In this work, we aim to determine an optimal strategy for accelerating high-resolution 3D myocardial T2 mapping. We quantitatively evaluate the performance of diverse methods involving different subsampling patterns and different reconstruction strategies relative to volume-by-volume SENSE reconstruction. Reconstructions which address all volumes as a single reconstruction problem (i.e. joint-sparsity SENSE or model-based SENSE) outperform volume-by-volume approaches, and variable density sampling outperforms equal spacing or CAIPIRIHNA undersampling. The T2 values observed in parametric maps proved to be more sensitive to data corruption than images themselves, limiting the degree of data reduction tolerable.
Image acquisition: Fully-sampled data was acquired at 3T with a respiratory navigator-gated free-breathing 3D T2 mapping method based on saturation and T2 preparation1 (T2-Prep) using a 32-channel cardiac array2. For N=8 normal subjects and N=3 naïve swine, 4 image volumes were acquired with T2-Prep TE$$$\in$$${0,25,35,45}ms. For one additional swine with acute myocardial infarction (MI) displaying significant edema (elevated T2), 3 volumes were acquired with T2-Prep TE$$$\in$$${0,25,45}ms. Image resolution was 1.25x1.25x5.0 mm3 for all scans.
Image reconstruction methods: We compared the performance of traditional SENSE3 in which each volume is subsampled and reconstructed independently to: 1) standard SENSE which reconstructs all volumes jointly, 2) joint-sparsity SENSE (JS-SENSE) which uses a sparsity transform to exploit the structural similarities4, and 3) model-based SENSE (MB-SENSE) which drives optimization with an exponential decay model5. For standard SENSE, JS-SENSE and MB-SENSE, data was retrospectively undersampled using optimal CAIPIRINHA6 and variable density random(VDR) sampling7. Images were reconstructed with no under-sampling,and with acceleration rate R$$$\in$$${2,3,4,5,6,7,8}.
reconstruction steps: Homodyne detection was applied to correct for partial echo sampling. Multi-channel images were combined by root-sum-squares. Sensitivity maps were computed using the fully sampled center of k-space. To better estimate the performance of random sampling paterns, 6 different random samplings with the same distribution were tested each dataset and the results were averaged (Fig. 1).
Image analysis: T2 was determined by linear regression of the log-transformed data. A 3D ROI was drawn manually and included the left ventricular myocardium (LV)spanning the whole heart. Four metrics were used to compare the performance of each reconstruction scheme on pixel in the ROI: 1) root mean square error (RMSE) of image intensity, 2) RMSE of T2, 3) bias of the mean T2, and 4) standard deviation (SD) of T2. For the swine with acute MI, the T2 values of normal cardiac tissue were assumed Gaussian-distributed1. A second Gaussian distribution (edematous tissue, with elevated T2) was fit and a threshold was chosen based on the optimal separation of the two distributions. The threshold was used to calculate the Jaccard index between the reference and the reconstructed images, with higher index values indicating data more consistent with reference data.
[1] Brittain J.H. et. al. Coronary angiography with magnetization-prepared T2 contrast. Magn Reson Med. 1995;33(5):689-696.
[2] Ding H. et. al. Three-dimensional whole-heart T2 mapping at 3T. Magn Reson Med.2015;74(3):803-816.
[3] Pruessmann K. P. et. al. SENSE: sensitivity encoding for fast MRI. Magn Reson Med. 1999;42(5):952-962.
[4] Majumdar A. and Rabab K. W. Accelerating multi-echo T2 weighted MR imaging: analysis prior group-sparse optimization. J Magn Reson. 2011;210(1):90-97.
[5] Samsonov A. Accelerated MR Parameter Mapping Using Robust Model-Consistency Reconstruction. Proc. Intl. Soc. Mag. Reson. Med. 2015(23):3711.
[6] Tsao J. et. al. Optimizing spatiotemporal sampling for k-t BLAST and k-t SENSE: Application to high-resolution real-time cardiac steady-state free precession. Magn Reson Med. 2005;53(6):1372-1382.
[7] Lustig M., David D. and John M. P. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med. 2007;58(6):1182-1195.
[8]Messroghli D.R. et. al. Modified Look-Locker inversion recovery (MOLLI) for high-resolution T1 mapping of the heart. Magn Reson Med. 2004;52(1):141-146