Sjölund J, Forsberg D, Andersson M, Knutsson H
Elekta Instrument AB, Kungstensgatan 18, Box 7593, SE-103 93 Stockholm, Sweden. Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden. Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Phys Med Biol. 2015 Jan 21;60(2):825-39. doi: 10.1088/0031-9155/60/2/825. Epub 2015 Jan 7.
Radiotherapy planning and attenuation correction of PET images require simulation of radiation transport. The necessary physical properties are typically derived from computed tomography (CT) images, but in some cases, including stereotactic neurosurgery and combined PET/MR imaging, only magnetic resonance (MR) images are available. With these applications in mind, we describe how a realistic, patient-specific, pseudo-CT of the head can be derived from anatomical MR images. We refer to the method as atlas-based regression, because of its similarity to atlas-based segmentation. Given a target MR and an atlas database comprising MR and CT pairs, atlas-based regression works by registering each atlas MR to the target MR, applying the resulting displacement fields to the corresponding atlas CTs and, finally, fusing the deformed atlas CTs into a single pseudo-CT. We use a deformable registration algorithm known as the Morphon and augment it with a certainty mask that allows a tailoring of the influence certain regions are allowed to have on the registration. Moreover, we propose a novel method of fusion, wherein the collection of deformed CTs is iteratively registered to their joint mean and find that the resulting mean CT becomes more similar to the target CT. However, the voxelwise median provided even better results; at least as good as earlier work that required special MR imaging techniques. This makes atlas-based regression a good candidate for clinical use.
正电子发射断层扫描（PET）图像的放射治疗计划和衰减校正需要模拟辐射传输。所需的物理特性通常从计算机断层扫描（CT）图像中获取，但在某些情况下，包括立体定向神经外科手术和PET/MR联合成像，仅有磁共振（MR）图像可用。考虑到这些应用，我们描述了如何从解剖学MR图像中得出逼真的、针对患者的头部伪CT。由于其与基于图谱的分割相似，我们将该方法称为基于图谱的回归。给定一个目标MR和一个包含MR与CT对的图谱数据库，基于图谱的回归通过将每个图谱MR与目标MR配准，将所得的位移场应用于相应的图谱CT，最后将变形后的图谱CT融合成一个单一的伪CT来实现。我们使用一种称为Morphon的可变形配准算法，并通过一个确定性掩码对其进行增强，该掩码允许调整某些区域对配准的影响。此外，我们提出了一种新颖的融合方法，其中将变形后的CT集合迭代地配准到它们的联合均值，并发现所得的均值CT与目标CT变得更加相似。然而，体素级中位数提供了更好的结果；至少与需要特殊MR成像技术的早期工作一样好。这使得基于图谱的回归成为临床应用的一个良好候选方法。
