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A detailed dosimetric comparative study of IMRT and VMAT in normal brain tissues for nasopharyngeal carcinoma patients treated with radiotherapy. 鼻咽癌放疗后正常脑组织IMRT与VMAT的剂量学比较研究。
Pub Date : 2023-01-01 DOI: 10.3389/fradi.2023.1190763
Kainan Shao, Shuang Zheng, Yajuan Wang, Xue Bai, Hongying Luo, Fenglei Du

Background: Radiotherapy (RT) is the primary treatment for nasopharyngeal carcinoma (NPC). However, it can cause implicit RT-induced injury by irradiating normal brain tissue. To date, there have been no detailed reports on the radiated exact location in the brain, the corresponding radiation dose, and their relationship.

Methods: We analyzed 803 Chinese NPC patients treated with RT and used a CT brain template in a Montreal Neurological Institute (MNI) space to compare the group differences in RT dose distribution for different RT technologies (IMRT or VMAT).

Results: Brain regions that received high doses (>50 Gy) of radiation were mainly located in parts of the temporal and limbic lobes, where radioactive damage often occurs. Brain regions that accepted higher doses with IMRT were mainly located near the anterior region of the nasopharyngeal tumor, while brain regions that accepted higher doses with VMAT were mainly located near the posterior region of the tumor. No significant difference was detected between IMRT and VMAT for T1 stage patients. For T2 stage patients, differences were widely distributed, with VMAT showing a significant dose advantage in protecting the normal brain tissue. For T3 stage patients, VMAT showed an advantage in the superior temporal gyrus and limbic lobe, while IMRT showed an advantage in the posterior cerebellum. For T4 stage patients, VMAT showed a disadvantage in protecting the normal brain tissue. These results indicate that IMRT and VMAT have their own advantages in sparing different organs at risk (OARs) in the brain for different T stages of NPC patients treated with RT.

Conclusion: Our approach for analyzing dosimetric characteristics in a standard MNI space for Chinese NPC patients provides greater convenience in toxicity and dosimetry analysis with superior localization accuracy. Using this method, we found interesting differences from previous reports: VMAT showed a disadvantage in protecting the normal brain tissue for T4 stage NPC patients.

背景:放疗是鼻咽癌(NPC)的主要治疗方法。然而,它可以通过照射正常脑组织引起隐性rt诱导的损伤。迄今为止,还没有关于辐射在大脑中的确切位置、相应的辐射剂量及其关系的详细报道。方法:我们分析了803例接受放疗的中国鼻咽癌患者,并使用蒙特利尔神经病学研究所(MNI)空间的CT脑模板,比较不同放疗技术(IMRT或VMAT)的放疗剂量分布的组间差异。结果:高剂量(>50 Gy)辐射的脑区主要位于颞叶和边缘叶部分,易发生放射性损伤。接受高剂量IMRT的脑区主要位于鼻咽肿瘤前部附近,而接受高剂量VMAT的脑区主要位于肿瘤后部附近。在T1期患者中,IMRT和VMAT无显著差异。对于T2期患者,差异分布广泛,VMAT在保护正常脑组织方面具有显著的剂量优势。对于T3期患者,VMAT在颞上回和边缘叶表现出优势,而IMRT在小脑后部表现出优势。对于T4期患者,VMAT在保护正常脑组织方面表现出劣势。这些结果表明,IMRT和VMAT在保留不同T期鼻咽癌患者脑内不同危险器官(OARs)方面具有各自的优势。结论:我们的方法在标准MNI空间中分析中国鼻咽癌患者的剂量学特征,为毒性和剂量学分析提供了更大的便利,定位精度更高。使用这种方法,我们发现了与先前报道的有趣差异:VMAT在保护T4期NPC患者的正常脑组织方面表现出劣势。
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引用次数: 1
Language function of the superior longitudinal fasciculus in patients with arteriovenous malformation as evidenced by automatic fiber quantification. 动静脉畸形患者上纵束语言功能的自动纤维定量研究。
Pub Date : 2023-01-01 DOI: 10.3389/fradi.2023.1121879
Fangrong Zong, Zhaoyi You, Leqing Zhou, Xiaofeng Deng

The superior longitudinal fasciculus (SLF) is a major fiber tract involved in language processing and has been used to investigate language impairments and plasticity in many neurological diseases. The SLF is divided into four main branches that connect with different cortex regions, with two branches (SLF II, SLF III) being directly related to language. However, most white matter analyses consider the SLF as a single bundle, which may underestimate the relationship between these fiber bundles and language function. In this study, we investigated the differences between branches of the SLF in patients with arteriovenous malformation (AVM), which is a unique model to investigate language reorganization. We analyzed diffusion tensor imaging data of AVM patients and healthy controls to generate whole-brain fiber tractography, and then segmented the SLF into SLF II and III based on their distinctive waypoint regions. The SLF, SLF II, and III were further quantified, and four diffusion parameters of three branches were compared between the AVMs and controls. No significant diffusivity differences of the whole SLF were observed between two groups, however, the right SLF II and III in AVMs showed significant reorganization or impairment patterns as compared to the controls. Results demonstrating the need to subtracting SLF branches when studying structure-function relationship in neurological diseases that have SLF damage.

上纵束(SLF)是参与语言加工的主要纤维束,已被用于研究许多神经系统疾病的语言障碍和可塑性。SLF分为四个主要分支,与不同的皮层区域相连,其中两个分支(SLF II、SLF III)与语言直接相关。然而,大多数白质分析将SLF视为单个束,这可能低估了这些纤维束与语言功能之间的关系。在这项研究中,我们研究了动静脉畸形(AVM)患者SLF分支的差异,这是研究语言重组的独特模型。我们分析AVM患者和健康对照的弥散张量成像数据,生成全脑纤维束图,然后根据不同的路点区域将SLF划分为SLF II和SLF III。进一步量化SLF、SLF II和SLF III,比较avm与对照组3支4项扩散参数。两组间整个SLF的扩散率无显著差异,但与对照组相比,avm的右侧SLF II和III表现出明显的重组或损伤模式。结果表明,在研究具有SLF损伤的神经疾病的结构-功能关系时,需要减去SLF分支。
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引用次数: 0
Robust and accurate pulmonary nodule detection with self-supervised feature learning on domain adaptation. 利用域适应性自监督特征学习实现稳健准确的肺结节检测
Pub Date : 2022-12-15 eCollection Date: 2022-01-01 DOI: 10.3389/fradi.2022.1041518
Jingya Liu, Liangliang Cao, Oguz Akin, Yingli Tian

Medical imaging data annotation is expensive and time-consuming. Supervised deep learning approaches may encounter overfitting if trained with limited medical data, and further affect the robustness of computer-aided diagnosis (CAD) on CT scans collected by various scanner vendors. Additionally, the high false-positive rate in automatic lung nodule detection methods prevents their applications in daily clinical routine diagnosis. To tackle these issues, we first introduce a novel self-learning schema to train a pre-trained model by learning rich feature representatives from large-scale unlabeled data without extra annotation, which guarantees a consistent detection performance over novel datasets. Then, a 3D feature pyramid network (3DFPN) is proposed for high-sensitivity nodule detection by extracting multi-scale features, where the weights of the backbone network are initialized by the pre-trained model and then fine-tuned in a supervised manner. Further, a High Sensitivity and Specificity (HS2) network is proposed to reduce false positives by tracking the appearance changes among continuous CT slices on Location History Images (LHI) for the detected nodule candidates. The proposed method's performance and robustness are evaluated on several publicly available datasets, including LUNA16, SPIE-AAPM, LungTIME, and HMS. Our proposed detector achieves the state-of-the-art result of 90.6% sensitivity at 1/8 false positive per scan on the LUNA16 dataset. The proposed framework's generalizability has been evaluated on three additional datasets (i.e., SPIE-AAPM, LungTIME, and HMS) captured by different types of CT scanners.

医学影像数据标注既昂贵又耗时。如果使用有限的医疗数据进行训练,有监督的深度学习方法可能会遇到过拟合的问题,并进一步影响计算机辅助诊断(CAD)对不同扫描仪供应商收集的 CT 扫描数据的稳健性。此外,肺结节自动检测方法的高假阳性率也阻碍了其在日常临床诊断中的应用。为了解决这些问题,我们首先引入了一种新颖的自学模式,通过从大规模无标注数据中学习丰富的特征代表来训练预训练模型,无需额外标注,从而保证了在新数据集上的一致检测性能。然后,提出了一种三维特征金字塔网络(3DFPN),通过提取多尺度特征进行高灵敏度结核检测,其中骨干网络的权重由预训练模型初始化,然后以监督方式进行微调。此外,还提出了一种高灵敏度和高特异性(HS2)网络,通过跟踪位置历史图像(LHI)上连续 CT 切片之间的外观变化来减少检测到的结节候选者的假阳性。我们在 LUNA16、SPIE-AAPM、LungTIME 和 HMS 等多个公开数据集上评估了所提方法的性能和鲁棒性。我们提出的检测器在 LUNA16 数据集上达到了 90.6% 的灵敏度,每次扫描的误报率为 1/8。我们还在由不同类型 CT 扫描仪采集的另外三个数据集(即 SPIE-AAPM、LungTIME 和 HMS)上评估了所提出框架的通用性。
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引用次数: 0
Improving disease classification performance and explainability of deep learning models in radiology with heatmap generators. 利用热图生成器提高放射学中深度学习模型的疾病分类性能和可解释性。
Pub Date : 2022-10-11 eCollection Date: 2022-01-01 DOI: 10.3389/fradi.2022.991683
Akino Watanabe, Sara Ketabi, Khashayar Namdar, Farzad Khalvati

As deep learning is widely used in the radiology field, the explainability of Artificial Intelligence (AI) models is becoming increasingly essential to gain clinicians' trust when using the models for diagnosis. In this research, three experiment sets were conducted with a U-Net architecture to improve the disease classification performance while enhancing the heatmaps corresponding to the model's focus through incorporating heatmap generators during training. All experiments used the dataset that contained chest radiographs, associated labels from one of the three conditions ["normal", "congestive heart failure (CHF)", and "pneumonia"], and numerical information regarding a radiologist's eye-gaze coordinates on the images. The paper that introduced this dataset developed a U-Net model, which was treated as the baseline model for this research, to show how the eye-gaze data can be used in multi-modal training for explainability improvement and disease classification. To compare the classification performances among this research's three experiment sets and the baseline model, the 95% confidence intervals (CI) of the area under the receiver operating characteristic curve (AUC) were measured. The best method achieved an AUC of 0.913 with a 95% CI of [0.860, 0.966]. "Pneumonia" and "CHF" classes, which the baseline model struggled the most to classify, had the greatest improvements, resulting in AUCs of 0.859 with a 95% CI of [0.732, 0.957] and 0.962 with a 95% CI of [0.933, 0.989], respectively. The decoder of the U-Net for the best-performing proposed method generated heatmaps that highlight the determining image parts in model classifications. These predicted heatmaps, which can be used for the explainability of the model, also improved to align well with the radiologist's eye-gaze data. Hence, this work showed that incorporating heatmap generators and eye-gaze information into training can simultaneously improve disease classification and provide explainable visuals that align well with how the radiologist viewed the chest radiographs when making diagnosis.

随着深度学习在放射学领域的广泛应用,人工智能(AI)模型的可解释性变得越来越重要,以便在使用模型进行诊断时赢得临床医生的信任。本研究采用 U-Net 架构进行了三组实验,以提高疾病分类性能,同时通过在训练过程中加入热图生成器来增强与模型重点相对应的热图。所有实验都使用了包含胸片、三种病症之一的相关标签("正常"、"充血性心力衰竭(CHF)"和 "肺炎")以及放射科医生在图像上注视坐标的数字信息的数据集。介绍该数据集的论文开发了一个 U-Net 模型,并将其作为本研究的基线模型,以展示如何在多模态训练中使用眼球数据来提高可解释性和进行疾病分类。为了比较本研究的三个实验组和基线模型的分类性能,测量了接收者工作特征曲线下面积(AUC)的 95% 置信区间(CI)。最佳方法的 AUC 为 0.913,95% CI 为 [0.860, 0.966]。基线模型最难分类的 "肺炎 "和 "心房颤动 "类别得到了最大改善,AUC 分别为 0.859(95% CI 为 [0.732,0.957])和 0.962(95% CI 为 [0.933,0.989])。性能最佳的建议方法的 U-Net 解码器生成的热图突出了模型分类中的决定性图像部分。这些预测的热图可用于解释模型的可解释性,同时也与放射科医生的眼动数据保持一致。因此,这项工作表明,在训练中加入热图生成器和眼动信息可同时改进疾病分类,并提供可解释的视觉效果,使之与放射科医生在诊断时查看胸片的方式完全一致。
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引用次数: 0
Blunt splenic injury: Assessment of follow-up CT utility using quantitative volumetry. 钝性脾损伤:定量容积法评价随访CT效用。
Pub Date : 2022-07-01 Epub Date: 2022-07-22 DOI: 10.3389/fradi.2022.941863
David Dreizin, Theresa Yu, Kaitlynn Motley, Guang Li, Jonathan J Morrison, Yuanyuan Liang

Purpose: Trials of non-operative management (NOM) have become the standard of care for blunt splenic injury (BSI) in hemodynamically stable patients. However, there is a lack of consensus regarding the utility of follow-up CT exams and relevant CT features. The purpose of this study is to determine imaging predictors of splenectomy on follow-up CT using quantitative volumetric measurements.

Methods: Adult patients who underwent a trial of non-operative management (NOM) with follow-up CT performed for BSI between 2017 and 2019 were included (n = 51). Six patients (12% of cohort) underwent splenectomy; 45 underwent successful splenic salvage. Voxelwise measurements of splenic laceration, hemoperitoneum, and subcapsular hematoma were derived from portal venous phase images of admission and follow-up scans using 3D slicer. Presence/absence of pseudoaneurysm on admission and follow-up CT was assessed using arterial phase images. Multivariable logistic regression was used to determine independent predictors of decision to perform splenectomy.

Results: Factors significantly associated with splenectomy in bivariate analysis incorporated in multivariate logistic regression included final hemoperitoneum volume (p = 0.003), final subcapsular hematoma volume (p = 0.001), change in subcapsular hematoma volume between scans (p = 0.09) and new/persistent pseudoaneurysm (p = 0.003). Independent predictors of splenectomy in the logistic regression were final hemoperitoneum volume (unit OR = 1.43 for each 100 mL change; 95% CI: 0.99-2.06) and new/persistent pseudoaneurysm (OR = 160.3; 95% CI: 0.91-28315.3). The AUC of the model incorporating both variables was significantly higher than AAST grading (0.91 vs. 0.59, p = 0.025). Mean combined effective dose for admission and follow up CT scans was 37.4 mSv.

Conclusion: Follow-up CT provides clinically valuable information regarding the decision to perform splenectomy in BSI patients managed non-operatively. Hemoperitoneum volume and new or persistent pseudoaneurysm at follow-up are independent predictors of splenectomy.

目的:非手术治疗已成为血流动力学稳定的钝性脾损伤(BSI)患者的标准治疗方法。然而,关于随访CT检查的效用和相关CT特征缺乏共识。本研究的目的是通过定量体积测量来确定脾切除术在随访CT上的影像学预测因素。方法:纳入2017年至2019年期间接受非手术治疗(NOM)试验并随访CT治疗BSI的成年患者(n = 51)。6例患者(占队列的12%)行脾切除术;45例成功进行脾脏抢救。脾裂伤、腹膜出血和荚膜下血肿的体素测量来自入院时门静脉相图像和随访时的三维切片机扫描。入院时假性动脉瘤的存在/不存在以及随访时的CT检查采用动脉期图像进行评估。采用多变量logistic回归确定决定行脾切除术的独立预测因素。结果:双变量分析中与脾切除术显著相关的因素包括最终腹膜血肿体积(p = 0.003)、最终囊下血肿体积(p = 0.001)、扫描间囊下血肿体积变化(p = 0.09)和新发/持续性假性动脉瘤(p = 0.003)。在logistic回归中脾脏切除术的独立预测因子为最终腹膜血容量(单位OR = 1.43 /每100 mL变化;95% CI: 0.99-2.06)和新发/持续性假性动脉瘤(OR = 160.3;95% ci: 0.91-28315.3)。合并这两个变量的模型的AUC显著高于AAST分级(0.91 vs. 0.59, p = 0.025)。入院和随访CT扫描的平均联合有效剂量为37.4 mSv。结论:随访CT为非手术治疗BSI患者是否行脾切除术提供了有临床价值的信息。腹腔内血量和随访时新发或持续的假性动脉瘤是脾切除术的独立预测因素。
{"title":"Blunt splenic injury: Assessment of follow-up CT utility using quantitative volumetry.","authors":"David Dreizin,&nbsp;Theresa Yu,&nbsp;Kaitlynn Motley,&nbsp;Guang Li,&nbsp;Jonathan J Morrison,&nbsp;Yuanyuan Liang","doi":"10.3389/fradi.2022.941863","DOIUrl":"https://doi.org/10.3389/fradi.2022.941863","url":null,"abstract":"<p><strong>Purpose: </strong>Trials of non-operative management (NOM) have become the standard of care for blunt splenic injury (BSI) in hemodynamically stable patients. However, there is a lack of consensus regarding the utility of follow-up CT exams and relevant CT features. The purpose of this study is to determine imaging predictors of splenectomy on follow-up CT using quantitative volumetric measurements.</p><p><strong>Methods: </strong>Adult patients who underwent a trial of non-operative management (NOM) with follow-up CT performed for BSI between 2017 and 2019 were included (<i>n</i> = 51). Six patients (12% of cohort) underwent splenectomy; 45 underwent successful splenic salvage. Voxelwise measurements of splenic laceration, hemoperitoneum, and subcapsular hematoma were derived from portal venous phase images of admission and follow-up scans using 3D slicer. Presence/absence of pseudoaneurysm on admission and follow-up CT was assessed using arterial phase images. Multivariable logistic regression was used to determine independent predictors of decision to perform splenectomy.</p><p><strong>Results: </strong>Factors significantly associated with splenectomy in bivariate analysis incorporated in multivariate logistic regression included final hemoperitoneum volume (<i>p</i> = 0.003), final subcapsular hematoma volume (<i>p</i> = 0.001), change in subcapsular hematoma volume between scans (<i>p</i> = 0.09) and new/persistent pseudoaneurysm (<i>p</i> = 0.003). Independent predictors of splenectomy in the logistic regression were final hemoperitoneum volume (unit OR = 1.43 for each 100 mL change; 95% CI: 0.99-2.06) and new/persistent pseudoaneurysm (OR = 160.3; 95% CI: 0.91-28315.3). The AUC of the model incorporating both variables was significantly higher than AAST grading (0.91 vs. 0.59, <i>p</i> = 0.025). Mean combined effective dose for admission and follow up CT scans was 37.4 mSv.</p><p><strong>Conclusion: </strong>Follow-up CT provides clinically valuable information regarding the decision to perform splenectomy in BSI patients managed non-operatively. Hemoperitoneum volume and new or persistent pseudoaneurysm at follow-up are independent predictors of splenectomy.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9479763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40368001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Normal Appearing Ischaemic Brain Tissue on CT and Outcome After Intravenous Alteplase. CT 上正常外观的缺血性脑组织与静脉注射阿替普酶后的预后
Pub Date : 2022-06-22 eCollection Date: 2022-01-01 DOI: 10.3389/fradi.2022.902165
Grant Mair, Joanna M Wardlaw

Background and aims: The visibility of ischaemic brain lesions on non-enhanced CT increases with time. Obviously hypoattenuating lesions likely represent infarction. Conversely, viable ischaemic brain lesions may be non-visible on CT. We tested whether patients with normal appearing ischaemic brain tissue (NAIBT) on their initial CT are identifiable, and if NAIBT yields better outcomes with alteplase.

Methods: With data from the Third International Stroke Trial (IST-3, a large randomized-controlled trial of intravenous alteplase for ischaemic stroke) we used receiver-operating characteristic analysis to find a baseline National Institutes of Health Stroke Scale (NIHSS) threshold for identifying patients who developed medium-large ischaemic lesions within 48 h. From patients with baseline CT (acquired <6 h from stroke onset), we used this NIHSS threshold for selection and tested whether favorable outcome after alteplase (6-month Oxford Handicap Score 0-2) differed between patients with NAIBT vs. with those with visible lesions on baseline CT using binary logistic regression (controlled for age, NIHSS, time from stroke onset to CT).

Results: From 2,961 patients (median age 81 years, median 2.6 h from stroke onset, 1,534 [51.8%] female, 1,484 [50.1%] allocated alteplase), NIHSS>11 best identified those with medium-large ischaemic lesions (area under curve = 0.79, sensitivity = 72.3%, specificity = 71.9%). In IST-3, 1,404/2,961 (47.4%) patients had baseline CT and NIHSS>11. Of these, 745/1,404 (53.1%) had visible baseline ischaemic lesions, 659/1,404 (46.9%) did not (NAIBT). Adjusted odds ratio for favorable outcome after alteplase was 1.54 (95% confidence interval, 1.01-2.36), p = 0.045 among patients with NAIBT vs. 1.61 (0.97-2.67), p = 0.066 for patients with visible lesions, with no evidence of an alteplase-NAIBT interaction (p-value = 0.895).

Conclusions: Patients with ischaemic stroke and NIHSS >11 commonly develop sizeable ischaemic brain lesions by 48 h that may not be visible within 6 h of stroke onset. Invisible ischaemic lesions may indicate tissue viability. In IST-3, patients with this clinical-radiological mismatch allocated to alteplase achieved more favorable outcome than those allocated to control.

背景和目的:非增强 CT 上缺血性脑损伤的可见度会随着时间的推移而增加。明显的低增强病变可能代表梗死。相反,有活力的脑缺血病变可能在 CT 上不可见。我们测试了初次 CT 显示正常缺血性脑组织(NAIBT)的患者是否可以被识别,以及 NAIBT 是否能在使用阿替普酶后获得更好的疗效:我们利用第三次国际脑卒中试验(IST-3,一项静脉注射阿替普酶治疗缺血性脑卒中的大型随机对照试验)的数据,采用受体运算特征分析法找到了一个基线美国国立卫生研究院脑卒中量表(NIHSS)阈值,用于识别在 48 小时内出现中大型缺血性病变的患者:从 2,961 名患者(中位年龄 81 岁,中位卒中发病时间 2.6 小时,1,534 名 [51.8%] 女性,1,484 名 [50.1%] 患者接受了阿替普酶治疗)中,NIHSS>11 最能识别中度大面积缺血性病变患者(曲线下面积 = 0.79,灵敏度 = 72.3%,特异性 = 71.9%)。在 IST-3 中,1,404/2,961(47.4%)名患者有基线 CT 和 NIHSS>11。其中,745/1,404(53.1%)人有可见的基线缺血性病变,659/1,404(46.9%)人没有(NAIBT)。阿替普酶治疗后良好预后的调整赔率为:NAIBT 患者为 1.54(95% 置信区间,1.01-2.36),p = 0.045;可见病变患者为 1.61(0.97-2.67),p = 0.066,没有证据表明阿替普酶与 NAIBT 之间存在交互作用(p 值 = 0.895):结论:缺血性卒中且 NIHSS >11 的患者通常会在 48 小时内出现相当大的脑缺血病变,而这些病变在卒中发生后 6 小时内可能不可见。看不见的缺血性病变可能预示着组织的存活能力。在 IST-3 中,与对照组相比,临床放射学不匹配的患者接受阿替普酶治疗的预后更佳。
{"title":"Normal Appearing Ischaemic Brain Tissue on CT and Outcome After Intravenous Alteplase.","authors":"Grant Mair, Joanna M Wardlaw","doi":"10.3389/fradi.2022.902165","DOIUrl":"10.3389/fradi.2022.902165","url":null,"abstract":"<p><strong>Background and aims: </strong>The visibility of ischaemic brain lesions on non-enhanced CT increases with time. Obviously hypoattenuating lesions likely represent infarction. Conversely, viable ischaemic brain lesions may be non-visible on CT. We tested whether patients with normal appearing ischaemic brain tissue (NAIBT) on their initial CT are identifiable, and if NAIBT yields better outcomes with alteplase.</p><p><strong>Methods: </strong>With data from the Third International Stroke Trial (IST-3, a large randomized-controlled trial of intravenous alteplase for ischaemic stroke) we used receiver-operating characteristic analysis to find a baseline National Institutes of Health Stroke Scale (NIHSS) threshold for identifying patients who developed medium-large ischaemic lesions within 48 h. From patients with baseline CT (acquired <6 h from stroke onset), we used this NIHSS threshold for selection and tested whether favorable outcome after alteplase (6-month Oxford Handicap Score 0-2) differed between patients with NAIBT vs. with those with visible lesions on baseline CT using binary logistic regression (controlled for age, NIHSS, time from stroke onset to CT).</p><p><strong>Results: </strong>From 2,961 patients (median age 81 years, median 2.6 h from stroke onset, 1,534 [51.8%] female, 1,484 [50.1%] allocated alteplase), NIHSS>11 best identified those with medium-large ischaemic lesions (area under curve = 0.79, sensitivity = 72.3%, specificity = 71.9%). In IST-3, 1,404/2,961 (47.4%) patients had baseline CT and NIHSS>11. Of these, 745/1,404 (53.1%) had visible baseline ischaemic lesions, 659/1,404 (46.9%) did not (NAIBT). Adjusted odds ratio for favorable outcome after alteplase was 1.54 (95% confidence interval, 1.01-2.36), p = 0.045 among patients with NAIBT vs. 1.61 (0.97-2.67), <i>p</i> = 0.066 for patients with visible lesions, with no evidence of an alteplase-NAIBT interaction (<i>p</i>-value = 0.895).</p><p><strong>Conclusions: </strong>Patients with ischaemic stroke and NIHSS >11 commonly develop sizeable ischaemic brain lesions by 48 h that may not be visible within 6 h of stroke onset. Invisible ischaemic lesions may indicate tissue viability. In IST-3, patients with this clinical-radiological mismatch allocated to alteplase achieved more favorable outcome than those allocated to control.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"2 ","pages":"902165"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10364986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10262400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Comparison of Image Quality and Radiation Dose Between Single-Energy and Dual-Energy Images for the Brain With Stereotactic Frames on Dual-Energy Cerebral CT. 双能脑CT立体定向帧单能和双能脑图像质量和辐射剂量的比较。
Pub Date : 2022-06-10 eCollection Date: 2022-01-01 DOI: 10.3389/fradi.2022.899100
Xiaojing Zhao, Wang Chao, Yi Shan, Jingkai Li, Cheng Zhao, Miao Zhang, Jie Lu

Background: Preoperative stereotactic planning of deep brain stimulation (DBS) using computed tomography (CT) imaging in patients with Parkinson's disease (PD) is of clinical interest. However, frame-induced metal artifacts are common in clinical practice, which can be challenging for neurosurgeons to visualize brain structures.

Objectives: To evaluate the image quality and radiation exposure of patients with stereotactic frame brain CT acquired using a dual-source CT (DSCT) system in single- and dual-energy modes.

Materials and methods: We included 60 consecutive patients with Parkinson's disease (PD) and randomized them into two groups. CT images of the brain were performed using DSCT (Group A, an 80/Sn150 kVp dual-energy mode; Group B, a 120 kVp single-energy mode). One set of single-energy images (120 kVp) and 10 sets of virtual monochromatic images (50-140 keV) were obtained. Subjective image analysis of overall image quality was performed using a five-point Likert scale. For objective image quality evaluation, CT values, image noise, signal-to-noise ratio (SNR), and contrast-to-noise (CNR) were calculated. The radiation dose was recorded for each patient.

Results: The mean effective radiation dose was reduced in the dual-energy mode (1.73 mSv ± 0.45 mSv) compared to the single-energy mode (3.16 mSv ± 0.64 mSv) (p < 0.001). Image noise was reduced by 46-52% for 120-140 keV VMI compared to 120 kVp images (both p < 0.01). CT values were higher at 100-140 keV than at 120 kVp images. At 120-140 keV, CT values of brain tissue showed significant differences at the level of the most severe metal artifacts (all p < 0.05). SNR was also higher in the dual-energy mode 90-140 keV compared to 120 kVp images, showing a significant difference between the two groups at 120-140 keV (all p < 0.01). The CNR was significantly better in Group A for 60-140 keV VMI compared to Group B (both p < 0.001). The highest subjective image scores were found in the 120 keV images, while 110-140 keV images had significantly higher scores than 120 kVp images (all p < 0.05).

Conclusion: DSCT images using dual-energy modes provide better objective and subjective image quality for patients with PD at lower radiation doses compared to single-energy modes and facilitate brain tissue visualization with stereotactic frame DBS procedures.

背景:应用计算机断层扫描(CT)成像对帕金森病(PD)患者进行术前立体定向脑深部刺激(DBS)计划具有临床意义。然而,框架诱导的金属伪影在临床实践中很常见,这对神经外科医生可视化大脑结构可能是一个挑战。目的:评价双源CT(DSCT)系统在单能和双能模式下获得的立体定向框架脑CT患者的图像质量和辐射暴露。材料和方法:我们纳入了60名连续的帕金森病患者,并将他们随机分为两组。使用DSCT进行大脑的CT图像(A组,80/Sn150kVp双能量模式;B组,120kVp单能量模式)。获得了一组单能量图像(120kVp)和10组虚拟单色图像(50-140keV)。使用五点Likert量表对整体图像质量进行主观图像分析。为了客观评估图像质量,计算了CT值、图像噪声、信噪比(SNR)和对比度与噪声(CNR)。记录每位患者的辐射剂量。结果:与单能量模式(3.16mSv±0.64mSv)相比,双能量模式下的平均有效辐射剂量(1.73mSv±0.45mSv)降低了(p<0.001)。与120kVp图像相比,120-140keV VMI图像噪声降低了46-52%(均p<0.01)。100-140keV图像的CT值高于120kVp。在120-140keV时,脑组织的CT值在最严重的金属伪影水平上显示出显著差异(均p<0.05)。与120kVp图像相比,90-140keV双能量模式下的SNR也更高,在120-140keV时,两组之间存在显著差异(均p<0.01)。在60-140keV VMI时,a组的CNR明显优于B组(均<0.001)。120keV图像的主观图像得分最高,而110-140keV图像的得分明显高于120kVp图像(均p<0.05)。结论:与单能量模式相比,双能量模式的DSCT图像在较低辐射剂量下为PD患者提供了更好的客观和主观图像质量,并有助于立体定向框架DBS程序的脑组织可视化。
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引用次数: 0
Informative and Reliable Tract Segmentation for Preoperative Planning. 用于术前规划的信息量大且可靠的韧带分段。
Pub Date : 2022-05-18 eCollection Date: 2022-01-01 DOI: 10.3389/fradi.2022.866974
Oeslle Lucena, Pedro Borges, Jorge Cardoso, Keyoumars Ashkan, Rachel Sparks, Sebastien Ourselin

Identifying white matter (WM) tracts to locate eloquent areas for preoperative surgical planning is a challenging task. Manual WM tract annotations are often used but they are time-consuming, suffer from inter- and intra-rater variability, and noise intrinsic to diffusion MRI may make manual interpretation difficult. As a result, in clinical practice direct electrical stimulation is necessary to precisely locate WM tracts during surgery. A measure of WM tract segmentation unreliability could be important to guide surgical planning and operations. In this study, we use deep learning to perform reliable tract segmentation in combination with uncertainty quantification to measure segmentation unreliability. We use a 3D U-Net to segment white matter tracts. We then estimate model and data uncertainty using test time dropout and test time augmentation, respectively. We use a volume-based calibration approach to compute representative predicted probabilities from the estimated uncertainties. In our findings, we obtain a Dice of ≈0.82 which is comparable to the state-of-the-art for multi-label segmentation and Hausdorff distance <10mm. We demonstrate a high positive correlation between volume variance and segmentation errors, which indicates a good measure of reliability for tract segmentation ad uncertainty estimation. Finally, we show that calibrated predicted volumes are more likely to encompass the ground truth segmentation volume than uncalibrated predicted volumes. This study is a step toward more informed and reliable WM tract segmentation for clinical decision-making.

识别白质(WM)束以确定术前手术计划的有力区域是一项具有挑战性的任务。人工WM束注释经常被使用,但这种方法耗时较长,存在评分者之间和评分者内部的差异,而且弥散核磁共振成像固有的噪声可能会使人工判读变得困难。因此,在临床实践中,手术时需要直接电刺激来精确定位 WM 束。WM束分割不可靠度的测量方法对于指导手术规划和操作非常重要。在本研究中,我们利用深度学习进行可靠的束分割,并结合不确定性量化来测量分割的不可靠度。我们使用三维 U-Net 对白质束进行分割。然后,我们分别使用测试时间遗漏和测试时间增强来估计模型和数据的不确定性。我们使用基于体积的校准方法,根据估计的不确定性计算出有代表性的预测概率。在我们的研究结果中,我们得到的 Dice 值≈0.82,与多标签分割和 Hausdorff 距离 mm 的最先进水平相当。我们证明了体积方差和分割误差之间的高度正相关性,这表明对切口分割和不确定性估计的可靠性有很好的衡量标准。最后,我们表明,与未经校准的预测体积相比,校准的预测体积更有可能包含地面实况分割体积。这项研究朝着为临床决策提供更明智、更可靠的 WM 道分割迈出了一步。
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引用次数: 0
Advanced MRI Protocols to Discriminate Glioma From Treatment Effects: State of the Art and Future Directions. 区分胶质瘤和治疗效果的先进 MRI 方案:技术现状与未来方向。
Pub Date : 2022-04-15 eCollection Date: 2022-01-01 DOI: 10.3389/fradi.2022.809373
Dania G Malik, Tanya J Rath, Javier C Urcuyo Acevedo, Peter D Canoll, Kristin R Swanson, Jerrold L Boxerman, C Chad Quarles, Kathleen M Schmainda, Terry C Burns, Leland S Hu

In the follow-up treatment of high-grade gliomas (HGGs), differentiating true tumor progression from treatment-related effects, such as pseudoprogression and radiation necrosis, presents an ongoing clinical challenge. Conventional MRI with and without intravenous contrast serves as the clinical benchmark for the posttreatment surveillance imaging of HGG. However, many advanced imaging techniques have shown promise in helping better delineate the findings in indeterminate scenarios, as posttreatment effects can often mimic true tumor progression on conventional imaging. These challenges are further confounded by the histologic admixture that can commonly occur between tumor growth and treatment-related effects within the posttreatment bed. This review discusses the current practices in the surveillance imaging of HGG and the role of advanced imaging techniques, including perfusion MRI and metabolic MRI.

在高级别胶质瘤(HGGs)的后续治疗中,如何区分真正的肿瘤进展和治疗相关影响(如假性进展和辐射坏死)是一项持续的临床挑战。静脉注射或不注射造影剂的传统磁共振成像是 HGG 治疗后监测成像的临床基准。然而,许多先进的成像技术已显示出帮助更好地描述不确定情况下的发现的前景,因为治疗后的影响往往会模仿传统成像上真正的肿瘤进展。在治疗后病床中,肿瘤生长和治疗相关效应之间通常会出现组织学混杂,这进一步加剧了上述挑战。本综述讨论了目前对 HGG 进行监测成像的做法以及先进成像技术(包括灌注 MRI 和代谢 MRI)的作用。
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引用次数: 0
Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model. 整合阿尔茨海默病的转录组学、基因组学和成像:联邦模式。
Pub Date : 2022-01-21 eCollection Date: 2021-01-01 DOI: 10.3389/fradi.2021.777030
Jianfeng Wu, Yanxi Chen, Panwen Wang, Richard J Caselli, Paul M Thompson, Junwen Wang, Yalin Wang

Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomics-the study of gene expression-also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person's genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.

每 9 个 65 岁及以上的老人中就有 1 人患有阿尔茨海默病(AD),随着全球人口的老龄化,该病已成为一个紧迫的公共卫生问题。在临床实践中,结构性磁共振成像(sMRI)是最容易获得和广泛使用的诊断成像方式。此外,全基因组关联研究(GWAS)和转录组学--基因表达研究--在了解注意力缺失症的病因和进展方面也发挥着重要作用。目前已开发出先进的成像遗传学系统,以发现持续影响大脑功能和结构的遗传因素。然而,迄今为止,大多数研究都集中在脑 sMRI 与 GWAS 或脑 sMRI 与转录组学之间的关系上。据我们所知,很少有方法能发现和推断 sMRI、GWAS 和转录组学之间的多模态关系。为了解决这个问题,我们提出了一个新的联合模型--基因型-表达-成像数据整合(GEIDI),以确定基因和转录组对大脑 sMRI 测量的影响。脑成像测量和基因表达之间的关系可在单核苷酸多态性(SNP)水平上取决于个人的基因型,从而使推论具有适应性和个性化。我们在公开的阿尔茨海默病神经影像倡议(ADNI)数据集上进行了大量实验。实验结果表明,在检测与阿尔茨海默病相关的遗传和转录组因素方面,我们提出的方法优于最先进的表达定量性状位点(eQTL)方法,而且在整合来自多个位点的数据时性能稳定。我们的GEIDI方法可为图像生物标志物、基因型和基因表达之间的关系提供新的见解,并有助于发现潜在的AD药物治疗的新基因靶点。
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引用次数: 0
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Frontiers in radiology
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