论文标题
生物医学图像重建的深度学习：调查
Deep Learning for Biomedical Image Reconstruction: A Survey
论文作者
论文摘要
医学成像是医学方面的宝贵资源，因为它可以在人体内部凝视，并为科学家和医生提供大量信息，对于理解，建模，诊断和治疗疾病必不可少。重建算法需要通过采集硬件收集的信号转换为可解释的图像。考虑到问题不足，在实际情况下，重建是一项具有挑战性的任务。尽管过去几十年来取得了令人印象深刻的进步，而改善了时间和空间分辨率，成本降低以及更广泛的适用性，但仍然可以预见到一些改进，例如减少患者的收购和重建时间，以减少患者的辐射和不适感，同时通过增加诊所和重新建设精度，从而减少辐射和不适。此外，在具有较小功率的手持设备中生物医学成像的部署需要精确度和延迟之间的良好平衡。
Medical imaging is an invaluable resource in medicine as it enables to peer inside the human body and provides scientists and physicians with a wealth of information indispensable for understanding, modelling, diagnosis, and treatment of diseases. Reconstruction algorithms entail transforming signals collected by acquisition hardware into interpretable images. Reconstruction is a challenging task given the ill-posed of the problem and the absence of exact analytic inverse transforms in practical cases. While the last decades witnessed impressive advancements in terms of new modalities, improved temporal and spatial resolution, reduced cost, and wider applicability, several improvements can still be envisioned such as reducing acquisition and reconstruction time to reduce patient's exposure to radiation and discomfort while increasing clinics throughput and reconstruction accuracy. Furthermore, the deployment of biomedical imaging in handheld devices with small power requires a fine balance between accuracy and latency.
