Published February 1, 2019 | Version v1
Journal article

T1 and extracellular volume fraction mapping in cardiac magnetic resonance: estimation of accuracy and precision of a novel algorithm

  • 1. Department of Public Health and Infectious Disease, 'Sapienza' University of Rome, Viale del Policlinico 155, 00161 Rome (Italy)
  • 2. Department of Medical Physics, Azienda Ospedaliera Universitaria Policlinico Umberto I, Viale del Policlinico 155, 00161 Rome (Italy)
  • 3. Department of Radiological, Oncological and Anatomo-Pathological Sciences, 'Sapienza' University of Rome, Viale del Policlinico 155, 00161 Rome (Italy)
  • 4. Department of Physics and NAST Center, 'Tor Vergata' University of Rome, Via della Ricerca Scientifica 1, Rome, 00179 (Italy)

Description

Magnetic resonance imaging is effective for non-invasive detection of myocardial diseases by extracellular volume fraction (ECV) estimation. A new methodology for T1 and ECV mapping is tested in this work, comparing results with other well-consolidated methods. The associated level of uncertainty for data was also estimated, to assess the reliability of the technique. A phantom with known T1 values was used for reference, and 60 subjects (40 controls, 20 diseased patients) were examined, using the modified look-locker inversion-recovery (MOLLI) method. Obtained T1 data were studied in terms of accuracy (difference with reference T1), paired comparisons with other methods, and Gamma-tool analysis with tolerances criteria of 4.21 mm for distance-to-agreement, and between 2%–5% for T1 difference. Accuracy and precision of the T1 mapping was analysed by phantom measurements, and the uncertainty of the ECV was estimated by T1 error propagation. Differences (in paired comparisons) of T1 datasets were not significant neither for statistical tests, nor for Bland–Altman analysis. T1 accuracy was between  −12% and  −1% across methods, slightly better for the tested method (mean accuracy in the T1 range of interest better than 2%). The Gamma analysis confirm slightly better results for the tested method than other methodologies. The new method improves the computational efficiency by a factor of 25 (at least), revealing to be also more suitable for Big Data-related applications. (note)

Availability note (English)

Available from http://dx.doi.org/10.1088/1361-6560/aafcca

Additional details

Identifiers

Publishing Information

Journal Title
Physics in Medicine and Biology
Journal Volume
64
Journal Issue
4
Journal Page Range
[9 p.]
ISSN
0031-9155
CODEN
PHMBA7