Institute of Nuclear Medicine, University College London, London, NW1, 2BU, UK.
Centre for Medical Imaging, University College London, London, NW1, 2PG, UK.
Med Phys. 2017 Jun;44(6):2379-2390. doi: 10.1002/mp.12253. Epub 2017 May 12.
Respiratory motion compensation in PET/CT and PET/MRI is essential as motion is a source of image degradation (motion blur, attenuation artifacts). In previous work, we developed a direct method for joint image reconstruction/motion estimation (JRM) for attenuation-corrected (AC) respiratory-gated PET, which uses a single attenuation-map (μ-map). This approach was successfully implemented for respiratory-gated PET/CT, but since it relied on an accurate μ-map for motion estimation, the question of its applicability in PET/MRI is open. The purpose of this work is to investigate the feasibility of JRM in PET/MRI and to assess the robustness of the motion estimation when a degraded μ-map is used.
We performed a series of JRM reconstructions from simulated PET data using a range of simulated Dixon MRI sequence derived μ-maps with wrong attenuation values in the lungs, from -100% (no attenuation) to +100% (double attenuation), as well as truncated arms. We compared the estimated motions with the one obtained from JRM in ideal conditions (no noise, true μ-map as an input). We also applied JRM on 4 patient datasets of the chest, 3 of them containing hot lesions. Patient list-mode data were gated using a principal component analysis method. We compared SUV values of the JRM reconstructed activity images and non motion-corrected images. We also assessed the estimated motion fields by comparing the deformed JRM-reconstructed activity with individually non-AC reconstructed gates.
Experiments on simulated data showed that JRM-motion estimation is robust to μ-map degradation in the sense that it produces motion fields similar to the ones obtained when using the true μ-map, regardless of the attenuation errors in the lungs (< 0.5% mean absolute difference with the reference motion field). When using a μ-map with truncated arms, JRM estimates a motion field that stretches the μ-map in order to match the projection data. Results on patient datasets showed that using JRM improves the SUV values of hot lesions significantly and suppresses motion blur. When the estimated motion fields are applied to the reconstructed activity, the deformed images are geometrically similar to the non-AC individually reconstructed gates.
Motion estimation by JRM is robust to variation of the attenuation values in the lungs. JRM successfully compensates for motion when applied to PET/MRI clinical datasets. It provides a potential alternative to existing methods where the motion fields are pre-estimated from separate MRI measurements.
在 PET/CT 和 PET/MRI 中进行呼吸运动补偿至关重要，因为运动是图像降级（运动模糊、衰减伪影）的一个来源。在之前的工作中，我们开发了一种用于衰减校正（AC）呼吸门控 PET 的联合图像重建/运动估计（JRM）的直接方法，该方法使用单个衰减图（μ 图）。这种方法已成功应用于呼吸门控 PET/CT，但由于它依赖于准确的 μ 图进行运动估计，因此其在 PET/MRI 中的适用性仍存在疑问。本研究的目的是探讨 JRM 在 PET/MRI 中的可行性，并评估在使用降级的 μ 图时运动估计的稳健性。
我们使用一系列模拟的 Dixon MRI 序列衍生的 μ 图进行了一系列 JRM 重建，这些 μ 图的肺部存在错误的衰减值，从-100%（无衰减）到+100%（双倍衰减），以及截断臂。我们将估计的运动与在理想条件下（无噪声，输入真实 μ 图）从 JRM 获得的运动进行了比较。我们还对 4 例胸部患者数据集应用了 JRM，其中 3 例包含热病变。使用主成分分析方法对患者列表模式数据进行门控。我们比较了 JRM 重建活动图像和非运动校正图像的 SUV 值。我们还通过比较变形后的 JRM 重建活动与单独的非 AC 重建门控来评估估计的运动场。
在模拟数据上的实验表明，JRM 运动估计对 μ 图退化具有鲁棒性，因为它产生的运动场与使用真实 μ 图时获得的运动场相似，无论肺部的衰减误差如何（与参考运动场的平均绝对差异<0.5%）。当使用截断臂的 μ 图时，JRM 会估计一个运动场，该运动场会拉伸 μ 图以匹配投影数据。对患者数据集的结果表明，使用 JRM 可显著提高热病变的 SUV 值，并抑制运动模糊。当将估计的运动场应用于重建的活动时，变形后的图像在几何上与单独的非 AC 重建门控相似。
JRM 的运动估计对肺部衰减值的变化具有鲁棒性。JRM 成功地补偿了应用于 PET/MRI 临床数据集的运动。它为现有的从单独的 MRI 测量中预估计运动场的方法提供了一种潜在的替代方法。
