Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, and Harvard Medical School, Charlestown, Massachusetts 02129, USA.
J Nucl Med. 2011 Jan;52(1):154-61. doi: 10.2967/jnumed.110.079343.
Head motion is difficult to avoid in long PET studies, degrading the image quality and offsetting the benefit of using a high-resolution scanner. As a potential solution in an integrated MR-PET scanner, the simultaneously acquired MRI data can be used for motion tracking. In this work, a novel algorithm for data processing and rigid-body motion correction (MC) for the MRI-compatible BrainPET prototype scanner is described, and proof-of-principle phantom and human studies are presented.
To account for motion, the PET prompt and random coincidences and sensitivity data for postnormalization were processed in the line-of-response (LOR) space according to the MRI-derived motion estimates. The processing time on the standard BrainPET workstation is approximately 16 s for each motion estimate. After rebinning in the sinogram space, the motion corrected data were summed, and the PET volume was reconstructed using the attenuation and scatter sinograms in the reference position. The accuracy of the MC algorithm was first tested using a Hoffman phantom. Next, human volunteer studies were performed, and motion estimates were obtained using 2 high-temporal-resolution MRI-based motion-tracking techniques.
After accounting for the misalignment between the 2 scanners, perfectly coregistered MRI and PET volumes were reproducibly obtained. The MRI output gates inserted into the PET list-mode allow the temporal correlation of the 2 datasets within 0.2 ms. The Hoffman phantom volume reconstructed by processing the PET data in the LOR space was similar to the one obtained by processing the data using the standard methods and applying the MC in the image space, demonstrating the quantitative accuracy of the procedure. In human volunteer studies, motion estimates were obtained from echo planar imaging and cloverleaf navigator sequences every 3 s and 20 ms, respectively. Motion-deblurred PET images, with excellent delineation of specific brain structures, were obtained using these 2 MRI-based estimates.
An MRI-based MC algorithm was implemented for an integrated MR-PET scanner. High-temporal-resolution MRI-derived motion estimates (obtained while simultaneously acquiring anatomic or functional MRI data) can be used for PET MC. An MRI-based MC method has the potential to improve PET image quality, increasing its reliability, reproducibility, and quantitative accuracy, and to benefit many neurologic applications.
在长时间的 PET 研究中，头部运动会难以避免，从而降低图像质量，并抵消使用高分辨率扫描仪的优势。在集成的 MR-PET 扫描仪中，一种潜在的解决方案是同时获取 MRI 数据，用于运动跟踪。在这项工作中，描述了一种用于 MRI 兼容 BrainPET 原型扫描仪的数据处理和刚体运动校正（MC）的新算法，并提出了原理验证的体模和人体研究。
为了考虑运动，根据 MRI 得出的运动估计，在沿射线方向（LOR）空间中处理 PET 提示和随机符合事件以及归一化后的灵敏度数据。对于每个运动估计，在标准的 BrainPET 工作站上的处理时间约为 16 秒。在扇形图空间中重新 bin 化后，对运动校正数据进行求和，并使用参考位置中的衰减和散射扇形图重建 PET 体积。首先使用 Hoffman 体模测试 MC 算法的准确性。接下来，进行了人体志愿者研究，并使用 2 种基于高时间分辨率的运动跟踪技术获得运动估计。
在考虑到 2 台扫描仪之间的不匹配后，可重复获得完全配准的 MRI 和 PET 体积。插入到 PET 列表模式中的 MRI 输出门允许在 0.2 毫秒内对 2 个数据集进行时间相关。通过在 LOR 空间中处理 PET 数据获得的 Hoffman 体模体积与通过使用标准方法处理数据并在图像空间中应用 MC 获得的体积相似，证明了该方法的定量准确性。在人体志愿者研究中，使用 echo planar imaging 和 cloverleaf navigator 序列，分别以 3 秒和 20 毫秒的间隔获取运动估计。使用这两种基于 MRI 的估计值，可以获得具有出色特定脑结构描绘的运动去模糊 PET 图像。
实现了用于集成的 MR-PET 扫描仪的基于 MRI 的 MC 算法。高时间分辨率的 MRI 得出的运动估计（在同时获取解剖或功能 MRI 数据时获得）可用于 PET MC。基于 MRI 的 MC 方法有可能改善 PET 图像质量，提高其可靠性、可重复性和定量准确性，并使许多神经应用受益。
