Krokos Georgios, MacKewn Jane, Dunn Joel, Marsden Paul
School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK.
EJNMMI Phys. 2023 Sep 11;10(1):52. doi: 10.1186/s40658-023-00569-0.
Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.
尽管自第一台PET-MR系统安装以来已有13年，但在已安装的混合PET系统总数中，这类扫描仪所占比例非常小。这与PET-CT扫描仪的迅速扩张形成了鲜明对比，PET-CT扫描仪在类似的时间框架内迅速确立了其在患者诊断中的重要性。主要障碍之一是开发一种准确、可重复且易于使用的衰减校正方法。制造商提供的MR方法与更成熟的基于CT或透射的衰减校正方法之间在PET图像上的定量差异，促使科学界不断努力开发一种强大而准确的替代方法。这些方法可大致分为四大类：(i)基于MR的方法、(ii)基于发射的方法、(iii)基于图谱的方法和(iv)基于机器学习的衰减校正方法，后者正迅速获得发展势头。第一种方法是基于对各种组织中的MR图像进行分割，并为每个组织分配一个预定义的衰减系数。基于发射的衰减校正方法旨在通过同时重建放射性分布和衰减图像来利用PET发射数据。基于图谱的衰减校正方法旨在通过使用包含普通人群CT或透射图像的数据库，根据新患者的MR图像预测CT或透射图像。最后，在机器学习方法中，通过利用图像的潜在特征，开发一个模型，该模型可以根据获取的MR或未进行衰减校正的PET图像预测所需图像。深度学习方法是这一类别的主要方法。与使用结构化数据构建模型的更传统机器学习相比，深度学习直接利用获取的图像来识别潜在特征。这篇最新综述在对PET-MR中的衰减校正方法进行分类后，梳理了相关文献。对每一类中的各种方法进行了描述和讨论。在分别探讨每一类方法之后，对当前状况和潜在的未来方法进行了总体概述，并对上述四类方法进行了比较。
