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The Picasso's skepticism on computer science and the dawn of generative AI: questions after the answers to keep "machines-in-the-loop". 毕加索对计算机科学的怀疑论与生成式人工智能的曙光:答案之后的问题,让 "机器在环中"。
IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-07-24 DOI: 10.1186/s41747-024-00485-7
Filippo Pesapane, Renato Cuocolo, Francesco Sardanelli

Starting from Picasso's quote ("Computers are useless. They can only give you answers"), we discuss the introduction of generative artificial intelligence (AI), including generative adversarial networks (GANs) and transformer-based architectures such as large language models (LLMs) in radiology, where their potential in reporting, image synthesis, and analysis is notable. However, the need for improvements, evaluations, and regulations prior to clinical use is also clear. Integration of LLMs into clinical workflow needs cautiousness, to avoid or at least mitigate risks associated with false diagnostic suggestions. We highlight challenges in synthetic image generation, inherent biases in AI models, and privacy concerns, stressing the importance of diverse training datasets and robust data privacy measures. We examine the regulatory landscape, including the 2023 Executive Order on AI in the United States and the 2024 AI Act in the European Union, which set standards for AI applications in healthcare. This manuscript contributes to the field by emphasizing the necessity of maintaining the human element in medical procedures while leveraging generative AI, advocating for a "machines-in-the-loop" approach.

从毕加索的名言("计算机是无用的,它们只能给你答案")开始,我们讨论了在放射学中引入生成式人工智能(AI),包括生成式对抗网络(GANs)和基于变换器的架构,如大型语言模型(LLMs),它们在报告、图像合成和分析方面的潜力引人注目。然而,在临床使用之前,显然还需要进行改进、评估和规范。将 LLM 纳入临床工作流程需要谨慎,以避免或至少降低与错误诊断建议相关的风险。我们强调了合成图像生成的挑战、人工智能模型的固有偏差和隐私问题,强调了多样化训练数据集和健全的数据隐私措施的重要性。我们研究了监管情况,包括美国的 2023 年人工智能行政命令和欧盟的 2024 年人工智能法案,这些法案为人工智能在医疗保健领域的应用制定了标准。本手稿强调在利用生成式人工智能的同时,有必要在医疗程序中保留人类元素,倡导 "机器在环 "的方法,从而为该领域做出贡献。
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引用次数: 0
Anisotropy component of DTI reveals long-term neuroinflammation following repetitive mild traumatic brain injury in rats. DTI 的各向异性成分揭示了大鼠重复性轻度脑损伤后的长期神经炎症。
IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-07-24 DOI: 10.1186/s41747-024-00490-w
Ching Cheng, Chia-Feng Lu, Bao-Yu Hsieh, Shu-Hui Huang, Yu-Chieh Jill Kao

Background: This study aimed to investigate the long-term effects of repetitive mild traumatic brain injury (rmTBI) with varying inter-injury intervals by measuring diffusion tensor metrics, including mean diffusivity (MD), fractional anisotropy (FA), and diffusion magnitude (L) and pure anisotropy (q).

Methods: Eighteen rats were randomly divided into three groups: short-interval rmTBI (n = 6), long-interval rmTBI (n = 6), and sham controls (n = 6). MD, FA, L, and q values were analyzed from longitudinal diffusion tensor imaging at days 50 and 90 after rmTBI. Immunohistochemical staining against neurons, astrocytes, microglia, and myelin was performed. Analysis of variance, Pearson correlation coefficient, and simple linear regression model were used.

Results: At day 50 post-rmTBI, lower cortical FA and q values were shown in the short-interval group (p ≤ 0.038). In contrast, higher FA and q values were shown for the long-interval group (p ≤ 0.039) in the corpus callosum. In the ipsilesional external capsule and internal capsule, no significant changes were found in FA, while lower L and q values were shown in the short-interval group (p ≤ 0.028) at day 90. The q values in the external capsule and internal capsule were negatively correlated with the number of microglial cells and the total number of astroglial cells (p ≤ 0.035).

Conclusion: Tensor scalar measurements, such as L and q values, are sensitive to exacerbated chronic injury induced by rmTBI with shorter inter-injury intervals and reflect long-term astrogliosis induced by the cumulative injury.

Relevance statement: Tensor scalar measurements, including L and q values, are potential DTI metrics for detecting long-term and subtle injury following rmTBI; in particular, q values may be used for quantifying remote white matter (WM) changes following rmTBI.

Key points: The alteration of L and q values was demonstrated after chronic repetitive mild traumatic brain injury. Changing q values were observed in the impact site and remote WM. The lower q values in the remote WM were associated with astrogliosis.

研究背景本研究旨在通过测量弥散张量指标,包括平均弥散率(MD)、分数各向异性(FA)、弥散幅度(L)和纯各向异性(q),研究不同损伤间隔的重复性轻度脑损伤(rmTBI)的长期影响:将 18 只大鼠随机分为三组:短间隔 rmTBI(n = 6)、长间隔 rmTBI(n = 6)和假对照组(n = 6)。对rmTBI后第50天和第90天的纵向弥散张量成像中的MD、FA、L和q值进行分析。对神经元、星形胶质细胞、小胶质细胞和髓鞘进行了免疫组化染色。采用了方差分析、皮尔逊相关系数和简单线性回归模型:结果:在脑损伤后第 50 天,短间隔组的皮质 FA 值和 q 值较低 (p ≤ 0.038)。相比之下,长间隔组胼胝体的 FA 值和 q 值较高(p ≤ 0.039)。在第 90 天,同侧外囊和内囊的 FA 没有发现显著变化,而短间隔组的 L 值和 q 值较低 (p ≤ 0.028)。外囊和内囊的 q 值与小胶质细胞数量和星形胶质细胞总数呈负相关(p ≤ 0.035):张量标度测量,如L值和q值,对损伤间隔较短的rmTBI诱发的慢性损伤加重很敏感,并能反映累积损伤诱发的长期星形胶质细胞病变:张量标度测量,包括 L 值和 q 值,是检测 rmTBI 后长期和细微损伤的潜在 DTI 指标;特别是,q 值可用于量化 rmTBI 后远端白质(WM)的变化:要点:慢性重复性轻度脑损伤后,L 值和 q 值发生了改变。在撞击部位和远端白质中观察到了q值的变化。远端WM中较低的q值与星形胶质细胞增多有关。
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引用次数: 0
Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study. 用于检测乳房 X 光照片上乳腺动脉钙化的深度传输学习:一项比较研究。
IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-07-15 DOI: 10.1186/s41747-024-00478-6
Nazanin Mobini, Davide Capra, Anna Colarieti, Moreno Zanardo, Giuseppe Baselli, Francesco Sardanelli

Introduction: Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs.

Material and methods: Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.

Results: The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images.

Conclusion: Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources.

Relevance statement: Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs.

Key points: • We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16's superior performance in localizing BAC.

简介乳房动脉钙化(BAC)是常规乳房 X 光检查中常见的偶然发现,被认为是心血管疾病(CVD)风险的性别特异性生物标志物。之前的研究表明,预训练卷积网络(CNN)VCG16 对自动检测 BAC 非常有效。在本研究中,我们通过与其他十种 CNN 的比较分析进一步测试了该方法:本回顾性研究纳入了 1,493 名女性的四视角标准乳腺 X 光检查结果,并由专家将其标记为 BAC 或非 BAC。比较研究使用了 11 个经过预训练的卷积网络(CNN),这些网络来自 Xception、VGG、ResNetV2、MobileNet 和 DenseNet 等五种架构,深度各不相同,并针对二元 BAC 分类任务进行了微调。性能评估包括接受者操作特征曲线下面积(AUC-ROC)分析、F1-分数(精确度和召回率的调和平均值)以及用于视觉解释的广义梯度加权类激活映射(Grad-CAM++):数据集显示,BAC 发生率为 194/1,493 名女性(13.0%)和 581/5,972 幅图像(9.7%)。在重新训练的模型中,VGG、MobileNet 和 DenseNet 的结果最有希望,在训练和独立测试子集中的 AUC-ROC 均大于 0.70。在测试 F1 分数方面,VGG16 排名第一,高于 MobileNet(0.51)和 VGG19(0.46)。定性分析显示,VGG16 生成的 Grad-CAM++ 热图始终优于其他生成的热图,能对图像中的钙化区域进行更精细、更有辨别力的定位:深度迁移学习在乳房 X 光照片的 BAC 自动检测中大有可为,其中相对较浅的网络表现出了卓越的性能,需要更短的训练时间和更少的资源:深度迁移学习是一种很有前途的方法,它能提高乳房 X 光照片上 BAC 的报告率,并有助于开发高效的工具,利用大规模乳房 X 光照片筛查计划对女性进行心血管风险分层:- 我们测试了不同的预训练卷积网络 (CNN),以检测乳房 X 光照片上的 BAC。- VGG和MobileNet表现出了良好的性能,超过了更深、更复杂的同类产品。- 使用 Grad-CAM++ 进行的可视化解释凸显了 VGG16 在定位 BAC 方面的卓越性能。
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引用次数: 0
Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance. 开发和验证四腔心肌磁共振的人工智能分割。
IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-07-12 DOI: 10.1186/s41747-024-00477-7
Hosamadin Assadi, Samer Alabed, Rui Li, Gareth Matthews, Kavita Karunasaagarar, Bahman Kasmai, Sunil Nair, Zia Mehmood, Ciaran Grafton-Clarke, Peter P Swoboda, Andrew J Swift, John P Greenwood, Vassilios S Vassiliou, Sven Plein, Rob J van der Geest, Pankaj Garg

Background: Cardiac magnetic resonance (CMR) in the four-chamber plane offers comprehensive insight into the volumetrics of the heart. We aimed to develop an artificial intelligence (AI) model of time-resolved segmentation using the four-chamber cine.

Methods: A fully automated deep learning algorithm was trained using retrospective multicentre and multivendor data of 814 subjects. Validation, reproducibility, and mortality prediction were evaluated on an independent cohort of 101 subjects.

Results: The mean age of the validation cohort was 54 years, and 66 (65%) were males. Left and right heart parameters demonstrated strong correlations between automated and manual analysis, with a ρ of 0.91-0.98 and 0.89-0.98, respectively, with minimal bias. All AI four-chamber volumetrics in repeatability analysis demonstrated high correlation (ρ = 0.99-1.00) and no bias. Automated four-chamber analysis underestimated both left ventricular (LV) and right ventricular (RV) volumes compared to ground-truth short-axis cine analysis. Two correction factors for LV and RV four-chamber analysis were proposed based on systematic bias. After applying the correction factors, a strong correlation and minimal bias for LV volumetrics were observed. During a mean follow-up period of 6.75 years, 16 patients died. On stepwise multivariable analysis, left atrial ejection fraction demonstrated an independent association with death in both manual (hazard ratio (HR) = 0.96, p = 0.003) and AI analyses (HR = 0.96, p < 0.001).

Conclusion: Fully automated four-chamber CMR is feasible, reproducible, and has the same real-world prognostic value as manual analysis. LV volumes by four-chamber segmentation were comparable to short-axis volumetric assessment.

Trials registration: ClinicalTrials.gov: NCT05114785.

Relevance statement: Integrating fully automated AI in CMR promises to revolutionise clinical cardiac assessment, offering efficient, accurate, and prognostically valuable insights for improved patient care and outcomes.

Key points: • Four-chamber cine sequences remain one of the most informative acquisitions in CMR examination. • This deep learning-based, time-resolved, fully automated four-chamber volumetric, functional, and deformation analysis solution. • LV and RV were underestimated by four-chamber analysis compared to ground truth short-axis segmentation. • Correction bias for both LV and RV volumes by four-chamber segmentation, minimises the systematic bias.

背景:四腔平面的心脏磁共振(CMR)可全面了解心脏的容积。我们的目标是开发一种使用四腔CMR进行时间分辨分割的人工智能(AI)模型:方法:使用 814 名受试者的回顾性多中心和多供应商数据训练全自动深度学习算法。对 101 名受试者组成的独立队列进行了验证、可重复性和死亡率预测评估:验证组群的平均年龄为 54 岁,男性 66 人(占 65%)。左心和右心参数在自动分析和手动分析之间显示出很强的相关性,ρ分别为0.91-0.98和0.89-0.98,偏差极小。重复性分析中的所有人工智能四腔容积均显示出高度相关性(ρ = 0.99-1.00),且无偏差。与地面实况短轴 cine 分析相比,自动四腔分析低估了左心室和右心室容积。根据系统性偏差,为左心室和右心室四腔分析提出了两个校正因子。应用校正因子后,观察到左心室容积测量的相关性很强,偏差很小。在平均 6.75 年的随访期间,16 名患者死亡。在逐步多变量分析中,手动分析(危险比 (HR) = 0.96,P = 0.003)和人工智能分析(HR = 0.96,P 结论:左房射血分数与死亡有独立关联:全自动四腔 CMR 是可行的、可重复的,并且在现实世界中具有与人工分析相同的预后价值。四腔分割得出的左心室容积与短轴容积评估结果相当:试验注册:ClinicalTrials.gov:NCT05114785.Relevance statement:在CMR中整合全自动人工智能有望彻底改变临床心脏评估,为改善患者护理和预后提供高效、准确和有价值的见解:- 四腔Cine序列仍然是CMR检查中信息量最大的采集之一。- 这套基于深度学习、时间分辨、全自动的四腔容积、功能和形变分析解决方案,可对四腔CT序列的左心室和左心室容积进行分析。- 与地面真实短轴分割相比,四腔分析低估了左心室和左心室容积。- 通过四腔分割纠正左心室和左心室容积偏差,最大限度地减少系统性偏差。
{"title":"Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance.","authors":"Hosamadin Assadi, Samer Alabed, Rui Li, Gareth Matthews, Kavita Karunasaagarar, Bahman Kasmai, Sunil Nair, Zia Mehmood, Ciaran Grafton-Clarke, Peter P Swoboda, Andrew J Swift, John P Greenwood, Vassilios S Vassiliou, Sven Plein, Rob J van der Geest, Pankaj Garg","doi":"10.1186/s41747-024-00477-7","DOIUrl":"10.1186/s41747-024-00477-7","url":null,"abstract":"<p><strong>Background: </strong>Cardiac magnetic resonance (CMR) in the four-chamber plane offers comprehensive insight into the volumetrics of the heart. We aimed to develop an artificial intelligence (AI) model of time-resolved segmentation using the four-chamber cine.</p><p><strong>Methods: </strong>A fully automated deep learning algorithm was trained using retrospective multicentre and multivendor data of 814 subjects. Validation, reproducibility, and mortality prediction were evaluated on an independent cohort of 101 subjects.</p><p><strong>Results: </strong>The mean age of the validation cohort was 54 years, and 66 (65%) were males. Left and right heart parameters demonstrated strong correlations between automated and manual analysis, with a ρ of 0.91-0.98 and 0.89-0.98, respectively, with minimal bias. All AI four-chamber volumetrics in repeatability analysis demonstrated high correlation (ρ = 0.99-1.00) and no bias. Automated four-chamber analysis underestimated both left ventricular (LV) and right ventricular (RV) volumes compared to ground-truth short-axis cine analysis. Two correction factors for LV and RV four-chamber analysis were proposed based on systematic bias. After applying the correction factors, a strong correlation and minimal bias for LV volumetrics were observed. During a mean follow-up period of 6.75 years, 16 patients died. On stepwise multivariable analysis, left atrial ejection fraction demonstrated an independent association with death in both manual (hazard ratio (HR) = 0.96, p = 0.003) and AI analyses (HR = 0.96, p < 0.001).</p><p><strong>Conclusion: </strong>Fully automated four-chamber CMR is feasible, reproducible, and has the same real-world prognostic value as manual analysis. LV volumes by four-chamber segmentation were comparable to short-axis volumetric assessment.</p><p><strong>Trials registration: </strong>ClinicalTrials.gov: NCT05114785.</p><p><strong>Relevance statement: </strong>Integrating fully automated AI in CMR promises to revolutionise clinical cardiac assessment, offering efficient, accurate, and prognostically valuable insights for improved patient care and outcomes.</p><p><strong>Key points: </strong>• Four-chamber cine sequences remain one of the most informative acquisitions in CMR examination. • This deep learning-based, time-resolved, fully automated four-chamber volumetric, functional, and deformation analysis solution. • LV and RV were underestimated by four-chamber analysis compared to ground truth short-axis segmentation. • Correction bias for both LV and RV volumes by four-chamber segmentation, minimises the systematic bias.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"77"},"PeriodicalIF":3.7,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11239622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591638","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: 0
High-resolution and highly accelerated MRI T2 mapping as a tool to characterise renal tumour subtypes and grades. 高分辨率和高度加速的磁共振成像 T2 图是描述肾脏肿瘤亚型和分级的工具。
IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-07-10 DOI: 10.1186/s41747-024-00476-8
Ines Horvat-Menih, Hao Li, Andrew N Priest, Shaohang Li, Andrew B Gill, Iosif A Mendichovszky, Susan T Francis, Anne Y Warren, Brent O'Carrigan, Sarah J Welsh, James O Jones, Antony C P Riddick, James N Armitage, Thomas J Mitchell, Grant D Stewart, Ferdia A Gallagher

Background: Clinical imaging tools to probe aggressiveness of renal masses are lacking, and T2-weighted imaging as an integral part of magnetic resonance imaging protocol only provides qualitative information. We developed high-resolution and accelerated T2 mapping methods based on echo merging and using k-t undersampling and reduced flip angles (TEMPURA) and tested their potential to quantify differences between renal tumour subtypes and grades.

Methods: Twenty-four patients with treatment-naïve renal tumours were imaged: seven renal oncocytomas (RO); one eosinophilic/oncocytic renal cell carcinoma; two chromophobe RCCs (chRCC); three papillary RCCs (pRCC); and twelve clear cell RCCs (ccRCC). Median, kurtosis, and skewness of T2 were quantified in tumours and in the normal-adjacent kidney cortex and were compared across renal tumour subtypes and between ccRCC grades.

Results: High-resolution TEMPURA depicted the tumour structure at improved resolution compared to conventional T2-weighted imaging. The lowest median T2 values were present in pRCC (high-resolution, 51 ms; accelerated, 45 ms), which was significantly lower than RO (high-resolution; accelerated, p = 0.012) and ccRCC (high-resolution, p = 0.019; accelerated, p = 0.008). ROs showed the lowest kurtosis (high-resolution, 3.4; accelerated, 4.0), suggestive of low intratumoural heterogeneity. Lower T2 values were observed in higher compared to lower grade ccRCCs (grades 2, 3 and 4 on high-resolution, 209 ms, 151 ms, and 106 ms; on accelerated, 172 ms, 160 ms, and 102 ms, respectively), with accelerated TEMPURA showing statistical significance in comparison (p = 0.037).

Conclusions: Both high-resolution and accelerated TEMPURA showed marked potential to quantify differences across renal tumour subtypes and between ccRCC grades.

Trial registration: ClinicalTrials.gov, NCT03741426 . Registered on 13 November 2018.

Relevance statement: The newly developed T2 mapping methods have improved resolution, shorter acquisition times, and promising quantifiable readouts to characterise incidental renal masses.

背景:目前还缺乏探查肾脏肿块侵袭性的临床成像工具,T2加权成像作为磁共振成像方案中不可或缺的一部分,只能提供定性信息。我们开发了基于回波合并、使用 k-t 欠采样和减小翻转角度(TEMPURA)的高分辨率加速 T2 映射方法,并测试了其量化肾脏肿瘤亚型和分级差异的潜力:对24例未接受过治疗的肾肿瘤患者进行了成像:7例肾肿瘤细胞瘤(RO)、1例嗜酸性/单核细胞肾细胞癌、2例嗜铬性RCC(chRCC)、3例乳头状RCC(pRCC)和12例透明细胞RCC(ccRCC)。对肿瘤和正常邻近肾皮质的 T2 中位数、峰度和偏度进行了量化,并对不同肾肿瘤亚型和不同级别的 ccRCC 进行了比较:与传统的 T2 加权成像相比,高分辨率 TEMPURA 能以更高的分辨率显示肿瘤结构。中位 T2 值最低的是 pRCC(高分辨率,51 毫秒;加速成像,45 毫秒),明显低于 RO(高分辨率;加速成像,p = 0.012)和 ccRCC(高分辨率,p = 0.019;加速成像,p = 0.008)。RO的峰度最低(高分辨率,3.4;加速度,4.0),表明瘤内异质性较低。与低级别ccRCC相比,高级别ccRCC的T2值更低(2、3和4级在高分辨率下分别为209毫秒、151毫秒和106毫秒;在加速TEMPURA下分别为172毫秒、160毫秒和102毫秒),加速TEMPURA与之相比具有统计学意义(p = 0.037):结论:高分辨率和加速 TEMPURA 在量化肾脏肿瘤亚型和 ccRCC 分级之间的差异方面都显示出明显的潜力:ClinicalTrials.gov, NCT03741426 .注册时间:2018年11月13日.相关性声明:新开发的T2映射方法具有更高的分辨率、更短的采集时间和有前景的可量化读数,可用于描述附带肾肿块的特征。
{"title":"High-resolution and highly accelerated MRI T2 mapping as a tool to characterise renal tumour subtypes and grades.","authors":"Ines Horvat-Menih, Hao Li, Andrew N Priest, Shaohang Li, Andrew B Gill, Iosif A Mendichovszky, Susan T Francis, Anne Y Warren, Brent O'Carrigan, Sarah J Welsh, James O Jones, Antony C P Riddick, James N Armitage, Thomas J Mitchell, Grant D Stewart, Ferdia A Gallagher","doi":"10.1186/s41747-024-00476-8","DOIUrl":"10.1186/s41747-024-00476-8","url":null,"abstract":"<p><strong>Background: </strong>Clinical imaging tools to probe aggressiveness of renal masses are lacking, and T2-weighted imaging as an integral part of magnetic resonance imaging protocol only provides qualitative information. We developed high-resolution and accelerated T2 mapping methods based on echo merging and using k-t undersampling and reduced flip angles (TEMPURA) and tested their potential to quantify differences between renal tumour subtypes and grades.</p><p><strong>Methods: </strong>Twenty-four patients with treatment-naïve renal tumours were imaged: seven renal oncocytomas (RO); one eosinophilic/oncocytic renal cell carcinoma; two chromophobe RCCs (chRCC); three papillary RCCs (pRCC); and twelve clear cell RCCs (ccRCC). Median, kurtosis, and skewness of T2 were quantified in tumours and in the normal-adjacent kidney cortex and were compared across renal tumour subtypes and between ccRCC grades.</p><p><strong>Results: </strong>High-resolution TEMPURA depicted the tumour structure at improved resolution compared to conventional T2-weighted imaging. The lowest median T2 values were present in pRCC (high-resolution, 51 ms; accelerated, 45 ms), which was significantly lower than RO (high-resolution; accelerated, p = 0.012) and ccRCC (high-resolution, p = 0.019; accelerated, p = 0.008). ROs showed the lowest kurtosis (high-resolution, 3.4; accelerated, 4.0), suggestive of low intratumoural heterogeneity. Lower T2 values were observed in higher compared to lower grade ccRCCs (grades 2, 3 and 4 on high-resolution, 209 ms, 151 ms, and 106 ms; on accelerated, 172 ms, 160 ms, and 102 ms, respectively), with accelerated TEMPURA showing statistical significance in comparison (p = 0.037).</p><p><strong>Conclusions: </strong>Both high-resolution and accelerated TEMPURA showed marked potential to quantify differences across renal tumour subtypes and between ccRCC grades.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov, NCT03741426 . Registered on 13 November 2018.</p><p><strong>Relevance statement: </strong>The newly developed T<sub>2</sub> mapping methods have improved resolution, shorter acquisition times, and promising quantifiable readouts to characterise incidental renal masses.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"76"},"PeriodicalIF":3.7,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11233479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141564725","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: 0
Sample size calculation for data reliability and diagnostic performance: a go-to review. 有关数据可靠性和诊断性能的样本量计算:一篇最新综述。
IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-07-05 DOI: 10.1186/s41747-024-00474-w
Caterina Beatrice Monti, Federico Ambrogi, Francesco Sardanelli

Sample size, namely the number of subjects that should be included in a study to reach the desired endpoint and statistical power, is a fundamental concept of scientific research. Indeed, sample size must be planned a priori, and tailored to the main endpoint of the study, to avoid including too many subjects, thus possibly exposing them to additional risks while also wasting time and resources, or too few subjects, failing to reach the desired purpose. We offer a simple, go-to review of methods for sample size calculation for studies concerning data reliability (repeatability/reproducibility) and diagnostic performance. For studies concerning data reliability, we considered Cohen's κ or intraclass correlation coefficient (ICC) for hypothesis testing, estimation of Cohen's κ or ICC, and Bland-Altman analyses. With regards to diagnostic performance, we considered accuracy or sensitivity/specificity versus reference standards, the comparison of diagnostic performances, and the comparisons of areas under the receiver operating characteristics curve. Finally, we considered the special cases of dropouts or retrospective case exclusions, multiple endpoints, lack of prior data estimates, and the selection of unusual thresholds for α and β errors. For the most frequent cases, we provide example of software freely available on the Internet.Relevance statement Sample size calculation is a fundamental factor influencing the quality of studies on repeatability/reproducibility and diagnostic performance in radiology.Key points• Sample size is a concept related to precision and statistical power.• It has ethical implications, especially when patients are exposed to risks.• Sample size should always be calculated before starting a study.• This review offers simple, go-to methods for sample size calculations.

样本量是科学研究的一个基本概念,即为达到预期终点和统计能力而应纳入研究的受试者人数。事实上,样本量必须事先规划,并根据研究的主要终点量身定制,以避免纳入过多受试者,从而可能使他们面临额外风险,同时浪费时间和资源;或纳入过少受试者,从而无法达到预期目的。我们对有关数据可靠性(可重复性/可再现性)和诊断性能的研究的样本量计算方法进行了简单的回顾。对于有关数据可靠性的研究,我们考虑了用于假设检验的科恩κ或类内相关系数(ICC)、科恩κ或ICC的估计以及布兰德-阿尔特曼分析。在诊断性能方面,我们考虑了准确性或灵敏度/特异性与参考标准的比较、诊断性能的比较以及接收者操作特征曲线下面积的比较。最后,我们还考虑了一些特殊情况,如辍学或回顾性病例排除、多终点、缺乏先前的数据估计以及选择不寻常的 α 和 β 误差阈值。对于最常见的情况,我们提供了可在互联网上免费获取的软件示例。相关性声明 样本大小计算是影响放射学重复性/可重复性和诊断性能研究质量的基本因素。关键点- 样本大小是一个与精确度和统计能力相关的概念。
{"title":"Sample size calculation for data reliability and diagnostic performance: a go-to review.","authors":"Caterina Beatrice Monti, Federico Ambrogi, Francesco Sardanelli","doi":"10.1186/s41747-024-00474-w","DOIUrl":"10.1186/s41747-024-00474-w","url":null,"abstract":"<p><p>Sample size, namely the number of subjects that should be included in a study to reach the desired endpoint and statistical power, is a fundamental concept of scientific research. Indeed, sample size must be planned a priori, and tailored to the main endpoint of the study, to avoid including too many subjects, thus possibly exposing them to additional risks while also wasting time and resources, or too few subjects, failing to reach the desired purpose. We offer a simple, go-to review of methods for sample size calculation for studies concerning data reliability (repeatability/reproducibility) and diagnostic performance. For studies concerning data reliability, we considered Cohen's κ or intraclass correlation coefficient (ICC) for hypothesis testing, estimation of Cohen's κ or ICC, and Bland-Altman analyses. With regards to diagnostic performance, we considered accuracy or sensitivity/specificity versus reference standards, the comparison of diagnostic performances, and the comparisons of areas under the receiver operating characteristics curve. Finally, we considered the special cases of dropouts or retrospective case exclusions, multiple endpoints, lack of prior data estimates, and the selection of unusual thresholds for α and β errors. For the most frequent cases, we provide example of software freely available on the Internet.Relevance statement Sample size calculation is a fundamental factor influencing the quality of studies on repeatability/reproducibility and diagnostic performance in radiology.Key points• Sample size is a concept related to precision and statistical power.• It has ethical implications, especially when patients are exposed to risks.• Sample size should always be calculated before starting a study.• This review offers simple, go-to methods for sample size calculations.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"79"},"PeriodicalIF":3.7,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11224179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141535589","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: 0
Detectability of intracranial vessel wall atherosclerosis using black-blood spectral CT: a phantom and clinical study. 利用黑血流频谱 CT 检测颅内血管壁动脉粥样硬化:一项模型和临床研究。
IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-07-03 DOI: 10.1186/s41747-024-00473-x
Fan Zhang, Hui Yao, Eran Langzam, Qinglin Meng, Xiao Meng, Rob J van der Geest, Chuncai Luo, Tengyuan Zhang, Jianyong Li, Jianmei Xiong, Weiwei Deng, Ke Chen, Yangrui Zheng, Jingping Wu, Fang Cui, Li Yang

Background: Computed tomography (CT) is the usual modality for diagnosing stroke, but conventional CT angiography reconstructions have limitations.

Methods: A phantom with tubes of known diameters and wall thickness was scanned for wall detectability, wall thickness, and contrast-to-noise ratio (CNR) on conventional and spectral black-blood (SBB) images. The clinical study included 34 stroke patients. Diagnostic certainty and conspicuity of normal/abnormal intracranial vessels using SBB were compared to conventional. Sensitivity/specificity/accuracy of SBB and conventional were compared for plaque detectability. CNR of the wall/lumen and quantitative comparison of remodeling index, plaque burden, and eccentricity were obtained for SBB imaging and high-resolution magnetic resonance imaging (hrMRI).

Results: The phantom study showed improved detectability of tube walls using SBB (108/108, 100% versus conventional 81/108, 75%, p < 0.001). CNRs were 75.9 ± 62.6 (mean ± standard deviation) for wall/lumen and 22.0 ± 17.1 for wall/water using SBB and 26.4 ± 15.3 and 101.6 ± 62.5 using conventional. Clinical study demonstrated (i) improved certainty and conspicuity of the vessels using SBB versus conventional (certainty, median score 3 versus 0; conspicuity, median score 3 versus 1 (p < 0.001)), (ii) improved sensitivity/specificity/accuracy of plaque (≥ 1.0 mm) detectability (0.944/0.981/0.962 versus 0.239/0.743/0.495) (p < 0.001), (iii) higher wall/lumen CNR of SBB of (78.3 ± 50.4/79.3 ± 96.7) versus hrMRI (18.9 ± 8.4/24.1 ± 14.1) (p < 0.001), and (iv) excellent reproducibility of remodeling index, plaque burden, and eccentricity using SBB versus hrMRI (intraclass correlation coefficient 0.85-0.94).

Conclusions: SBB can enhance the detectability of intracranial plaques with an accuracy similar to that of hrMRI.

Relevance statement: This new spectral black-blood technique for the detection and characterization of intracranial vessel atherosclerotic disease could be a time-saving and cost-effective diagnostic step for clinical stroke patients. It may also facilitate prevention strategies for atherosclerosis.

Key points: • Blooming artifacts can blur vessel wall morphology on conventional CT angiography. • Spectral black-blood (SBB) images are generated from material decomposition from spectral CT. • SBB images reduce blooming artifacts and noise and accurately detect small plaques.

背景:计算机断层扫描(CT)是诊断中风的常用方法,但传统的 CT 血管造影重建存在局限性:方法:对一个已知直径和壁厚的管子模型进行扫描,以检测管壁的可探测性、壁厚以及常规和光谱黑血(SBB)图像的对比度-噪声比(CNR)。临床研究包括 34 名中风患者。使用 SBB 与传统方法比较了正常/异常颅内血管的诊断确定性和清晰度。比较了 SBB 和传统方法对斑块检测的敏感性/特异性/准确性。SBB成像和高分辨率磁共振成像(hrMRI)获得了管壁/管腔的CNR以及重塑指数、斑块负荷和偏心率的定量比较:结果:模型研究显示,使用 SBB 提高了管壁的可探测性(108/108,100% 与传统的 81/108,75% 相比,P 结论:SBB 可提高管壁的可探测性:SBB 可以提高颅内斑块的可探测性,其准确性与 hrMRI 相似:这种用于检测和描述颅内血管动脉粥样硬化疾病的新型光谱黑血技术可为临床卒中患者提供省时、经济的诊断步骤。它还有助于动脉粥样硬化的预防策略:- 要点:在传统 CT 血管造影术中,出血伪影会模糊血管壁形态。- 光谱黑血(SBB)图像由光谱 CT 的物质分解生成。- SBB 图像可减少出血伪影和噪音,并能准确检测出小斑块。
{"title":"Detectability of intracranial vessel wall atherosclerosis using black-blood spectral CT: a phantom and clinical study.","authors":"Fan Zhang, Hui Yao, Eran Langzam, Qinglin Meng, Xiao Meng, Rob J van der Geest, Chuncai Luo, Tengyuan Zhang, Jianyong Li, Jianmei Xiong, Weiwei Deng, Ke Chen, Yangrui Zheng, Jingping Wu, Fang Cui, Li Yang","doi":"10.1186/s41747-024-00473-x","DOIUrl":"10.1186/s41747-024-00473-x","url":null,"abstract":"<p><strong>Background: </strong>Computed tomography (CT) is the usual modality for diagnosing stroke, but conventional CT angiography reconstructions have limitations.</p><p><strong>Methods: </strong>A phantom with tubes of known diameters and wall thickness was scanned for wall detectability, wall thickness, and contrast-to-noise ratio (CNR) on conventional and spectral black-blood (SBB) images. The clinical study included 34 stroke patients. Diagnostic certainty and conspicuity of normal/abnormal intracranial vessels using SBB were compared to conventional. Sensitivity/specificity/accuracy of SBB and conventional were compared for plaque detectability. CNR of the wall/lumen and quantitative comparison of remodeling index, plaque burden, and eccentricity were obtained for SBB imaging and high-resolution magnetic resonance imaging (hrMRI).</p><p><strong>Results: </strong>The phantom study showed improved detectability of tube walls using SBB (108/108, 100% versus conventional 81/108, 75%, p < 0.001). CNRs were 75.9 ± 62.6 (mean ± standard deviation) for wall/lumen and 22.0 ± 17.1 for wall/water using SBB and 26.4 ± 15.3 and 101.6 ± 62.5 using conventional. Clinical study demonstrated (i) improved certainty and conspicuity of the vessels using SBB versus conventional (certainty, median score 3 versus 0; conspicuity, median score 3 versus 1 (p < 0.001)), (ii) improved sensitivity/specificity/accuracy of plaque (≥ 1.0 mm) detectability (0.944/0.981/0.962 versus 0.239/0.743/0.495) (p < 0.001), (iii) higher wall/lumen CNR of SBB of (78.3 ± 50.4/79.3 ± 96.7) versus hrMRI (18.9 ± 8.4/24.1 ± 14.1) (p < 0.001), and (iv) excellent reproducibility of remodeling index, plaque burden, and eccentricity using SBB versus hrMRI (intraclass correlation coefficient 0.85-0.94).</p><p><strong>Conclusions: </strong>SBB can enhance the detectability of intracranial plaques with an accuracy similar to that of hrMRI.</p><p><strong>Relevance statement: </strong>This new spectral black-blood technique for the detection and characterization of intracranial vessel atherosclerotic disease could be a time-saving and cost-effective diagnostic step for clinical stroke patients. It may also facilitate prevention strategies for atherosclerosis.</p><p><strong>Key points: </strong>• Blooming artifacts can blur vessel wall morphology on conventional CT angiography. • Spectral black-blood (SBB) images are generated from material decomposition from spectral CT. • SBB images reduce blooming artifacts and noise and accurately detect small plaques.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"78"},"PeriodicalIF":3.7,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11219652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141493753","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: 0
7 T and beyond: toward a synergy between fMRI-based presurgical mapping at ultrahigh magnetic fields, AI, and robotic neurosurgery. 7 T 及以上:实现超高磁场下基于 fMRI 的术前绘图、人工智能和机器人神经外科手术之间的协同作用。
IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-07-01 DOI: 10.1186/s41747-024-00472-y
Mohamed L Seghier

Presurgical evaluation with functional magnetic resonance imaging (fMRI) can reduce postsurgical morbidity. Here, we discuss presurgical fMRI mapping at ultra-high magnetic fields (UHF), i.e., ≥ 7 T, in the light of the current growing interest in artificial intelligence (AI) and robot-assisted neurosurgery. The potential of submillimetre fMRI mapping can help better appreciate uncertainty on resection margins, though geometric distortions at UHF might lessen the accuracy of fMRI maps. A useful trade-off for UHF fMRI is to collect data with 1-mm isotropic resolution to ensure high sensitivity and subsequently a low risk of false negatives. Scanning at UHF might yield a revival interest in slow event-related fMRI, thereby offering a richer depiction of the dynamics of fMRI responses. The potential applications of AI concern denoising and artefact removal, generation of super-resolution fMRI maps, and accurate fusion or coregistration between anatomical and fMRI maps. The latter can benefit from the use of T1-weighted echo-planar imaging for better visualization of brain activations. Such AI-augmented fMRI maps would provide high-quality input data to robotic surgery systems, thereby improving the accuracy and reliability of robot-assisted neurosurgery. Ultimately, the advancement in fMRI at UHF would promote clinically useful synergies between fMRI, AI, and robotic neurosurgery.Relevance statement This review highlights the potential synergies between fMRI at UHF, AI, and robotic neurosurgery in improving the accuracy and reliability of fMRI-based presurgical mapping.Key points• Presurgical fMRI mapping at UHF improves spatial resolution and sensitivity.• Slow event-related designs offer a richer depiction of fMRI responses dynamics.• AI can support denoising, artefact removal, and generation of super-resolution fMRI maps.• AI-augmented fMRI maps can provide high-quality input data to robotic surgery systems.

通过功能磁共振成像(fMRI)进行术前评估可降低术后发病率。鉴于目前人们对人工智能(AI)和机器人辅助神经外科手术的兴趣与日俱增,我们在此讨论超高磁场(UHF)(即≥ 7 T)下的术前 fMRI 映像。亚毫米级 fMRI 图谱的潜力有助于更好地了解切除边缘的不确定性,不过超高频的几何失真可能会降低 fMRI 图谱的准确性。超高频 fMRI 的一个有效权衡方法是收集 1 毫米各向同性分辨率的数据,以确保高灵敏度和较低的假阴性风险。超高频扫描可能会重新激发对慢速事件相关 fMRI 的兴趣,从而提供更丰富的 fMRI 反应动态描述。人工智能的潜在应用涉及去噪和去除伪影、生成超分辨率 fMRI 图谱以及解剖图和 fMRI 图之间的精确融合或核心配准。后者可受益于 T1 加权回声平面成像的使用,以更好地显示大脑激活。这种人工智能增强的 fMRI 地图将为机器人手术系统提供高质量的输入数据,从而提高机器人辅助神经外科手术的准确性和可靠性。最终,超高频 fMRI 的进步将促进 fMRI、人工智能和机器人神经外科之间产生临床有用的协同效应。 相关性声明 本综述强调了超高频 fMRI、人工智能和机器人神经外科之间在提高基于 fMRI 的术前映射的准确性和可靠性方面的潜在协同效应。人工智能可支持去噪、去除伪影和生成超分辨率的 fMRI 地图,人工智能增强的 fMRI 地图可为机器人手术系统提供高质量的输入数据。
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引用次数: 0
Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study. 用于腰椎磁共振成像加速的深度学习重建:一项前瞻性研究。
IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-06-21 DOI: 10.1186/s41747-024-00470-0
Hui Tang, Ming Hong, Lu Yu, Yang Song, Mengqiu Cao, Lei Xiang, Yan Zhou, Shiteng Suo

Background: We compared magnetic resonance imaging (MRI) turbo spin-echo images reconstructed using a deep learning technique (TSE-DL) with standard turbo spin-echo (TSE-SD) images of the lumbar spine regarding image quality and detection performance of common degenerative pathologies.

Methods: This prospective, single-center study included 31 patients (15 males and 16 females; aged 51 ± 16 years (mean ± standard deviation)) who underwent lumbar spine exams with both TSE-SD and TSE-DL acquisitions for degenerative spine diseases. Images were analyzed by two radiologists and assessed for qualitative image quality using a 4-point Likert scale, quantitative signal-to-noise ratio (SNR) of anatomic landmarks, and detection of common pathologies. Paired-sample t, Wilcoxon, and McNemar tests, unweighted/linearly weighted Cohen κ statistics, and intraclass correlation coefficients were used.

Results: Scan time for TSE-DL and TSE-SD protocols was 2:55 and 5:17 min:s, respectively. The overall image quality was either significantly higher for TSE-DL or not significantly different between TSE-SD and TSE-DL. TSE-DL demonstrated higher SNR and subject noise scores than TSE-SD. For pathology detection, the interreader agreement was substantial to almost perfect for TSE-DL, with κ values ranging from 0.61 to 1.00; the interprotocol agreement was almost perfect for both readers, with κ values ranging from 0.84 to 1.00. There was no significant difference in the diagnostic confidence or detection rate of common pathologies between the two sequences (p ≥ 0.081).

Conclusions: TSE-DL allowed for a 45% reduction in scan time over TSE-SD in lumbar spine MRI without compromising the overall image quality and showed comparable detection performance of common pathologies in the evaluation of degenerative lumbar spine changes.

Relevance statement: Deep learning-reconstructed lumbar spine MRI protocol enabled a 45% reduction in scan time compared with conventional reconstruction, with comparable image quality and detection performance of common degenerative pathologies.

Key points: • Lumbar spine MRI with deep learning reconstruction has broad application prospects. • Deep learning reconstruction of lumbar spine MRI saved 45% scan time without compromising overall image quality. • When compared with standard sequences, deep learning reconstruction showed similar detection performance of common degenerative lumbar spine pathologies.

背景:我们比较了使用深度学习技术重建的腰椎磁共振成像(MRI)涡轮自旋回波图像(TSE-DL)与标准涡轮自旋回波图像(TSE-SD)在图像质量和常见退行性病变检测性能方面的差异:这项前瞻性单中心研究共纳入了 31 名患者(男性 15 人,女性 16 人;年龄 51 ± 16 岁(平均 ± 标准差)),他们都接受了腰椎检查,并同时进行了 TSE-SD 和 TSE-DL 采集,以检测脊柱退行性疾病。图像由两名放射科医生进行分析,并使用 4 点李克特量表对图像质量、解剖标志物的定量信噪比 (SNR) 以及常见病变的检测进行评估。采用了配对样本 t 检验、Wilcoxon 检验和 McNemar 检验、非加权/线性加权 Cohen κ 统计法和类内相关系数:TSE-DL和TSE-SD方案的扫描时间分别为2:55分钟和5:17分钟。TSE-DL的整体图像质量明显更高,或者TSE-SD和TSE-DL之间没有明显差异。与 TSE-SD 相比,TSE-DL 的信噪比和主体噪声得分更高。在病理检测方面,TSE-DL 的读数间一致性很高,几乎达到完美,κ值在 0.61 到 1.00 之间;两个读数的协议间一致性几乎达到完美,κ值在 0.84 到 1.00 之间。两种序列的诊断可信度和常见病理的检出率没有明显差异(P≥0.081):TSE-DL使腰椎核磁共振成像的扫描时间比TSE-SD减少了45%,同时不影响整体图像质量,在评估腰椎退行性病变时对常见病变的检测性能相当:深度学习重建的腰椎核磁共振成像方案与传统重建相比,扫描时间缩短了45%,图像质量和常见退行性病变的检测性能相当:- 采用深度学习重建的腰椎磁共振成像具有广阔的应用前景。- 腰椎核磁共振成像的深度学习重建可节省45%的扫描时间,且不影响整体图像质量。- 与标准序列相比,深度学习重建对常见的腰椎退行性病变具有相似的检测性能。
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引用次数: 0
Impact of reconstruction parameters on the accuracy of myocardial extracellular volume quantification on a first-generation, photon-counting detector CT. 重建参数对第一代光子计数探测器 CT 心肌细胞外容积量化准确性的影响。
IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-06-19 DOI: 10.1186/s41747-024-00469-7
Chiara Gnasso, Daniel Pinos, U Joseph Schoepf, Milan Vecsey-Nagy, Gilberto J Aquino, Nicola Fink, Emese Zsarnoczay, Robert J Holtackers, Jonathan Stock, Pal Suranyi, Akos Varga-Szemes, Tilman Emrich

Background: The potential role of cardiac computed tomography (CT) has increasingly been demonstrated for the assessment of diffuse myocardial fibrosis through the quantification of extracellular volume (ECV). Photon-counting detector (PCD)-CT technology may deliver more accurate ECV quantification compared to energy-integrating detector CT. We evaluated the impact of reconstruction settings on the accuracy of ECV quantification using PCD-CT, with magnetic resonance imaging (MRI)-based ECV as reference.

Methods: In this post hoc analysis, 27 patients (aged 53.1 ± 17.2 years (mean ± standard deviation); 14 women) underwent same-day cardiac PCD-CT and MRI. Late iodine CT scans were reconstructed with different quantum iterative reconstruction levels (QIR 1-4), slice thicknesses (0.4-8 mm), and virtual monoenergetic imaging levels (VMI, 40-90 keV); ECV was quantified for each reconstruction setting. Repeated measures ANOVA and t-test for pairwise comparisons, Bland-Altman plots, and Lin's concordance correlation coefficient (CCC) were used.

Results: ECV values did not differ significantly among QIR levels (p = 1.000). A significant difference was observed throughout different slice thicknesses, with 0.4 mm yielding the highest agreement with MRI-based ECV (CCC = 0.944); 45-keV VMI reconstructions showed the lowest mean bias (0.6, 95% confidence interval 0.1-1.4) compared to MRI. Using the most optimal reconstruction settings (QIR4. slice thickness 0.4 mm, VMI 45 keV), a 63% reduction in mean bias and a 6% increase in concordance with MRI-based ECV were achieved compared to standard settings (QIR3, slice thickness 1.5 mm; VMI 65 keV).

Conclusions: The selection of appropriate reconstruction parameters improved the agreement between PCD-CT and MRI-based ECV.

Relevance statement: Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility.

Key points: • CT is increasingly promising for myocardial tissue characterization, assessing focal and diffuse fibrosis via late iodine enhancement and ECV quantification, respectively. • PCD-CT offers superior performance over conventional CT, potentially improving ECV quantification and its agreement with MRI-based ECV. • Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility.

背景:心脏计算机断层扫描(CT)在通过量化细胞外容积(ECV)评估弥漫性心肌纤维化方面的潜在作用已日益得到证实。与能量积分探测器 CT 相比,光子计数探测器 (PCD) -CT 技术可提供更准确的 ECV 定量。我们以基于磁共振成像(MRI)的 ECV 为参照,评估了重建设置对 PCD-CT ECV 定量准确性的影响:在这项事后分析中,27 名患者(年龄为 53.1 ± 17.2 岁(平均 ± 标准差);14 名女性)在同一天接受了心脏 PCD-CT 和 MRI 检查。晚期碘 CT 扫描采用不同的量子迭代重建级别(QIR 1-4)、切片厚度(0.4-8 毫米)和虚拟单能成像级别(VMI,40-90 千伏)进行重建;对每种重建设置的 ECV 进行量化。采用重复测量方差分析和 t 检验进行配对比较、布兰-阿尔特曼图和林氏一致性相关系数(CCC):不同 QIR 水平的 ECV 值差异不大(p = 1.000)。不同切片厚度的 ECV 值差异明显,其中 0.4 mm 与 MRI ECV 值的一致性最高(CCC = 0.944);与 MRI 相比,45-keV VMI 重建的平均偏差最小(0.6,95% 置信区间 0.1-1.4)。与标准设置(QIR3,切片厚度 1.5 mm;VMI 65 keV)相比,使用最理想的重建设置(QIR4,切片厚度 0.4 mm;VMI 45 keV),平均偏差减少了 63%,与基于 MRI 的 ECV 的一致性提高了 6%:结论:选择适当的重建参数可提高 PCD-CT 和基于 MRI 的心血管动态图像之间的一致性:与磁共振成像相比,定制 PCD-CT 重建参数可优化 ECV 定量,从而提高其临床实用性:- CT在心肌组织特征描述方面的应用前景越来越广,可分别通过晚期碘增强和ECV量化评估局灶性和弥漫性纤维化。- PCD-CT 比传统 CT 性能更优越,有可能改善 ECV 定量及其与基于 MRI 的 ECV 的一致性。- 与磁共振成像相比,定制 PCD-CT 重建参数可优化 ECV 定量,从而提高其临床实用性。
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European Radiology Experimental
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