3D free‐breathing cardiac magnetic resonance fingerprinting

Abstract

Purpose To develop a novel respiratory motion compensated three‐dimensional (3D) cardiac magnetic resonance fingerprinting (cMRF) approach for whole‐heart myocardial T1 and T2 mapping from a free‐breathing scan. Methods Two‐dimensional (2D) cMRF has been recently proposed for simultaneous, co‐registered T1 and T2 mapping from a breath‐hold scan; however, coverage is limited. Here we propose a novel respiratory motion compensated 3D cMRF approach for whole‐heart myocardial T1 and T2 tissue characterization from a free‐breathing scan. Variable inversion recovery and T2 preparation modules are used for parametric encoding, respiratory bellows driven localized autofocus is proposed for beat‐to‐beat translation motion correction and a subspace regularized reconstruction is employed to accelerate the scan. The proposed 3D cMRF approach was evaluated in a standardized T1/T2 phantom in comparison with reference spin echo values and in 10 healthy subjects in comparison with standard 2D MOLLI, SASHA and T2‐GraSE mapping techniques at 1.5 T. Results 3D cMRF T1 and T2 measurements were generally in good agreement with reference spin echo values in the phantom experiments, with relative errors of 2.9% and 3.8% for T1 and T2 (T2 < 100 ms), respectively. in vivo left ventricle (LV) myocardial T1 values were 1054 ± 19 ms for MOLLI, 1146 ± 20 ms for SASHA and 1093 ± 24 ms for the proposed 3D cMRF; corresponding T2 values were 51.8 ± 1.6 ms for T2‐GraSE and 44.6 ± 2.0 ms for 3D cMRF. LV coefficients of variation were 7.6 ± 1.6% for MOLLI, 12.1 ± 2.7% for SASHA and 5.8 ± 0.8% for 3D cMRF T1, and 10.5 ± 1.4% for T2‐GraSE and 11.7 ± 1.6% for 3D cMRF T2. Conclusion The proposed 3D cMRF can provide whole‐heart, simultaneous and co‐registered T1 and T2 maps with accuracy and precision comparable to those of clinical standards in a single free‐breathing scan of about 7 min.

Simultaneous T1, T2, and T1ρ cardiac magnetic resonance fingerprinting for contrast agent–free myocardial tissue characterization

“…Phantom studies show that the T 1 , T 2 , and T 1⍴ maps obtained with the proposed MRF approach are comparable and in good agreement with those obtained from SE-prepared conventional scans. An excellent correlation 1,20,28,36,44 The T 1⍴ -MRF shows excellent correlation with T 1⍴ -prepared SE reference, and only a slight underestimation for MRF values was observed for high T 1⍴ values, which may be F I G U R E 3 Short-axis view of T 1 , T 2 , and T 1ρ quantitative maps in 3 representative healthy subjects. Conventional maps (top row) of T 1 -MOLLI, T 2 -GRASE, and T 1⍴ -TFE are compared with the proposed inherently co-registered T 1 , T 2 , and T 1⍴ cardiac MRF maps (bottom row) obtained from an approximate 16-second single scan caused by an insufficient encoding power provided by the SL pulse preparations for the highest T 1⍴ values.…”

“…magnetic resonance fingerprinting, mapping cardiac MRI, multiparametric mapping, T 1⍴ as previously proposed. 28 Briefly, the following reconstruction algorithm solves the problem:…”

Synergistic multi-contrast cardiac magnetic resonance image reconstruction

“…HD-PROST has been successfully applied to multi-contrast CMR, including accelerated free-breathing 3D high-resolution myocardial T2 [54] and T1ρ [14] mapping. HD-PROST has also been combined with LRI for cardiac MRF methods [55][56][57][58], such as water-fat Dixon cardiac MRF [58] and 3D free-breathing cardiac MRF [55], as well as free-running 3D myocardial T1/T2 mapping [24,59]. Example water-fat images and parametric maps from Dixon cardiac MRF are shown in figure 5.…”

Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer

“…Early approaches attempted to use zero-filled reconstruction to produce the images for each time point [ 17 ], however subsequent studies demonstrated that remaining aliasing artefacts can affect the multiparametric quantification [ 27 ]. Thus, several undersampled reconstruction approaches have been proposed exploiting the temporal and spatial redundancies in the highly undersampled time series of images [ 28 , 29 , 30 ].…”