论文标题
带有变压器的蒙面壁式模型，用于不适合的计算机断层扫描重建：初步研究
Masked Sinogram Model with Transformer for ill-Posed Computed Tomography Reconstruction: a Preliminary Study
论文作者
论文摘要
计算机断层扫描（CT）是一种成像技术，其中以不同角度（称为投影或扫描）收集有关对象的信息。然后，通过解决反问题来产生显示切片内部结构的横截面图像。受辐射剂量，投影角，产生的图像等某些因素的限制可能是嘈杂的或包含伪影。受到《变压器在自然语言处理》中的成功的启发，这项初步研究的核心思想是将层析成像的投影视为单词标记，而整个横截面（又称Sinogram）的整体扫描是在自然语言处理的背景下作为句子。然后，我们通过训练蒙版辛图模型（MSM）和微调MSM来探索基础模型的想法，以了解各种下游应用程序，包括在数据收集限制下（例如Photon-budget）和数据驱动的解决方案，以近似CT重建的逆问题解决方案。本研究中使用的模型和数据可在https://github.com/lzhengchun/tomotx上获得。
Computed Tomography (CT) is an imaging technique where information about an object are collected at different angles (called projections or scans). Then the cross-sectional image showing the internal structure of the slice is produced by solving an inverse problem. Limited by certain factors such as radiation dosage, projection angles, the produced images can be noisy or contain artifacts. Inspired by the success of transformer for natural language processing, the core idea of this preliminary study is to consider a projection of tomography as a word token, and the whole scan of the cross-section (A.K.A. sinogram) as a sentence in the context of natural language processing. Then we explore the idea of foundation model by training a masked sinogram model (MSM) and fine-tune MSM for various downstream applications including CT reconstruction under data collections restriction (e.g., photon-budget) and a data-driven solution to approximate solutions of the inverse problem for CT reconstruction. Models and data used in this study are available at https://github.com/lzhengchun/TomoTx.
