Dynamic contrast enhanced (DCE) MRI is a promising technique to quantitatively evaluate the inflammatory status of atherosclerosis noninvasively. However, its demanding sampling requirement leads to sacrifices in slide resolution, coverage, and/or temporal resolution in the applications to vessel wall imaging. In this work we designed accelerated dynamic T1-mapping technique using Low Rank Tensor (LRT) framework to achieve 3D high-resolution quantitative DCE of the carotid arteries.
We designed a dynamic T1 mapping method based on the Low-Rank Tensor (LRT) framework4,5 to exploit the high correlation between images with different saturation recovery times and different contrast enhancement phases to allow vastly accelerated imaging.
Sequence Design: A saturation recovery-prepared low angle shot readout (SR-FLASH) was employed (Figure 1). Cartesian acquisition with randomized reordering in ky and kz directions was implemented according to a variable-density Gaussian distribution. A k-space center line was collected every 8 lines as training data4 for the LRT subspace.
LRT Image Reconstruction: A 5-D image I(x,y,z,TI,TE) is formed with three spatial dimensions, a saturation recovery time dimension τ, and a DCE time dimension t. This image can be reshaped as a 3-way tensor A with dimensions indexing voxel location r=(x,y,z), τ, and t. The resulting low-rank tensor can be factored as A(1)=UΦ, where A(1) is the tensor collapsed into matrix form, Φ contains tensor subspace basis functions describing T1 relaxation and contrast agent dynamics, and where U contains spatial coefficients. Image reconstruction follows a two-step process wherein we first determine Φ from subspace training data4 and then recover U by fitting Φ to the remainder of the sparsely sampled data: ˆU=argmin where \mathbf{d} is the measured data, \mathrm{E}(\cdot) describes MRI encoding and sampling, \mathrm{TV}(\cdot) is the total variation regularization functional, and is the regularization parameter.
Imaging Protocol: All data were acquired on a 3T Siemens Verio scanner. Accuracy of T1 mapping was tested in T1 phantoms6 and compared with a standard inversion-prepared spin echo method. Normal subjects without known carotid atherosclerosis (N=6) were scanned using the following parameters: coronal orientation, spatial resolution=0.7mm isotropic, FOV=150x150x26mm3, \alpha = 8^\circ, TR=600ms, scan time=12mins, DCE temporal footprint=2.08s. Gd contrast media was administered at the rate of 1.0ml/sec with 20ml saline flush (Gadovist, 0.1mmol/kg).
Motion Correction: An automatic algorithm was developed to exclude and interpolate the motion-corrupted data in the tensor based on spike detection in the principal temporal basis function of the LRT subspace.
Figure 2 compares the T1 quantification between the proposed method and the reference in static phantoms showing high agreement (r=0.97, p<0.001).
Figure 3 is a representative image set of multi-phase DCE images from a normal subject. Multiple SR phases allowed T1 quantification for direct estimation of contrast concentration. Three key DCE phases are shown along horizontal axis as examples.
Figure 4 demonstrates the effects of motion correction. Images after motion correction showed sharper delineation and less artifacts.
Figure 5 shows the real-time signal evolution, dynamic T1 mapping, and AUC mapping. Vessel wall vp and Ktrans was 0.276 and 0.121 ±0.02 min-1, respectively.
1 Kerwin, W. et al. Quantitative magnetic resonance imaging analysis of neovasculature volume in carotid atherosclerotic plaque. Circulation 107, 851-856 (2003).
2 Dong, L. et al. Carotid artery atherosclerosis: effect of intensive lipid therapy on the vasa vasorum--evaluation by using dynamic contrast-enhanced MR imaging. Radiology 260, 224-231, doi:10.1148/radiol.11101264 (2011).
3 Calcagno, C. et al. Three-dimensional dynamic contrast-enhanced MRI for the accurate, extensive quantification of microvascular permeability in atherosclerotic plaques. NMR Biomed 28, 1304-1314, doi:10.1002/nbm.3369 (2015).
4 He, J. et al. Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors. IEEE transactions on medical imaging 35, 2119-2129, doi:10.1109/TMI.2016.2550204 (2016).
5 Christodoulou, A. G. et al. Fast dynamic electron paramagnetic resonance (EPR) oxygen imaging using low-rank tensors. Journal of magnetic resonance 270, 176-182, doi:10.1016/j.jmr.2016.07.006 (2016).
6 Stanisz, G. J. et al. T1, T2 relaxation and magnetization transfer in tissue at 3T. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 54, 507-512, doi:10.1002/mrm.20605 (2005).