By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose CT reconstruction is a holy grail to minimize cancer risks and genetic damages, especially for children. With the development of medical CT technologies, the iterative algorithms are widely used to reconstruct decent CT images from a low-dose scan. Recently, artificial intelligence (AI) techniques have shown a great promise in further reducing CT radiation dose to the next level. In this paper, we demonstrate that AI-powered CT reconstruction offers diagnostic image quality at an ultra-low-dose level comparable to that of radiography. Specifically, here we develop a Split Unrolled Grid-like Alternative Reconstruction (SUGAR) network, in which deep learning, physical modeling and image prior are integrated. The reconstruction results from clinical datasets show that excellent images can be reconstructed using SUGAR from 36 projections. This approach has a potential to change future healthcare.
翻译:根据ALARA(As Low As Liorable Aleasable Alevenable)原则,超低剂量CT重建是尽量减少癌症风险和遗传损害、特别是对儿童而言的神圣的薄弱环节。随着医疗CT技术的发展,迭代算法被广泛用于从低剂量扫描中重建像样的CT图像。最近,人工智能(AI)技术在进一步将CT辐射剂量降低到下一个水平方面表现出了巨大的希望。在本文中,我们证明AI-动力CT重建提供了诊断性图像质量,其水平与放射学水平相比超低。具体地说,我们在这里开发了一个分解的网状替代重建(SUGAR)网络,在该网络中,先将深度学习、物理模型和图像整合在一起。临床数据集的重建结果表明,利用SUGAR(SGAR)的预测可以重建极好的图像。这一方法有可能改变未来的保健。