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A Myocardial Reorientation Method Based on Feature Point Detection for Quantitative Analysis of PET Myocardial Perfusion Imaging.

Computer Methods and Programs in Biomedicine(2025)SCI 2区SCI 3区

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Abstract
OBJECTIVE:Reorienting cardiac positron emission tomography (PET) images to the transaxial plane is essential for cardiac PET image analysis. This study aims to design a convolutional neural network (CNN) for automatic reorientation and evaluate its generalizability. METHODS:An artificial intelligence (AI) method integrating U-Net and the differentiable spatial to numeric transform module (DSNT-U) was proposed to automatically position three feature points (Papex, Pbase, and PRV), with these three points manually located by an experienced radiologist as the reference standard (RS). A second radiologist performed manual location for reproducibility evaluation. The DSNT-U, initially trained and tested on a [11C]acetate dataset (training/testing: 40/17), was further compared with a CNN-spatial transformer network (CNN-STN). The network fine-tuned with 4 subjects was tested on a [13N]ammonia dataset (n = 30). The performance of the DSNT-U was evaluated in terms of coordinates, volume, and quantitative indexes (pharmacokinetic parameters and total perfusion deficit). RESULTS:The proposed DSNT-U successfully achieved automatic myocardial reorientation for both [11C]acetate and [13N]ammonia datasets. For the former dataset, the intraclass correlation coefficients (ICCs) between the coordinates predicted by the DSNT-U and the RS exceeded 0.876. The average normalized mean squared error (NMSE) between the short-axis (SA) images obtained through DSNT-U-based reorientation and the reference SA images was 0.051 ± 0.043. For pharmacokinetic parameters, the R² between the DSNT-U and the RS was larger than 0.968. Compared with the CNN-STN, the DSNT-U demonstrated a higher ICC between the estimated rigid transformation parameters and the RS. After fine-tuning on the [13N]ammonia dataset, the average NMSE between the SA images reoriented by the DSNT-U and the reference SA images was 0.056 ± 0.046. The ICC between the total perfusion deficit (TPD) values computed from DSNT-U-derived images and the reference values was 0.981. Furthermore, no significant differences were observed in the performance of the DSNT-U prediction among subjects with different genders or varying myocardial perfusion defect (MPD) statuses. CONCLUSIONS:The proposed DSNT-U can accurately position Papex, Pbase, and PRV on the [11C]acetate dataset. After fine-tuning, the positioning model can be applied to the [13N]ammonia perfusion dataset, demonstrating good generalization performance. This method can adapt to data of different genders (with or without MPD) and different tracers, displaying the potential to replace manual operations.
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Key words
Cardiac PET,Automatic reorientation,Artificial intelligence (AI),Pharmacokinetic parameters,Myocardial perfusion defect
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要点】:本研究提出了一种基于特征点检测的心肌重新定位方法,使用卷积神经网络(CNN)自动定位心肌图像中的三个特征点,并评估了方法的泛化性能。

方法】:采用集成了U-Net和可微分空间到数值转换模块(DSNT-U)的AI方法,自动定位心尖点(Papex)、心底点(Pbase)和右心室点(PRV),并与传统的CNN-空间转换网络(CNN-STN)进行比较。

实验】:DSNT-U最初在[11C]醋酸数据集上进行训练和测试(训练/测试:40/17),随后在4个受试者上进行微调并在[13N]氨水数据集(n=30)上进行测试。性能评估包括坐标、体积和定量指数(药代动力学参数和总灌注缺损)。结果显示,DSNT-U在两个数据集上均成功实现了心肌的自动重新定位。在[11C]醋酸数据集上,DSNT-U预测的坐标与参考标准之间的ICC超过0.876;在[13N]氨水数据集上,微调后的DSNT-U平均NMSE为0.056 ± 0.046,TPD值的ICC为0.981。此外,DSNT-U在不同性别和不同心肌灌注缺损(MPD)状态的受试者中的性能无显著差异。