Use of Early T1 Mapping for MRI in Acute Myocarditis

Quantitative cardiac MRI parameters for assessment of myocarditis in children and adolescents: a systematic review and meta-analysis

AIM

To evaluate the role of quantitative cardiac magnetic resonance imaging (CMRI) parameters in myocarditis, including acute and chronic myocarditis (AM and CM), for children and adolescents.

MATERIALS AND METHODS

PRISMA principles were followed. PubMed, EMBASE, Web of Science, Cochrane Library, and grey literature were searched. The Newcastle-Ottawa Scale (NOS) and the Agency for Healthcare Research and Quality (AHRQ) checklist were utilised for quality assessment. Quantitative CMRI parameters were extracted and a meta-analysis was performed in comparison with healthy controls. The overall effect size was measured as the weighted mean difference (WMD).

RESULTS

Ten quantitative CMRI parameters of seven studies were analysed. Compared with the control group, the myocarditis group reported longer native T1 relaxation time (WMD=54.00, 95% confidence interval [CI]: 33.21,74.79, p<0.001), longer T2 relaxation time (WMD=2.13, 95% CI: 0.98, 3.28, p<0.001), increased extracellular volume (ECV; WMD=3.13, 95% CI: 1.34,4.91, p=0.001), elevated early gadolinium enhancement (EGE) ratio (WMD=1.47, 95% CI: 0.65,2.28, p<0.001), and increased T2-weighted ratio (WMD=0.43, 95% CI: 0.21,0.64, p<0.001). The AM group had longer native T1 relaxation times (WMD=72.02, 95% CI: 32.78,111.27, p<0.001), increased T2-weighted ratios (WMD=0.52, 95% CI: 0.21,0.84 p=0.001), and impaired left ventricular ejection fractions (LVEF; WMD=-5.84, 95% CI: -9.69, -1.99, p=0.003). Impaired LVEF (WMD=-2.24, 95% CI: -3.32, -1.17, p<0.001) was observed in the CM group.

CONCLUSION

Statistical differences can be observed in some CMRI parameters between patients with myocarditis and healthy controls; however, apart from native T1 mapping, there were no large differences in other parameters between two groups, which may reveal the limited benefit of CMRI in assessing myocarditis in children and adolescents.

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T₁ ), the spin-spin relaxation time (T₂ ), 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 T₁ -, T₂ -, 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.