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使用UNET变压器网络进行图像重建,用于快速和低剂量PET扫描

Sanaz Kaviani¹, Amirhossein Sanaat², Mersede Mokri¹

¹Faculty of Medicine, University of Montreal, Montreal, Canada; University of Montreal Hospital Research Centre (CRCHUM), Montreal, Canada.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

|November 25, 2023

概括

这项研究介绍了TrUNET-MAPEM,这是一种用于低计数正子发射断层扫描 (PET) 成像的深度学习模型. 这种新的方法通过减少噪音来提高图像质量,同时保留关键细节,提高诊断准确度.

科学领域:

医疗成像医学成像
深度学习 (Deep Learning) 是一种深度学习.
计算科学计算科学

背景情况:

低剂量和快速的正子发射断层扫描 (PET) 成像对于患者的安全和舒适至关重要.
从低数量的PET数据中重建高质量的图像是具有挑战性的,因为噪音和数据有限.
有效的无色化和图像增强对于准确的PET扫描解释至关重要.

研究的目的:

开发一种基于深度学习的准确方法,用于低计数PET图像重建.
通过减少噪音和保存细节来提高重建的PET图像的质量.
结合UNET和变压器网络的优势,以进行增强的PET图像重建.

主要方法:

TrUNET-MAPEM模型将UNET变压器调节器与最大后期期望最大化 (MAPEM) 算法集成在一起.
一个损失函数,结合结构相似度指数 (SSIM) 和平均平方误差 (MSE) 被用于准确性评估.
使用模拟 (Brainweb幻影) 和真实患者数据 (Siemens Biograph mMR) 评估性能,与OSEM,MAPOSEM和3D-UNET进行比较.

主要成果:

与最先进的方法相比,TrUNET-MAPEM在模拟和真实患者数据上取得了更高的性能.
对于患者数据,该模型的平均PSNR为33.72dB,SSIM为0.955,rRMSE为0.39.
该模型成功地重建了光滑的图像,同时保留了边缘等基本特征,在所有评估的指标中表现优于其他方法.

结论:

TrUNET-MAPEM模型代表了低计数PET图像重建的重大进步.
该方法为早期疾病检测和诊断提供了潜在的临床应用.
结果表明,与现有算法相比,噪声降低和边缘保护优越.

关键词:

深度学习重建的重建低剂量和快速的PET扫描.后期期望最大化 (MAPEM) 的方法变压器网络的变压器网络.

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