Convex optimization algorithms in medical image reconstruction - In the age of AI

Jingyan Xu, Frédéric Noo

Research output: Contribution to journalReview articlepeer-review


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.

Original languageEnglish (US)
Article number07TR01
JournalPhysics in medicine and biology
Issue number7
StatePublished - Apr 7 2022


  • artificial intelligence
  • convex optimization
  • deep learning (DL)
  • first order methods
  • inverse problems
  • machine learning (ML)
  • model based image reconstruction

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging


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