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Updated: Jan 11, 2026

Cardiac Magnetic Resonance Imaging at 7 Tesla
Published on: January 6, 2019
Free-Breathing Multi-Slice Co-Registered Cardiac T1, T2, and ADC Mapping With Spin-Echo Echo Planar Imaging
Dingheng Mai1,2, Danielle Kara1,3, Yuchi Liu4
1Cardiovascular Innovation Research Center, Heart Vascular Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Purpose:
To develop a free-breathing method for multi-slice co-registered cardiac T1, T2, and ADC maps using single-shot spin-echo echo planar imaging (SE-EPI).
Methods:
T1, T2, and diffusion weighted images in five slices were acquired with SE-EPI readouts during free breathing using interleaved acquisition of slices, non-selective inversion recovery, varying echo times (TE), and second-order motion-compensated diffusion gradients in three orthogonal directions with different b values. All images were registered to a single target image followed by parameter fitting to generate co-registered T1/T2/ADC maps. This approach was evaluated in phantom measurements, 13 healthy volunteers, two myocardial infarction (MI) swine, and two cardiac amyloidosis (CA) patients by comparison to reference mapping techniques. Qualitative assessments were performed by two expert readers.
Results:
Phantom experiments showed strong agreement with reference measurements (R2 > 0.96). In volunteers, myocardial T1 values were higher than MOLLI (1341 ± 59 vs. 1252 ± 36 ms, p = 0.003); the proposed T2 values were shorter than T2prep-FLASH (38.7 ± 2.2 vs. 41.0 ± 2.2 ms; p = 0.009), and mean ADC values were comparable to the reference cDTI-derived mean diffusivity (MD) values (1.57 ± 0.05 vs. 1.56 ± 0.05 μm2/ms; p = 0.404). Proposed relaxation maps received significantly lower quality scores than references, and diffusivity maps were comparable. Myocardial abnormalities in CA patients and MI swine were consistent with conventional methods.
Conclusions:
The proposed method enables five-slice, co-registered, and free-breathing myocardial T1, T2, and ADC maps in less than 5 min, facilitating integrated multi-contrast tissue characterization in cardiac MRI. Future work will aim to optimize map quality and expand clinical applications.
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