MEDISIP, Department of Electronics and Information Systems, Ghent University-IBBT-IBiTech, Ghent, Belgium.
J Nucl Med. 2010 May;51(5):812-8. doi: 10.2967/jnumed.109.065425.
One of the challenges in PET/MRI is the derivation of an attenuation map to correct the PET image for attenuation. Different methods have been suggested for deriving the attenuation map from an MR image. Because the low signal intensity of cortical bone on images acquired with conventional MRI sequences makes it difficult to detect this tissue type, these methods rely on some sort of anatomic precondition to predict the attenuation map, raising the question of whether these methods will be usable in the clinic when patients may exhibit anatomic abnormalities.
We propose the use of the transverse relaxation rate, derived from images acquired with an ultrashort echo time sequence to classify the voxels into 1 of 3 tissue classes (bone, soft tissue, or air), without making any assumptions on patient anatomy. Each voxel is assigned a linear attenuation coefficient corresponding to its tissue class. A reference CT scan is used to determine the voxel-by-voxel accuracy of the proposed method. The overall accuracy of the MRI-based attenuation correction is evaluated using a method that takes into account the nonlocal effects of attenuation correction.
As a proof of concept, the head of a pig was used as a phantom for imaging. The new method yielded a correct tissue classification in 90% of the voxels. Five human brain PET/CT and MRI datasets were also processed, yielding slightly worse voxel-by-voxel performance, compared to a CT-derived attenuation map. The PET datasets were reconstructed using the segmented MRI attenuation map derived with the new method, and the resulting images were compared with segmented CT-based attenuation correction. An average error of around 5% was found in the brain.
The feasibility of using the transverse relaxation rate map derived from ultrashort echo time MR images for the estimation of the attenuation map was shown on phantom and clinical brain data. The results indicate that the new method, compared with CT-based attenuation correction, yields clinically acceptable errors. The proposed method does not make any assumptions about patient anatomy and could therefore also be used in cases in which anatomic abnormalities are present.
正电子发射断层扫描/磁共振成像（PET/MRI）中的一个挑战是为衰减校正而从磁共振图像推导衰减图。已经提出了从 MR 图像推导衰减图的不同方法。由于常规 MRI 序列获得的图像中皮质骨的低信号强度使得难以检测到这种组织类型，因此这些方法依赖于某种解剖学前提来预测衰减图，这就提出了一个问题，即当患者可能存在解剖异常时，这些方法在临床上是否可用。
我们提出使用从超短回波时间序列获得的图像中的横向弛豫率来将体素分类为 3 种组织类型之一（骨、软组织或空气），而无需对患者解剖结构做出任何假设。为每个体素分配与其组织类型相对应的线性衰减系数。使用参考 CT 扫描来确定所提出方法的体素逐体素准确性。使用考虑衰减校正的非局部效应的方法来评估基于 MRI 的衰减校正的整体准确性。
作为概念验证，猪的头部被用作成像的体模。新方法在 90%的体素中产生了正确的组织分类。还处理了 5 个人脑 PET/CT 和 MRI 数据集，与 CT 衍生的衰减图相比，体素逐体素的性能略有下降。使用新方法获得的分段 MRI 衰减图重建了 PET 数据集，并将生成的图像与基于 CT 的分段衰减校正进行了比较。在大脑中发现平均误差约为 5%。
在体模和临床脑部数据上证明了使用源自超短回波时间 MR 图像的横向弛豫率图来估计衰减图的可行性。结果表明，与 CT 基于衰减校正相比，新方法产生了可接受的临床误差。该方法对患者解剖结构没有任何假设，因此也可以用于存在解剖异常的情况。
