The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner facilitates the simultaneous acquisition of metabolic information via PET and morphological information with high soft-tissue contrast using MRI. Although PET/MRI facilitates the capture of high-accuracy fusion images, its major drawback can be attributed to the difficulty encountered when performing attenuation correction, which is necessary for quantitative PET evaluation. The combined PET/MRI scanning requires the generation of attenuation-correction maps from MRI owing to no direct relationship between the gamma-ray attenuation information and MRIs. While MRI-based bone-tissue segmentation can be readily performed for the head and pelvis regions, the realization of accurate bone segmentation via chest CT generation remains a challenging task. This can be attributed to the respiratory and cardiac motions occurring in the chest as well as its anatomically complicated structure and relatively thin bone cortex. This paper presents a means to minimise the anatomical structural changes without human annotation by adding structural constraints using a modality-independent neighbourhood descriptor (MIND) to a generative adversarial network (GAN) that can transform unpaired images. The results obtained in this study revealed the proposed U-GAT-IT + MIND approach to outperform all other competing approaches. The findings of this study hint towards possibility of synthesising clinically acceptable CT images from chest MRI without human annotation, thereby minimising the changes in the anatomical structure.
翻译:PET/MRI)扫描仪有助于通过PET和具有高软质对比度的形态信息使用MRI同时获取代谢信息。尽管PET/MRI有助于捕获高精度聚合图像,但其主要缺点可归因于在进行衰减校正时遇到的困难,这是定量PET评价所必需的。PET/MRI综合扫描需要从MRI生成衰减-校正地图,因为伽马射线减速信息与MMIS之间没有直接关系。虽然基于MRI的骨质分解可以方便地为头部和骨质区域进行,但通过胸部CT生成实现准确的骨质分解仍是一项艰巨的任务。这可归因于胸部发生的呼吸和心力运动,以及其解剖复杂的结构,以及相对薄的骨质皮。本文展示了一种手段,通过使用不依赖模式的内脏结构图解分解法,将基于模型的骨质分解分解分解图解结果添加到不依赖模型的磁性G。