Matthias Schloegl^{1}, Martin Holler^{2}, Oliver Maier^{1}, Thomas Benkert^{3}, Kristian Bredies^{2}, Kai Tobias Block^{3}, and Rudolf Stollberger^{1}

This work describes the use of ICTGV regularization for highly accelerated T_{1} and T_{2}
quantification. For increased robustness of quantitative MRI multiple
parameter encodings are necessary. With conventional encoding, this
strategy increases scan time, in particular for T_{1}. By using
appropriate subsampling and iterative image reconstruction with ICTGV regularization, high quantification quality is achieved up to an
acceleration factor of 16.

DISCUSSION AND CONCLUSION

For both investigated qMRI applications the reconstruction quality of the parameter maps remains stable and in high accordance with the fully-sampled reference up to high acceleration factors. Anatomical details are depicted sharply. This is also reflected by the quantitative, statistical analysis of the histogram of WM and GM. ICTGV achieves better performance than a low-rank/sparse regularization for high acceleration factors. For the VFA method, more residual noise is introduced compared to the MESE method, which results from the higher number of parameter encodings possible for MESE (32 echoes vs 10 flip-angles). These can be acquired without additional scan time for a fixed number of slices. In the current work, the VFA method has been simplified to slice-by-slice processing, but could also be processed as 3D-parameter dataset to explore additional redundancies (with increased computational effort). In comparison to model-based reconstruction the proposed approach does not restrict the reconstruction to a specific signal-model (e.g. mono-exponential). The signal model used for the actual quantification can then be adjusted in dependency of the investigated data after the reconstruction. In summary, ICTGV regularization, originally developed for dynamic MRI data, can be successfully applied to accelerate MR parameter mapping with very high acceleration factors.BioTechMed-Graz, Graz, Austria

Funded by the Austrian Science Fund (FWF) SFB-F3209-18

NVIDIA Corporation Hardware grant support

1.Velikina JV, Alexander AL, Samsonov AA "Accelerating MR Parameter Mapping using Sparsity promoting regularization in parametric dimension." MRM 2013; 70(5):1263-73

2. Zhao B, Lu W, Hitchens
TK, Lam F, Ho C, Liang ZP
"Accelerated MR
Parameter Mapping with Low-Rank and Sparsity Constraints."
MRM 2015; 72(2):489-498

3. Block KT, Uecker M, Frahm J "Model-Based Iterative Reconstruction for Radial Fast Spin-Echo MRI." IEEE Trans. Med. Imag. 2009; 28(11)

4. Sumpf TJ, Uecker M, Boretius S, Frahm J "Model-based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin-echo." Magn Reson Imag 2011; 34(2):420–428

5. Schloegl M, Holler M, Schwarzl A, Bredies K, Stollberger R "Infimal Convolution of Total Generalized Variation Functionals for dynamic MRI." MRM 2016; doi:10.1002/mrm.26352

6. Homer J, Beevers MS "Driver-equilibrium single-pulse observation of T1 relaxation. A re-evaluation of a rapid “new” method for determining NMR spin-lattice relaxation times." J Magn Reson 1985; 63: 287-297.

7. Deoni SC, Rutt BK, and Peters TM "Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state." MRM 2003; 49: 515-526.

8. Block KT, Chandarana H, Fatterpekar G, et al. "Improving the Robustness of Clinical T1-Weighted MRI Using Radial VIBE."
MAGNETOM Flash 2013; 5:6-11

9. Sacolick LI, Wiesinger F, Hancu I, Vogel MW "B1 mapping by Bloch-Siegert shift." MRM 2010; 63:1315-22

10. Ahmad R, Xue H., Giri, S, Ding Y, Craft J, Simonetti OP "Variable Density Incoherent Spatiotemporal Acquisition (VISTA) for Highly Accelerated Cardiac MRI" MRM 2015; 74(5):1266-1278

11. Otazo R, Candes E, Sodickson DK "Low-Rank Plus Sparse Matrix Decomposition for Accelerated Dynamic MRI with Separation of Background and Dynamic Components." MRM 2015; 73(3):1125-1136

12. Uecker M, Ong F, Tamir JI et al.
"Berkeley Advanced Reconstruction Toolbox.", In Proc. Intl. Soc. Mag. Reson. Med. 23 2015;2486

Figure 1: Comparison of T_{1} maps computed from the fully sampled VFA flip-angle
dataset (TR/TE=5.31/2.46ms,
$$$\alpha \in \{1,3,5,7,9,11,13,15,17,19\}$$$ °, 1x1x3mm³) with T_{1} maps computed from reconstructed subsampled data $$$(R=9,14,17)$$$ using ICTGV regularization (1^{st} and 2^{nd}
row (closeup) and a
low-rank/sparse regularization (3^{rd} row)

Figure 2: Comparison of T_{2} and M_{0} maps computed (1^{st} echo neglected) from the
fully sampled MESE dataset (TR/TE_{1} = 3110/11 ms, ΔTE=11ms, 1x1x5mm³) with T_{2} and M_{0} maps computed from subsampled data $$$(R=8,12,16)$$$ with ICTGV regularization.

Figure 3: Statistical analysis of the distribution of T_{1} (VFA method) and T_{2}
(MESE method) values within a gray (GM) and white matter (WM)
segmented region-of-interest for different subsampling factors
reconstructed with ICTGV (a,c) and a low-rank/sparse (b) regularization.

Figure 4: T_{1} maps gained from the ICTGV reconstructed subsampled VFA flip-angle
radial VIBE dataset with golden angle acquisition (TR/TE = 4.51/2.11 ms,
$$$ \alpha \in \{1,3,5,7,9,11,13,15,17,19 \}$$$°, 1.1x1.1x3mm³) for (spf=55, 34, 21) spokes-per-frame, which corresponds to accelerations of (R=7.3, 11.8, 19).