Kulathilake, K. A. Saneera Hemantha and Abdullah, Nor Aniza and Md Sabri, Aznul Qalid and Lai, Khin Wee (2023) A review on Deep Learning approaches for low-dose Computed Tomography restoration. Complex & Intelligent Systems, 9 (3). pp. 2713-2745. ISSN 2199-4536, DOI https://doi.org/10.1007/s40747-021-00405-x.
Full text not available from this repository.Abstract
Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.
| Item Type: | Article |
|---|---|
| Funders: | World Bank-funded Accelerating Higher Education Expansion and Development Operation, Sri Lanka [Grant no. AHEAD/PhD/R1-PART-2/ENG&TECH/105], Universiti Malaya [Grant no. IIRG012C-2019], Centre for Ionic Liquids, University of Malaya, Institute of Research Management and Services, University of Malaya |
| Uncontrolled Keywords: | Deep Learning; Generative adversarial networks; Optimization; Medical datasets; Structure preservation; Denoising |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence Faculty of Computer Science & Information Technology > Department of Computer System & Technology Faculty of Engineering > Department of Biomedical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 03 Nov 2025 07:01 |
| Last Modified: | 03 Nov 2025 07:01 |
| URI: | http://eprints.um.edu.my/id/eprint/48566 |
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