Technical Note: Fully automatic segmental relaxometry (FASTR) for cardiac magnetic resonance T1 mapping

Purpose Cardiac relaxometry techniques, particularly T1 mapping, have recently gained clinical importance in various cardiac pathologies. Myocardial T1 and extracellular volume are usually calculated from manual identification of left ventricular epicardial and endocardial regions. This is a laborious process, particularly for large volume studies. Here we present a fully automated relaxometry framework (FASTR) for segmental analysis of T1 maps (both native and postcontrast) and partition coefficient (lambda). Methods Patients (N = 11) were imaged postacute myocardial infarction on a 1.5T clinical scanner. The scan protocol involved CINE-SSFP imaging, native, and post-contrast T1 mapping using the Modified Look-Locker Inversion (MOLLI) recovery sequence. FASTR consisted of automatic myocardial segmentation of spatio-temporally coregistered CINE images as an initial guess, followed by refinement of the contours on the T1 maps to derive segmental T1 and lambda. T1 and lambda were then compared to those obtained from two trained expert observers. Results Robust endocardial and epicardial contours were achieved on T1 maps despite the presence of infarcted tissue. Relative to experts, FASTR resulted in myocardial Dice coefficients (native T1: 0.752 +/- 0.041; postcontrast T1: 0.751 +/- 0.057) that were comparable to interobserver Dice (native T1: 0.803 +/- 0.045; postcontrast T1: 0.799 +/- 0.054). There were strong correlations observed for T1 and lambda derived from experts and FASTR (native T1: r = 0.83; postcontrast T1: r = 0.87; lambda: r = 0.78; P < 0.0001), which were comparable to inter-expert correlation coefficients (native T1: r = 0.90; postcontrast T1: r = 0.93; lambda: r = 0.80; P < 0.0001). Conclusions Our fully automated framework, FASTR, can generate accurate myocardial segmentations for native and postcontrast MOLLI T1 analysis without the need for manual intervention. Such a design is appealing for high volume clinical protocols.