Accelerated 3D multi-echo spin-echo sequence with a subspace constrained reconstruction for whole mouse brain $T_2$ mapping

Purpose: To accelerate whole-brain quantitative $T_2$ mapping in preclinical imaging setting.

Methods: A three-dimensional (3D) multi-echo spin echo sequence was highly undersampled with a variable density Poisson distribution to reduce the acquisition time. Advanced iterative reconstruction based on linear subspace constraints was employed to recover high-quality raw images. Different subspaces, generated using exponential or extended-phase graph (EPG) simulations or from low-resolution calibration images, were compared. The subspace dimension was investigated in terms of $T_2$ precision. The method was validated on a phantom containing a wide range of $T_2$ and was then applied to monitor metastasis growth in the mouse brain at 4.7T. Image quality and $T_2$ estimation were assessed for 3 acceleration factors (6/8/10).

Results: The EPG-based dictionary gave robust estimations of a large range of $T_2$ . A subspace dimension of 6 was the best compromise between $T_2$ precision and image quality. Combining the subspace constrained reconstruction with a highly undersampled dataset enabled the acquisition of whole-brain $T_2$ maps, the detection and the monitoring of metastasis growth of less than 500 $\mu m^3$ .

Conclusion: Subspace-based reconstruction is suitable for 3D $T_2$ mapping. This method can be used to reach an acceleration factor up to 8, corresponding to an acquisition time of 25 min for an isotropic 3D acquisition of 156 $\mu$ m on the mouse brain, used here for monitoring metastases growth.