Convex optimization algorithms in medical image reconstruction—in the age of AI. (7th April 2022)

• Record Type: Journal Article
• Authors: Xu, Jingyan; Noo, Frédéric
• Is Part Of: Physics in medicine & biology. Volume 67:Number 7(2022)
• Publication Date: 2022-04-07
• Subjects: inverse problems -- convex optimization -- first order methods -- machine learning (ML) -- deep learning (DL) -- model based image reconstruction -- artificial intelligence; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac3842
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: The past decade has seen the rapid growth of model based image reconstruction (MBIR) algorithms, which are often applications or adaptations of convex optimization algorithms from the optimization community. We review some state-of-the-art algorithms that have enjoyed wide popularity in medical image reconstruction, emphasize known connections between different algorithms, and discuss practical issues such as computation and memory cost. More recently, deep learning (DL) has forayed into medical imaging, where the latest development tries to exploit the synergy between DL and MBIR to elevate the MBIR's performance. We present existing approaches and emerging trends in DL-enhanced MBIR methods, with particular attention to the underlying role of convexity and convex algorithms on network architecture. We also discuss how convexity can be employed to improve the generalizability and representation power of DL networks in general.