Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends. (15th October 2020)

• Record Type: Journal Article
• Authors: Feng, Li; Ma, Dan; Liu, Fang
• Other Names: Zhang Hui guestEditor.; Alexander Daniel C. guestEditor.; Shen Dinggang guestEditor.; Yap Pew‐Thian guestEditor.
• Is Part Of: NMR in biomedicine. Volume 35:Number 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-15
• Subjects: artificial intelligence -- deep learning -- image reconstruction -- MR relaxometry -- parameter mapping -- quantitative MRI; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4416
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract : Quantitative mapping of MR tissue parameters such as the spin‐lattice relaxation time ( T 1 ), the spin‐spin relaxation time ( T 2 ), and the spin‐lattice relaxation in the rotating frame ( T 1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast‐weighted (eg T 1 ‐, T 2 ‐, or T 1ρ ‐weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep‐learning‐based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state‐of‐the‐art spatiotemporal acceleration techniques, model‐based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges. Abstract : Quantitative mapping of MR parameters has demonstrated improved assessment for a wide range of diseases. The rise of deep learning has been revolutionizing many aspects of MRI research. The goal of this paper is to discuss the application of deep learning for quantitative MR relaxometry and to review emerging deep‐learning‐based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness.