA) and linear, planar, and spherical anisotropy ratios(C_L, C_P, and C_S, respectively) of the diffusion eigenvalues were measured in white matter(WM) and gray matter(GM) areas associated with early axonal degenerative processes(in both the brain and the spinal cord). Specifically, in WM structures(corpus callosum, corticospinal tract, and spinal cord funiculi) as the disease progressed, FA, C_L, and C_P values decreased, whereas C_S values increased. In GM structures(prefrontal cortex, hippocampus, and central spinal cord) FA and C_P decreased, whereas the C_L and C_S values were unchanged or slightly smaller. Histological studies of a fluorescent mice model(YFP, G93 A-SOD1 mouse) corroborated the early alterations in neuronal morphology and axonal connectivity measured by DTI.Conclusions: Changes in diffusion tensor shape were observed in this animal model at the early, nonsymptomatic stages of ALS. Further studies of C_L, C_P, and C_S as imaging biomarkers should be undertaken to refine this neuroimaging tool for future clinical use in the detection of the early stages of ALS.展开更多
Objective: To establish an orthotopic bladder cancer model bearing human bladder cancer for experimental research, and monitor tumor progression by magnetic resonance imaging (MRI). Methods: The mucosa was mechanicall...Objective: To establish an orthotopic bladder cancer model bearing human bladder cancer for experimental research, and monitor tumor progression by magnetic resonance imaging (MRI). Methods: The mucosa was mechanically damaged transurethrally under direct vision, and then human bladder cancer cell line T24 was inoculated into the bladders of BALB/c nude mice to establish orthotopic bladder cancer model. To find a suitable concentration of Gd-DTPA for this re- search. MRI was performed weekly to assess tumor growth, using Gd-DTPA as contrast agent. The pathologic morphology of the bladders and other specimens were observed with HE stain. Results: All the 25 mice developed bladder cancer after inoculation. The best concentration of Gd-DTPA was 1.408 mg/mL. On MRI, no change in the bladders was observed on day 7 after inoculation, filling defect in the bladders, accordant to actual tumor size, was detected on days 14, 21 and 28. Pathologic examination showed that tumor grew in the mucosa or superficial muscle of bladder on day 7, confined in muscle layer on days 14–28, and invaded serosa on day 35. Conclusion: Transurethrally damaged bladder mucosa under direct vision and instilled bladder cancer cell T24, we successfully established an orthotopic bladder cancer model. Tumor growth simulated the progression of human bladder cancer approximately. MRI was a reliable way for dynamic detection of murine orthotopic bladder tumor.展开更多
The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interes...The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration.Nevertheless,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices.Thus,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and regression.Themodel was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable hyper-parameters.The experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,respectively.This model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification procedures.Therefore,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.展开更多
基金supported by the National Natural Science Foundation of China(6153102061471383)
文摘An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.
文摘This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These segmented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radiologist.
基金supported by the National Natural Science Foundation of China(Grant No.61271323)the Open Project from State Key Laboratory of MillimeterWaves,China(Grant No.K200913)
文摘Ultra-wideband （UWB） microwave imaging is a promising method for breast cancer detection based on the large contrast of electric parameters between the malignant tumor and its surrounded normal breast organisms. In the case of multiple tumors being present, the conventional imaging approaches may be ineffective to detect all the tumors clearly. In this paper, a progressive processing method is proposed for detecting more than one tumor. The method is divided into three stages： primary detection, refocusing and image optimization. To test the feasibility of the approach, a numerical breast model is developed based on the realistic magnetic resonance image （MRI）. Two tumors are assumed embedded in different positions. Successful detection of a 3.6 mm-diameter tumor at a depth of 42 mm is achieved. The correct information of both tumors is shown in the reconstructed image, suggesting that the progressive processing method is promising for multi-tumor detection.
基金Magnetic Laboratory Visiting Scientist Program,Grant/Award Number 327Chicago Biomedical Consortium,Grant/Award Number 085740National Science Foundation,Grant/Award Number DMR-1157490。
文摘Objective: Cell structural changes are one of the main features observed during the development of amyotrophic lateral sclerosis(ALS). In this work, we propose the use of diffusion tensor imaging(DTI) metrics to assess specific ultrastructural changes in the central nervous system during the early neurodegenerative stages of ALS.Methods: Ultra-high field MRI and DTI data at 17.6 T were obtained from fixed, excised mouse brains, and spinal cords from ALS(G93 A-SOD1) mice.Results: Changes in fractional anisotropy(FA) and linear, planar, and spherical anisotropy ratios(C_L, C_P, and C_S, respectively) of the diffusion eigenvalues were measured in white matter(WM) and gray matter(GM) areas associated with early axonal degenerative processes(in both the brain and the spinal cord). Specifically, in WM structures(corpus callosum, corticospinal tract, and spinal cord funiculi) as the disease progressed, FA, C_L, and C_P values decreased, whereas C_S values increased. In GM structures(prefrontal cortex, hippocampus, and central spinal cord) FA and C_P decreased, whereas the C_L and C_S values were unchanged or slightly smaller. Histological studies of a fluorescent mice model(YFP, G93 A-SOD1 mouse) corroborated the early alterations in neuronal morphology and axonal connectivity measured by DTI.Conclusions: Changes in diffusion tensor shape were observed in this animal model at the early, nonsymptomatic stages of ALS. Further studies of C_L, C_P, and C_S as imaging biomarkers should be undertaken to refine this neuroimaging tool for future clinical use in the detection of the early stages of ALS.
基金Supported by the grants from the Talented Scholar Foundation of Jiangsu Province (No. RC2003094)the Sci-Tech research Foundation of Jiangsu Province (No. H200517)"The Six Talent Peak" Project Foundation of Jiangsu Province (06-B-021)
文摘Objective: To establish an orthotopic bladder cancer model bearing human bladder cancer for experimental research, and monitor tumor progression by magnetic resonance imaging (MRI). Methods: The mucosa was mechanically damaged transurethrally under direct vision, and then human bladder cancer cell line T24 was inoculated into the bladders of BALB/c nude mice to establish orthotopic bladder cancer model. To find a suitable concentration of Gd-DTPA for this re- search. MRI was performed weekly to assess tumor growth, using Gd-DTPA as contrast agent. The pathologic morphology of the bladders and other specimens were observed with HE stain. Results: All the 25 mice developed bladder cancer after inoculation. The best concentration of Gd-DTPA was 1.408 mg/mL. On MRI, no change in the bladders was observed on day 7 after inoculation, filling defect in the bladders, accordant to actual tumor size, was detected on days 14, 21 and 28. Pathologic examination showed that tumor grew in the mucosa or superficial muscle of bladder on day 7, confined in muscle layer on days 14–28, and invaded serosa on day 35. Conclusion: Transurethrally damaged bladder mucosa under direct vision and instilled bladder cancer cell T24, we successfully established an orthotopic bladder cancer model. Tumor growth simulated the progression of human bladder cancer approximately. MRI was a reliable way for dynamic detection of murine orthotopic bladder tumor.
基金supported by the Ministry of Higher Education(MOHE)through the Fundamental Research Grant Scheme(FRGS)(FRGS/1/2020/TK0/UTHM/02/16)the Universiti Tun Hussein Onn Malaysia(UTHM)through an FRGS Research Grant(Vot K304).
文摘The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration.Nevertheless,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices.Thus,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and regression.Themodel was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable hyper-parameters.The experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,respectively.This model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification procedures.Therefore,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.
