目的:开发一种用于PET/CT和PET/MRI的通用病变识别算法,验证它,探索影响绩效的因素。
方法:2022AutoPetChallenge的1014PET/CT数据集用于基于2D和3D分数残差(F-Res)模型训练病变检测模型。为了将其扩展到PET/MRI,开发了将MR图像转换为合成CT(sCT)的网络,使用41组全身MR和相应的CT数据。38例患者的PET/CT和PET/MRI数据用于验证通用病变识别算法。使用信噪比(SNR)和对比噪声比(CNR)评估图像质量。总病变糖酵解(TLG),代谢性肿瘤体积(MTV),和病变计数从得到的病变面具计算。经验丰富的医生审查并纠正了模型的输出,建立地面真理。通过检测精度评估病变检测深度学习模型在不同PET图像上的性能,精度,召回,和骰子系数。具有小于1的检测准确度评分(DAS)的数据用于异常值的分析。
结果:与PET/CT相比,PET/MRI扫描的延迟时间明显更长(135±45分钟vs61±12分钟),信噪比更低(6.17±1.11vs9.27±2.77)。然而,CNR值相似(7.37±5.40vs5.86±6.69)。PET/MRI检测到更多的病变(平均差异为-3.184)。TLG和MTV在PET/CT和PET/MRI之间没有显着差异(TLG:119.18±203.15vs123.57±151.58,p=0.41;MTV:36.58±57.00vs39.16±48.34,p=0.33)。在异常值分析中包括总共12个PET/CT和14个PET/MRI数据集。异常值分析显示肠道PET/CT异常,输尿管,和肌肉,而PET/MRI异常在肠道,睾丸,和低示踪剂吸收区域,输尿管(PET/CT)和肠/睾丸(PET/MRI)假阳性。
结论:深度学习病变检测模型在PET/CT和PET/MRI中均表现良好。SNR,CNR和重建参数对识别精度的影响最小,但注射后延迟时间显著。
OBJECTIVE: Develop a universal lesion recognition algorithm for PET/CT and PET/MRI, validate it, and explore factors affecting performance.
METHODS: The 2022 AutoPet Challenge\'s 1014 PET/CT dataset was used to train the lesion detection model based on 2D and 3D fractional-residual (F-Res) models. To extend this to PET/MRI, a network for converting MR images to synthetic CT (sCT) was developed, using 41 sets of whole-body MR and corresponding CT data. 38 patients\' PET/CT and PET/MRI data were used to verify the universal lesion recognition algorithm. Image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Total lesion glycolysis (TLG), metabolic tumor volume (MTV), and lesion count were calculated from the resultant lesion masks. Experienced physicians reviewed and corrected the model\'s outputs, establishing the ground truth. The performance of the lesion detection deep-learning model on different PET images was assessed by detection accuracy, precision, recall, and dice coefficients. Data with a detection accuracy score (DAS) less than 1 was used for analysis of outliers.
RESULTS: Compared to PET/CT, PET/MRI scans had a significantly longer delay time (135 ± 45 min vs 61 ± 12 min) and lower SNR (6.17 ± 1.11 vs 9.27 ± 2.77). However, CNR values were similar (7.37 ± 5.40 vs 5.86 ± 6.69). PET/MRI detected more lesions (with a mean difference of -3.184). TLG and MTV showed no significant differences between PET/CT and PET/MRI (TLG: 119.18 ± 203.15 vs 123.57 ± 151.58, p = 0.41; MTV: 36.58 ± 57.00 vs 39.16 ± 48.34, p = 0.33). A total of 12 PET/CT and 14 PET/MRI datasets were included in the analysis of outliers. Outlier analysis revealed PET/CT anomalies in intestines, ureters, and muscles, while PET/MRI anomalies were in intestines, testicles, and low tracer uptake regions, with false positives in ureters (PET/CT) and intestines/testicles (PET/MRI).
CONCLUSIONS: The deep learning lesion detection model performs well with both PET/CT and PET/MRI. SNR, CNR and reconstruction parameters minimally impact recognition accuracy, but delay time post-injection is significant.