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Purpose: Breast cancer ranks first among cancers affecting women's health. Our goal is to develop a fast, high-precision, and fully automated breast cancer detection algorithm to improve the early detection rate of breast cancer. Methods: We compare different object detection algorithms, including anchor-based and anchor-free object detection algorithms for detecting breast lesions. Finally, we find that the fully convolutional onestage object detection (FCOS) showed the best performance in the detection of breast lesions, which is an anchor-free algorithm. 1) Considering that the detection of breast lesions requires the context information of the ultrasound images, we introduce the non-local technique, which models long-range dependency between pixels to the FCOS algorithm, providing the global context information for the detection of the breast lesions. 2) The variety of shapes and sizes of breast lesions makes detection difficult. We propose a new deformable spatial attention (DSA) module and add it to the FCOS algorithm. Results: The detection performance of the original FCOS is that the average precision (AP) for benign lesions is 0.818, and for malignant lesions is 0.888. The FCOS with a non-local module improves the performance of the breast detection; the AP of benign lesions was 0.819, and that of malignant lesions was 0.894. Combining the DSA module with the FCOS improves the performance of breast detection; the AP for benign lesions and malignant lesions is 0.840 and 0.899, respectively. Conclusion: We propose two methods to improve the FCOS algorithm from different perspectives to improve its performance in detecting breast lesions. We find that FCOS combined with DSA is beneficial in improving the localization and classification of breast tumors and can provide auxiliary diagnostic advice for ultrasound physicians, which has a certain clinical application value.

Introduction: In some hospitals in remote areas, due to the lack of MRI scanners with high magnetic field intensity, only low-resolution MRI images can be obtained, hindering doctors from making correct diagnoses. In our study, higher-resolution images were obtained through low-resolution MRI images. Moreover, as our algorithm is a lightweight algorithm with a small number of parameters, it can be carried out in remote areas under the condition of the lack of computing resources. Moreover, our algorithm is of great clinical significance in providing references for doctors' diagnoses and treatment in remote areas. Methods: We compared different super-resolution algorithms to obtain high-resolution MRI images, including SRGAN, SPSR, and LESRCNN. A global skip connection was applied to the original network of LESRCNN to use global semantic information to get better performance. Results: Experiments reported that our network improved SSMI by 0.8% and also achieved an obvious increase in PSNR, PI, and LPIPS compared to LESRCNN in our dataset. Similar to LESRCNN, our network has a very short running time, a small number of parameters, low time complexity, and low space complexity while ensuring high performance compared to SRGAN and SPSR. Five MRI doctors were invited for a subjective evaluation of our algorithm. All agreed on significant improvements and that our algorithm could be used clinically in remote areas and has great value. Conclusion: The experimental results demonstrated the performance of our algorithm in super-resolution MRI image reconstruction. It allows us to obtain high-resolution images in the absence of high-field intensity MRI scanners, which have great clinical significance. The short running time, a small number of parameters, low time complexity, and low space complexity ensure that our network can be used in grassroots hospitals in remote areas that lack computing resources. We can reconstruct high-resolution MRI images in a short time, thus saving time for patients. Our algorithm can be biased towards practical applications; however, doctors have affirmed the clinical value of our algorithm.

Echocardiography plays an important role in the clinical diagnosis of cardiovascular diseases. Cardiac function assessment by echocardiography is a crucial process in daily cardiology. However, cardiac segmentation in echocardiography is a challenging task due to shadows and speckle noise. The traditional manual segmentation method is a time-consuming process and limited by inter-observer variability. In this paper, we present a fast and accurate echocardiographic automatic segmentation framework based on Convolutional neural networks (CNN). We propose FAUet, a segmentation method serially integrated U-Net with coordinate attention mechanism and domain feature loss from VGG19 pre-trained on the ImageNet dataset. The coordinate attention mechanism can capture long-range dependencies along one spatial direction and meanwhile preserve precise positional information along the other spatial direction. And the domain feature loss is more concerned with the topology of cardiac structures by exploiting their higher-level features. In this research, we use a two-dimensional echocardiogram (2DE) of 88 patients from two devices, Philips Epiq 7C and Mindray Resona 7T, to segment the left ventricle (LV), interventricular septal (IVS), and posterior left ventricular wall (PLVW). We also draw the gradient weighted class activation mapping (Grad-CAM) to improve the interpretability of the segmentation results. Compared with the traditional U-Net, the proposed segmentation method shows better performance. The mean Dice Score Coefficient (Dice) of LV, IVS, and PLVW of FAUet can achieve 0.932, 0.848, and 0.868, and the average Dice of the three objects can achieve 0.883. Statistical analysis showed that there is no significant difference between the segmentation results of the two devices. The proposed method can realize fast and accurate segmentation of 2DE with a low time cost. Combining coordinate attention module and feature loss with the original U-Net framework can significantly increase the performance of the algorithm.

Breast cancer ranks first among cancers affecting women’s health. Our work aims to realize the intelligence of the medical ultrasound equipment with limited computational capability, which is used for the assistant detection of breast lesions. We embed the high-computational deep learning algorithm into the medical ultrasound equipment with limited computational capability by two techniques: (1) lightweight neural network: considering the limited computational capability of ultrasound equipment, a lightweight neural network is designed, which greatly reduces the amount of calculation. And we use the technique of knowledge distillation to train the low-precision network helped with the high-precision network; (2) asynchronous calculations: consider four frames of ultrasound images as a group; the image of the first frame of each group is used as the input of the network, and the result is respectively fused with the images of the fourth to seventh frames. An amount of computation of 30 GFLO/frame is required for the proposed lightweight neural network, about 1/6 of that of the large high-precision network. After trained from scratch using the knowledge distillation technique, the detection performance of the lightweight neural network (sensitivity = 89.25%, specificity = 96.33%, the average precision [AP] = 0.85) is close to that of the high-precision network (sensitivity = 98.3%, specificity = 88.33%, AP = 0.91). By asynchronous calculation, we achieve real-time automatic detection of 24 fps (frames per second) on the ultrasound equipment. Our work proposes a method to realize the intelligence of the low-computation-power ultrasonic equipment, and successfully achieves the real-time assistant detection of breast lesions. The significance of the study is as follows: (1) The proposed method is of practical significance in assisting doctors to detect breast lesions; (2) our method provides some practical and theoretical support for the development and engineering of intelligent equipment based on artificial intelligence algorithms.

Background: Ultrasound is one of the preferred choices for early screening of dense breast cancer. Clinically, doctors have to manually write the screening report, which is time-consuming and laborious, and it is easy to miss and miswrite. Aim: We proposed a new pipeline to automatically generate AI breast ultrasound screening reports based on ultrasound images, aiming to assist doctors in improving the efficiency of clinical screening and reducing repetitive report writing. Methods: AI efficiently generated personalized breast ultrasound screening preliminary reports, especially for benign and normal cases, which account for the majority. Doctors then make simple adjustments or corrections based on the preliminary AI report to generate the final report quickly. The approach has been trained and tested using a database of 4809 breast tumor instances. Results: Experimental results indicate that this pipeline improves doctors' work efficiency by up to 90%, greatly reducing repetitive work. Conclusion: Personalized report generation is more widely recognized by doctors in clinical practice than non-intelligent reports based on fixed templates or options to fill in the blanks.

Accurate annotation of the medical image is the crucial step for image artificial intelligence (AI) clinical application. However, annotating medical image will incur a lot of annotation efforts and expense due to its high complexity and needing experienced doctors. In order to reduce the cost of annotation, several active learning methods have been proposed previously. However, the focus of these methods is to reduce the number of annotation candidates with little regard for doctor's workload, which is not enough for medical image annotation, since even annotating a small amount of data will take a lot of time for doctors. In order to effectively reduce the workload of doctors, we developed a new framework for medical image annotation. First, by combining active learning and U-shape network, we employed a suggestive annotation strategy to select the most effective annotation candidates. We then exploited a fine annotation platform to alleviate annotating efforts on each candidate and utilized a new criterion to quantitatively calculate the efforts of doctors. In this paper, we used MR brain tissue segmentation as an example to evaluate the proposed method. The extensive experiments with the IBSR18 and MRBrainS18 Challenge datasets showed that with the proposed strategy, only 60% of the annotation candidates were needed to achieve the most advanced segmentation performance, and the annotation workload of doctors can be alleviated by at least 44%, 44%, and 47% on cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM), respectively. Meanwhile, our proposed method was proven to be effective for annotation of clinical data collected from our hospital, where the annotation workload of doctors was alleviated by at least 32% on WM and the time of annotating each clinical candidate is expected to be saved by at least 64%.

Accurate segmentation of brain tissue in magnetic resonance images (MRI) is a difficult task due to different types of brain abnormalities. In this paper, we review the deformation method focus on the construction of diffeomorphisms to generate the 2D MRI image grids and extend it to the 3D MRI images, address clearly a new formation of the deformation problem for moving domains and construction of diffeomorphisms through a completely different approach. The idea is to control directly the first order differential operators including the Jacobian determinant (JD), divergence (DIV), and curl vector (CV) generated by the 2D and 3D MRI image grids and use them as CNN channels with other modalities (T1-weighted, T1-IR, and T2-FLAIR) or single T1-weighted modality to improve the performance of brain segmentation. More importantly, we discuss the influence of three optimization parameters to the generation of the 2D and 3D MRI image grids by both theoretical analysis and the numerical experiments. We test this method on the IBSR dataset and MRBrainS18 dataset based on VoxResNet and verify the influence of three parameters on the accuracy of MRI brain segmentation. Finally, we also compare the segmentation performance of our method in three networks, including 2D U-Net, 3D U-Net, and VoxResNet. We believe the proposed method can advance the performance in brain segmentation and clinical diagnosis.

Accurate registration of medical images is vital for doctor’s diagnosis and quantitative analysis. In this paper, we propose a new unsupervised learning network SA-VoxelMorph for 3D deformable medical image registration. In this paper, we design a novel network architecture SAU-Net by introducing a new Binary Spatial Attention Module (BSAM) into skip connection of 3D U-Net, which can make full use of the spatial information extracted from the encoding and corresponding decoding stage. Moreover, from variational method, control function can better control the generation of registration field $\phi$. Therefore, we also propose a new registration loss function with novel smoothing term based on optimal control method to generate better $\phi$. We verify our method on two datasets including ADNI and PPMI, and obtain excellent results on magnetic resonance image (MRI) registration with higher average Dice scores and better diffeomorphic registration fields compared with other state-of-the-art methods. The experimental results show the method can achieve better performance in brain MRI registration.

Deformable medical image registration is vital for doctor's diagnosis and quantitative analysis. In this paper, we propose a novel unsupervised learning model (denoted as BSADM) for 3D diffeomorphic medical image registration. Inspired by spatial attention module, we propose a new network architecture BSAU-Net by introducing a novel Binary Spatial Attention Module (BSAM) into skip connection, which can take full advantages of the spatial information extracted from the encoding path and corresponding decoding path. In addition, from variational method in differential geometry, monitor function f is used to control the Jacobian determinant (JD) of registration field ɸ. So, we also propose a novel orientation-consistent regularization loss to penalize the local regions with negative Jacobian determinant, which further encourages the diffeomorphic property of the transformations. We verify our method on two datasets including ADNI and PPMI dataset, and obtain excellent improvement on magnetic resonance (MR) image registration with higher average Dice scores and better diffeomorphic registration.

Recently deep learning-based networks have achieved advanced performance in medical image segmentation. However, the development of deep learning is slow in magnetic resonance image (MRI) segmentation of normal brain tissues. In this paper, inspired by channel attention module, we propose a new architecture, Binary Channel Attention U-Net (BCAU- Net), by introducing a novel Binary Channel Attention Module (BCAM) into skip connection of U-Net, which can take full advantages of the channel information extracted from the encoding path and corresponding decoding path. To better aggregate multiscale spatial information of the feature map, spatial pyramid pooling (SPP) modules with different pooling operations are used in BCAM instead of original average-pooling and max-pooling operations. We verify this model on two datasets including IBSR and MRBrainS18, and obtain better performance on MRI brain segmentation compared with other methods. We believe the proposed method can advance the performance in brain segmentation and clinical diagnosis.

With the development of deep learning, the structures of convolutional neural networks (CNNs) are becoming more complex and the performance of expression recognition is getting better. However, the classification mechanism of CNN is still a black box. The main problem is that CNNs have a great number of parameters, which makes it difficult to analyze them clearly. In this paper, we explain the essence of deep learning from the perspective of manifold geometry. The main purpose of deep learning especially CNN is to learn the probability distributions on manifolds. And we design a neural network based on the facial expression recognition to explore the classification mechanism of CNN. By using the deconvolution visualization method, we qualitatively verify that the trained CNN forms a detector for specific facial action unit (FAU) and each neuron of CNN is a specific manifold feature extractor for facial images. Moreover, we design a distance function to measure the differences of activation value distributions on the same feature map of FAU. The greater the distance, the more sensitive the feature map is to the FAU. The results show that the mapping relationship between FAUs and feature maps of CNN is determined, the trained CNN has generated an internal detector for each FAU to extract the facial manifold feature.

Optimization of loss function is one of the research directions in medical image registration. A loss function of registration is the sum of two terms: a similarity term Lsim (Φ) and a smoothing term Lsmooth(Φ). From variational method in differential geometry, control function is essential to generate better registration field Φ. Here, we propose a new registration loss function with novel smoothing terms using VoxelMorph based on control function and Laplacian operator. We divide the process into two steps. The first step is based on Laplacian operator. We replace the gradient of registration field Φ in Lsmooth (Φ) by the Laplacian of Φ. In the second step, we add the term control function F to the Lsmooth (Φ) in the first step, which is the key contribution of our method. We verify our method on two datasets including ADNI and IBSR, and obtain excellent improvement on MR image registration, with better convergence and gets higher average Dice and lower percentage of non-positive Jacobian locations compared with original loss function.

Objective: Accurate and fast automatic segmentation of organs is a key step for efficient planning for radiation therapy. In this paper, we propose the Multi-mode CNNs, which is for accurate and fast automatic segmentation of mandible, parotid glands, brainstem, optic nerves, cerebellum, eyes, lens, pituitary, thyroid, temporal lobes, brain and head respectively in 3D CT image of head and neck.Methods: The proposed Multi-mode CNNs consist of three convolutional neural networks. The first CNN is a simple classification network to distinguish slices of head and neck. It accelerates the next step by reducing the search space. The second network is a 3DCNN for locating the region of interest (ROI) of organs. It can enhance the robustness of the algorithm to CT images from different hospitals. The third network is a full convolution network (FCN) based on U-Net. This is a pixel-wise detailed segmented network to classify voxels in a region of interest and generate segmentation results. We used public and clinical datasets to evaluate our algorithm. The public datasets are from the MICCAI 2015 Head and Neck Auto Segmentation Grand Challenge. For the private datasets, we collected 3D CT images of 88 patients from A and B Hospital.Results: For the public datasets, our segmentation results of the nine organs in the challenge surpassed the first rank. For the private datasets, clinical trials have proved that it is effective for real image data in different hospitals. The average total segmentation time for one patient with above whole organs is less than one minute, which indicates proposed method having clinical value.Conclusion: The proposed Multi-mode CNNs can be used to accurate and fast automatic segmentation of organs in radiation therapy. It can assist doctors in outlining the organs during the planning of radiation therapy in the clinic.

Breast cancer has become the biggest threat to female health. Ultrasonic diagnosis of breast cancer based on artificial intelligence is basically a classification of benign and malignant tumors, which does not meet clinical demand. Besides, the current target detection method performs poorly in detecting small lesions, while it is clinically required to detect nodules below 2 mm. The objective of this study is to (a) propose a diagnostic method based on Breast Imaging Reporting and Data System (BI-RADS) and (b) increase its detectability of small lesions. We modified the framework of Faster R-CNN (Faster Region-based Convolutional Neural Network) by introducing multi-scale feature extraction and multi-resolution candidate bound extraction into the network. Then, it was trained using 852 images of BI-RADS C2, 739 images of C3, and 1662 images of malignancy (BI-RADS 4a/4b/4c/5/6). We compared our model with unmodified Faster R-CNN and YOLO v3 (You Only Look Once v3). The mean average precision (mAP) is significantly increased to 0.913, while its average detection speed is slightly declined to 4.11 FPS (frames per second). Meanwhile, its detectivity of small lesions is effectively improved. Moreover, we also tentatively applied our model on video sequences and got satisfactory results. We modified Faster R-CNN and trained it partly based on BI-RADS. Its detectability of lesions, as well as small nodules, was significantly improved. In view of wide coverage of dataset and satisfactory test results, our method can basically meet clinical needs.

With the increase of the aging population, the old-age care industry faces new challenges. Mutual support for the elderly as a new model of old-age care has attracted much attention. Although Time Banks have provided a mutual assistance pension solution, still, there are some problems. This paper combines the blockchain with the time bank to build a blockchain time bank, and solves the problems faced by the current time banks. At the same time, the article innovates the mutual help pension model and broadens the user base and participating institutions, which makes the pension system more complete and rich.

Objective The precise segmentation of organs at risk (OARs) is of importance for improving therapeutic outcomes and reducing injuries of patients undergoing radiotherapy. In this study, we developed a new approach for accurate computed tomography (CT) image segmentation of the eyes and surrounding organs, which is first locating then segmentation (FLTS). Methods The FLTS approach was composed of two steps: (a) classification of CT images using convolutional neural networks (CNN), and (b) segmentation of the eyes and surrounding organs using modified U-shape networks. In order to obtain optimal performance, we enhanced our training datasets by random jitter and rotation. Results This model was trained and verified using the clinical datasets that were delineated by experienced physicians. The dice similarity coefficient (DSC) was employed to evaluate the performance of our segmentation method. The average DSCs for the segmentation of the pituitary, left eye, right eye, left eye lens, right eye lens, left optic nerve, and right optic nerve were 90%, 94%, 93.5%, 84.5%, 84.3%, 80.3%, and 82.2%, respectively. Conclusion We developed a new network-based approach for rapid and accurate CT image segmentation of the eyes and surrounding organs. This method is accurate and efficient, and is suitable for clinical use.

High radiation dose in computed tomography (CT) scans increases the lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with total variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, the low-contrast structures tend to be smoothed out by the TV regularization, posing a great challenge for the TV method. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV (EPTV) regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing energy consisting of an EPTV norm and a data fidelity term posed by the x-ray projections. The EPTV term is proposed to preferentially perform smoothing only on the non-edge part of the image in order to better preserve the edges, which is realized by introducing a penalty weight to the original TV norm. During the reconstruction process, the pixels at the edges would be gradually identified and given low penalty weight. Our iterative algorithm is implemented on graphics processing unit to improve its speed. We test our reconstruction algorithm on a digital NURBS-based cardiac-troso phantom, a physical chest phantom and a Catphan phantom. Reconstruction results from a conventional filtered backprojection (FBP) algorithm and a TV regularization method without edge-preserving penalty are also presented for comparison purposes. The experimental results illustrate that both the TV-based algorithm and our EPTV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under a low-dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of low-contrast structures and therefore maintain acceptable spatial resolution.

Respiratory motion results in significant motion blur in thoracic positron emission tomography (PET) imaging. Existing approaches to correct the blurring artifact involve acquiring the images in gated mode and using complicated reconstruction algorithms. In this paper, we propose a post-reconstruction framework to estimate respiratory motion and reduce the motion blur of PET images acquired in ungated mode. Our method includes two steps: one is to use minmax directional derivative analysis and local auto-correlation analysis to identify the two parameters blur direction and blur extent, respectively, and another is to employ WRL, à trous wavelet-denoising modified Richardson-Lucy (RL) deconvolution, to reduce the motion blur based on identified parameters. The mobile phantom data were first used to test the method before it was applied to 32 cases of clinical lung tumor PET data. Results showed that the blur extent of phantom images in different directions was accurately identified, and WRL can remove the majority of motion blur within ten iterations. The blur extent of clinical images was estimated to be 12.1 ± 3.7 mm in the direction of 74 ± 3° relative to the image horizontal axis. The quality of clinical images was significantly improved, both from visual inspection and quantitative evaluation after deconvolution. It was demonstrated that WRL outperforms RL and a Wiener filter in reducing the motion blur with one to two more iterations. The proposed method is easy to implement and thus could be a useful tool to reduce the effect of respiration in ungated thoracic PET imaging.

Content-based image retrieval for medical images is a primary technique for computer-aided diagnosis. While it is a premise for computer-aided diagnosis system to build an efficient medical image database which is paid less attention than that it deserves. In this paper, we provide an efficient approach to develop the archives of large brain CT medical data. Medical images are securely acquired along with relevant diagnosis reports and then cleansed, validated and enhanced. Then some sophisticated image processing algorithms including image normalization and registration are applied to make sure that only corresponding anatomy regions could be compared in image matching. A vector of features is extracted by non-negative tensor factorization and associated with each image, which is essential for the content-based image retrieval. Our experiments prove the efficiency and promising prospect of this database building method for computer-aided diagnosis system. The brain CT image database we built could provide radiologists with a convenient access to retrieve pre-diagnosed, validated and highly relevant examples based on image content and obtain computer-aided diagnosis.

Studies of non-invasive glucose measurement with optical coherence tomography (OCT) in tissue-simulating phantoms and biological tissues show that glucose has an effect on the OCT signal slope. Choosing an efficient fitting range to calculate the OCT signal slope is important because it helps to improve the precision of glucose measurement. In this paper, we study the problem in two ways: (1) scattering-induced change of OCT signal slope versus depth in intralipid suspensions with different concentrations based on Monte Carlo (MC) simulations and experiments and (2) efficient fitting range for glucose measurement in 3% and 10% intralipid. The results show that the OCT signal slope expresses a contrary change with scattering coefficient below a certain depth in high intralipid concentrations, so that there is an effective fitting depth. With an efficient fitting range from 100 μm to the effective fitting depth, the precision of glucose measurement can be 4.4 mM for 10% intralipid and 2.2 mM for 3% intralipid.

Classification of gene expression samples is a core task in microarray data analysis. How to reduce thousands of genes and to select a suitable classifier are two key issues for gene expression data classification. This paper introduces a framework on combining both feature extraction and classifier simultaneously. Considering the non-negativity, high dimensionality and small sample size, we apply a discriminative mixture model which is designed for non-negative gene express data classification via non-negative matrix factorization (NMF) for dimension reduction. In order to enhance the sparseness of training data for fast learning of the mixture model, a generalized NMF is also adopted. Experimental results on several real gene expression datasets show that the classification accuracy, stability and decision quality can be significantly improved by using the generalized method, and the proposed method can give better performance than some previous reported results on the same datasets.

Sparse angular Computer Tomography (CT) is a rapidly developing imaging modality that reconstructs high-quality images from sparse data toward low-dose x-rays. The effectiveness of conventional total variation (TV) algorithm is limited by the over-smoothness on the edges and slow convergence. To mitigate this drawback, we proposed an improved fast iterative reconstruction method based on the minimization of selective image TV. The presented selective TV model is derived by linking the regularity metric to the local gradient of images, and selectively applies different degrees of regularization (the value of p) to background and potential signal locations for the purpose of preserving the edge details. In order to further speed up the convergence, we draws on a fast variant of The Iterative-Shrinkage-Thresholding Algorithm (ISTA), which uses a special linear combination of the two previous iterate results as the initial value of next iteration for more accurate correction. Experiments on simulated Shepp-Logan phantom are performed. The results demonstrated that the new method not only protected the edge of the image characteristics, but also significantly improved the convergence speed of the iterative reconstruction.

The purpose of this study is to evaluate the dosimetric gain of the adaptive radiotherapy (ART) over conventional intensity modulated radiation therapy (IMRT) by using as the deformable image registration technique for dose mapping and summation to assess the dosimetric difference on target and organs at risk (OARs) dose. We retrospectively collected planning CT images and fractional CT images from nine lung cancer patients who were treated with adaptive IMRT. During fractional treatments, new tumor and organ contours are generated using the deformable image registration and a new IMRT plan is generated for the follow up treatments for each patient. The resulting deformation moving vector fields are also used to map and accumulate dose from the first 20 fractions to the sequential 10 fractions for final total dose calculation. The organs at risk (OARs) and the tumor of dosimetric parameters are compared between the ART and conventional IMRT plan. The evaluation results showed that when using the ART technique, the mean GTV volume is reduced by 53.2%, the mean tumor dose is increased by 0.41Gy, the mean lung V20 and V30 are decreased by 2.17% and 3.32%, the mean heart V30 and V40 are decreased by 1.14% and 2.98%, respectively, and the maximum dose of spinal cord is decreased by 1.21Gy when compared with the conventional IMRT. This work demonstrated the feasibility of ART to achieve better target coverage and OARs sparing over the conventional IMRT, and it will potentially reduce the radiation side effect and increase the local control rate.

To realize dose deformation and accumulation using image registration technology, and evaluate the anatomic changes and corresponding dosimetric variations for patients with nasopharyngeal carcinoma (NPC). 15 patients with NPC treated with IMRT were selected in this study, the original treatment plan(Plan1(CT1)) based on the first CT scan(CT1) and the second plan(Plan2(CT2)) based on the repeated CT scan(CT2) were calculated using the planning system Raystation Version 4.5 (RaySearch Laboratories AB, Stockholm, Sweden). Plan1(CT2) was acquired by deforming the dose of Plan1(CT1) to CT2 using the hybrid deformation image registration method, which express to perform the original plan without re-planning. Assessing the dose distribution and deciding whether to modify the plan, if need it, Plan2(CT2) was planned based on CT2 images, Plan1+2(CT2) that expressing the actual exposure dose after modification was generated by accumulating the dose of Plan1(CT2) and Plan2(CT2). The dose distribution was compared among Plan1(CT1), Plan2(CT2) and Plan1+2(CT2). Compared with CT1, the volume of GTVnx, GTVnd and CTV2 in CT2 decreased by 36.3%, 36.3% and 49.8% respectively, and the left and right parotid gland decreased in volume by 34.6% and 34.6%. Compared with Plan1(CT1), due to the changes of organ size and location, the dose to target organ were significantly decreased in Plan1(CT2) (the dose received by 95% of the target (D95) to GTVnx, GTVnd and CTV2 were decreased by 2.9%, 6.2% and 3.3%, respectively), and the dose to normal tissue were increased (the dose to right parotid gland, brain stem and spinal cord were increased by 5.5%, 1.4% and 8.9%, respectively). With repeated CT and re-planning after 20 times as shown in Plan1+2(CT2), the dose delivered to target were significantly increased (Compared with Plan1(CT2), the dose in Plan1+2(CT2) received by 95% of the target (D95) to GTVnx, GTVnd and CTV2 were increased by 2.9%, 3.3% and 3.7%, respectively), and at the same time the dose to normal tissue were decreased as much as possible (the mean dose to the left and right parotid gland were decreased by 3.0% and 7.4%, and the max dose to the brain stem and spinal cord were decreased by 14.1% and 7.7%). During the course of adaptive radiotherapy, the volume of parotid gland and target significantly decreased. Re-planning after 20 times could ensure adequate dose to target and safe dose to the normal tissues.

Heavy-ions have the similar characteristic of depth-dose distribution with protons, but exhibit enhanced physical and radiobiological benefits. With increasing development in technical and clinical research, more facilities are being installed in the world. At the same time, many critical techniques of heavy-ion therapy facility were optimized and completed. This paper classified and reviewed the basic structure of heavy-ion system equipments, especially the accelerator, gantry, nozzle , TPS.

The accurate and effective algorithm for segmenting image is very useful in many fields, especially in medical images. In this paper we introduced a novel method that focuses on segmenting the brain MR Image that is important for neural diseases. Because of many noises embedded in the acquiring procedure, such as eddy currents, susceptibility artifacts, rigid body motion and intensity inhomogeneity, segmenting the brain MR image is a difficult work. In this algorithm, we overcame the inhomogeneity shortage, by modifying the objective function by compensating its immediate neighborhood effect using Gaussian smooth method for decreasing the influence of the inhomogeneity and increasing the segmenting accuracy. Using simulate image and clinical MRI data, experiments show that our proposed algorithm is effective.

Lumen segmentation of the carotid artery is an important preprocessing step with clinical application, because it facilitates subsequent analysis, including stenosis grading, the detection and quantification of plaque components in the vessel wall. But it is a challenging task owing to the low and varying contrast between the surrounding tissues. Plus, intensity inhomogeneity often occurs in medical images from different modalities, which causes undesirable side effects when the traditional Chan-Vese (CV) model is applied. In order to overcome the intensity inhomogeneity problem we improve the CV model by adding a local fitting term, which incorporates both local information and global information. We also introduce a penalizing energy to avoid the time-consuming re-initialization procedure in traditional level set method.We propose and validate a semi-automatic method for lumen segmentation of carotid artery in computed tomography angiography(CTA). First we manually select a seedpoint on the lumen area, then the segmentation is automatically obtained using a level set. By combining both global and local statistical information, we manage to overcome the inhomogeneous intensity distribution in the CTA images and provide more satisfying segmentation result than CV model. Experiments on carotid artery CTA data have demonstrated the efficiency and accuracy of our model, in addition our model is also less sensitive to the location of the initial contour. Therefore our method has the potential to replace the manual procedure of lumen segmentation in CTA, which is of great value for doctors with clinical applications.

This paper proposes a framework for detecting the suspected abnormal region of the bladder wall via magnetic resonance (MR) cystography. Volume based features are used. First, the bladder wall is divided into several layers, based on which a path from each voxel on the inner border to the outer border is found. By using the path length to measure the wall thickness and a bent rate term to measure the geometry property of the voxels on the inner border, the seed voxels representing the abnormalities on the inner border are determined. Then, by tracing the path from each seed, a weighted bent rate term is constructed to determine the suspected voxels, which are on the path and inside the bladder wall. All the suspected voxels are grouped together for the abnormal region. This work is significantly different from most of the previous computer aided bladder tumor detection reports on two aspects. First of all, the T1-weighted MR images are used which give better image contrast and texture information for the bladder wall, comparing with the computed tomography images. Secondly, while most previous reports detected the abnormalities and indicated them on the reconstructed three-dimensional bladder model by surface rendering, we further determine the possible region of the abnormality inside the bladder wall. This work aims at a non-invasive procedure for bladder tumor detection and abnormal region delineation, which has the potential for further clinical analysis such as the invasion depth of the tumor and virtual cystoscopy diagnosis. Five datasets including two patients and three volunteers were used to test the presented method, all the tumors were detected by the method, and the overlap rates of the regions delineated by the computer against the experts were measured. The results demonstrated the potential of the method for detecting bladder wall abnormal regions via MR cystography.