We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Deep learning for biomedical image reconstruction: a survey.
- Authors
Ben Yedder, Hanene; Cardoen, Ben; Hamarneh, Ghassan
- Abstract
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-posedness 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. The design of fast, robust, and accurate reconstruction algorithms is a desirable, yet challenging, research goal. While the classical image reconstruction algorithms approximate the inverse function relying on expert-tuned parameters to ensure reconstruction performance, deep learning (DL) allows automatic feature extraction and real-time inference. Hence, DL presents a promising approach to image reconstruction with artifact reduction and reconstruction speed-up reported in recent works as part of a rapidly growing field. We review state-of-the-art image reconstruction algorithms with a focus on DL-based methods. First, we examine common reconstruction algorithm designs, applied metrics, and datasets used in the literature. Then, key challenges are discussed as potentially promising strategic directions for future research.
- Subjects
IMAGE reconstruction; DEEP learning; IMAGE reconstruction algorithms; FEATURE extraction; INVERSE functions; SCIENTISTS
- Publication
Artificial Intelligence Review, 2021, Vol 54, Issue 1, p215
- ISSN
0269-2821
- Publication type
Article
- DOI
10.1007/s10462-020-09861-2