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Abstract：
UNASSIGNED: To develop a deep learning approach that enables ultra-low-dose, 1% of the standard clinical dosage (3 MBq/kg), ultrafast whole-body PET reconstruction in cancer imaging.
UNASSIGNED: In this Health Insurance Portability and Accountability Act-compliant study, serial fluorine 18-labeled fluorodeoxyglucose PET/MRI scans of pediatric patients with lymphoma were retrospectively collected from two cross-continental medical centers between July 2015 and March 2020. Global similarity between baseline and follow-up scans was used to develop Masked-LMCTrans, a longitudinal multimodality coattentional convolutional neural network (CNN) transformer that provides interaction and joint reasoning between serial PET/MRI scans from the same patient. Image quality of the reconstructed ultra-low-dose PET was evaluated in comparison with a simulated standard 1% PET image. The performance of Masked-LMCTrans was compared with that of CNNs with pure convolution operations (classic U-Net family), and the effect of different CNN encoders on feature representation was assessed. Statistical differences in the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and visual information fidelity (VIF) were assessed by two-sample testing with the Wilcoxon signed rank t test.
UNASSIGNED: The study included 21 patients (mean age, 15 years ± 7 [SD]; 12 female) in the primary cohort and 10 patients (mean age, 13 years ± 4; six female) in the external test cohort. Masked-LMCTrans-reconstructed follow-up PET images demonstrated significantly less noise and more detailed structure compared with simulated 1% extremely ultra-low-dose PET images. SSIM, PSNR, and VIF were significantly higher for Masked-LMCTrans-reconstructed PET (P < .001), with improvements of 15.8%, 23.4%, and 186%, respectively.
UNASSIGNED: Masked-LMCTrans achieved high image quality reconstruction of 1% low-dose whole-body PET images.Keywords: Pediatrics, PET, Convolutional Neural Network (CNN), Dose Reduction Supplemental material is available for this article. © RSNA, 2023.
UNASSIGNED: In this Health Insurance Portability and Accountability Act-compliant study, serial fluorine 18-labeled fluorodeoxyglucose PET/MRI scans of pediatric patients with lymphoma were retrospectively collected from two cross-continental medical centers between July 2015 and March 2020. Global similarity between baseline and follow-up scans was used to develop Masked-LMCTrans, a longitudinal multimodality coattentional convolutional neural network (CNN) transformer that provides interaction and joint reasoning between serial PET/MRI scans from the same patient. Image quality of the reconstructed ultra-low-dose PET was evaluated in comparison with a simulated standard 1% PET image. The performance of Masked-LMCTrans was compared with that of CNNs with pure convolution operations (classic U-Net family), and the effect of different CNN encoders on feature representation was assessed. Statistical differences in the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and visual information fidelity (VIF) were assessed by two-sample testing with the Wilcoxon signed rank t test.
UNASSIGNED: The study included 21 patients (mean age, 15 years ± 7 [SD]; 12 female) in the primary cohort and 10 patients (mean age, 13 years ± 4; six female) in the external test cohort. Masked-LMCTrans-reconstructed follow-up PET images demonstrated significantly less noise and more detailed structure compared with simulated 1% extremely ultra-low-dose PET images. SSIM, PSNR, and VIF were significantly higher for Masked-LMCTrans-reconstructed PET (P < .001), with improvements of 15.8%, 23.4%, and 186%, respectively.
UNASSIGNED: Masked-LMCTrans achieved high image quality reconstruction of 1% low-dose whole-body PET images.Keywords: Pediatrics, PET, Convolutional Neural Network (CNN), Dose Reduction Supplemental material is available for this article. © RSNA, 2023.
摘要：
■要开发一种能够实现超低剂量的深度学习方法，1％的标准临床剂量（3MBq/kg），癌症成像中的超快全身PET重建。
■在这项符合健康保险可移植性和责任法案的研究中，我们在2015年7月至2020年3月期间,从两个跨大陆医疗中心回顾性收集了小儿淋巴瘤患者的氟18标记氟代脱氧葡萄糖PET/MRI系列扫描.基线和随访扫描之间的全局相似性用于开发Masked-LMCTrans，纵向多模态协同注意卷积神经网络（CNN）变换器，提供来自同一患者的系列PET/MRI扫描之间的交互和联合推理。与模拟的标准1％PET图像相比，评估了重建的超低剂量PET的图像质量。将Masked-LMCTrans的性能与具有纯卷积运算（经典U-Net家族）的CNN的性能进行了比较，并评估了不同CNN编码器对特征表示的影响。结构相似性指数度量(SSIM)的统计差异，峰值信噪比(PSNR)，和视觉信息保真度(VIF)通过双样本检验和Wilcoxon符号秩t检验进行评估。
■该研究包括21名患者(平均年龄，15岁±7[SD]；12名女性)的主要队列和10名患者(平均年龄，13岁±4岁；六名女性）在外部测试队列中。与模拟的1％极超低剂量PET图像相比，掩蔽LMCTrans重建的后续PET图像显示出明显更少的噪声和更详细的结构。SSIM,PSNR,Masked-LMCTrans重建PET的VIF明显更高（P<0.001），改善了15.8%，23.4%,186%,分别。
■Masked-LMCTrans实现了1％低剂量全身PET图像的高图像质量重建。关键词：儿科，PET,卷积神经网络(CNN)剂量减少补充材料可用于本文。©RSNA,2023年。
■在这项符合健康保险可移植性和责任法案的研究中，我们在2015年7月至2020年3月期间,从两个跨大陆医疗中心回顾性收集了小儿淋巴瘤患者的氟18标记氟代脱氧葡萄糖PET/MRI系列扫描.基线和随访扫描之间的全局相似性用于开发Masked-LMCTrans，纵向多模态协同注意卷积神经网络（CNN）变换器，提供来自同一患者的系列PET/MRI扫描之间的交互和联合推理。与模拟的标准1％PET图像相比，评估了重建的超低剂量PET的图像质量。将Masked-LMCTrans的性能与具有纯卷积运算（经典U-Net家族）的CNN的性能进行了比较，并评估了不同CNN编码器对特征表示的影响。结构相似性指数度量(SSIM)的统计差异，峰值信噪比(PSNR)，和视觉信息保真度(VIF)通过双样本检验和Wilcoxon符号秩t检验进行评估。
■该研究包括21名患者(平均年龄，15岁±7[SD]；12名女性)的主要队列和10名患者(平均年龄，13岁±4岁；六名女性）在外部测试队列中。与模拟的1％极超低剂量PET图像相比，掩蔽LMCTrans重建的后续PET图像显示出明显更少的噪声和更详细的结构。SSIM,PSNR,Masked-LMCTrans重建PET的VIF明显更高（P<0.001），改善了15.8%，23.4%,186%,分别。
■Masked-LMCTrans实现了1％低剂量全身PET图像的高图像质量重建。关键词：儿科，PET,卷积神经网络(CNN)剂量减少补充材料可用于本文。©RSNA,2023年。
