Super-Resolution in Medical Imaging

• Published in: Computer journal Vol. 52; no. 1; pp. 43 - 63
• Main Author: Greenspan, Hayit
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.01.2009; Oxford Publishing Limited (England)
• Subjects: Algorithms; Brain research; Computer science; Energy; Hypothesis testing; Localization; Magnetic resonance imaging; Medical imaging; Medical technology; Noise; Sensors; Signal to noise ratio; Tomography; X-rays; MRI; super-resolution; medical imaging; PET
• ISSN: 0010-4620; 1460-2067; 0010-4620
• DOI: 10.1093/comjnl/bxm075

This paper provides an overview on super-resolution (SR) research in medical imaging applications. Many imaging modalities exist. Some provide anatomical information and reveal information about the structure of the human body, and others provide functional information, locations of activity for specific activities and specified tasks. Each imaging system has a characteristic resolution, which is determined based on physical constraints of the system detectors that are in turn tuned to signal-to-noise and timing considerations. A common goal across systems is to increase the resolution, and as much as possible achieve true isotropic 3-D imaging. SR technology can serve to advance this goal. Research on SR in key medical imaging modalities, including MRI, fMRI and PET, has started to emerge in recent years and is reviewed herein. The algorithms used are mostly based on standard SR algorithms. Results demonstrate the potential in introducing SR techniques into practical medical applications.