XRayWizard: Reconstructing 3‐D lung surfaces from a single 2‐D chest x‐ray image via Vision Transformer

作者：Zhiyi Shi, Kaiwen Geng, Xiaoyan Zhao, Farhad Mahmoudi, Christopher Haas, Joseph K. Leader, Emrah Duman, Jiantao Pu · 发表于：Medical Physics · 年份：2023 · DOI：10.1002/mp.16781 · 被引用次数：7 · 研究领域：COVID-19 diagnosis using AI、Ultrasound in Clinical Applications、Digital Radiography and Breast Imaging

BACKGROUND: Chest x-ray is widely utilized for the evaluation of pulmonary conditions due to its technical simplicity, cost-effectiveness, and portability. However, as a two-dimensional (2-D) imaging modality, chest x-ray images depict limited anatomical details and are challenging to interpret. PURPOSE: To validate the feasibility of reconstructing three-dimensional (3-D) lungs from a single 2-D chest x-ray image via Vision Transformer (ViT). METHODS: We created a cohort of 2525 paired chest x-ray images (scout images) and computed tomography (CT) acquired on different subjects and we randomly partitioned them as follows: (1) 1800 - training set, (2) 200 - validation set, and (3) 525 - testing set. The 3-D lung volumes segmented from the chest CT scans were used as the ground truth for supervised learning. We developed a novel model termed XRayWizard that employed ViT blocks to encode the 2-D chest x-ray image. The aim is to capture global information and establish long-range relationships, thereby improving the performance of 3-D reconstruction. Additionally, a pooling layer at the end of each transformer block was introduced to extract feature information. To produce smoother and more realistic 3-D models, a set of patch discriminators was incorporated. We also devised a novel method to incorporate subject demographics as an auxiliary input to further improve the accuracy of 3-D lung reconstruction. Dice coefficient and mean volume error were used as performance metrics as...