In Cell reports. Medicine
3D digital subtraction angiography (DSA) reconstruction from rotational 2D projection X-ray angiography is an important basis for diagnosis and treatment of intracranial aneurysms (IAs). The gold standard requires approximately 133 different projection views for 3D reconstruction. A method to significantly reduce the radiation dosage while ensuring the reconstruction quality is yet to be developed. We propose a self-supervised learning method to realize 3D-DSA reconstruction using ultra-sparse 2D projections. 202 cases (100 from one hospital for training and testing, 102 from two other hospitals for external validation) suspected to be suffering from IAs were conducted to analyze the reconstructed images. Two radiologists scored the reconstructed images from internal and external datasets using eight projections and identified all 82 lesions with high diagnostic confidence. The radiation dosages are approximately 1/16.7 compared with the gold standard method. Our proposed method can help develop a revolutionary 3D-DSA reconstruction method for use in clinic.
Zhao Huangxuan, Zhou Zhenghong, Wu Feihong, Xiang Dongqiao, Zhao Hui, Zhang Wei, Li Lin, Li Zhong, Huang Jia, Hu Hongyao, Liu Chengbo, Wang Tao, Liu Wenyu, Ma Jinqiang, Yang Fan, Wang Xinggang, Zheng Chuansheng
cerebrovascular diseases, clinical study, deep learning, digital subtraction angiography, medical imaging reconstruction