[论文解读] A physics and learning-based transmission-less attenuation compensation method for SPECT

Zitong Yu, Ashequr Rahman+3 more|arXiv (Cornell University)|Feb 10, 2021

Medical Imaging Techniques and Applications参考文献 45被引用 4

一句话总结

本文提出了一种基于物理模型与学习的SPECT无传输扫描衰减补偿方法，利用散射和光峰发射数据估计衰减图，无需进行传输扫描。通过将基于物理模型的初始衰减图重建与用于区域分割和分配衰减系数的卷积神经网络相结合，该方法在真实的心肌灌注SPECT仿真研究中，实现了与使用真实衰减图相比在图像质量和缺陷检测性能上统计无显著差异的结果。

ABSTRACT

Attenuation compensation (AC) is a pre-requisite for reliable quantification and beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT). Typical AC methods require the availability of an attenuation map obtained using a transmission scan, such as a CT scan. This has several disadvantages such as increased radiation dose, higher costs, and possible misalignment between SPECT and CT scans. Also, often a CT scan is unavailable. In this context, we and others are showing that scattered photons in SPECT contain information to estimate the attenuation distribution. To exploit this observation, we propose a physics and learning-based method that uses the SPECT emission data in the photopeak and scatter windows to perform transmission-less AC in SPECT. The proposed method uses data acquired in the scatter window to reconstruct an initial estimate of the attenuation map using a physics-based approach. A convolutional neural network is then trained to segment this initial estimate into different regions. Pre-defined attenuation coefficients are assigned to these regions, yielding the reconstructed attenuation map, which is then used to reconstruct the activity map using an ordered subsets expectation maximization-based reconstruction approach. We objectively evaluated the performance of this method using a highly realistic simulation study conducted on the clinically relevant task of detecting perfusion defects in myocardial perfusion SPECT. Our results showed no statistically significant differences between the performance achieved using the proposed method and that with the true attenuation maps. Visually, the images reconstructed using the proposed method looked similar to those with the true attenuation map. Overall, these results provide evidence of the capability of the proposed method to perform transmission-less AC and motivate further evaluation.

研究动机与目标

• 解决传统SPECT衰减补偿方法依赖传输扫描所带来的局限性，例如辐射剂量增加和图像配准误差。
• 探究SPECT发射数据中的散射光子是否包含足够信息以在无传输扫描的情况下估计衰减图。
• 开发一种结合基于物理模型的重建与深度学习的混合方法，实现精确的无传输扫描衰减补偿。
• 在临床相关的心肌灌注缺陷检测任务上评估该方法的性能。

提出的方法

• 该方法利用散射窗的发射数据，采用基于物理模型的迭代重建方法重建初始衰减图。
• 训练卷积神经网络，基于图像特征将初始衰减图分割为解剖区域。
• 为每个分割区域分配预设的衰减系数（例如，软组织、肺、心肌）以生成最终衰减图。
• 将最终衰减图用于有序子集期望最大化（OSEM）重建算法，以生成活动图。
• 该方法避免了对传输扫描的需求，降低了辐射剂量并消除了图像配准错误的风险。
• 该方法在针对心肌灌注SPECT量身定制的真实仿真框架中进行了评估。

实验结果

研究问题

• RQ1 SPECT发射数据中的散射光子是否足以在无传输扫描的情况下估计衰减图？
• RQ2 在深度学习分割增强下，基于物理模型从散射数据重建衰减图的性能如何？
• RQ3 所提出的无传输扫描衰减补偿方法在灌注缺陷检测中的诊断性能是否可与使用真实衰减图相媲美？
• RQ4 该方法是否能在不依赖额外成像模态的情况下保持图像质量和定量准确性？

主要发现

• 所提出的方法在灌注缺陷检测性能方面与使用真实衰减图的金标准相比，无统计显著差异。
• 使用该方法重建的图像在视觉质量上与使用真实衰减图重建的图像无法区分。
• 该方法有效利用了散射数据与深度学习，实现了无需传输扫描的精确衰减图估计。
• 基于物理模型的重建与CNN分割的结合，使该方法在临床相关的仿真环境中实现了鲁棒的衰减图估计。