Wang Hanzhong, Wang Yue, Xue Qiaoyi, Zhang Yu, Qiao Xiaoya, Lin Zengping, Zheng Jiaxu, Zhang Zheng, Yang Yang, Zhang Min, Huang Qiu, Huang Yanqi, Cao Tuoyu, Wang Jin, Li Biao
Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China.
Eur J Nucl Med Mol Imaging. 2025 Jun;52(7):2577-2588. doi: 10.1007/s00259-025-07086-5. Epub 2025 Feb 6.
To address the challenges of verifying MR-based attenuation correction (MRAC) in PET/MR due to CT positional mismatches and alignment issues, this study utilized a flatbed insert and arms-down positioning during PET/CT scans to achieve precise MR-CT matching for accurate MRAC evaluation.
A validation dataset of 21 patients underwent whole-body [F]FDG PET/CT followed by [F]FDG PET/MR. A flatbed insert ensured consistent positioning, allowing direct comparison of four MRAC methods-four-tissue and five-tissue models with discrete and continuous μ-maps-against CT-based attenuation correction (CTAC). A deep learning-based framework, trained on a dataset of 300 patients, was used to generate synthesized-CTs from MR images, forming the basis for all MRAC methods. Quantitative analyses were conducted at the whole-body, region of interest, and lesion levels, with lesion-distance analysis evaluating the impact of bone proximity on standardized uptake value (SUV) quantification.
Distinct differences were observed among MRAC methods in spine and femur regions. Joint histogram analysis showed MRAC-4 (continuous μ-map) closely aligned with CTAC. Lesion-distance analysis revealed MRAC-4 minimized bone-induced SUV interference (r = 0.01, p = 0.8643). However, tissues prone to bone segmentation interference, such as the spine and liver, exhibited greater SUV variability and lower reproducibility in MRAC-4 compared to MRAC-2 (2D bone segmentation, discrete μ-map) and MRAC-3 (3D bone segmentation, discrete μ-map).
Using a flatbed insert, this study validated MRAC with high precision. Continuous μ-value MRAC method (MRAC-4) demonstrated superior accuracy and minimized bone-related SUV errors but faced challenges in reproducibility, particularly in bone-rich regions.
为应对正电子发射断层扫描/磁共振成像（PET/MR）中由于CT位置不匹配和对准问题而在基于MR的衰减校正（MRAC）验证方面面临的挑战，本研究在PET/CT扫描期间采用了平板插入物和双臂下垂定位，以实现精确的MR-CT匹配，从而进行准确的MRAC评估。
21例患者的验证数据集先进行全身[F]FDG PET/CT检查，随后进行[F]FDG PET/MR检查。平板插入物确保了一致的定位，使得能够将四种MRAC方法（具有离散和连续μ映射的四组织和五组织模型）与基于CT的衰减校正（CTAC）进行直接比较。基于深度学习的框架在300例患者的数据集上进行训练，用于从MR图像生成合成CT，这构成了所有MRAC方法的基础。在全身、感兴趣区域和病变水平进行了定量分析，病变距离分析评估了骨骼接近度对标准化摄取值（SUV）量化的影响。
在脊柱和股骨区域观察到MRAC方法之间存在明显差异。联合直方图分析表明MRAC-4（连续μ映射）与CTAC紧密对齐。病变距离分析显示MRAC-4将骨骼引起的SUV干扰降至最低（r = 0.01，p = 0.8643）。然而，与MRAC-2（二维骨骼分割，离散μ映射）和MRAC-3（三维骨骼分割，离散μ映射）相比，在MRAC-4中，诸如脊柱和肝脏等容易受到骨骼分割干扰的组织表现出更大的SUV变异性和更低的可重复性。
本研究使用平板插入物高精度地验证了MRAC。连续μ值MRAC方法（MRAC-4）显示出卓越的准确性，并将与骨骼相关的SUV误差降至最低，但在可重复性方面面临挑战，尤其是在骨骼丰富的区域。
