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Adenosine stress T1-mapping on cardiovascular magnetic resonance (CMR) can differentiate between normal, ischemic, infarcted, and remote myocardial tissue classes without the need for contrast agents. Regadenoson, a selective coronary vasodilator, is often used in stress perfusion imaging when adenosine is contra-indicated, and has advantages in ease of administration, safety profile, and clinical workflow. We aimed to characterize the regadenoson stress T1-mapping response in healthy individuals, and to investigate its ability to differentiate between myocardial tissue classes in patients with coronary artery disease (CAD). Eleven healthy controls and 25 patients with CAD underwent regadenoson stress perfusion CMR, as well as rest and stress ShMOLLI T1-mapping. Native T1 values and stress T1 reactivity were derived for normal myocardium in healthy controls and for different myocardial tissue classes in patients with CAD. Healthy controls had normal myocardial native T1 values at rest (931 ± 22 ms) with significant global regadenoson stress T1 reactivity (δT1 = 8.2 ± 0.8% relative to baseline; p < 0.0001). Infarcted myocardium had significantly higher resting T1 (1215 ± 115 ms) than ischemic, remote, and normal myocardium (all p < 0.0001) with an abolished stress T1 response (δT1 = −0.8% [IQR: −1.9–0.5]). Ischemic myocardium had elevated resting T1 compared to normal (964 ± 57 ms; p < 0.01) with an abolished stress T1 response (δT1 = 0.5 ± 1.6%). Remote myocardium in patients had comparable resting T1 to normal (949 ms [IQR: 915–973]; p = 0.06) with blunted stress reactivity (δT1 = 4.3% [IQR: 3.1–6.3]; p < 0.0001). Healthy controls demonstrate significant stress T1 reactivity during regadenoson stress. Regadenoson stress and rest T1-mapping is a viable alternative to adenosine and exercise for the assessment of CAD and can distinguish between normal, ischemic, infarcted, and remote myocardium. • Regadenoson has advantages over adenosine in terms of administration, safety profile, and clinical workflow. • There are distinct tissue characteristics for normal, ischemic, infarcted, and remote myocardium. • Healthy controls demonstrate significant stress T1 reactivity during vasodilator stress. • Regadenoson stress T1-mapping can distinguish between different myocardial tissue classes. • Regadenoson stress T1-mapping is a viable alternative to adenosine and exercise for the assessment of coronary artery disease. [ABSTRACT FROM AUTHOR]

Cardiovascular magnetic resonance T1-mapping is increasingly used for tissue characterization, commonly based on Modified Look-Locker Inversion recovery (MOLLI). However, there are numerous MOLLI variants with differing normal ranges. This lack of standardization presents confusion and difficulty in inter-center comparisons, hindering widespread adoption of T1-mapping. To address this, we performed a structured literature search for native left ventricular myocardial T1-mapping in healthy humans measured using MOLLI variants at 1.5 and 3 Tesla, across scanner vendors. We then used k-means clustering to structure normal MOLLI-T1 values according to magnetic field strength, and investigated correlations between common imaging parameters: repetition time (TR), echo time (TE), flip angle (FA). We analyzed data from 2207 healthy controls in 76 independent reports. Normal MOLLI-T1 standard deviations varied by 11-fold, and dependencies on TE, TR, and FA differed between 1.5 T and 3 T, thwarting meaningful T1 standardization even within a single field strength, including the use of Z-score. However, divergent MOLLI-T1 norms may be structured using data clustering. For 1.5 T, two clusters emerged: Cluster1 1.5T : T1 = 958 ± 16 ms (n = 1280); Cluster2 1.5T : T1 = 1027 ± 19 ms (n = 386). For 3 T, three clusters emerged: Cluster1 3T : T1 = 1160 ± 21 ms (n = 330); Cluster2 3T : T1 = 1067 ± 18 ms (n = 178); Cluster3 3T : T1 = 1227 ± 19 ms (n = 41). We then propose the concept of an online calculator for assigning local norms to a known MOLLI-T1 cluster, allowing benchmarking against published norms. Clustered structuring allows T1 standardization of widely-divergent MOLLI variants, benchmarking local norms (usually based on smaller samples) against published norms (larger samples). This may increase confidence and quality control in method implementation, facilitating wider clinical adoption of T1-mapping. • Normal myocardial T1 values vary widely across different MOLLI T1-mapping methods. • Clustered structuring via an online calculator is practical for T1 standardization. • Clustered structuring allows benchmarking of local norms against published norms. • Clustered structuring increases confidence and quality in T1-mapping implementation. [ABSTRACT FROM AUTHOR]

Myocardial T1-mapping is increasingly used in multicentre studies and trials. Inconsistent image analysis introduces variability, hinders differentiation of diseases, and results in larger sample sizes. We present a systematic approach to standardize T1-map analysis by human operators to improve accuracy and consistency. We developed a multi-step training program for T1-map post-processing. The training dataset contained 42 left ventricular (LV) short-axis T1-maps (normal and diseases; 1.5 and 3 Tesla). Contours drawn by two experienced human operators served as reference for myocardial T1 and wall thickness (WT). Trainees (n = 26) underwent training and were evaluated by: (a) qualitative review of contours; (b) quantitative comparison with reference T1 and WT. The mean absolute difference between reference operators was 8.4 ± 6.3 ms (T1) and 1.2 ± 0.7 pixels (WT). Trainees' mean discrepancy from reference in T1 improved significantly post-training (from 8.1 ± 2.4 to 6.7 ± 1.4 ms; p < 0.001), with a 43% reduction in standard deviation (SD) (p = 0.035). WT also improved significantly post-training (from 0.9 ± 0.4 to 0.7 ± 0.2 pixels, p = 0.036), with 47% reduction in SD (p = 0.04). These experimentally-derived thresholds served to guide the training process: T1 (±8 ms) and WT (±1 pixel) from reference. A standardized approach to CMR T1-map image post-processing leads to significant improvements in the accuracy and consistency of LV myocardial T1 values and wall thickness. Improving consistency between operators can translate into 33–72% reduction in clinical trial sample-sizes. This work may: (a) serve as a basis for re-certification for core-lab operators; (b) translate to sample-size reductions for clinical studies; (c) produce better-quality training datasets for machine learning. • T1-mapping MRI is increasingly being employed as a Cardiovascular MRI technique in clinical studies and trials. • Standardisation of T1 mapping post-processing is still limited, hindering reproducibility and consistency across centres. • High-quality manual contouring of T1 maps is crucial to ensure good quality training data for machine learning algorithms. • Our training programme shows statistically significant reduction in discrepancy between operators analysing T1 maps. [ABSTRACT FROM AUTHOR]