Utility of 18 F‐FDG PET/CT scan to diagnose the etiology of fever of unknown origin in patients on dialysis

Introduction: Studies on fever of unknown origin (FUO) in patients of chronic kidney disease and end stage renal disease patients on dialysis were not many. In this study, we used 18 F‐FDG PET/CT scan whole body survey for detection of hidden infection, in patients on dialysis, labelled as FUO. Methods: In this retrospective study, 20 patients of end stage renal disease on dialysis were investigated for the cause of FUO using 18F‐FDG PET/CT scan. All these patients satisfied the definition of FUO as defined by Petersdorf and Beeson. Any focal abnormal site of increased FDG concentration detected by PET/CT, either a solitary or multiple lesions was documented and at least one of the detected abnormal sites of radio tracer concentration was further examined for histopathology. Findings: All patients were on renal replacement therapy. Of these, 18 were on hemodialysis and two were on peritoneal dialysis. 18F‐FDG PET/CT scan showed metabolically active lesions in 15 patients and metabolically quiescent in five patients. After 18F‐FDG PET/CT scan all, but one patient had a change in treatment for fever. Anti‐tuberculous treatment was given in 15 patients, antibiotics in four patients and anti‐malaria treatment in one patient. Discussion: The present study is first study of 18F‐FDG PET/CT scan in patients of end stage renal disease on dialysis with FUO. The study showed that the 18 F FDG PET/CT scan may present an opportunity to attain the diagnosis in end stage renal disease patients on dialysis with FUO.     

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.     

图象重建的神经网络法

以平行射束扫描的 X射线 CT为例 ,提出一种运用 Hopfield人工神经元网络实现从投影中重建图象的方法 .首先将图象重建问题转化为最小范数准则下的优化问题 ,然后构造相应的 Hopfield神经网络模型 ,网络的稳态解 ,即为重建问题的解 ,计算机模拟实验结果证明了方法的正确性     

一种改进的交互式CT胸部图像肺实质分割方法

提出一种交互式的肺实质分割算法,该算法充分利用序列CT图像相邻层中肺实质轮廓变化缓慢的特点,结合且改进了Live-Wire模型、Snake模型以及轮廓插值方法.并辅以操作人员的专业知识.首先人工的在序列CT图像中选取肺实质的关键层,然后通过Live-Wire模型交互式的勾勒其轮廓,再进行轮廓插值得到其他层肺实质的初始轮廓,最后通过Snake模型演化得到所有层的肺实质准确分割结果,并加以手工修正.实验结果表明,该算法能快速准确的从序列CT图像中分割出肺实质.     

基于三参数Chirplet变换的ARM检测

根据反辐射导弹(ARM)回波信号的特征,选择Chirplet变换5个参数中的3个,得到三参数Chirplet变换,并结合自适应线性预测滤波算法,提出一种基于三参数Chirplet变换的ARM检测方法。该方法可有效缩小搜索范围和降低输入信噪比要求,且可借助FFT实现。仿真结果表明其可在大载机回波干扰和低信噪比环境下快速准确地检测出ARM回波,实现实时告警。     

安检中X光断层扫描加速迭代算法研究

随着安检要求的日益提高,计算机断层扫描的安检方式正成为X光安检中越来越重要的部分.为了保证行李扫描的检测效率,加快断层扫描图像的迭代计算速度,提出了一种采用小波变换域取代图像空间来测算图像小波系数的估值方法.该方法以交替最小化算法为框架,在逼近系数时,创建一个替代函数,随着迭代次数的增加,基于同一标准的小波树得以更新扩展,每层的细节系数均更新,小波树也依此扩展,以此达到加速迭代的效果.实验结果表明,该方法比原交替最小化算法最大能减少66.7％的迭代运算次数.     

工业CT在航空机载传感器可靠性提升中的应用

利用工业CT技术对某型航空发动机数控系统的总温传感器故障件进行了检测,从而无损、高效、准确地检测到故障点,并利用其提供的传感器内部信息对故障原因进行了分析,采取了相应的改进措施。通过环境试验验证了改进措施的有效性,表明该型传感器的可靠性得到了显著提高。因此,工业CT为机载传感器的故障检测提供了一种行之有效的新途径、新思路,可以在其可靠性提升上推广应用。     

小波在基于CT图像的肝脏疾病良恶性分类中的研究

肝癌是一种常见的恶性肿瘤,近年来发病率呈缓慢上升的趋势,病死率也随之上升。文章利用小波在特征提取和模式识别方面的独特优势,提取了基于小波和灰度共生矩阵的纹理特征,结合遗传算法进行特征选择和优化,用KNN分类器设计出高精确度的肝脏疾病良恶性分类器。采用肝脏CT平扫图像,将肝癌与其他的良性病变进行分类,探讨了小波的不同性质及特征提取方式对分类结果的影响,对小波在肝脏CT图像良恶性分类中的研究有指导意义。     

Extracting 3D information on bone remodeling in the proximity of titanium implants in SRμCT image volumes

Bone-implant integration is measured in several ways. Traditionally and routinely, 2D histological sections of samples, containing bone and the biomaterial, are stained and analyzed using a light microscope. Such histological section provides detailed cellular information about the bone regeneration in the proximity of the implant. However, this information reflects the integration in only a very small fraction, a 10 μm thick slice, of the sample. In this study, we show that feature values quantified on 2D sections are highly dependent on the orientation and the placement of the section, suggesting that a 3D analysis of the whole sample is of importance for a more complete judgment of the bone structure in the proximity of the implant. We propose features describing the 3D data by extending the features traditionally used for 2D-analysis. We present a method for extracting these features from 3D image data and we measure them on five 3D SRμCT image volumes.We also simulate cuts through the image volume positioned at all possible section positions. These simulations show that the measurement variations due to the orientation of the section around the center line of the implant are about 30%.     

CT增强扫描中的小肝癌的自动检测

提出一种用模板匹配来检测CT增强扫描中的小肝癌的算法,该算法先利用改进的区域生长方法提取出完整的肝部以减少模板匹配的计算量,然后简化Y Lee等人的模板匹配方法,从而达到加快计算速度的目的。