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Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI.
European Journal of Nuclear Medicine and Molecular Imaging ( IF 9.1 ) Pub Date : 2019-07-01 , DOI: 10.1007/s00259-019-04380-x
Hossein Arabi 1 , Guodong Zeng 2 , Guoyan Zheng 2, 3 , Habib Zaidi 1, 4, 5, 6
Affiliation  

OBJECTIVE Quantitative PET/MR imaging is challenged by the accuracy of synthetic CT (sCT) generation from MR images. Deep learning-based algorithms have recently gained momentum for a number of medical image analysis applications. In this work, a novel sCT generation algorithm based on deep learning adversarial semantic structure (DL-AdvSS) is proposed for MRI-guided attenuation correction in brain PET/MRI. MATERIALS AND METHODS The proposed DL-AdvSS algorithm exploits the ASS learning framework to constrain the synthetic CT generation process to comply with the extracted structural features from CT images. The proposed technique was evaluated through comparison to an atlas-based sCT generation method (Atlas), previously developed for MRI-only or PET/MRI-guided radiation planning. Moreover, the commercial segmentation-based approach (Segm) implemented on the Philips TF PET/MRI system was included in the evaluation. Clinical brain studies of 40 patients who underwent PET/CT and MR imaging were used for the evaluation of the proposed method under a two-fold cross validation scheme. RESULTS The accuracy of cortical bone extraction and CT value estimation were investigated for the three different methods. Atlas and DL-AdvSS exhibited similar cortical bone extraction accuracy resulting in a Dice coefficient of 0.78 ± 0.07 and 0.77 ± 0.07, respectively. Likewise, DL-AdvSS and Atlas techniques performed similarly in terms of CT value estimation in the cortical bone region where a mean error (ME) of less than -11 HU was obtained. The Segm approach led to a ME of -1025 HU. Furthermore, the quantitative analysis of corresponding PET images using the three approaches assuming the CT-based attenuation corrected PET (PETCTAC) as reference demonstrated comparative performance of DL-AdvSS and Atlas techniques with a mean standardized uptake value (SUV) bias less than 4% in 63 brain regions. In addition, less that 2% SUV bias was observed in the cortical bone when using Atlas and DL-AdvSS approaches. However, Segm resulted in 14.7 ± 8.9% SUV underestimation in the cortical bone. CONCLUSION The proposed DL-AdvSS approach demonstrated competitive performance with respect to the state-of-the-art atlas-based technique achieving clinically tolerable errors, thus outperforming the commercial segmentation approach used in the clinic.

中文翻译:

用于脑部PET / MRI的MRI引导衰减校正的新型对抗性语义结构深度学习。

目的定量PET / MR成像受到MR图像生成合成CT(sCT)的准确性的挑战。基于深度学习的算法最近在许多医学图像分析应用中得到了发展。在这项工作中,提出了一种基于深度学习对抗语义结构(DL-AdvSS)的新型sCT生成算法,用于脑PET / MRI的MRI引导衰减校正。材料与方法所提出的DL-AdvSS算法利用ASS学习框架来约束合成CT生成过程,以符合从CT图像中提取的结构特征。通过与基于Atlas的sCT生成方法(Atlas)进行比较,对该技术进行了评估,该方法以前是为仅MRI或PET / MRI引导的辐射计划而开发的。而且,评估中包括在Philips TF PET / MRI系统上实施的基于商业细分的方法(Segm)。在两次交叉验证方案下,对40位接受了PET / CT和MR成像的患者进行了临床脑研究,以评估该方法的有效性。结果研究了三种不同方法的皮质骨提取的准确性和CT值估计。Atlas和DL-AdvSS表现出相似的皮质骨提取精度,Dice系数分别为0.78±0.07和0.77±0.07。同样,DL-AdvSS和Atlas技术在获得平均误差(ME)小于-11 HU的皮质骨区域中,在CT值估计方面表现相似。Segm方法得出的ME为-1025 HU。此外,假设基于CT的衰减校正的PET(PETCTAC)作为参考,使用这三种方法对相应PET图像进行的定量分析证明了DL-AdvSS和Atlas技术的比较性能,其中63的平均标准摄取值(SUV)偏差小于4%大脑区域。此外,当使用Atlas和DL-AdvSS方法时,在皮质骨中观察到的SUV偏差小于2%。但是,Segm导致皮质骨SUV低估14.7±8.9%。结论所提出的DL-AdvSS方法在实现基于临床的可容忍错误的基于最新图集的技术方面展示了竞争性能,从而胜过了临床上使用的商业细分方法。
更新日期:2019-07-01
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