Cartesian dictionary-based native T1 and T2 mapping of the myocardium

TL;DR

This paper introduces Multimapping, a novel Cartesian sampling-based technique for simultaneous native myocardial T1 and T2 mapping, demonstrating promising accuracy and clinical applicability in phantom, healthy, and patient studies.

Contribution

The study presents a new dictionary-based method for combined T1 and T2 mapping using Cartesian sampling, improving efficiency and clinical relevance.

Findings

Good agreement with reference T1 and T2 values in phantom

Higher T1 and lower T2 variability in vivo compared to reference

Effective detection of increased T1 and T2 in patients

Abstract

Purpose: To implement and evaluate a new dictionary-based technique for native myocardial T1 and T2 mapping using Cartesian sampling. Methods: The proposed technique (Multimapping) consisted of single-shot Cartesian image acquisitions in 10 consecutive cardiac cycles, with inversion pulses in cycle 1 and 5, and T2 preparation (TE: 30ms, 50ms and 70ms) in cycles 8-10. Multimapping was simulated for different T1 and T2, where entries corresponding to the k-space centers were matched to acquired data. Experiments were performed in a phantom, 16 healthy subjects and three patients with cardiovascular disease. Results: Multimapping phantom measurements showed good agreement with reference values for both T1 and T2, with no discernable heart-rate dependency for T1 and T2 within the range of myocardium. In vivo mean T1 in healthy subjects was significantly higher using Multimapping (T1=1114+/-14ms) compared to the reference (T1=991+/-26ms) (p<0.01). Mean Multimapping T2 (47.1+/-1.3ms) and T2 spatial variability (5.8+/-1.0ms) was significantly lower compared to the reference (T2=54.7+/-2.2ms, p<0.001; spatial variability=8.4+/-2.0ms, p<0.01). Increased T1 and T2 was detected in all patients using Multimapping. Conclusions: Multimapping allows for simultaneous native myocardial T1 and T2 mapping with a conventional Cartesian trajectory, demonstrating promising in vivo image quality and parameter quantification results.