Deep learning for whole-body medical image generation
Deep learning for whole-body medical image generation
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DOI：
10.1007/s00259-021-05413-0
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发表时间：
2021-05-22
影响因子：
9.1
通讯作者：
Veit-Haibach, Patrick
中科院分区：
文献类型：
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作者：
Schaefferkoetter, Joshua;Yan, Jianhua;Veit-Haibach, Patrick
基于深度卷积网络的人工智能（AI）算法在图像变换任务方面取得了显著的成功。生成对抗网络（GAN）和不需要配对数据的训练方法已经取得了最先进的成果。最近，这些技术已被应用于医学领域的跨域图像翻译。目的本研究探讨了医学成像中的深度学习转换。它的动机是确定可推广的方法，这将满足整个人体的质量和解剖精度的同时要求。具体而言，在PET/MR系统上采集的全身MR患者数据用于生成合成CT图像体积。评价了这些合成CT数据用于PET衰减校正（AC）的能力，并与当前基于MR的衰减校正（MR-AC）方法进行了比较，后者通常使用多相狄克逊序列来分割各种组织类型。材料和方法这项工作的目的是调查一般的MR到CT体积变换的GAN系统的技术性能，并评估性能的PET AC生成的图像。使用包含匹配的当天PET/MR和PET/CT患者扫描的数据集进行验证。结果采用多种训练技术相结合的方法，生成了高质量、解剖学准确的合成图像。发现直接从CT数据计算的mu图的值与从合成CT图像导出的值之间的相关性高于从默认分段狄克逊方法导出的值。在整个身体上，两种MR-AC方法之间的重建PET活动总量相似，但合成CT方法在定量特定区域的示踪剂摄取方面具有更高的准确性。结论本文报道的研究结果证明了该技术的可行性及其在改善PET/MR系统衰减校正某些方面的潜力。此外，这项工作可能有更大的影响，建立通用的方法，跨模态，全身转换的医学成像。无监督深度学习技术可以生成高质量的合成图像，但可能需要额外的约束来保持生成数据的医学完整性。
Background Artificial intelligence (AI) algorithms based on deep convolutional networks have demonstrated remarkable success for image transformation tasks. State-of-the-art results have been achieved by generative adversarial networks (GANs) and training approaches which do not require paired data. Recently, these techniques have been applied in the medical field for cross-domain image translation. Purpose This study investigated deep learning transformation in medical imaging. It was motivated to identify generalizable methods which would satisfy the simultaneous requirements of quality and anatomical accuracy across the entire human body. Specifically, whole-body MR patient data acquired on a PET/MR system were used to generate synthetic CT image volumes. The capacity of these synthetic CT data for use in PET attenuation correction (AC) was evaluated and compared to current MR-based attenuation correction (MR-AC) methods, which typically use multiphase Dixon sequences to segment various tissue types. Materials and methods This work aimed to investigate the technical performance of a GAN system for general MR-to-CT volumetric transformation and to evaluate the performance of the generated images for PET AC. A dataset comprising matched, same-day PET/MR and PET/CT patient scans was used for validation. Results A combination of training techniques was used to produce synthetic images which were of high-quality and anatomically accurate. Higher correlation was found between the values of mu maps calculated directly from CT data and those derived from the synthetic CT images than those from the default segmented Dixon approach. Over the entire body, the total amounts of reconstructed PET activities were similar between the two MR-AC methods, but the synthetic CT method yielded higher accuracy for quantifying the tracer uptake in specific regions. Conclusion The findings reported here demonstrate the feasibility of this technique and its potential to improve certain aspects of attenuation correction for PET/MR systems. Moreover, this work may have larger implications for establishing generalized methods for inter-modality, whole-body transformation in medical imaging. Unsupervised deep learning techniques can produce high-quality synthetic images, but additional constraints may be needed to maintain medical integrity in the generated data.