- Workshop presentation
- Open Access
Relaxation time mapping technique development improves disease detectability
© Witschey et al. 2016
- Published: 27 January 2016
- Myocardial Disease
- Incremental Improvement
- Elevated Heart Rate
- Monte Carlo Data
- Optical Flow Algorithm
Relaxation time mapping is increasingly important for myocardial tissue characterization and potentially offers a quantitative descriptor of occult or heterogeneous myocardial disease. Recent techniques that reduce motion sensitivity, image artifacts and correct for elevated heart rates (HR) have made substantial advances in map accuracy and precision, but it is unclear how these improvements quantitatively lead to better disease detection. We used T1rho maps from normal subjects to generate variance-matched Monte Carlo data and retrospectively analyzed the effect that incremental improvements in mapping technology have on disease detectability.
T1rho MRI data was acquired in 14 normal subjects using a 2D single-shot T1rho-prepared balanced steady-state free-precession sequence with a 90x-SLy-180y-SL-y-90-x pulse cluster with 8 images (TSL = 2-50 ms and B1 = 500 Hz) at 1.5 T. Motion correction (MoCo) was performed using an optical flow algorithm. HR correction was performed with an initial dummy scan to presaturate the longitudinal magnetization. Uncorrected, HR corrected, MoCo, and dual MoCo and HR corrected images were contoured and mean relaxation times were estimated using a 6 segment model (QMass, Medis). Monte Carlo simulations (10k trials) were performed with matched-variance and true disease scores at ΔT1rho = +5,10 and 15 ms. For per-patient disease detectability, ROC curves were generated for each level. For study-based group disease detectability, 1-way ANOVA was performed with Bonferroni correction.
In conclusion, the Monte Carlo methods introduced here demonstrate that incremental improvements in mapping techniques by MoCo and HR correction have substantial improvements in disease detectability. This will be important for future technique development performance comparisons, myocardial disease detection in individual patients and for correct sample size determination in clinical trials.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.