研究非衰减校正 PET 图像在 PET 数据衰减校正中的应用。

Investigating the use of nonattenuation corrected PET images for the attenuation correction of PET data.

机构信息

Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, MS-366, Houston, Texas 77005, USA.

出版信息

Med Phys. 2013 Aug;40(8):082508. doi: 10.1118/1.4816304.

DOI:10.1118/1.4816304

PMID:23927353

Abstract

PURPOSE

The aim of this study is to investigate the feasibility of using the nonattenuated PET images (PET-NAC) as a means for the AC of PET data.

METHODS

A three-step iterative segmentation process is proposed. In step 1, a patient's body contour is segmented from the PET-NAC using an active contour algorithm. Voxels inside the contour are then assigned a value of 0.096 cm(-1) to represent the attenuation coefficient of soft tissue at 511 keV. This segmented attenuation map is then used to correct for attenuation the raw PET data and the resulting PET images are used as the input to Step 2 of the process. In step 2, the lung region is segmented using an optimal thresholding approach and the corresponding voxels are assigned a value of 0.024 cm(-1) representing the attenuation coefficients of lung tissue at 511 keV. The updated attenuation map is then used for a second time to correct for attenuation the raw PET data, and the resulting PET images are used as the input to step 3. The purpose of Step 3 is to delineate parts of the heart and liver in the lung contour using a region growing approach since these parts were unavoidably excluded in the lung contour in step 2. These parts are then corrected by using a value of 0.096 cm(-1) in the attenuation map. Finally the attenuation coefficients of the bed are included based on CT images to eliminate the impact of the couch on the accuracy of AC. The final attenuation map is then used to AC the raw PET data and generates the final PET image, which we name iterative AC PET (PET-IAC). To assess the proposed segmentation approach, a phantom and 14 patients (with a total of 55 lesions including bone) were scanned on a GE Discovery-RX PET∕CT scanner. PET-IAC images were generated using the proposed process and compared to those of CT-AC PET (PET-CTAC). Visual inspection, lesion SUV, and voxel by voxel histograms between PET-IAC and PET-CTAC for phantom and patient studies were performed to assess the accuracy of image quantification.

RESULTS

Visual inspection showed a small difference in lung parenchyma between the PET-IAC and PET-CTAC. Tumor SUV based on PET-IAC were on average different by 3%±9% (6%±7%) compared to the SUVs from the PET-CTAC in the phantom (patient) studies. For bone lesions only, the average difference was 3%±6%. The histogram comparing PET-CTAC and PET-IAC resulted in an average regression line of y=(1.08±0.07)x+(0.00007±0.0013), with R2=0.978±0.0057.

CONCLUSIONS

Preliminary results suggest that PET-NAC for the AC of PET images is feasible. Such an approach can potentially be used for dedicated PET or PET∕MR hybrid systems while minimizing scan time or potential image artifacts, respectively.

摘要

目的

本研究旨在探讨使用未经衰减校正的 PET 图像（PET-NAC）作为 PET 数据衰减校正（AC）的方法。

方法

提出了一个三步迭代分割过程。在步骤 1 中，使用主动轮廓算法从 PET-NAC 中分割出患者的身体轮廓。轮廓内的体素被赋值为 0.096cm(-1)，代表 511keV 时软组织的衰减系数。该分割的衰减图随后用于校正原始 PET 数据的衰减，得到的 PET 图像作为过程第二步的输入。在步骤 2 中，使用最佳阈值方法分割肺区，并将相应的体素赋值为 0.024cm(-1)，代表 511keV 时肺组织的衰减系数。更新后的衰减图再次用于第二次校正原始 PET 数据，得到的 PET 图像作为步骤 3 的输入。步骤 3 的目的是使用区域生长方法在肺轮廓中描绘心脏和肝脏的部分，因为这些部分在步骤 2 中不可避免地被排除在肺轮廓之外。然后使用衰减图中的 0.096cm(-1)值对这些部分进行校正。最后，根据 CT 图像包含床位的衰减系数，以消除床对 AC 准确性的影响。最后，衰减图用于校正原始 PET 数据，并生成最终的 PET 图像，我们称之为迭代 AC PET（PET-IAC）。为了评估所提出的分割方法，使用 GE Discovery-RX PET∕CT 扫描仪对一个体模和 14 名患者（共 55 个病灶，包括骨骼）进行了扫描。使用所提出的方法生成了 PET-IAC 图像，并与 CT-AC PET（PET-CTAC）进行了比较。对体模和患者研究的 PET-IAC 和 PET-CTAC 的图像进行了视觉检查、病灶 SUV 和体素直方图分析，以评估图像定量的准确性。

结果

视觉检查显示 PET-IAC 和 PET-CTAC 之间肺实质有细微差异。与 PET-CTAC 相比，体模（患者）研究中基于 PET-IAC 的肿瘤 SUV 平均低 3%±9%（6%±7%）。对于骨骼病变，平均差异为 3%±6%。比较 PET-CTAC 和 PET-IAC 的直方图得到平均回归线 y=(1.08±0.07)x+(0.00007±0.0013)，R2=0.978±0.0057。