论文标题
通过3D形状感应改进计算机断层扫描（CT）重建
Improving Computed Tomography (CT) Reconstruction via 3D Shape Induction
论文作者
论文摘要
胸部计算机断层扫描（CT）成像为肺部传染病（例如结核病（TB））的诊断和管理增添了宝贵的见解。但是，由于成本和资源的限制，只有X射线图像可用于初步诊断或在治疗过程中进行后续比较成像。由于其投影性，X射线图像可能更难解释临床医生。缺乏公开配对的X射线和CT图像数据集使训练3D重建模型的挑战。此外，胸部X射线放射学可能依赖于具有不同图像质量的不同设备方式，并且潜在的种群疾病频谱可能会在输入中产生多样性。我们提出了形状诱导，也就是说，在没有CT监督的情况下从X射线中学习3D CT的形状，作为一种新型技术，可以在训练重建模型的训练过程中结合现实的X射线分布。我们的实验表明，此过程既提高了产生的CT的感知质量，也可以提高肺传染病的下游分类的准确性。
Chest computed tomography (CT) imaging adds valuable insight in the diagnosis and management of pulmonary infectious diseases, like tuberculosis (TB). However, due to the cost and resource limitations, only X-ray images may be available for initial diagnosis or follow up comparison imaging during treatment. Due to their projective nature, X-rays images may be more difficult to interpret by clinicians. The lack of publicly available paired X-ray and CT image datasets makes it challenging to train a 3D reconstruction model. In addition, Chest X-ray radiology may rely on different device modalities with varying image quality and there may be variation in underlying population disease spectrum that creates diversity in inputs. We propose shape induction, that is, learning the shape of 3D CT from X-ray without CT supervision, as a novel technique to incorporate realistic X-ray distributions during training of a reconstruction model. Our experiments demonstrate that this process improves both the perceptual quality of generated CT and the accuracy of down-stream classification of pulmonary infectious diseases.
