一种处理多模态心脏PET/MRI数据的新型无监督机器学习策略
A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data
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中文总结 AI 辅助
研究针对致心律失常性左心室心肌病诊断难题，利用PET/MRI数据，采用两步聚类等方法，对患者图像进行处理分析，生成健康报告，经交叉验证和在更大队列上验证，能准确识别异常，有助于表征心肌异质性。
中文摘要 AI 辅助
致心律失常性左心室心肌病是一种因缺乏金标准诊断标准而难以诊断的遗传性心肌疾病。同时进行PET/MR成像并结合多参数定量分析，有助于识别与心肌病表型和进展相关的不同特征。本初步研究聚焦于处理PET/MRI数据的方法策略，包括患者间数据关联和区域分析。对99例经基因诊断的致心律失常性左心室心肌病患者的T1和T2图、LGE及¹⁸F-FDG-PET图像应用两步聚类。先对每位患者图像独立进行z标准化并汇总为单一体积后聚类成超体素，再通过光谱聚类得到32个患者间超体素组。为每个聚类和模态分配“异常”分数以可视化可能与疾病相关的异常区域，并生成自动化文本和靶心健康报告，在重复嵌套交叉验证中与心脏成像评估进行比较。该方法在167个数字体模的更大队列上进一步验证。聚类生成的报告准确识别了大多数心脏科医生的观察结果（患者重复嵌套交叉验证中BA = 0.76 ± 0.04，体模上BA ≥ 0.8），且识别出的异常聚类与视觉观察紧密匹配，有助于识别图像上不同程度的纤维化或炎症。此方法能更系统地处理多模态PET/MRI数据以表征致心律失常性左心室心肌病患者的心肌异质性。
英文摘要
Arrhythmogenic left ventricular cardiomyopathy is a genetic myocardial disease difficult to diagnose due to the lack of gold standard criteria. Simultaneous PET/MR imaging, combined with multiparametric quantitative analysis, could facilitate the identification of different profiles related to the phenotype and progression of cardiomyopathy. This preliminary study focuses on a methodological strategy for dealing with PET/MRI data, including inter-patient data linkage and regional analysis. Two-step clustering was applied to T1 and T2 maps, LGE, and 18F-FDG-PET images of 99 patients genetically diagnosed with arrhythmogenic left ventricular cardiomyopathy. Each patient's images were independently z-scored and summed into a single volume, which was clustered into supervoxels. Thirty-two inter-patient groups of supervoxels were obtained by spectral clustering. An "abnormality" score was assigned to each cluster and modality, and used to visualise abnormal regions likely associated with disease. They enabled the generation of automated textual and bullseye health reports for each patient, which were compared with cardiac imager assessments using balanced accuracy in repeated nested cross-validation. This approach was further validated on a larger cohort of 167 numerical phantoms. The reports generated by clustering accurately identified most of the cardiac physicians' observations (BA = 0.76 $\pm$ 0.04 in repeated nested cross-validation on patients, and BA $\ge$ 0.8 on phantoms). Furthermore, the identified abnormal clusters closely matched their visual observations, facilitating the identification of varying degrees of fibrosis or inflammation on the images. This approach enables a more systematic handling of multimodal PET/MRI data to characterise myocardial heterogeneity in arrhythmogenic left ventricular cardiomyopathy patients.
发表机构
- Nantes Université, CHU Nantes（南特大学，南特大学医院中心）
- Siemens Healthineers France（西门子医疗法国公司）
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