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首页> 外文会议>Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >Disease Quantification on PET/CT Images without Object Delineation
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Disease Quantification on PET/CT Images without Object Delineation

机译：没有物体描绘的PET / CT图像上的疾病定量

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The derivation of quantitative information from images to make quantitative radiology (QR) clinically practical continues to face a major image analysis hurdle because of image segmentation challenges. This paper presents a novel approach to disease quantification (DQ) via positron emission tomography/computed tomography (PET/CT) images that explores how to decouple DQ methods from explicit dependence on object segmentation through the use of only object recognition results to quantify disease burden. The concept of an object-dependent disease map is introduced to express disease severity without performing explicit delineation and partial volume correction of either objects or lesions. The parameters of the disease map are estimated from a set of training image data sets. The idea is illustrated on 20 lung lesions and 20 liver lesions derived from ~(18)F-2-fluoro-2-deoxy-D-glucose (FDG)-PET/CT scans of patients with various types of cancers and also on 20 NEMA PET/CT phantom data sets. Our preliminary results show that, on phantom data sets, "disease burden" can be estimated to within 2% of known absolute true activity. Notwithstanding the difficulty in establishing true quantification on patient PET images, our results achieve 8% deviation from "true" estimates, with slightly larger deviations for small and diffuse lesions where establishing ground truth becomes really questionable, and smaller deviations for larger lesions where ground truth set up becomes more reliable. We are currently exploring extensions of the approach to include fully automated body-wide DQ, extensions to just CT or magnetic resonance imaging (MRI) alone, to PET/CT performed with radiotracers other than FDG, and other functional forms of disease maps.
机译：由于图像分割挑战，图像从图像中的定量信息从图像进行定量放射学（QR）的定量信息临床实际情况继续面对主要的图像分析障碍。本文介绍了通过正电子发射断层扫描/计算断层扫描/计算机断层扫描（PET / CT）图像的疾病量化（DQ）的新方法，该图像探讨了如何通过仅使用对象识别结果来定量疾病负担来解耦DQ方法对对象分割的明确依赖性。引入对象依赖性疾病图的概念以表达疾病严重程度，而不表现出对物体或病变的显式描绘和部分体积校正。疾病地图的参数估计来自一组训练图像数据集。这些想法是由20肺病变和20次肝脏病变衍生自〜（18）F-2-氟-2-脱氧-D-葡萄糖（FDG）-PET / CT扫描的各种类型癌症的肺病变和20种NEMA PET / CT幻影数据集。我们的初步结果表明，在幻影数据集上，“疾病负担”可以估计到已知绝对真实活动的2％以内。尽管难以建立对患者宠物图像的真实量化，但我们的结果达到了8％的差异，从“真实”的估计，小和漫射病变的偏差略大，其中建立地面真理变得真正有质疑，以及较大病变的较小偏差设置变得更加可靠。我们目前正在探索了该方法的扩展来包括全自动全身性DQ，扩展只是CT或核磁共振成像（MRI）单独，以PET / CT具有比其他FDG放射性示踪剂，和疾病的地图的其它功能形式进行。

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