侧颅底冠状位组织学与CT断层的对照

目的 制作颞骨火棉胶冠状位断层连续薄层切片,获取数字化图片库。方法 用2例尸头标本进行螺旋CT扫描后制作断层标本火棉胶包埋后,用大型轮式切片机沿冠状位切片,厚度100μm,每片均摄影并保存于计算机。选取代表性层面的胶片进行HE染色。将断层照片与HE染色照
片和CT片进行对照观察,对代表性层面结构特点进行描述。结果 共获取侧颅底数字化连续冠状位薄层断面标本数据集 2套。断层图像分辨率1920×2560像素,结构毗邻关系显示清晰。与HE照片和CT片进行对照观察,可以更清楚地定位断层结构。结论 火棉胶包埋技术是制作颞骨大切
片,获取高分辨率的数字化图片数据集的理想方法。冠状位对听小骨、Prussak‘s间隙、颈静脉窝、耳蜗与中颅窝的关系,颈内动脉升段及膝部、面神经垂直段、内听道与中颅窝的关系等显示较佳。     

Patient position verification using CT images.

The use of ions in the radiotherapy of cancer patients requires an accurate patient positioning in order to exploit its potential benefits. Using CT images as the basis for the setup verification offers the advantage of a high in-plane resolution in combination with a geometrically accurate, volumetric information. Before each fraction a single CT slice is acquired at the isocenter level after the positioning procedure. This single slice is registered to the planning CT cube using automated image registration algorithms. Thus any erreonous translation or rotation can be detected and quantified. The registration process involves the interpolation of the volumetric data, the calculation of an energy function, and the minimization of this energy function. Several data interpolation functions as well as minimization algorithms were compared. CT studies with a head phantom were performed in which defined translations and rotations were simulated by moving a motor-driven treatment chair. Different slice thicknesses and anatomical sites were studied to investigate their potential influence on the registration accuracy. The accuracy of the registration was found to be a fraction of a voxel size for suitable combinations of algorithms (typically better than 0.16 mm/deg). A significant dependancy of the registration accuracy on the CT slice thickness and the anatomical site was found (the accuracy ranges from 0.05 mm/deg to 0.16 mm/deg depending on the site). The calculation time is dependant on the used algorithms and the magnitude of the setup error. For the standard combination of algorithms as proposed by the authors (Downhill Simplex minimization with Trilinear interpolation) the typical calculation time is about 20 s for a Sun UltraSPARC processor. Taking into account the mechanical accuracy of the setup device (motor-driven chair) the registration of CT images is thus a useful tool for detecting and quantifying any significant error in the patient position.     

Liver segmentation for CT images using GVF snake

Accurate liver segmentation on computed tomography (CT) images is a challenging task especially at sites where surrounding tissues (e.g., stomach, kidney) have densities similar to that of the liver and lesions reside at the liver edges. We have developed a method for semiautomatic delineation of the liver contours on contrast-enhanced CT images. The method utilizes a snake algorithm with a gradient vector flow (GVF) field as its external force. To improve the performance of the GVF snake in the segmentation of the liver contour, an edge map was obtained with a Canny edge detector, followed by modifications using a liver template and a concavity removal algorithm. With the modified edge map, for which unwanted edges inside the liver were eliminated, the GVF field was computed and an initial liver contour was formed. The snake algorithm was then applied to obtain the actual liver contour. This algorithm was extended to segment the liver volume in a slice-by-slice fashion, where the result of the preceding slice constrained the segmentation of the adjacent slice. 551 two-dimensional liver images from 20 volumetric images with colorectal metastases spreading throughout the livers were delineated using this method, and also manually by a radiologist for evaluation. The difference ratio, which is defined as the percentage ratio of mismatching volume between the computer and the radiologist's results, ranged from 2.9% to 7.6% with a median value of 5.3%.     

Reconstruction of patient-specific femurs using X-ray and sparse CT images

Three-dimensional patient-specific bone models have been used in computer-aided planning and biomechanical analysis of orthopaedic surgeries. Reconstruction methods using X-ray images have been developed recently. However, these reconstruction methods have limited ability to generate femur models with severe rotational deformities. In this study, a new X-ray-based reconstruction method was proposed using the free form deformation method with two X-ray images and three CT images. The obtained femur model is closer to a CT-based 3D femur model in comparison with the reconstruction method using only X-ray images. This method will have benefits for many clinical and biomechanical applications.     

Computer-aided detection of early interstitial lung diseases using low-dose CT images

This study aims to develop a new computer-aided detection (CAD) scheme to detect early interstitial lung disease (ILD) using low-dose computed tomography (CT) examinations. The CAD scheme classifies each pixel depicted on the segmented lung areas into positive or negative groups for ILD using a mesh-grid-based region growth method and a multi-feature-based artificial neural network (ANN). A genetic algorithm was applied to select optimal image features and the ANN structure. In testing each CT examination, only pixels selected by the mesh-grid region growth method were analyzed and classified by the ANN to improve computational efficiency. All unselected pixels were classified as negative for ILD. After classifying all pixels into the positive and negative groups, CAD computed a detection score based on the ratio of the number of positive pixels to all pixels in the segmented lung areas, which indicates the likelihood of the test case being positive for ILD. When applying to an independent testing dataset of 15 positive and 15 negative cases, the CAD scheme yielded the area under receiver operating characteristic curve (AUC = 0.884 ± 0.064) and 80.0% sensitivity at 85.7% specificity. The results demonstrated the feasibility of applying the CAD scheme to automatically detect early ILD using low-dose CT examinations.     

CT影像中筛前动脉与颅底的关系及在鼻窦炎手术中的意义

目的 通过CT影像探讨筛前动脉（AEA）与颅底的解剖关系及其在鼻窦炎手术中的意义。方法 回顾性研究2017年1月～2017年8月共52例鼻窦炎患者,所有患者均行鼻窦CT扫描及重建,测量AEA与颅底的距离并分型,测量AEA至额嘴的距离,并研究其与AEA悬空的关系。记录眶上筛房（SOEC）的发生率并以卡方检验分析SOEC与AEA悬空的关系,测量筛板外侧板的深度并进行Keros分型,以 Spearman 相关系数分析Keros分型与AEA悬空的关系。结果 AEA在CT图像中辨别率为100%, Ⅰ型为AEA嵌于颅骨内,占 42.3%（44/104）,Ⅱ型为AEA紧贴颅底,占18.3%（19/104）,Ⅲ型为AEA悬空于筛窦内,占39.4%（41/104）,即AEA的悬空率为39.4%,至颅底的平均距离为（3.8±1.5）mm。AEA至额嘴平均距离（14.1±2.2）mm,其距离在AEA悬空与非悬空组中差异无统计学意义（t=0.740,P>0.05）。在Keros分型中,Ⅰ型占51.9%（54/104）,Ⅱ型占37.5%（39/104）,Ⅲ型占10.6%（11/104）。Keros分型与AEA发生悬空之间的Spearman相关系数为0.505（P<0.001),为中度正相关。SOEC发生率为17.3%（17/104）,有SOEC的患者与无SOEC的患者的AEA悬空发生率差异存在统计学意义（χ2=4.3287,P<0.05）。结论 当SOEC存在或Keros分型级别较高时,AEA的悬空率明显升高,术前进行CT影像学检查可以识别颅底解剖情况,明确AEA与颅底的位置关系,进而降低术中AEA的损伤风险。     

基于卷积神经网络的宫颈CT图像的金属伪影去除

目的:为了消除宫颈CT图像中存在的金属伪影,提出一种利用卷积神经网络（CNN）去除金属伪影的策略。方法:首先通过数值仿真得到金属伪影图像与目标图像（无伪影图像）,构造训练测试数据集,利用含金属伪影的宫颈CT图像和对应的无伪影图像训练已搭建的CNN,进而得到去除宫颈CT图像金属伪影的CNN模型。结果:训练网络之前金属伪影图像与目标图像峰值信噪比（PSNR）平均值为26.098 0 dB。不同尺寸（25×25、50×50、100×100）的图像块训练网络得到去除金属伪影的图像与目标图像PSNR平均值分别为34.607 9、38.375 1、38.183 8 dB。结论:通过对仿真数据和临床数据进行实验,研究结果表明,本文方法能够快速有效地消除宫颈CT图像中的金属伪影,并且可以保留完整的组织结构信息。     

Lung metastases detection in CT images using 3D template matching

The aim of this study is to demonstrate a novel, fully automatic computer detection method applicable to metastatic tumors to the lung with a diameter of 4-20 mm in high-risk patients using typical computed tomography (CT) scans of the chest. Three-dimensional (3D) spherical tumor appearance models (templates) of various sizes were created to match representative CT imaging parameters and to incorporate partial volume effects. Taking into account the variability in the location of CT sampling planes cut through the spherical models, three offsetting template models were created for each appearance model size. Lung volumes were automatically extracted from computed tomography images and the correlation coefficients between the subregions around each voxel in the lung volume and the set of appearance models were calculated using a fast frequency domain algorithm. To determine optimal parameters for the templates, simulated tumors of varying sizes and eccentricities were generated and superposed onto a representative human chest image dataset. The method was applied to real image sets from 12 patients with known metastatic disease to the lung. A total of 752 slices and 47 identifiable tumors were studied. Spherical templates of three sizes (6, 8, and 10 mm in diameter) were used on the patient image sets; all 47 true tumors were detected with the inclusion of only 21 false positives. This study demonstrates that an automatic and straightforward 3D template-matching method, without any complex training or postprocessing, can be used to detect small lung metastases quickly and reliably in the clinical setting.     

Range of motion measurement of an artificial hip joint using CT images

Total hip arthroplasty (THA) is one of the most effective treatments for osteoarthritis and rheumatoid arthritis. Dislocation of the femoral head from the acetabular socket is a major problem of THA. To prevent dislocation, it is important to know the range of motion (ROM) after THA. Although various studies on the ROM were carried out, there exist only a few reports on ROM evaluation in individual patients. This is because in clinical cases, bone-to-bone and bone-to-component contacts must be considered besides the impingement of components. In this study, a new method for evaluating ROM of internal/external rotation, which takes into account all combinations of contacts between the bones and components, was proposed. A computer simulation demonstrated that the RMS error of the proposed method was approximately 3°. The method was applied to 33 THAs under various conditions of flexion and adduction angles. The method was able to detect any type of impingement. The evaluated ROM was in good agreement with that measured during the THA operation (correlation coefficient = 0.91).     

Fast segmentation of bone in CT images using 3D adaptive thresholding

Fast bone segmentation is often important in computer-aided medical systems. Thresholding-based techniques have been widely used to identify the object of interest (bone) against dark backgrounds. However, the darker areas that are often present in bone tissue may adversely affect the results obtained using existing thresholding-based segmentation methods. We propose an automatic, fast, robust and accurate method for the segmentation of bone using 3D adaptive thresholding. An initial segmentation is first performed to partition the image into bone and non-bone classes, followed by an iterative process of 3D correlation to update voxel classification. This iterative process significantly improves the thresholding performance. A post-processing step of 3D region growing is used to extract the required bone region. The proposed algorithm can achieve sub-voxel accuracy very rapidly. In our experiments, the segmentation of a CT image set required on average less than 10 s per slice. This execution time can be further reduced by optimizing the iterative convergence process.     

Optimal femoral head contour segmentation in CT images using dynamic programming.

We present a new contour segmentation method for femoral head detection in CT images. The principal idea underlying our approach is to represent the contour using active rays. Each ray has internal energy (continuity and smoothness constraints), as well as external energy (image-oriented edges and global a-priori knowledge). The causal nature of this representation allows a globally optimal, non-iterative solution to be found using dynamic programming. Moreover, even pathological bone structures can be automatically segmented. Several clinical situations on pelvis CT scan with hip degenerative change shows that this approach is more efficient than conventional methods. In this paper, we describe our method and discuss how it can be used to create graphical 3D models of the hip that are suitable for preoperative planning.     

Automatic delineation of body contours on cone-beam CT images using a delineation booster

In radiotherapy, cone-beam computerized tomography (CBCT) scans are used for position correction for various tumour sites. At the start of the treatment, a CT scan that serves as input for a treatment planning is acquired. A CBCT scan is made prior to the irradiation of the tumour. Because there might be significant interfractional tumour movement, online recalculation of the dose improves decision making on how to proceed. A prerequisite for such recalculation is an accurately delineated body contour. In this note, we present an automatic delineation method for the body contour in the unprocessed CBCT scans, that employs a novel delineation boosting technique. The main idea of this technique is to construct an accurate delineation by combining the strength of several edge detectors in an innovative way. Quantitative validation reveals that the algorithm performs comparably with the manual delineations of two trained observers. Furthermore, because of the generic nature of the delineation boosting procedure, the algorithm can easily be extended with additional edge detectors to further increase the accuracy. Finally, the processing time of one scan when delineated manually is 3?h, and the total processing time is 24?min for one scan if the algorithm is used in its present form. Current investigation includes the conversion of the Matlab algorithm to C++?and the development of a visual tool to quickly detect which automatically delineated slices need manual correction. From this we expect further speeding up of the process, allowing online computation.     

Volumetric analysis of CT orbital images

The determination of the volume of eye muscles is a problem of considerable interest in Graves' ophtalmopathy both with respect to the diagnostic quantisation of the disease and to the measured assessment of the effectiveness of pharmacological treatment. The aim of this research is to design and test an advanced method for processing computerised tomography (CT) orbital images in order to obtain a three-dimensional reconstruction of infra-orbital muscle structures and to analyse them from a morphometric viewpoint. CT images of subjects suffering from Graves' disease were acquired before and after pharmacological treatment with immunosuppressors. They were then processed along with CT images of an anatomical phantom of known volume and fusiform morphology in order to assess the reliability of the procedure and to calculate the effect of the different modalities of acquisition and processing of CT images on the error in volume calculation.     

Artefacts of PET/CT images

Positron emission tomography (PET) is a non-invasive imaging modality, which is clinically widely used both for diagnosis and accessing therapy response in oncology, cardiology and neurology.Fusing PET and CT images in a single dataset would be useful for physicians who could read the functional and the anatomical aspects of a disease in a single shot.The use of fusion software has been replaced in the last few years by integrated PET/CT systems, which combine a PET and a CT scanner in the same gantry. CT images have the double function to correct PET images for attenuation and can fuse with PET for a better visualization and localization of lesions. The use of CT for attenuation correction yields several advantages in terms of accuracy and patient comfort, but can also introduce several artefacts on PET-corrected images.PET/CT image artefacts are due primarily to metallic implants, respiratory motion, use of contrast media and image truncation. This paper reviews different types artefacts and their correction methods.PET/CT improves image quality and image accuracy. However, to avoid possible pitfalls the simultaneous display of both Computed Tomography Attenuation Corrected (CTAC) and non corrected PET images, side by side with CT images is strongly recommended.     

Segmentation of bone CT images and assessment of bone structure using measures of complexity

A nondestructive and noninvasive method for numeric characterization (quantification) of the structural composition of human bone tissue has been developed and tested. In order to quantify and to compare the structural composition of bones from 2D computed tomography (CT) images acquired at different skeletal locations, a series of robust, versatile, and adjustable image segmentation and structure assessment algorithms were developed. The segmentation technique facilitates separation from cortical bone and standardizes the region of interest. The segmented images were symbol-encoded and different aspects of the bone structural composition were quantified using six different measures of complexity. These structural examinations were performed on CT images of bone specimens obtained at the distal radius, humeral mid-diaphysis, vertebral body, femoral head, femoral neck, proximal tibia, and calcaneus. In addition, the ability of the noninvasive and nondestructive measures of complexity to quantify trabecular bone structure was verified by comparing them to conventional static histomorphometry performed on human fourth lumbar vertebral bodies. Strong correlations were established between the measures of complexity and the histomorphometric parameters except for measures expressing trabecular thickness. Furthermore, the ability of the measures of complexity to predict vertebral bone strength was investigated by comparing the outcome of the complexity analysis of the CT images with the results of a biomechanical compression test of the third lumbar vertebral bodies from the same population as used for histomorphometry. A multiple regression analysis using the proposed measures including structure complexity index, structure disorder index, trabecular network index, index of a global ensemble, maximal L-block, and entropy of x-ray attenuation distribution revealed an excellent relationship (r=0.959, r2=0.92) between the measures of complexity and compressive bone strength. In conclusion, the image segmentation techniques and the assessment of bone architecture by measures of complexity have been successfully applied to analyze high-resolution peripheral quantitative computed tomography (pQCT) and CT images obtained from the distal radius, humeral mid-diaphysis, third and fourth lumbar vertebral bodies, proximal femur, proximal tibia, and calcaneus. The proposed approach is of broad interest as it can be applied for the quantification of structures and textures originating from different imaging modalities in other fields of science.     

基于改进阈值的VGG网络的新冠肺炎CT图像自动诊断算法

肺部CT能够较准确地鉴定新冠肺炎病例,但医生工作量较大,本研究提出一种基于改进阈值的VGG网络的新冠肺炎CT图像自动诊断算法,通过该模型可快速准确地完成新冠肺炎病例的自动识别,为进一步控制其传播提供帮助。通过比较卷积神经网络VGG中的VGG-11、VGG-13、VGG-16,获得准确率较高的新冠肺炎CT图像自动诊断模型VGG-13,并在此基础上通过改进阈值的方式使准确率由86%提高到了89%,进一步提高诊断的准确性。