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CT imaging-based radiomics signatures improve prognosis prediction in postoperative colorectal cancer. 基于CT成像的放射组学特征提高结直肠癌术后预后预测。
IF 3 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2023-01-01 DOI: 10.3233/XST-230090
Yan Kong, Muchen Xu, Xianding Wei, Danqi Qian, Yuan Yin, Zhaohui Huang, Wenchao Gu, Leyuan Zhou

Objective: To investigate the use of non-contrast-enhanced (NCE) and contrast-enhanced (CE) CT radiomics signatures (Rad-scores) as prognostic factors to help improve the prediction of the overall survival (OS) of postoperative colorectal cancer (CRC) patients.

Methods: A retrospective analysis was performed on 65 CRC patients who underwent surgical resection in our hospital as the training set, and 19 patient images retrieved from The Cancer Imaging Archive (TCIA) as the external validation set. In training, radiomics features were extracted from the preoperative NCE/CE-CT, then selected through 5-fold cross validation LASSO Cox method and used to construct Rad-scores. Models derived from Rad-scores and clinical factors were constructed and compared. Kaplan-Meier analyses were also used to compare the survival probability between the high- and low-risk Rad-score groups. Finally, a nomogram was developed to predict the OS.

Results: In training, a clinical model achieved a C-index of 0.796 (95% CI: 0.722-0.870), while clinical and two Rad-scores combined model performed the best, achieving a C-index of 0.821 (95% CI: 0.743-0.899). Furthermore, the models with the CE-CT Rad-score yielded slightly better performance than that of NCE-CT in training. For the combined model with CE-CT Rad-scores, a C-index of 0.818 (95% CI: 0.742-0.894) and 0.774 (95% CI: 0.556-0.992) were achieved in both the training and validation sets. Kaplan-Meier analysis demonstrated a significant difference in survival probability between the high- and low-risk groups. Finally, the areas under the receiver operating characteristics (ROC) curves for the model were 0.904, 0.777, and 0.843 for 1, 3, and 5-year survival, respectively.

Conclusion: NCE-CT or CE-CT radiomics and clinical combined models can predict the OS for CRC patients, and both Rad-scores are recommended to be included when available.

目的:探讨非对比增强(NCE)和对比增强(CE) CT放射组学特征(Rad-scores)作为预测结直肠癌(CRC)术后患者总生存期(OS)的预后因素。方法:回顾性分析在我院行手术切除的65例结直肠癌患者作为训练集,从癌症影像档案(the Cancer Imaging Archive, TCIA)检索的19例患者图像作为外部验证集。在训练中,从术前NCE/CE-CT中提取放射组学特征,然后通过5倍交叉验证LASSO Cox法选择并用于构建rad评分。根据rad评分和临床因素建立模型并进行比较。Kaplan-Meier分析也用于比较高风险和低风险拉德评分组之间的生存概率。最后,开发了一个nomogram来预测OS。结果:在训练中,临床模型的C-index为0.796 (95% CI: 0.722-0.870),临床和两个rad评分联合模型的C-index表现最好,为0.821 (95% CI: 0.743-0.899)。此外,具有CE-CT rad评分的模型在训练中的表现略优于NCE-CT模型。对于与CE-CT rad评分相结合的模型,训练集和验证集的c指数分别为0.818 (95% CI: 0.742-0.894)和0.774 (95% CI: 0.556-0.992)。Kaplan-Meier分析显示高危组和低危组的生存率有显著差异。最后,该模型1年、3年和5年生存率的受试者工作特征(ROC)曲线下面积分别为0.904、0.777和0.843。结论:NCE-CT或CE-CT放射组学及临床联合模型可预测结直肠癌患者的OS,建议在有条件时纳入两种rad评分。
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引用次数: 0
Applying a nomogram based on preoperative CT to predict early recurrence of laryngeal squamous cell carcinoma after surgery. 应用基于术前CT的形态图预测喉鳞癌术后早期复发。
IF 3 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2023-01-01 DOI: 10.3233/XST-221320
Yao Yao, Chuanliang Jia, Haicheng Zhang, Yakui Mou, Cai Wang, Xiao Han, Pengyi Yu, Ning Mao, Xicheng Song

Purpose: To identify the value of a computed tomography (CT)-based radiomics model to predict probability of early recurrence (ER) in patients diagnosed with laryngeal squamous cell carcinoma (LSCC) after surgery.

Materials and method: Pre-operative CT scans of 140 LSCC patients treated by surgery are reviewed and selected. These patients are randomly split into the training set (n = 97) and test set (n = 43). The regions of interest of each patient were delineated manually by two senior radiologists. Radiomics features are extracted from CT images acquired in non-enhanced, arterial, and venous phases. Variance threshold, one-way ANOVA, and least absolute shrinkage and selection operator algorithm are used for feature selection. Then, radiomics models are built with five algorithms namely, k-nearest neighbor (KNN), logistic regression (LR), linear support vector machine (LSVM), radial basis function SVM (RSVM), and polynomial SVM (PSVM). Clinical factors are selected using univariate and multivariate logistic regressions. Last, a radiomics nomogram incorporating the radiomics signature and clinical factors is built to predict ER and its efficiency is evaluated by receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) is also used to evaluate clinical usefulness.

Results: Four features are remarkably associated with ER in patients with LSCC. Applying to test set, the area under the ROC curves (AUCs) of KNN, LR, LSVM, RSVM, and PSVM are 0.936, 0.855, 0.845, 0.829, and 0.794, respectively. The radiomics nomogram shows better discrimination (with AUC: 0.939, 95% CI: 0.867-0.989) than the best radiomics model and the clinical model. Predicted and actual ERs in the calibration curves are in good agreement. DCA shows that the radiomics nomogram is clinically useful.

Conclusion: The radiomics nomogram, as a noninvasive prediction tool, exhibits favorable performance for ER prediction of LSCC patients after surgery.

目的:探讨基于计算机断层扫描(CT)的放射组学模型在预测喉鳞癌(LSCC)术后早期复发(ER)概率中的价值。材料与方法:回顾并选择140例经手术治疗的LSCC患者的术前CT扫描。这些患者被随机分为训练集(n = 97)和测试集(n = 43)。每位患者感兴趣的区域由两位资深放射科医师手动划定。放射组学特征是从非增强期、动脉期和静脉期获得的CT图像中提取的。采用方差阈值、单因素方差分析、最小绝对收缩和选择算子算法进行特征选择。然后,利用k近邻(KNN)、逻辑回归(LR)、线性支持向量机(LSVM)、径向基函数支持向量机(RSVM)和多项式支持向量机(PSVM)五种算法构建放射组学模型。临床因素选择采用单因素和多因素logistic回归。最后,建立结合放射组学特征和临床因素的放射组学nomogram预测ER,并通过受试者工作特征(ROC)曲线和校准曲线评价其有效性。决策曲线分析(DCA)也用于评估临床有用性。结果:四个特征与LSCC患者的ER显著相关。应用于测试集,KNN、LR、LSVM、RSVM、PSVM的ROC曲线下面积(auc)分别为0.936、0.855、0.845、0.829、0.794。放射组学nomogram (AUC: 0.939, 95% CI: 0.867 ~ 0.989)优于最佳放射组学模型和临床模型。校正曲线上的预测电阻抗与实际电阻抗吻合较好。DCA显示放射组学图在临床上是有用的。结论:放射组学影像学作为一种无创预测工具,对LSCC术后ER预测有较好的效果。
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引用次数: 1
Application of radiomics based on chest CT-enhanced dual-phase imaging in the immunotherapy of non-small cell lung cancer. 基于胸部CT增强双相成像的放射组学在癌症免疫治疗中的应用。
IF 3 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2023-01-01 DOI: 10.3233/XST-230189
Ze-Peng Ma, Xiao-Lei Li, Kai Gao, Tian-Le Zhang, Heng-Di Wang, Yong-Xia Zhao

Objective: To explore the value of applying computed tomography (CT) radiomics based on different CT-enhanced phases to determine the immunotherapeutic efficacy of non-small cell lung cancer (NSCLC).

Methods: 106 patients with NSCLC who underwent immunotherapy are randomly divided into training (74) and validation (32) groups. CT-enhanced arterial and venous phase images of patients before treatment are collected. Region-of-interest (ROI) is segmented on the CT-enhanced images, and the radiomic features are extracted. One-way analysis of variance and least absolute shrinkage and selection operator (LASSO) are used to screen the optimal radiomics features and analyze the association between radiomics features and immunotherapy efficacy. The area under receiver-operated characteristic curves (AUC) along with the sensitivity and specificity are computed to evaluate diagnostic effectiveness.

Results: LASSO regression analysis screens and selects 6 and 8 optimal features in the arterial and venous phases images, respectively. Applying to the training group, AUCs based on CT-enhanced arterial and venous phase images are 0.867 (95% CI:0.82-0.94) and 0.880 (95% CI:0.86-0.91) with the sensitivities of 73.91% and 76.19%, and specificities of 66.67% and 72.19%, respectively, while in validation group, AUCs of the arterial and venous phase images are 0.732 (95% CI:0.71-0.78) and 0.832 (95% CI:0.78-0.91) with sensitivities of 75.00% and 76.00%, and specificities of 73.07% and 75.00%, respectively. There are no significant differences between AUC values computed from arterial phases and venous phases images in both training and validation groups (P < 0.05).

Conclusion: The optimally selected radiomics features computed from CT-enhanced different-phase images can provide new imaging marks to evaluate efficacy of the targeted therapy in NSCLC with a high diagnostic value.

目的:探讨基于不同CT增强期的计算机断层扫描(CT)放射组学在判断癌症(NSCLC)免疫治疗效果中的价值。方法:将106例接受免疫治疗的NSCLC患者随机分为训练组(74例)和验证组(32例)。收集患者治疗前的CT增强动脉和静脉期图像。在CT增强图像上分割感兴趣区域(ROI),并提取放射学特征。单向方差分析和最小绝对收缩选择算子(LASSO)用于筛选最佳放射组学特征,并分析放射组学特性与免疫治疗疗效之间的关系。计算受试者操作特征曲线下面积(AUC)以及灵敏度和特异性,以评估诊断有效性。结果:LASSO回归分析分别在动脉期和静脉期图像中筛选出6个和8个最佳特征。应用于训练组,基于CT增强动脉和静脉期图像的AUCs分别为0.867(95%CI:0.82-0.94)和0.880(95%CI:0.86-0.91),敏感性分别为73.91%和76.19%,特异性分别为66.67%和72.19%,而在验证组,动脉期和静脉期图像的AUC分别为0.732(95%可信区间:0.71-0.78)和0.832(95%置信区间:0.78-0.91),敏感性分别为75.00%和76.00%,特异性分别为73.07%和75.00%。在训练组和验证组中,根据动脉期和静脉期图像计算的AUC值之间没有显著差异(P <  结论:从CT增强的不同相位图像中计算出的最佳放射组学特征可以为评估靶向治疗NSCLC的疗效提供新的成像标记,具有较高的诊断价值。
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引用次数: 0
Dosimetric properties of PASSAG polymer gel dosimeter in electron beam radiotherapy using magnetic resonance imaging. PASSAG聚合物凝胶剂量计在电子束放射治疗中的磁共振成像剂量学特性。
IF 3 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2023-01-01 DOI: 10.3233/XST-230073
Tiancheng Zhang, Yasir Q Almajidi, Sameer A Awad, Firas Rahi Alhachami, Maher Abdulfadhil Gatea, Wesam R Kadhum

Background: Several physical factors such as photon beam energy, electron beam energy, and dose rate may affect the dosimetric properties of polymer gel dosimeters. The photon beam energy and dose rate dependence of PASSAG gel dosimeter were previously evaluated.

Objective: This study aims to assess the dosimetric properties of the optimized PASSAG gel samples in various electron beam energies.

Methods: The optimized PASSAG gel samples are first fabricated and irradiated to various electron energies (5, 7, 10 and 12 MeV). Then, the response (R2) and sensitivity of gel samples are analyzed by magnetic resonance imaging technique at a dose range of 0 to 10 Gy, scanning room temperature range of 15 to 22 °C, and post-irradiation time range of 1 to 30 days.

Results: The R2-dose response and sensitivity of gel samples do not change under the evaluated electron beam energies (the differences are less than 5%). Furthermore, a dose resolution range of 11 to 38 cGy is obtained for the gel samples irradiated to different electron beam energies. Moreover, the findings show that the R2-dose response and sensitivity dependence of gel samples on electron beam energy varies over different scanning room temperatures and post-irradiation times.

Conclusion: The dosimetric assessment of the optimized PASSAG gel samples provides the promising data for this dosimeter during electron beam radiotherapy.

背景:光子束能量、电子束能量和剂量率等物理因素可能影响聚合物凝胶剂量计的剂量测定性能。对PASSAG凝胶剂量计的光子束流能量和剂量率依赖性进行了评价。目的:研究优化后的PASSAG凝胶样品在不同电子束能量下的剂量学特性。方法:首先制备优化后的PASSAG凝胶样品,并在不同电子能(5、7、10和12 MeV)下辐照。在0 ~ 10 Gy的剂量范围、15 ~ 22℃的室温扫描范围和1 ~ 30天的辐照后时间范围内,通过磁共振成像技术分析凝胶样品的响应(R2)和灵敏度。结果:凝胶样品的r2剂量响应和灵敏度在评价的电子束能量下没有变化(差异小于5%)。此外,凝胶样品在不同电子束能量照射下的剂量分辨范围为11 ~ 38 cGy。此外,研究结果表明,凝胶样品的r2剂量响应和灵敏度依赖于电子束能量随扫描室温和辐照后时间的不同而变化。结论:优化后的PASSAG凝胶样品的剂量学评价为该剂量计在电子束放疗中的应用提供了有前景的数据。
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引用次数: 0
Diagnostic performance of the thyroid imaging reporting and data system improved by color-coded acoustic radiation force pulse imaging. 彩色编码声辐射力脉冲成像提高甲状腺影像报告和数据系统的诊断性能。
IF 3 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2023-01-01 DOI: 10.3233/XST-221359
Kai-Mei Lian, Teng Lin

Objective: To explore the value of color-coded virtual touch tissue imaging (CCV) using acoustic radiation force pulse technology (ARFI) in diagnosing malignant thyroid nodules.

Methods: Images including 189 thyroid nodules were collected as training samples and a binary logistic regression analysis was used to calculate regression coefficients for Thyroid Imaging Reporting and Data System (TI-RADS) and CCV. An integrated prediction model (TI-RADS+CCV) was then developed based on the regression coefficients. Another testing dataset involving 40 thyroid nodules was used to validate and compare the diagnostic performance of TI-RADS, CCV, and the integrated predictive models using the receiver operating characteristic (ROC) curves.

Results: Both TI-RADS and CCV are independent predictors. The diagnostic performance advantage of CCV is insignificant compared to TI-RADS (P = 0.61). However, the diagnostic performance of the integrated prediction model is significantly higher than that of TI-RADS or CCV (all P < 0.05). Applying to the validation image dateset, the integrated predictive model yields an area under the curve (AUC) of 0.880.

Conclusions: Developing a new predictive model that integrates the regression coefficients calculated from TI-RADS and CCV enables to achieve the superior performance of thyroid nodule diagnosis to that of using TI-RADS or CCV alone.

目的:探讨声辐射力脉冲技术(ARFI)彩色编码虚拟触摸组织成像(CCV)在甲状腺恶性结节诊断中的价值。方法:收集189张甲状腺结节图像作为训练样本,采用二元logistic回归分析计算甲状腺影像学报告与数据系统(TI-RADS)和CCV的回归系数。基于回归系数建立TI-RADS+CCV综合预测模型。另一个包含40个甲状腺结节的测试数据集用于验证和比较TI-RADS、CCV和使用受试者工作特征(ROC)曲线的综合预测模型的诊断性能。结果:TI-RADS和CCV均为独立预测因子。与TI-RADS相比,CCV的诊断性能优势不显著(P = 0.61)。结论:将TI-RADS和CCV计算的回归系数进行整合,建立新的预测模型,可以获得比单独使用TI-RADS或CCV更好的甲状腺结节诊断效果。
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引用次数: 0
Analytical reconstructions of full-scan multiple source-translation computed tomography under large field of views. 大视场下全扫描多源平移计算机断层扫描的解析重建。
IF 3 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2023-01-01 DOI: 10.3233/XST-230138
Zhisheng Wang, Yue Liu, Shunli Wang, Xingyuan Bian, Zongfeng Li, Junning Cui

This paper is to investigate the high-quality analytical reconstructions of multiple source-translation computed tomography (mSTCT) under an extended field of view (FOV). Under the larger FOVs, the previously proposed backprojection filtration (BPF) algorithms for mSTCT, including D-BPF and S-BPF (their differences are different derivate directions along the detector and source, respectively), make some errors and artifacts in the reconstructed images due to a backprojection weighting factor and the half-scan mode, which deviates from the intention of mSTCT imaging. In this paper, to achieve reconstruction with as little error as possible under the extremely extended FOV, we combine the full-scan mSTCT (F-mSTCT) geometry with the previous BPF algorithms to study the performance and derive a suitable redundancy-weighted function for F-mSTCT. The experimental results indicate FS-BPF can get high-quality, stable images under the extremely extended FOV of imaging a large object, though it requires more projections than FD-BPF. Finally, for different practical requirements in extending FOV imaging, we give suggestions on algorithm selection.

本文旨在研究扩展视场(FOV)下多源平移计算机断层扫描(mSTCT)的高质量分析重建。在较大视场下,已有的mSTCT反投影滤波(backprojection filtering, BPF)算法,包括D-BPF和S-BPF算法(它们的区别在于沿检测器和源的导数方向不同),由于反投影加权因子和半扫描模式,在重建图像中产生了一些误差和伪影,偏离了mSTCT成像的初衷。本文将全扫描mSTCT (F-mSTCT)几何结构与已有的BPF算法相结合,研究了F-mSTCT几何结构的性能,并推导出适合于F-mSTCT的冗余加权函数。实验结果表明,虽然FS-BPF比FD-BPF需要更多的投影量,但在成像大物体的极大视场下,FS-BPF可以获得高质量、稳定的图像。最后,针对扩展视场成像的不同实际需求,给出了算法选择建议。
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引用次数: 1
VDVM: An automatic vertebrae detection and vertebral segment matching framework for C-arm X-ray image identification. VDVM:用于c臂x射线图像识别的自动椎体检测和椎段匹配框架。
IF 3 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2023-01-01 DOI: 10.3233/XST-230025
Ruyi Zhang, Yiwei Hu, Kai Zhang, Guanhua Lan, Liang Peng, Yabin Zhu, Wei Qian, Yudong Yao

Background: C-arm fluoroscopy, as an effective diagnosis and treatment method for spine surgery, can help doctors perform surgery procedures more precisely. In clinical surgery, the surgeon often determines the specific surgical location by comparing C-arm X-ray images with digital radiography (DR) images. However, this heavily relies on the doctor's experience.

Objective: In this study, we design a framework for automatic vertebrae detection as well as vertebral segment matching (VDVM) for the identification of vertebrae in C-arm X-ray images.

Methods: The proposed VDVM framework is mainly divided into two parts: vertebra detection and vertebra matching. In the first part, a data preprocessing method is used to improve the image quality of C-arm X-ray images and DR images. The YOLOv3 model is then used to detect the vertebrae, and the vertebral regions are extracted based on their position. In the second part, the Mobile-Unet model is first used to segment the vertebrae contour of the C-arm X-ray image and DR image based on vertebral regions respectively. The inclination angle of the contour is then calculated using the minimum bounding rectangle and corrected accordingly. Finally, a multi-vertebra strategy is applied to measure the visual information fidelity for the vertebral region, and the vertebrae are matched based on the measured results.

Results: We use 382 C-arm X-ray images and 203 full length X-ray images to train the vertebra detection model, and achieve a mAP of 0.87 in the test dataset of 31 C-arm X-ray images and 0.96 in the test dataset of 31 lumbar DR images. Finally, we achieve a vertebral segment matching accuracy of 0.733 on 31 C-arm X-ray images.

Conclusions: A VDVM framework is proposed, which performs well for the detection of vertebrae and achieves good results in vertebral segment matching.

背景:c臂透视作为一种有效的脊柱外科诊疗手段,可以帮助医生更精确地进行手术操作。在临床手术中,外科医生通常通过比较c臂x线图像与数字x线图像(DR)来确定具体的手术位置。然而,这在很大程度上依赖于医生的经验。目的:在本研究中,我们设计了一个自动椎骨检测和椎段匹配(VDVM)框架,用于识别c臂x射线图像中的椎骨。方法:提出的VDVM框架主要分为两部分:椎体检测和椎体匹配。第一部分采用数据预处理方法提高c臂x射线图像和DR图像的图像质量。然后使用YOLOv3模型检测椎体,并根据其位置提取椎体区域。在第二部分中,首先利用Mobile-Unet模型分别对c臂x射线图像和基于椎体区域的DR图像进行椎体轮廓分割。然后使用最小边界矩形计算轮廓的倾斜角并进行相应的校正。最后,采用多椎体策略测量椎体区域的视觉信息保真度,并根据测量结果对椎体进行匹配。结果:我们使用382张c臂x线图像和203张全长x线图像来训练椎体检测模型,在31张c臂x线图像的测试数据集中实现了0.87的mAP,在31张腰椎DR图像的测试数据集中实现了0.96的mAP。最后,我们在31张c臂x射线图像上实现了0.733的椎段匹配精度。结论:提出了一种VDVM框架,该框架对椎体的检测效果较好,在椎段匹配方面取得了较好的效果。
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引用次数: 0
Correction of motion artifact in CL based on MAFusNet. 基于mausnet的CL运动伪影校正。
IF 3 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2023-01-01 DOI: 10.3233/XST-221335
Tong Jia, Liu Shi, Cunfeng Wei, Rongjian Shi, Baodong Liu

Computed laminography (CL) is one of the best methods for nondestructive testing of plate-like objects. If the object and the detector move continually while the scanning is being done, the data acquisition efficiency of CL will be significantly increased. However, the projection images will contain motion artifact as a result. A multi-angle fusion network (MAFusNet) is presented in order to correct the motion artifact of CL projection images considering the properties of CL projection images. The multi-angle fusion module significantly increases the ability of MAFusNet to deblur by using data from nearby projection images, and the feature fusion module lessens information loss brought on by data flow between the encoders. In contrast to conventional deblurring networks, the MAFusNet network employs synthetic datasets for training and performed well on realistic data, proving the network's outstanding generalization. The multi-angle fusion-based network has a significant improvement in the correction effect of CL motion artifact through ablation study and comparison with existing classical deblurring networks, and the synthetic training dataset can also significantly lower the training cost, which can effectively improve the quality and efficiency of CL imaging in industrial nondestructive testing.

计算机层析成像(CL)是板状物体无损检测的最佳方法之一。如果在扫描过程中,物体和探测器不断移动,则CL的数据采集效率将显著提高。然而,投影图像会因此包含运动伪影。针对CL投影图像本身的特点,提出了一种多角度融合网络(MAFusNet)来校正CL投影图像的运动伪影。多角度融合模块显著提高了mausnet利用附近投影图像的数据去模糊的能力,特征融合模块减少了编码器之间数据流动带来的信息丢失。与传统的去模糊网络相比,MAFusNet网络使用合成数据集进行训练,并在现实数据上表现良好,证明了网络出色的泛化能力。通过烧蚀研究和与现有经典去模糊网络的比较,基于多角度融合的网络对CL运动伪影的校正效果有了明显的提高,并且合成的训练数据集也可以显著降低训练成本,可以有效地提高工业无损检测中CL成像的质量和效率。
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引用次数: 0
BiRPN-YOLOvX: A weighted bidirectional recursive feature pyramid algorithm for lung nodule detection. BiRPN-YOLOvX:一种用于肺结节检测的加权双向递归特征金字塔算法。
IF 3 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2023-01-01 DOI: 10.3233/XST-221310
Liying Han, Fugai Li, Hengyong Yu, Kewen Xia, Qiyuan Xin, Xiaoyu Zou

Background: Lung cancer has the second highest cancer mortality rate in the world today. Although lung cancer screening using CT images is a common way for early lung cancer detection, accurately detecting lung nodules remains a challenged issue in clinical practice.

Objective: This study aims to develop a new weighted bidirectional recursive pyramid algorithm to address the problems of small size of lung nodules, large proportion of background region, and complex lung structures in lung nodule detection of CT images.

Methods: First, the weighted bidirectional recursive feature pyramid network (BiPRN) is proposed, which can increase the ability of network model to extract feature information and achieve multi-scale fusion information. Second, a CBAM_CSPDarknet53 structure is developed to incorporate an attention mechanism as a feature extraction module, which can aggregate both spatial information and channel information of the feature map. Third, the weighted BiRPN and CBAM_CSPDarknet53 are applied to the YOLOvX model for lung nodule detection experiments, named BiRPN-YOLOvX, where YOLOvX represents different versions of YOLO. To verify the effectiveness of our weighted BiRPN and CBAM_ CSPDarknet53 algorithm, they are fused with different models of YOLOv3, YOLOv4 and YOLOv5, and extensive experiments are carried out using the publicly available lung nodule datasets LUNA16 and LIDC-IDRI. The training set of LUNA16 contains 949 images, and the validation and testing sets each contain 118 images. There are 1987, 248 and 248 images in LIDC-IDRI's training, validation and testing sets, respectively.

Results: The sensitivity of lung nodule detection using BiRPN-YOLOv5 reaches 98.7% on LUNA16 and 96.2% on LIDC-IDRI, respectively.

Conclusion: This study demonstrates that the proposed new method has potential to help improve the sensitivity of lung nodule detection in future clinical practice.

背景:肺癌是当今世界上死亡率第二高的癌症。虽然利用CT图像进行肺癌筛查是早期发现肺癌的常用方法,但在临床实践中,准确发现肺结节仍然是一个具有挑战性的问题。目的:针对肺结节CT图像检测中存在的肺结节体积小、背景区域占比大、肺结构复杂等问题,提出一种新的加权双向递归金字塔算法。方法:首先,提出加权双向递归特征金字塔网络(BiPRN),提高网络模型提取特征信息的能力,实现多尺度信息融合;其次,构建CBAM_CSPDarknet53结构,将注意力机制作为特征提取模块,对特征图的空间信息和通道信息进行聚合;第三,将加权BiRPN和CBAM_CSPDarknet53应用到YOLOvX模型中进行肺结节检测实验,命名为BiRPN-YOLOvX,其中YOLOvX代表不同版本的YOLO。为了验证加权BiRPN和CBAM_ CSPDarknet53算法的有效性,将它们与YOLOv3、YOLOv4和YOLOv5的不同模型融合,并使用公开的肺结节数据集LUNA16和LIDC-IDRI进行了大量实验。LUNA16的训练集包含949张图像,验证集和测试集各包含118张图像。LIDC-IDRI的训练集、验证集和测试集分别有1987张、248张和248张图像。结果:BiRPN-YOLOv5对LUNA16和LIDC-IDRI肺结节的检测灵敏度分别为98.7%和96.2%。结论:本研究表明,新方法在未来的临床实践中有可能有助于提高肺结节检测的敏感性。
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引用次数: 0
Feasibility of Tc-99 m sestamibi uptake quantification with few-projection emission tomography. 少投影发射层析成像技术量化Tc-99微波辐射吸收的可行性。
IF 3 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2023-01-01 DOI: 10.3233/XST-221303
A M García-Esparza, H M Garnica-Garza

Background: Molecular breast imaging uses Tc-99 m sestamibi to obtain functional images of the breast. Determining the Tc-99 m sestamibi uptake in volumes of interest in the breast may be useful in assessing the response to neoadjuvant chemotherapy or for the purposes of breast cancer risk assessment.

Purpose: To determine, using Monte Carlo simulation, if emission tomography can be used to quantify the uptake of Tc-99 m sestamibi in molecular breast imaging and if so, to determine the accuracy as a function of the number of projections used in the reconstruction process.

Methods: In this study, two voxelized breast models are implemented with different ratios of fibroglandular to fatty tissue and tumoral masses of varying dimensions. Monte Carlo simulation is used to calculate sets of projections, which assumes that each tumoral mass contains a given Tc-99 m activity. Projections are also calculated for a calibration phantom in order to correlate the known activity with the image pixel value. For each case, the total number of calculated projections is 36 and the reconstruction is carried out for 36, 18, 9, 7 and 5 projections, respectively, using an open source image reconstruction toolbox.

Results: Study data show that determination of Tc-99 m sestamibi uptake with and average error of 7% can be carried out with as little as 7 projections.

Conclusions: Molecular breast emission tomography enables to accurately determine the Tc-99 m sestamibi tumoral mass uptake with the number of projections very close to the number of images currently acquired in clinical practice.

背景:分子乳房成像使用tc - 99m sestamibi获得乳房的功能图像。确定tc - 99msestamibi在乳腺感兴趣的体积中的摄取可能有助于评估对新辅助化疗的反应或用于乳腺癌风险评估。目的:通过蒙特卡罗模拟,确定发射层析成像是否可以用于量化Tc-99 m sestamibi在乳腺分子成像中的摄取,如果可以,确定其准确性作为重建过程中使用的投影数量的函数。方法:在本研究中,采用不同比例的纤维腺与脂肪组织和不同尺寸的肿瘤块体素化乳房模型。蒙特卡罗模拟用于计算投影集,假设每个肿瘤肿块包含给定的tc - 99m活性。为了将已知活动与图像像素值相关联,还计算了校准幻影的投影。对于每种情况,计算的投影总数为36个,分别对36个、18个、9个、7个和5个投影进行重建,使用开源图像重建工具箱。结果:研究数据表明,仅用7个投影即可测定Tc-99的吸收,平均误差为7%。结论:分子乳腺发射断层扫描能够准确判断tc - 99m肿瘤肿块的摄取,其投影数量与目前临床实践中获得的图像数量非常接近。
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Journal of X-Ray Science and Technology
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