Model-based super-resolution reconstruction of T-2 maps

Purpose High-resolution isotropic T-2 mapping of the human brain with multi-echo spin-echo (MESE) acquisitions is challenging. When using a 2D sequence, the resolution is limited by the slice thickness. If used as a 3D acquisition, specific absorption rate limits are easily exceeded due to the high power deposition of nonselective refocusing pulses. A method to reconstruct 1-mm(3) isotropic T-2 maps is proposed based on multiple 2D MESE acquisitions. Data were undersampled (10-fold) to compensate for the prolonged scan time stemming from the super-resolution acquisition. Theory and Methods The proposed method integrates a classical super-resolution with an iterative model-based approach to reconstruct quantitative maps from a set of undersampled low-resolution data. The method was tested on numerical and multipurpose phantoms, and in vivo data. T-2 values were assessed with a region-of-interest analysis using a single-slice spin-echo and a fully sampled MESE acquisition in a phantom, and a MESE acquisition in healthy volunteers. Results Numerical simulations showed that the best trade-off between acceleration and number of low-resolution datasets is 10-fold acceleration with 4 acquisitions (acquisition time = 18 min). The proposed approach showed improved resolution over low-resolution images for both phantom and brain. Region-of-interest analysis of the phantom compartments revealed that at shorter T-2, the proposed method was comparable with the fully sampled MESE. For the volunteer data, the T-2 values found in the brain structures were consistent across subjects (8.5-13.1 ms standard deviation). Conclusion The proposed method addresses the inherent limitations associated with high-resolution T-2 mapping and enables the reconstruction of 1 mm(3) isotropic relaxation maps with a 10 times faster acquisition.


Published in:
Magnetic Resonance In Medicine
Year:
Sep 13 2019
Publisher:
Hoboken, WILEY
ISSN:
0740-3194
1522-2594
Keywords:




 Record created 2019-09-29, last modified 2019-12-05


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