Meta-Radiology最新文献_第2页

Consistency-driven state-space model for incomplete multimodal MRI brain tumor segmentation 不完全多模态MRI脑肿瘤分割的一致性驱动状态空间模型

Meta-Radiology

Pub Date : 2025-10-09 DOI: 10.1016/j.metrad.2025.100185

Debao Liu , Xiaozhi Zhang , Hong Zhou , Kok Lay Teo

Brain tumor segmentation based on multimodal magnetic resonance imaging (MRI) plays a crucial role in clinical diagnosis and treatment planning. However, the absence or unavailability of certain modalities often hampers segmentation performance in real-world clinical settings. In this study, we propose a novel consistency-driven state-space model (SSM), which incorporates learnable consistency features into the SSM architecture to establish a robust framework for brain tumor segmentation under incomplete modality conditions. This approach introduces an innovative strategy for explicitly capturing cross-modal consistency within the context of efficient long-range dependency modeling, specifically designed for segmentation tasks involving missing modalities. Specifically, we design a scale-aware fusion block that integrates learnable consistency features to aggregate modality-specific information at an early stage. Subsequently, a Mamba-based multimodal consistency fusion technique is employed, enabling efficient long-range dependency modeling with linear computational complexity. To prevent modality bias, we further introduce a progressive attention weighting module that dynamically balances modality-specific features. Additionally, an adaptive feature correction mechanism is incorporated to refine both modality-specific and consistency features along both spatial and channel dimensions. The proposed method effectively facilitates modality integration while minimizing conflicts that may arise from directly fusing potentially inconsistent modalities. Comprehensive experiments conducted on the BraTS2018 and BraTS2020 datasets demonstrate that our model surpasses existing state-of-the-art approaches under various incomplete modality scenarios.

基于多模态磁共振成像（MRI）的脑肿瘤分割在临床诊断和治疗规划中起着至关重要的作用。然而，缺乏或不可用的某些模式往往阻碍分割性能在现实世界的临床设置。在这项研究中，我们提出了一种新的一致性驱动状态空间模型（SSM），该模型将可学习的一致性特征融入到SSM架构中，以建立一个不完全模态条件下脑肿瘤分割的鲁棒框架。该方法引入了一种创新策略，用于在高效的远程依赖建模的上下文中显式捕获跨模态一致性，该策略专门为涉及缺失模态的分割任务而设计。具体来说，我们设计了一个规模感知融合块，它集成了可学习的一致性特征，以便在早期阶段聚合特定于模态的信息。随后，采用基于mamba的多模态一致性融合技术，实现了具有线性计算复杂度的高效远程依赖关系建模。为了防止模态偏差，我们进一步引入了一个渐进的注意力加权模块，该模块可以动态平衡模态特定的功能。此外，还采用了自适应特征校正机制，以沿着空间和通道维度改进特定于模态和一致性的特征。提出的方法有效地促进了模态集成，同时最大限度地减少了直接融合潜在不一致模态可能产生的冲突。在BraTS2018和BraTS2020数据集上进行的综合实验表明，我们的模型在各种不完全模态场景下优于现有的最先进方法。

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引用次数: 0

Transformer-based integration of radiomics and deep learning for differentiating lipid-poor adrenal adenomas from malignant tumors 基于转换器的放射组学和深度学习的整合用于区分低脂肾上腺腺瘤和恶性肿瘤

Meta-Radiology

Pub Date : 2025-10-01 DOI: 10.1016/j.metrad.2025.100183

Kai Zhao , Zhongqi Sun , Hao Jiang , Zhixuan Zou , Qiong Wu , Yanjie Xin , Xiangru Liu , Huijie Jiang

Purpose

To evaluate the effectiveness of a Transformer model based on contrast-enhanced computed tomography (CECT) that integrates radiomics and deep learning features in differentiating adrenal lipid-poor adenomas (LPA) and malignant tumors (MT).

Methods

This retrospective study included 282 patients with adrenal tumors from two medical centers between October 2018 and October 2024. The patients were classified into adrenal (LPA) and adrenal (MT) groups. Radiomics and deep learning features were extracted from CECT images. A total of 240 patients from the first center were randomly divided into Training Set and Test Set at a 7:3 ratio, while 42 patients from the second center served as an External Validation Set. A Transformer algorithm was employed to integrate radiomics and deep learning features for building predictive models. Its self-attention mechanism was utilized to capture intrinsic associations within each feature type and to uncover hidden information related to clinical outcomes. Additionally, a Radiomics model, a Deep Learning model (DL_model), and a Traditional Combined model integrating radiomics and deep learning features were constructed. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and radar chart. Calibration curves and decision curve analysis (DCA) were employed to assess the predictive accuracy and clinical net benefit of the models. Furthermore, radiomics feature activation maps and gradient-weighted class activation mapping (Grad-CAM) were utilized to visualize radiomics and deep learning features, respectively.

Results

The Transformer model achieved the best predictive performance in the training, test, and external validation sets, with AUCs of 0.949, 0.917, and 0.852, respectively. The DeLong test indicated that the performance differences between this model and the other models were statistically significant. Furthermore, the radar chart illustrated that the Transformer model achieved superior overall performance, and DCA confirmed its higher clinical net benefit compared with the other models.

Conclusion

The Transformer model that integrates radiomics and deep learning features can accurately distinguish between LPA and MT. Furthermore, the visual analysis of radiomics feature activation maps and Grad-CAM intuitively illustrates the distribution of radiomics and deep learning features, enhancing their potential for clinical application in preoperative assessment of adrenal tumors.

目的评估基于对比增强计算机断层扫描（CECT）的Transformer模型在鉴别肾上腺脂质低下腺瘤（LPA）和恶性肿瘤（MT）中的有效性，该模型结合放射组学和深度学习特征。方法回顾性研究纳入2018年10月至2024年10月来自两家医疗中心的282例肾上腺肿瘤患者。将患者分为肾上腺素（LPA）组和肾上腺素（MT）组。从CECT图像中提取放射组学和深度学习特征。第一中心240例患者按7:3的比例随机分为Training Set和Test Set，第二中心42例患者作为External Validation Set。利用Transformer算法整合放射组学和深度学习特征，构建预测模型。它的自我注意机制被用来捕捉每个特征类型的内在联系，并揭示与临床结果相关的隐藏信息。此外，构建了放射组学模型、深度学习模型（DL_model）和传统放射组学与深度学习相结合的组合模型。利用接收机工作特性曲线（ROC）下面积和雷达图评估模型性能。采用校正曲线和决策曲线分析（DCA）来评估模型的预测准确性和临床净效益。此外，利用放射组学特征激活图和梯度加权类激活图（Grad-CAM）分别可视化放射组学和深度学习特征。结果Transformer模型在训练集、测试集和外部验证集的预测性能最佳，auc分别为0.949、0.917和0.852。DeLong检验表明，该模型与其他模型的性能差异具有统计学意义。此外，雷达图显示Transformer模型具有优越的综合性能，DCA证实其与其他模型相比具有更高的临床净效益。结论整合放射组学和深度学习特征的Transformer模型可以准确区分LPA和MT，并且通过放射组学特征激活图的可视化分析和Grad-CAM可以直观地说明放射组学和深度学习特征的分布，增强其在肾上腺肿瘤术前评估中的临床应用潜力。

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引用次数: 0

Frontal cortical gyrification mediates the association between hippocampal subfield microstructure and cognitive function in Parkinson's disease: A NODDI study highlighting olfactory dysfunction 前额皮质回旋介导帕金森病海马亚区微观结构和认知功能之间的关联：一项NODDI研究强调了嗅觉功能障碍

Meta-Radiology

Pub Date : 2025-09-15 DOI: 10.1016/j.metrad.2025.100182

Xin Liu , Jun Hu , Yule Zeng , Ping Yan , Ziling Huang , Liangwu Chen , Shicheng He , Yudie Xie , Beisha Tang , Weiyi Liu , Tianyao Wang , Hong Zhou

Background

Parkinson's disease (PD) is characterized by the progressive degeneration of dopaminergic neurons. Olfactory dysfunction and cognitive impairment are common and often co-occurring non-motor symptoms in PD. However, the neural mechanisms underlying their association remain unclear.

Methods

This cross-sectional study analyzed 72 patients with Parkinson's disease (PD). Based on the Hyposmia Rating Scale (HRS), they were classified into two groups: those with olfactory dysfunction (PD-OD) and those without (PD-NOD). Simple mediation models (PROCESS Model 4) were applied to both the overall PD group and the PD-OD subgroup to assess the potential mediating role of bilateral frontal cortical local gyrification index (LGI) values in the association between neurite orientation dispersion and density imaging (NODDI) parameters of the hippocampal subfields and cognitive performance.

Results

Microstructural alterations in the left subiculum and bilateral presubiculum were significantly associated with cognitive performance in PD patients. These associations were partially mediated by gyrification of the right pars opercularis, with significant indirect effects observed (c–c′ = −0.108, −0.121, and −0.126, respectively; all p < 0.05). The mediation effect was more prominent in PD-OD patients (c–c′ = −0.141, −0.138, and −0.132, respectively; all p < 0.05), indicating greater hippocampal–frontal structural disruption in this subgroup.

Conclusions

There is a significant structural association among hippocampal subfield microstructure, frontal cortical gyrification, and cognitive dysfunction in PD. Gyrification of the right pars opercularis plays a critical mediating role in the relationship between hippocampal microstructural alterations and cognitive impairment, with this mediation effect being more pronounced in PD-OD patients.

帕金森氏病（PD）以多巴胺能神经元进行性变性为特征。嗅觉功能障碍和认知障碍是PD患者常见的非运动症状。然而，它们之间联系的神经机制尚不清楚。方法对72例帕金森病（PD）患者进行横断面研究。根据嗅觉功能减退评定量表（HRS）将患者分为嗅觉功能障碍组（PD-OD）和无嗅觉功能障碍组（PD-NOD）。将简单中介模型（PROCESS Model 4）应用于PD整体组和PD- od亚组，评估双侧额叶皮质局部回化指数（LGI）值在海马亚区神经突定向分散和密度成像（NODDI）参数与认知表现之间的关联中的潜在中介作用。结果PD患者左肩带下和双侧肩带前的微结构改变与认知能力显著相关。这些关联部分由右侧包部的旋转介导，观察到显著的间接影响（c-c′分别= - 0.108,- 0.121和- 0.126；均p <； 0.05）。调解作用在PD-OD患者中更为突出（c-c′分别= - 0.141、- 0.138和- 0.132；均p <； 0.05），表明该亚组海马额叶结构破坏更严重。结论PD患者海马亚区微结构、额叶皮质回缩与认知功能障碍存在显著的结构关联。右侧包部旋回在海马微结构改变与认知功能障碍的关系中起着重要的中介作用，这种中介作用在PD-OD患者中更为明显。

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引用次数: 0

Radiomic analysis to predict arrhythmias in athletes by echocardiography: Artificial intelligence vs. formal methods 通过超声心动图预测运动员心律失常的放射组学分析：人工智能与正式方法

Meta-Radiology

Pub Date : 2025-09-02 DOI: 10.1016/j.metrad.2025.100174

Giulia Varriano , Ester Lagonigro , Giuseppe Prisco, Marialucia Spina, Klara Komici, Germano Guerra, Antonella Santone

The diagnosis and management of premature ventricular beats (PVBs) in athletes remain challenging due to the potential for underlying cardiac pathology. While the electrocardiogram is essential for detecting electrical abnormalities, echocardiography is crucial for evaluating structural heart disease. This study explores the use of radiomics analysis applied to apical 4-chamber echocardiography to automatically and preemptively identify PVB risk, better characterize athlete cardiac remodeling, and enable early detection of pathological changes.We evaluated and compared the limitations and potential of Artificial Intelligence (AI) and Formal Methods (FM) for this task. Using data from 723 athletes, we processed echocardiography videos and extracted over 100 features per athlete to develop robust classifiers.Our findings demonstrate that radiomics can power automated decision-support systems for diagnosis. While AI-based models offer powerful predictive capabilities, FM presents a compelling, mathematically rigorous, and explainable alternative. Both modalities are useful for the early detection of structural substrates underlying PVBs. The collaboration between physicians and computer scientists is crucial to unlocking new frontiers in radiomics and advancing personalized medicine.

由于潜在的心脏病理，运动员室性早搏（pvb）的诊断和管理仍然具有挑战性。虽然心电图对检测电异常至关重要，但超声心动图对评估结构性心脏病至关重要。本研究探索将放射组学分析应用于根尖4室超声心动图，以自动和先发制人地识别PVB风险，更好地表征运动员心脏重构，并能够早期发现病理变化。我们评估并比较了人工智能（AI）和形式化方法（FM）在这项任务中的局限性和潜力。使用来自723名运动员的数据，我们处理了超声心动图视频，并提取了每个运动员超过100个特征，以开发健壮的分类器。我们的研究结果表明，放射组学可以为诊断的自动决策支持系统提供动力。虽然基于人工智能的模型提供了强大的预测能力，但FM提供了一个引人注目的、数学上严谨的、可解释的替代方案。这两种方式都有助于早期检测PVBs下的结构基底。医生和计算机科学家之间的合作对于打开放射组学的新领域和推进个性化医疗至关重要。

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引用次数: 0

Advances in neuroimaging applications of quantitative susceptibility mapping 定量易感图谱在神经影像学中的应用进展

Meta-Radiology

Pub Date : 2025-09-01 DOI: 10.1016/j.metrad.2025.100148

Shuxin Ma , Wencan Fu , Chao Chai , Huiying Wang , Ke Lv , Chenxi Zhao , E. Mark Haacke , Sagar Buch , Shuang Xia

This review article delves into the advancements of quantitative susceptibility mapping (QSM) in neuroimaging, highlighting its utility in detecting and quantifying magnetic susceptibility differences in tissues, particularly for paramagnetic substances like iron and diamagnetic substances such as calcifications in the brain. QSM has revolutionized the diagnosis and monitoring of neurodegenerative diseases by enabling the precise measurement of brain iron deposition and blood oxygen saturation. The review is partitioned into three sections. The first section underscores QSM's role in clinical applications related to microhemorrhages, cerebral amyloidosis, intracranial hematomas, and cerebrovascular malformations. The second section focuses on QSM's application in mapping iron content in neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease. The final section discusses QSM's potential in assessing stroke by measuring oxygen saturation. The article also outlines the basic theory and development of QSM, emphasizing the importance of echo time selection for accurate QSM results. Challenges in clinical applications and future directions, including the integration of AI technology for image reconstruction and data analysis, are also discussed. QSM's ability to differentiate between microbleeds and calcifications, assess dynamic susceptibility changes in intracranial hematomas, and guide thrombolytic strategies in acute cerebrovascular disease is highlighted. The review concludes by emphasizing the need for further optimization of QSM algorithms and the expansion of its applications in biomedical imaging.

本文综述了定量易感性制图（QSM）在神经影像学中的研究进展，重点介绍了其在检测和定量组织磁化率差异方面的应用，特别是对铁等顺磁性物质和脑钙化等反磁性物质。QSM通过精确测量脑铁沉积和血氧饱和度，彻底改变了神经退行性疾病的诊断和监测。这篇综述分为三个部分。第一部分强调了QSM在与微出血、脑淀粉样变性、颅内血肿和脑血管畸形相关的临床应用中的作用。第二部分重点介绍QSM在帕金森病和阿尔茨海默病等神经退行性疾病中铁含量制图中的应用。最后一节讨论了通过测量血氧饱和度来评估脑卒中的QSM的潜力。本文还概述了QSM的基本理论和发展，强调了回波时间选择对准确的QSM结果的重要性。还讨论了临床应用中的挑战和未来方向，包括将人工智能技术集成到图像重建和数据分析中。QSM能够区分微出血和钙化，评估颅内血肿的动态易感性变化，并指导急性脑血管疾病的溶栓策略。最后强调了QSM算法的进一步优化和在生物医学成像中的应用。

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引用次数: 0

Cardiac ECV mapping: Underlying concepts and clinical applications 心脏ECV制图：基本概念和临床应用

Meta-Radiology

Pub Date : 2025-09-01 DOI: 10.1016/j.metrad.2025.100168

Guo-Jun Zhu , Simran Qureshi , Ward Hedges , Chong-Wen Wu , Lian-Ming Wu

Myocardial Extracellular volume fraction (ECV) mapping, based on cardiac magnetic resonance (CMR), is a crucial technique for assessing myocardial histological changes by evaluating extracellular matrix expansion. In recent years, cardiac ECV mapping has gained significant attention in both basic research and clinical settings, particularly for its role in myocardial fibrosis and other cardiovascular diseases. This review explores the measurement of ECV mapping, its diagnostic and prognostic value in ischemic and non-ischemic cardiovascular diseases, and emerging advancements in ECV mapping techniques.

心肌细胞外体积分数（ECV）制图，基于心脏磁共振（CMR），是通过评估细胞外基质扩张来评估心肌组织学变化的关键技术。近年来，心脏ECV制图在基础研究和临床环境中都受到了极大的关注，特别是其在心肌纤维化和其他心血管疾病中的作用。本文综述了ECV作图的测量方法，在缺血性和非缺血性心血管疾病中的诊断和预后价值，以及ECV作图技术的新进展。

引用次数: 0

2-[18F]FDG PET/CT identifies metabolic activity linked to myocardial deformation in hypertrophic cardiomyopathy 2-[18F]FDG PET/CT鉴定肥厚性心肌病患者与心肌变形相关的代谢活动

Meta-Radiology

Pub Date : 2025-08-26 DOI: 10.1016/j.metrad.2025.100173

Patrícia Marques-Alves , Maria João Ferreira , Rodolfo Silva , João Borges-Rosa , Andreia Gomes , Antero Abrunhosa , Miguel Castelo-Branco , Wael Jaber , Lino Gonçalves

Purpose

Hypertrophic cardiomyopathy (HCM) is a highly heterogeneous disease, which makes prognostic assessment challenging. We aim to explore the relationship between myocardial metabolism, evaluated via 2-[18F]FDG PET/CT, and myocardial deformation, through strain analysis, in HCM patients.

Methods

We included 30 patients with non-obstructive HCM who underwent echocardiography with left ventricular (LV-GLS), left atrial (LA) strain and LV myocardial work (MW) analysis. Myocardial metabolism through 2-[¹⁸F]FDG PET/CT was evaluated using dedicated software. Cardiac magnetic resonance was also performed, evaluating fibrosis with late gadolinium enhancement (LGE). After a follow-up period of 2 ± 0.5 years, a subsequent echocardiography was performed.

Results

Mean age was 54 years and 57 % patients were male. Mean myocardial maximal wall thickness (MMWT) was 20.3 ± 4.4 mm and correlated with impaired myocardial deformation (LA and LV strain, LV-MW indexes). Increased 2-[¹⁸F]FDG uptake was present in 53 % patients. Hypermetabolism extension was higher in patients with higher MMWT (18.4 ± 19.7 % vs 3.07 ± 7.1 %, p = 0.013; rho = 0.46, p = 0.012), impaired LV-GLS (rho 0.37, p = 0.042) and impaired MW indexes (rho = −0.41, p = 0.034). Hypermetabolism was also correlated with LGE (rho 0.47, p = 0.028). At follow-up, there was a significant decrease in LV ejection fraction (61.2 ± 4.5 % to 55.1 ± 6.7 %, p = 0.014), anterior myocardial strain (−13.2 ± 4.4 to −9.5 ± 4.4, p = 0.035) and LA strain. This was particularly noted in patients that presented with 2-[¹⁸F]FDG uptake and also LGE.

Conclusions

Our study highlights key correlations between 2-[¹⁸F]FDG uptake, myocardial hypertrophy, and impaired deformation. These findings suggest a progression from hypermetabolism to fibrosis and deformation impairment, potentially driving functional decline in HCM.

目的肥厚性心肌病（HCM）是一种高度异质性的疾病，这使得预后评估具有挑战性。我们旨在探讨心肌代谢（通过2-[18F]FDG PET/CT评估）与心肌变形（通过应变分析）在HCM患者中的关系。方法30例非阻塞性HCM患者行超声心动图左室（LV- gls）、左房（LA）应变和左室心肌功（MW）分析。使用专用软件通过2-[18F]FDG PET/CT评估心肌代谢。同时进行心脏磁共振，用晚期钆增强（LGE）评估纤维化。随访2±0.5年后，进行超声心动图检查。结果患者平均年龄54岁，男性占57%。平均心肌最大壁厚（MMWT）为20.3±4.4 mm，与心肌变形受损（左室、左室应变、LV- mw指数）相关。53%的患者出现2-[18F]FDG摄取增加。MMWT高（18.4±19.7% vs 3.07±7.1%,p = 0.013; rho = 0.46, p = 0.012）、LV-GLS受损（rho = 0.37, p = 0.042）和MW指数受损（rho = - 0.41, p = 0.034）患者的高代谢延伸程度更高。高代谢也与LGE相关（rho 0.47, p = 0.028）。随访时左室射血分数（61.2±4.5%至55.1±6.7%,p = 0.014）、前心肌应变（- 13.2±4.4至- 9.5±4.4,p = 0.035）和左室应变均显著降低。这在出现2-[18F]FDG摄取和LGE的患者中尤为明显。结论我们的研究强调了2-[18F]FDG摄取与心肌肥大和变形受损之间的关键相关性。这些发现提示从高代谢到纤维化和变形损伤的进展，可能导致HCM功能下降。

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引用次数: 0

Intelligent analysis of chest X-ray based on multi-modal instruction tuning 基于多模态指令调谐的胸部x射线智能分析

Meta-Radiology

Pub Date : 2025-08-19 DOI: 10.1016/j.metrad.2025.100172

Junjie Yao , Junhao Wang , Zhenxiang Xiao , Xinlin Hao , Xi Jiang

Chest X-ray plays a crucial role in the screening and diagnosis of chest diseases. Due to the complexity of pathological manifestations and limitations of radiologists' experience, the accuracy and efficiency of diagnosing chest diseases need to be further improved. In recent years, deep learning has made significant progress in chest X-ray image analysis, while existing methods mainly rely on uni-modal visual information, overlooking the prior knowledge related to disease category descriptions embedded in medical text data, making it challenging to fully understand the deep semantics of chest X-ray images. To address these challenges, inspired by the Instruction-ViT model, we adopt instruction tuning techniques to integrate medical textual information into the fine-tuning process of the visual model. Furthermore, a contrastive learning loss is employed to align textual and visual features, thereby enhancing the model's capacity to understand and differentiate complex pathological patterns. Experimental results demonstrate that the model integrating medical text information outperforms uni-modal models in various evaluation metrics, confirming that with instruction tuning, our model can effectively utilize medical text as prior knowledge to improve the performance of visual models in chest disease diagnosis. Furthermore, we conduct an interpretability analysis of the model's decision-making process, revealing that the regions attended to by the model highly correspond to the radiographic manifestations of different diseases, demonstrating the model's interpretability to a certain degree.

胸部x线在胸部疾病的筛查和诊断中起着至关重要的作用。由于病理表现的复杂性和放射科医师经验的局限性，胸部疾病诊断的准确性和效率有待进一步提高。近年来，深度学习在胸部x线图像分析方面取得了重大进展，但现有方法主要依赖于单模态视觉信息，忽略了医学文本数据中嵌入的疾病类别描述相关的先验知识，难以充分理解胸部x线图像的深度语义。为了解决这些挑战，受instruction - vit模型的启发，我们采用指令调优技术将医学文本信息集成到视觉模型的微调过程中。此外，使用对比学习损失来对齐文本和视觉特征，从而增强模型理解和区分复杂病理模式的能力。实验结果表明，集成医学文本信息的模型在各种评价指标上优于单模态模型，验证了通过指令调优，我们的模型可以有效地利用医学文本作为先验知识来提高视觉模型在胸部疾病诊断中的性能。进一步，我们对模型的决策过程进行了可解释性分析，发现模型所关注的区域与不同疾病的影像学表现高度对应，说明模型具有一定的可解释性。

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引用次数: 0

Application of imaging in pulmonary function assessment 影像学在肺功能评价中的应用

Meta-Radiology

Pub Date : 2025-08-13 DOI: 10.1016/j.metrad.2025.100167

Naishu Xie , Jiayu Wang , Rui Zhao , Jiankang Wu , Weiwei Meng , Huihui Zeng , Yan Chen

Pulmonary function test (PFT) is the most common and effective method for functional diagnosis and screening of respiratory diseases, but it is not suitable for all patients and the procedure is relatively complex. With advances in imaging technology and the application of artificial intelligence, imaging can not only circumvent the limitations of PFTs but also reconstruct images, quantify lung structure, and predict pulmonary functional responses. This article reviews the application of different imaging tests in pulmonary function, the measurement of pulmonary function in different diseases, and the latest advances in artificial intelligence.

肺功能检查（Pulmonary function test， PFT）是呼吸系统疾病最常用、最有效的功能诊断和筛查方法，但并非适用于所有患者，且操作相对复杂。随着成像技术的进步和人工智能的应用，成像不仅可以绕过PFTs的局限性，还可以重建图像，量化肺结构，预测肺功能反应。本文综述了不同影像学检查在肺功能中的应用，不同疾病肺功能的测量，以及人工智能的最新进展。

引用次数: 0

Dopamine receptor- and noradrenaline transporter-related disruptions are associated with depression and cognitive performance in COVID-19 survivors 多巴胺受体和去甲肾上腺素转运蛋白相关的破坏与COVID-19幸存者的抑郁和认知表现有关

Meta-Radiology

Pub Date : 2025-08-05 DOI: 10.1016/j.metrad.2025.100170

Yao Wang , Ziwei Yang , Xiao Liang , Lin Wu , Chengsi Wu , Jiankun Dai , Yuan Cao , Xianjun Zeng , Meng Li , Fuqing Zhou

Purpose

This study aims to explore the relationships between neural activity, neurovascular coupling (NVC), and neurotransmitter receptors, and to investigate their association with emotion and cognition in COVID-19 survivors.

Materials and methods

A total of 42 COVID-19 survivors and 30 matched healthy controls (HCs) were recruited. Regional homogeneity (ReHo) and functional connectivity strength (FCS) were calculated -to assess local and global neural activity, respectively. Cerebral blood flow (CBF) was characterized as brain perfusion. Regional NVC was evaluated using CBF/ReHo and CBF/FCS ratios at the voxel level. Neurotransmitter receptor maps were derived from the JuSpace toolbox, which integrates positron emission tomography (PET) and single-photon emission computed tomography (SPECT) data from healthy populations. These maps included 16 receptor/transporters, such as dopamine, serotonin, norepinephrine and glutamate receptors, among others. Spatial correlations between neural activity, NVC and neurotransmitter receptor maps were subsequently analyzed in COVID-19 survivors.

Results

Whether examining neural activity or NVC, COVID-19 survivors primariily exhibiteddecreased ReHo or CBF/ReHo pattern compared to HC. Moreover, the neurotransmitter receptor distributions showed strong associations exclusively with local neural activity (e.g., ReHo) and NVC (e.g., CBF/ReHo) in COVID-19 survivors. Specifically, the spatial pattern of ReHo correlated with dopamine receptors, glutamate receptors, and noradrenaline transporters, but the CBF/ReHo correlated only with dopamine receptors. Importantly, the correlation coefficients between ReHo and dopamine receptors or noradrenaline transporters were associated with cognitive performance in COVID-19 survivors. Conversely, the correlation coefficients between CBF/ReHo and dopamine were correlated with depression in COVID-19 survivors.

Conclusion

COVID-19 survivors exhibit disruptions in local neural activity and NVC related to dopamine receptors and noradrenaline transporters. These alterations are associated with depression and cognitive impairment, suggesting a potential molecular basis for impaired neural and neurovascular function.

目的探讨新冠肺炎幸存者神经活动、神经血管偶联（NVC）和神经递质受体之间的关系，以及它们与情绪和认知的关系。材料和方法共招募了42名COVID-19幸存者和30名匹配的健康对照（hc）。计算区域均匀性（ReHo）和功能连接强度（FCS），分别评估局部和全局神经活动。脑血流（CBF）表征为脑灌注。在体素水平上使用CBF/ReHo和CBF/FCS比率来评估区域NVC。神经递质受体图谱来源于JuSpace工具箱，该工具箱集成了健康人群的正电子发射断层扫描（PET）和单光子发射计算机断层扫描（SPECT）数据。这些地图包括16种受体/转运蛋白，如多巴胺、血清素、去甲肾上腺素和谷氨酸受体等。随后分析了COVID-19幸存者的神经活动、NVC和神经递质受体图谱之间的空间相关性。结果无论是检测神经活动还是NVC，与HC相比，COVID-19幸存者主要表现为ReHo或CBF/ReHo模式降低。此外，在COVID-19幸存者中，神经递质受体分布仅与局部神经活动（如ReHo）和NVC（如CBF/ReHo）有很强的相关性。具体来说，ReHo的空间模式与多巴胺受体、谷氨酸受体和去甲肾上腺素转运蛋白相关，但CBF/ReHo仅与多巴胺受体相关。重要的是，ReHo与多巴胺受体或去甲肾上腺素转运蛋白之间的相关系数与COVID-19幸存者的认知表现相关。相反，CBF/ReHo和多巴胺之间的相关系数与COVID-19幸存者的抑郁相关。结论covid -19幸存者表现出与多巴胺受体和去甲肾上腺素转运体相关的局部神经活动和NVC破坏。这些改变与抑郁和认知障碍有关，提示神经和神经血管功能受损的潜在分子基础。

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