Andreia C. Freitas^{1}, Inês Sousa^{1}, Andreia S. Gaspar^{1}, Rui P.A.G. Teixeira^{2}, Joseph V. Hajnal^{2}, and Rita G. Nunes^{1,2}

^{1}ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico – Universidade de Lisboa, Lisbon, Portugal, ^{2}Centre for the Developing Brain, King's College London, London, United Kingdom

T_{2} mapping provides valuable tissue-specific MR information. To enable shorter scan times, multi spin-echo (MSE) sequences are commonly used but the achieved T_{2} accuracy using conventional mono-exponential fitting is poor. Improvements are possible by matching the measured signal to a pre-computed dictionary. Although simultaneous B_{1}+ estimation is feasible, previous work demonstrated a bimodal behaviour. We investigate further improvements in B_{1}+ accuracy using an iterative pixel-neighborhood based method (the Fusion Bootstrap Moves Solver), comparing different levels of spatial regularization. Improved B_{1}+ accuracy and recovery of spatially smooth maps was demonstrated both in simulated and *in-vivo* brain data.

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Figure 1: Numerical phantom simulations: (a) B_{1}+ and (h) T_{2} ground truth. (b) B_{1}+ and (i) T_{2} maps computed with EMC and different FBMS regularizations (c-g and j-n). Effective simultaneous B_{1}+ estimation is possible with EMC (b), however with the presence of inhomogeneous regions (texture), where a smooth spatial variation should be expected. B_{1}+ spatial smoothness improves with FBMS at higher ɑ_{B1} (f-g). Similar effect seen in T_{2} maps (add of textured noise) that can be counteracted with increase of ɑ_{T2}

Figure 2: Numerical simulations: (a) B_{1}+ and (b) T_{2} error computed with EMC and FBMS relative to ground truth. B_{1}+ accuracy improved with FBMS compared to EMC matching (dashed line), proportionally to ɑ_{B1} regularization. Effect of ɑ_{T2} regularization is also visible. T_{2} accuracy worsened with FBMS regularization due to enforced smoothness in T_{2} mapping which is not naturally expected (as T_{2} changes with tissue-specific properties), resulting in added noise in the corresponding T_{2} maps (see Figure 1).

Figure 3: B_{1}+/T_{2 }brain maps: (a) B_{1}+ map obtained with AFI. (b) B1+ map obtained with EMC matching. (c-f) B_{1}+ maps obtained with FBMS regularization (ɑ_{B1/T2}). (g) T_{2} map obtained with mono-exponential fit. (h) T_{2} map obtained with EMC matching. (i-l) T_{2} maps obtained with FBMS regularization (ɑ_{B1/T2}). While inhomogeneous spatial distribution is seen in B_{1}+ EMC maps, FBMS improves spatial smoothness ((b) and (e) - green arrows). Textured noise increased in corresponding T_{2} FBMS maps ((k) – white arrows).

Figure 4:* In-vivo* B_{1}+/T_{2} local variations metric. (a-d) Local variations maps obtained from calculating local standard deviation within a pixel neighborhood in B_{1}+ maps obtained with EMC and FBMS. Brighter intensity areas indicate higher local variation within the pixel neighborhood. (e) Mean B_{1}+ local variation calculated from B_{1}+ EMC/FBMS maps. B_{1}+ local variations decrease proportionally to ɑ_{B1}, resulting in improved overall spatial smoothness. (f) Mean T_{2} local variations calculated from T_{2} FBMS maps.

Figure 5: Mean B_{1}+/T_{2} error with EMC and FBMS within a cohort of 3 subjects. (a) B_{1}+ error calculated from B_{1}+ EMC/FBMS maps compared to AFI (here used as reference). Similarly to simulations, *in-vivo* B_{1}+ accuracy improved proportionally to FBMS ɑ_{B1}, resulting in a lower B_{1}+ error than EMC method (dashed line). (b) T_{2} error calculated from T_{2} FBMS maps compared to EMC (here used as reference). However, T_{2} accuracy worsens with FBMS compared to EMC. This effect can be partially counteracted with ɑ_{T2}.