Artificial intelligence in multiparametric magnetic resonance imaging: A review

Cheng Li, Wen Li, Chenyang Liu, Hairong Zheng, Jing Cai, Shanshan Wang

Research output: Journal article publicationReview articleAcademic researchpeer-review

18 Citations (Scopus)

Abstract

Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning–based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availabilities of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI.

Original languageEnglish
Pages (from-to)e1024-e1054
JournalMedical Physics
Volume49
Issue number10
DOIs
Publication statusPublished - Oct 2022

Keywords

  • deep learning
  • disease diagnosis
  • machine learning
  • medical image analysis
  • MRI-guided radiotherapy
  • radiomics

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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