Brilliance型16排螺旋CT的故障分析与维修

针对日常使用中遇到的Brilliance 16排螺旋CT高压系统、机架以及控制器局域网络(CAN)通讯等故障现象进行分析与维修,最终通过更换滤波电容、机架数据采集控制器(DMC)电源以及信号刷等配件得以排除故障。通过对故障准确判断以及合理运用厂家Service模式下的维修工具软件快速解决故障,保障了影像科检查工作的顺利进行,从而提高设备的稳定性。     

Computed tomography reference dimensions for identification of stented surgical mitral bioprostheses valve size

BackgroundSelection of the transcatheter heart valve size for a mitral valve-in-valve procedure is based on the type and manufacturer’s labelled size. However, accurate information of surgical heart valve (SHV) size may not be available in the patient’s medical record. The purpose of this study is to establish reference data for computed tomography (CT) dimensions of commonly used mitral SHV in order to determine the manufacturer’s labelled size from a cardiac CT data set.MethodsCT datasets of 105 patients with surgical mitral bioprosthesis and available manufacturer labeled datasets were included in the analysis. CT derived valve dimensions were assessed by two observers using multiplanar reformats aligned with the basal sewing ring. A circular region of interest was used in a standardized fashion to minimize influence of image acquisition and reconstruction parameters. Interobserver variability was assessed by Bland-Altman analysis.ResultsThe CT-derived dimensions were stratified by valve size and type, and SHV properties were demonstrated for 5 common valve types. Variability of measurements was small and inter-observer limits of agreement were narrow. Stratified by SHV type, no overlap was noted for CT-derived dimensions among different SHV sizes . A reference table of CT characteristics of surgical mitral bioprosthesis types was created.ConclusionThe study provides reference CT data for determining the manufacturers’ labeled SHV size across a range of commonly used mitral SHVs. The findings will be important to help identify types of surgical mitral bioprosthesis utilizing CT characteristics for patients without SHV size documentation.     

28例原发性卵黄囊瘤患者的CT表现分析

目的分析28例原发性卵黄囊瘤(YST)患者的CT表现。方法选择2015年2月至2019年12月收治的28例YST患者的临床资料进行回顾性分析,8例YST患者入院后均予以CT检查,然后对患者的CT影像学特征和临床资料数据进行分析比较。结果本研究中28例YST患者肿瘤位于性腺的有17例(60.71%),其中位于睾丸、卵巢处的分别有7例、10例,占比分别为60.71%、41.18%,均为单侧发病;9例(39.29%)位于性腺外,其中位于骶尾部、前纵隔、阴道的分别有6例、2例、1例,占比分别为66.67%、22.22%、11.11%。CT检测可见肿瘤"浅分叶",且呈"椭网形",18例(64.29%)肿瘤边界较为清晰,9例(32.14%)呈"深分叶状"形态不规则;8例(28.57%)边界模糊,肿瘤大小分析可见,肿瘤位于前纵隔、卵巢的相对较大,位于阴道、睾丸、骶尾部的相对较小。28例YST患者中,单纯型21例YST(75.00%),混合型7例(25.00%)。结论原发性卵黄囊瘤进行CT检测尤为重要,其CT表现具有一定特征性,可为临床治疗提供一定的依据。     

A comprehensive 3D CT based classification of intertrochanteric fracture

IntroductionDespite advancements in surgical techniques complications like implant failure is very common after the fixation of intertrochanteric fractures. Classifying these complex fractures based on plain radiographs underestimates the complexity of these fractures which in turn leads to complications. We propose a comprehensive classification of the intertrochanteric fractures based on 3D Non Contrast Computed Tomography (3D NCCT) scan.Material and methodsA total of 102 patients (51 males and 51 females) with intertrochanteric fractures were included in this study conducted over a time period of 22 months in a Tertiary care center in North India. NCCT proximal femur of the intertrochanteric fracture patients was done to formulate a new CT classification system and classify all fractures. Intra and inter-observer reliability was tested using kappa variance.ResultsNew classification system was proposed which included 3 main and a total of 6 groups. All the fractures were classifiable into the new system. Kappa variance of the study showed a good intra and interobserver reliability (0.95 and 0.90) proving clinical agreement of the classification.ConclusionThis new 3D-CT based classification has the advantages of being easy, comprehensible with high intra and inter-observer reliability. This 3DCT based classification can prove to be useful to detect occult intertrochanteric fractures undetectable in plain radiographs as well as choosing the optimum treatment plan.     

NEMA NU 2-2018标准在PET/CT系统性能测试中的作用评价

目的 评价美国国家电器制造协会(National Electrical Manufactures Association, NEMA)最新标准(NU 2-2018)在正电子发射型计算机断层显像/电子计算机断层显像(positron emission tomography/computed tomography, PET/CT)设备性能检测中的作用。 方法 依据最新的NEMA NU 2-2018标准,检测西门子Biograph Vision PET/CT的空间分辨率、灵敏度、散射分数、计数丢失、随机符合、飞行时间分辨率、计数丢失率和随机符合校正精度、图像质量、衰减和散射校正精度及PET与CT配准精度指标。 结果 距视野中心1 cm处横向和轴向空间分辨率分别为3.75 mm和3.76 mm;在视野中心和轴向10 cm处的灵敏度分别为16.83 kcps/MBq和16.67 kcps/MBq;放射性浓度为27.37 kBq/mL时,最大等效噪声计数率为258.26 kcps,散射分数为38.58%;系统时间分辨率为209.82 ps;图像质量模型的对比度恢复系数范围为88.9%~96.2%,背景变异系数范围为2.05%~6.80%,平均肺插件残余误差为2.43%;计数丢失和随机符合校正最大误差为3.9%;距离床板末端 5 cm 和 100 cm处,在距视野中心Y轴1 cm处,PET和CT的配准精度分别为0.46 mm和1.07 mm,在距视野中心X轴20 cm处,PET和CT的配准精度分别为1.06 mm和1.45 mm,在距视野中心Y轴20 cm处PET和CT的配准精度分别为0.85 mm和1.15 mm。 结论 NEMA NU 2-2018标准检测条件更加接近临床,能更好地反映PET/CT设备的系统性能。     

An MRI sequence independent convolutional neural network for synthetic head CT generation in proton therapy

A magnetic resonance imaging (MRI) sequence independent deep learning technique was developed and validated to generate synthetic computed tomography (sCT) scans for MR guided proton therapy. 47 meningioma patients previously undergoing proton therapy based on pencil beam scanning were divided into training (33), validation (6), and test (8) cohorts. T₁, T₂, and contrast enhanced T₁ (T1CM) MRI sequences were used in combination with the planning CT (pCT) data to train a 3D U-Net architecture with ResNet-Blocks. A hyperparameter search was performed including two loss functions, two group sizes of normalisation, and depth of the network. Training outcome was compared between models trained for each individual MRI sequence and for all sequences combined. The performance was evaluated based on a metric and dosimetric analysis as well as spot difference maps. Furthermore, the influence of immobilisation masks that are not visible on MRIs was investigated. Based on the hyperparameter search, the final model was trained with fixed features per group for the group normalisation, six down-convolution steps, an input size of 128 × 192 × 192, and feature loss. For the test dataset for body/bone the mean absolute error (MAE) values were on average 79.8/216.3 Houndsfield unit (HU) when trained using T1 images, 71.1/186.1 HU for T2, and 82.9/236.4 HU for T1CM. The structural similarity metric (SSIM) ranged from 0.95 to 0.98 for all sequences. The investigated dose parameters of the target structures agreed within 1% between original proton treatment plans and plans recalculated on sCTs. The spot difference maps had peaks at ±0.2 cm and for 98% of all spots the difference was less than 1 cm. A novel MRI sequence independent sCT generator was developed, which suggests that the training phase of neural networks can be disengaged from specific MRI acquisition protocols. In contrast to previous studies, the patient cohort consisted exclusively of actual proton therapy patients (i.e. “real-world data”).     

DEep learning-based rapid Spiral Image REconstruction (DESIRE) for high-resolution spiral first-pass myocardial perfusion imaging

The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) technique for high-resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage, to provide fast and accurate image reconstruction for both single-slice (SS) and simultaneous multislice (SMS) acquisitions. Three-dimensional U-Net–based image enhancement architectures were evaluated for high-resolution spiral perfusion imaging at 3 T. The SS and SMS MB = 2 networks were trained on SS perfusion images from 156 slices from 20 subjects. Structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized root mean square error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5: excellent; 1: poor). Excellent performance was demonstrated for the proposed technique. For SS, SSIM, PSNR, and NRMSE were 0.977 [0.972, 0.982], 42.113 [40.174, 43.493] dB, and 0.102 [0.080, 0.125], respectively, for the best network. For SMS MB = 2 retrospective data, SSIM, PSNR, and NRMSE were 0.961 [0.950, 0.969], 40.834 [39.619, 42.004] dB, and 0.107 [0.086, 0.133], respectively, for the best network. The image quality scores were 4.5 [4.1, 4.8], 4.5 [4.3, 4.6], 3.5 [3.3, 4], and 3.5 [3.3, 3.8] for SS DESIRE, SS L1-SPIRiT, MB = 2 DESIRE, and MB = 2 SMS-slice-L1-SPIRiT, respectively, showing no statistically significant difference (p = 1 and p = 1 for SS and SMS, respectively) between L1-SPIRiT and the proposed DESIRE technique. The network inference time was ~100 ms per dynamic perfusion series with DESIRE, while the reconstruction time of L1-SPIRiT with GPU acceleration was ~ 30 min. It was concluded that DESIRE enabled fast and high-quality image reconstruction for both SS and SMS MB = 2 whole-heart high-resolution spiral perfusion imaging.