Derivative-Hilbert-Backprojection based image reconstruction from truncated projections in helical cone-beam CT

In helical cone-beam computed tomography(CT), Feldkamp-Davis-Kress(FDK) based image reconstruction algorithms are by far the most popular. However, artifacts are commonly met in the presence of lateral projection truncation. The reason is that the ramp filter is global. To restrain the truncation artifacts, an approximate reconstruction formula is proposed based on the Derivative-Hilbert-Backprojection(DHB) framework. In the method, the first order derivative filter is followed by the Hilbert transform. Since the filtered projection values are almost zero by the first order derivative filter, the following Hilbert transform has little influence on the projection values, even though the projections are laterally truncated. The proposed method has two main advantages. First, it has comparable computational efficiency and image quality as well as the conventional helical FDK algorithm for non-truncated projections. The second advantage is that images can be reconstructed with acceptable quality and much lower computational cost in comparison to the Laplace operator based algorithm in cases with truncated projections. To point out the advantages of our method, simulations on the computer and real data experiments on our laboratory industrial cone-beam CT are conducted. The simulated and experimental results demonstrate that the method is feasible for image reconstruction in the case of projection truncation.

Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.

CT灌注成像联合血清D-D、MMP-9对急性脑梗死患者溶栓后出血转化的预测价值研究

目的探讨计算机断层扫描(CT)灌注成像(CTP)联合血清D-二聚体(D-D)、金属基质蛋白酶-9(MMP-9)对急性脑梗死患者溶栓后出血转化的预测价值。方法选择2020年1月至2021年12月我院收治的急性脑梗死患者94例,根据患者7d内头颅CT结果分为出血转化组28例及未出血转化组66例。比较两组CTP参数及血清D-D、MMP-9水平,应用Logistic回归分析溶栓后出血转化的影响因素,应用受试者工作特征(ROC)曲线分析CTP联合血清D-D、MMP-9对急性脑梗死患者溶栓后出血转化的预测价值。结果出血转化组相对脑血容量(rCBV)、相对脑血流量(rCBF)显著低于非出血转化组,相对达峰时间(rTTP)高于非出血转化组(P<0.05)。出血转化组入院时血清D-D、MMP-9水平显著高于非出血转化组(P<0.05)。出血转化组年龄、心源性栓塞型比例、大面积脑梗死比例、溶栓前收缩压、溶栓前空腹血糖(FPG)、溶栓前美国国立卫生研究院卒中量表(NIHSS)评分及吸烟、合并糖尿病比例均显著高于未出血转化组(P<0.05),而两组性别、BMI、酗酒、合并高血压、溶栓前收缩压、溶栓前舒张压、溶栓前血小板比较无统计学意义(P>0.05)。Logistic回归分析显示,rCBV≤0.65、rCBF≤0.25、rTTP≥1.03s、血清D-D≥1.28mg/L、血清MMP-9≥251.98μg/L、年龄≥61.59岁、TOAST分型心源性栓塞型、大面积脑梗死、溶栓前收缩压≥145.30 mmHg、溶栓前FPG≥7.11mmol/L、溶栓前NIHSS评分≥14.87分是急性脑梗死患者溶栓后出血转化的危险因素(P<0.05)。ROC曲线分析显示,CTP联合血清D-D、MMP-9对急性脑梗死患者溶栓后出血转化预测有较高的敏感度、特异度,曲线下面积(AUC)为0.838。结论CTP联合血清D-D、MMP-9对急性脑梗死患者溶栓后出血转化预测有较高的诊断价值,危险因素较多。

A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection

Coronavirus has infected more than 753 million people,ranging in severity from one person to another,where more than six million infected people died worldwide.Computer-aided diagnostic(CAD)with artificial intelligence(AI)showed outstanding performance in effectively diagnosing this virus in real-time.Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients.This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs.We used the methodology of systematic reviews and meta-analyses(PRISMA)flow method.This research aims to systematically analyze the supervised deep learning methods,open resource datasets,data augmentation methods,and loss functions used for various segment shapes of COVID-19 infection from computerized tomography(CT)chest images.We have selected 56 primary studies relevant to the topic of the paper.We have compared different aspects of the algorithms used to segment infected areas in the CT images.Limitations to deep learning in the segmentation of infected areas still need to be developed to predict smaller regions of infection at the beginning of their appearance.

Research on Automatic Elimination of Laptop Computer in Security CT Images Based on Projection Algorithm and YOLOv7-Seg

In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.

The diagnostic value of 3D spiral CT imaging of cholangiopancreatic ducts on obstructive jaundice

Objective: Computerized tomography (CT) plays an important role in the diagnosis of diseases of biliary tract. Recently, three dimensions (3D) spiral CT imaging has been used in surgical diseases gradually. This study was designed to evaluate the diagnostic value of 3D spiral CT imaging of cholangiopancreatic ducts on obstructive jaundice. Methods: Thirty patients with obstructive jaundice had received B-mode ultrasonography, CT, percutaneous transhepatic cholangiography (PTC) or endoscopic retrograde cholangiopancreatography (ERCP), and 3D spiral CT imaging of cholangiopancreatic ducts preoperatively. Then the diagnose accordance rate of these examinational methods were compared after operations. Results: The diagnose accordance rate of 3D spiral CT imaging of cholangiopancreatic ducts was higher than those of B-mode ultrasonography, CT, or single PTC or ERCP, which showed clear images of bile duct tree and pathological changes. As to malignant obstructive jaundice, this examinational technique could clearly display the adjacent relationship between tumor and liver tissue, biliary ducts, blood vessels, and intrahepatic metastases. Conclusion: 3D spiral CT imaging of cholangiopancreatic ducts has significant value for obstructive diseases of biliary ducts, which provides effective evidence for the feasibility of tumor-resection and surgical options.

利用X射线CT技术研究根系三维构型的进展

【目的】X射线CT扫描具有非破坏性、高分辨率等特点,近年来被广泛应用于研究植物根系三维构型。本文介绍了X射线CT技术研究植物根系的原理和方法,并重点阐述了CT图像提取和定量根系的方法。【主要进展】X射线CT可以穿透土体进而获取土体三维结构,并能通过图像分析准确定量土体内部的细节特征(如根系、孔隙等)。目前根系结构研究中应用的CT主要有医用CT、工业显微CT、同步辐射CT等,分别用于研究不同大小和分辨率要求的样品。应用CT扫描根系样品时,样品的制备和扫描参数的选择非常重要,样品的尺寸、容器的材质、扫描功率和扫描时长等因素都会影响CT图像质量。CT图像中提取根系的方法包括全局阈值法、区域生长法、根系追踪法、深度学习法等,结合图像预处理和后处理能更完整地提取根系。利用ImageJ、Rootrak、RooTh等软件,可以定量根系形态学参数(体积、根长、直径等),还可以分析根系三维分布特征(根系生长角度、根系生长深度等)。【展望】从CT图像中提取根系的算法在实际应用中仍存在很多问题,开发提取根系的先进算法,准确获取根系三维构型并将其定量表征,仍将是CT技术用于根系研究的重要方向。由于CT设备性能的特点,扫描样品尺寸和图像分辨率的矛盾仍然是关键问题,X射线CT硬件设备的创新和发展对其在根系研究中更加广泛和有效的应用具有重要意义。

基于DCT的电网金属材料金相压缩算法

为了实现对电网金属材料的定量金相分析,加快存储和传输电网金属材料金相显微组织图像的速度,对基于离散余弦变换(DCT)的JPEG图像压缩算法进行了研究。采用基于DCT的JPEG图像压缩算法,以MATLAB图像处理工具箱为软件平台对系统进行试验。试验结果表明,所设计的压缩算法满足定量金相分析图像压缩的设计要求,既可保证有较高的压缩比,又可保证较好的图像质量,大大提高了图像压缩的效率,实现了电网金属材料金相显微组织图像数据的压缩。

Feldkamp-type-VOI锥束CT重建算法加速的研究

本文具体介绍了一种Feldkamp-type感兴趣区域锥束CT重建方法。首先,我们分析了算法的特点;继而利用锥束扫描模式存在的对称性的反投影优化性质进行算法优化;最后,通过Shepp-Logan数值模拟实验显示优化方法可有效提高图像重建的速度且几乎不影响图像质量。

Computer‑aided CT image processing and modeling method for tibia microstructure

We present a method for computed tomography(CT)image processing and modeling for tibia microstructure,achieved by using computer graphics and fractal theory.Given the large-scale image data of tibia species with DICOM standard for clinical applications,we take advantage of algorithms such as image binarization,hot pixel removing and close operation to obtain visually clear image for tibia microstructure.All of these images are based on 20 CT scanning images with 30μm slice thickness and 30μm interval and continuous changes in pores.For each pore,we determine its profile by using an improved algorithm for edge detection.Then,to calculate its three-dimensional fractal dimension,we measure the circumference perimeter and area of the pores of bone microstructure using a line fitting method based on the least squares.Subsequently,we put forward an algorithm for the pore profiles through ellipse fitting.The results show that the pores have significant fractal characteristics because of the good linear correlation between the perimeter and the area parameters in log–log scale coordinates system,and the ratio of the elliptical short axis to the long axis through ellipse fitting tends to 0.6501.Based on support vector machine and structural risk minimization principle,we put forward a mapping database theory of structure parameters among the pores of CT images and fractal dimension,Poisson’s ratios,porosity and equivalent aperture.On this basis,we put forward a new concept for 3D modeling called precision-measuring digital expressing to reconstruct tibia microstructure for human hard tissue.

Study on threshold segmentation of multi-resolution 3D human brain CT image

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper.In this method,first,original 3D human brain image information is collected,and CT image filtering is performed to the collected information through the gradient value decomposition method,and edge contour features of the 3D human brain CT image are extracted.Then,the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points,and the 3D human brain CT image is reconstructed with the salient feature point as center.Simulation results show that the method proposed in this paper can provide accuracy up to 100%when the signal-to-noise ratio is 0,and with the increase of signal-to-noise ratio,the accuracy provided by this method is stable at 100%.Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is signicantly better than traditional methods in pathological feature estimation accuracy,and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

双源CT双能量肺灌注成像联合血浆D-二聚体检测在急性肺动脉栓塞患者诊断及危险分层中的分析

目的:探究双源CT双能量肺灌注成像(DEPI)联合血浆D-二聚体(D-D)检测在急性肺动脉栓塞(APE)患者临床诊断及危险分层中的应用价值。方法:选取我院收治的疑似APE患者370例,入院后均行DEPI检查,并检测血浆中D-D水平,经病理诊断,确诊为APE 160例,根据Wells评分和修正Geneva评分量表将其分为低危组(n=40)、中危组(n=47)和高危组(n=73)。比较DEPI与D-D检测对APE的诊断效能及各组栓塞分数、灌注缺损分数、RV/LV、D-D水平。结果:DEPI诊断APE敏感度及特异度为85.1%、96.5%,D-D检测诊断APE敏感度及特异度为97.2%、62.7%,二者联合诊断敏感度及特异度为89.4%、83.7%;高危组栓塞分数、灌注缺损分数、RV/LV及D-D水平均显著高于低危组和中危组,中危组栓塞分数、灌注缺损分数、RV/LV及D-D水平亦显著高于低危组(P<0.05)。结论:DEPI联合血浆D-D检测可用于临床诊断APE,评估其危险分层。

PC-BASED SURFACE RECONSTRUCTION OF MEDICAL CT IMAGES

It is an active research area to reconstruct 3-D object and display its visible surfacesfrom cross-sectional images. In this paper, the methods of reconstructing 3-D object from medicalCT images and displaying the visible surfaces are discussed. A polygon approximation methodthat forms polygon with the same number of segment points and a fast interpolation method forcross-sectional contours are presented at first. Then the voxel set of a human liver is reconstructed.And then the liver voxel set is displayed using depth and gradient shading methods. The softwareis written in C programming language at a microcomputer image processing system with a PC/ATcomputer as the host and a PC-VISION board as the image processing unit. The result of theprocessing is satisfying.

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.

Visualization of Three-dimensional Human Data Based on CT Image

Three-dimensional medical image visualization becomes an essential part for medical field, including computer aided diagnosis, surgery planning and simulation, artificial limb surgery, radiotherapy planning, and teaching etc. In this paper, marching cubes algorithm is adopted to reconstruct the 3-D images for the CT image sequence in DICOM format under theVC＋＋6.0 and the visual package VTK platform. The relatively simple interactive operations such as rotation and transfer can be realized on the platform. Moreover, the normal vector and interior point are calculated to form the virtual clipping plane, which is then used to incise the 3-D object. Information of the virtual slice can be obtained, in the mean while the virtual slice images are displayed on the screen. The technique can realize the real time interaction extraction of virtual slice on 3-D CT image. The cuboids structured can be zoomed, moved and eircumrotated by operating mouse to incise the 3-D reconstruction object. Real time interaction can be realized by clipping the reconstruction object. The coordinates can be acquired by the mouse clicking in the 3D space, to realize the point mouse pick-up as well angle and distance interactive measurement. We can get quantitative information about 3-D images through measurement.

急性胸部创伤的螺旋CT影像诊断分析与临床价值研究

探究螺旋CT影像在急性胸部创伤诊断中的应用价值。随机选取孟村回族自治县医院2023年1月—2023年6月符合标准的200例急性胸部创伤患者为研究对象,分为观察组和对照组,观察组应用螺旋CT开展诊断,对照组采用传统方式开展诊断。比较两组患者的检查结果,并进行统计学分析。结果显示,观察组的各项数据明显优于对照组。研究发现,螺旋CT在急性胸部创伤诊断中具有积极意义,可以在临床上应用。

多层螺旋CT及其后处理技术对隐匿性骨折的诊断价值

目的 探讨多层螺旋CT(MSCT)二维(MPR、MIP)和三维(VRT、SSD)重建技术对复杂结构的隐匿性骨折的诊断价值。资料与方法 对4 2例外伤患者普通X线平片中未显示而临床怀疑有骨折的患者进行MSCT扫描后,通过特殊的CT图像处理软件包在工作站上进行后处理(MPR、MIP、VRT及SSD) ,得到外伤部位骨骼高清晰度的二维和三维图像后,由两位有经验的放射科医师共同阅片,以共同意见为准。结果 4 2例X线平片中未显示骨折的患者经MSCT重建后,18例显示有骨折,其中枕骨骨折2例,肩胛骨骨折2例,肋骨骨折4例,脊椎骨折3例,骨盆骨折3例,股骨颈骨折3例,跟骨骨折1例。结论 MSCT二维和三维重建对临床怀疑骨折而普通X线不能确诊为骨折的病例能准确地作出诊断,并对治疗方案的选择及预后的估计有重要价值。

16层螺旋CT结肠三维成像与仿真内镜的临床应用研究

目的 探讨 16层螺旋CT三维成像与仿真内镜在结肠肿瘤诊断中的应用价值。资料与方法 应用Lightspeed 16CT对 2 6例纤维结肠镜活检证实的结肠癌 (19例 )和结肠息肉 (7例 )进行容积扫描 ,在AW4 .2工作站进行三维成像和仿真内镜观察 ,并与纤维结肠镜结果相比较。结果 2 5例三维成像效果良好 ,仿真内镜与纤维结肠镜显示基本一致 ,1例因肠道准备不彻底 ,成像效果欠佳 ,但不影响诊断 ;CT仿真结肠镜 (CTVC)结合 2D多平面重建(MPR)和透明显示 (RaySum)图像 ,可以提供更多信息 ,有助于病变的准确定位、定性。结论 16层螺旋CT仿真内镜结合三维成像能达到与纤维结肠镜同样的敏感性和特异性 ,加上MPR。

螺旋CT三维成像在颈椎损伤病变中的应用

目的 探讨螺旋CT三维成像 (MPR、SSD)在颈椎损伤中的应用价值。材料与方法 经X线和 /或螺旋CT证实的颈椎损伤 15例 ,行螺旋CT扫描 ,并作MPR和SSD成像 ,13例扫描参数为层厚 3mm ,床速 4.5mm/s ,重建间距 1.5mm ;2例为层厚 5mm ,床速5mm/s ,重建间距 2mm。SSD最低阈值选择 15 0～ 180。结果 椎体及附件骨折 10例 ,其中 6例伴环齿关节脱位 ,1例椎体滑脱。椎体旋转脱位 4例 ,成角滑脱 1例。 15例中 3例椎管狭窄并硬脊膜受压。MPR结合轴位图像可完全明确骨折、脱位及椎管情况。SSD对颈椎表面骨折、椎体旋转及成角脱位显示良好 ,立体感强 ,但对椎体内骨裂或骨折、椎管内病变显示差。结论 颈椎损伤应首选CT检查。轴位图像、MPR及SSD联合应用 ,能明确病变性质 ,立体、直观、多方位提供临床医师所需信息 ,有利于治疗方案的选择。

颌面外科及正畸中三维CT的临床应用

目的 探讨三维CT在颌面外科和正畸中的应用价值。资料与方法 颌面部骨折 5 5例 ,颌面部肿瘤 14例 ,颌骨及牙发育畸形 14例。所有病例均经螺旋CT扫描后进行骨的表面重建 ,肿瘤患者还行肿瘤区的多平面重建 ,颌骨畸形牙咬合错位者利用牙科软件行牙体的表面重建和牙根的多平面重建。重建图像分别与原始轴位图、手术所见对照 ,并与正畸模型进行对照。结果 (1) 5 5例颌面部骨折和 14例颌面部肿瘤三维重建图像显示的骨折、肿瘤部位与原始轴位图显示一致 ,与手术所见相符 ,而且立体感更强。 (2 ) 14例颌骨及牙发育畸形三维重建图像清楚显示颌骨畸形、牙咬合错位牙根方向的情况。根据三维图像制订、实施手术方案和矫形正畸计划 ,获得良好的效果。结论 三维CT可准确地显示颌骨的病变 ,可多方位、多角度观察病变 ,立体感强 ,有助于颌面外科疾病的治疗、手术计划的制订和实施 ,牙科软件行牙的表面重建及牙根的多平面重建 ,能清楚地显示各个牙根的走向 ,为牙的矫形。