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.
A Comprehensive Survey on Magnetic Resonance Image Reconstruction
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Big Data Institute, Central South University（中南大学大数据研究院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Science（中国科学院深圳先进技术研究院）
Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California Nano Systems Institute (CNSI)（加州纳米系统研究所）；David Geffen School of Medicine, University of California, Los Angeles（加利福尼亚大学洛杉矶分校大卫·格芬医学院）
Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methods
School of Computing Sciences, University of East Anglia（东英吉利大学计算科学学院）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Idrak AI Ltd（伊德拉克人工智能有限公司）
AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data
ShanghaiTech University（上海科技大学）；Chinese Academy of Sciences（中国科学院）；Shanghai Institute of Microsystem and Information Technology（上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging
University of Chinese Academy of Sciences（中国科学院大学）；Xidian University（西安电子科技大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Princeton University（普林斯顿大学）；Vrije Universiteit Brussel（布鲁塞尔自由大学）
