Multi-parameter mapping, fat/water separation and functional imaging with a two-sequence brain morphometry protocol
Andre Jan Willem van der Kouwe1, Fikret Isik Karahanoglu1, Matthew Dylan Tisdall1, Paul Wighton1, Himanshu Bhat2, Thomas Benner3, and Jonathan R Polimeni1

1Athinoula A. Martinos Center, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, 2Siemens Healthcare, Charlestown, MA, United States, 3Siemens Healthcare, Erlangen, Germany


We present an efficient two-sequence protocol for quantifying multiple parameters in a 1 mm isotropic brain morphometry examination. The protocol comprises a multiple gradient echo (TE), multiple inversion (TI) time MPRAGE (MEMPxRAGE) and a two-flip-angle balanced SSFP (TrueFISP) sequence. Proton density and T1 maps are estimated from the MEMPxRAGE data using the multi-TI data and a Bloch simulation. With the T1 map and TrueFISP data, the T2 map is estimated using DESPOT2. Fat, water and B0 maps are obtained from the multi-TE data using the IDEAL algorithm. The MEMPxRAGE scan includes embedded 3D EPI-based navigators encoding low resolution functional information.


Time efficiency is paramount in MR examinations, to minimize patient discomfort and cost, and to minimize the likelihood of motion. We present a two-sequence brain examination shorter than 10 minutes that yields 1 mm isotropic maps of proton density (PD), T1 and T2 relaxation times, separate water and fat images, and a B0 field map. The examination also provides a low-SNR T2* map and a functional time-series suitable for coarse, low resolution resting state analysis. In previous work1 we demonstrated PD and T1 mapping using a multiple gradient echo (TE), multiple inversion time (TI) MPRAGE sequence (MEMPxRAGE) with inner-loop GRAPPA acceleration and volumetric navigators (vNavs) for real-time motion correction2. Here we extend this approach by adding a two-flip-angle balanced SSFP (TrueFISP) sequence, from which T2 is estimated3. With 1 mm isotropic PD, T1 and T2 maps, many contrasts can be synthesized beyond the original acquisition4, and images can be resampled in any plane. We demonstrate that the multiple TE data can be analyzed with the IDEAL algorithm5 to extract water and fat volumes6 and a B0 field map, at 1 mm isotropic resolution. A volume suitable for automatic cortical and subcortical segmentation by FreeSurfer is generated by combining the data. The vNavs from the MPRAGE sequence are 3D-EPI-based and T2*-weighted (therefore BOLD-weighted)7.


The combined protocol was tested with a volunteer, in accordance with the Partners HealthCare IRB. The following two scans were collected on a Siemens 3 T Skyra with a 32 channel head coil: 3D MEMPxRAGE (TR 2530 ms, TE 1.63/3.38/5.13/6.88 ms, TI 1150/2050 ms, flip angle 7°/7°, BW 723 Hz/px, 176 sagittal slices, 256 mm FoV, 13 mm3 resolution, Tacq 5:39 min:s) with embedded vNavs (TR 20 ms, TE 11 ms, flip angle 2°, BW 4464 Hz/px, 40 sagittal slices, 240 mm FoV, 63 mm3 resolution) and 3D TrueFISP (TR 7.17 ms, TE 3.59 ms, flip angle 15°/70°, geometry and bandwidth match MEMPxRAGE, Tacq 4:16 min:s).


Figure 1 shows PD and T1 maps, estimated by fitting the observed MEMPxRAGE data (four TEs, two TIs) to a table of predictions generated using a Bloch simulation of the MPRAGE sequence1,8. From the estimated T1 data and the two-flip-angle TrueFISP data, the DESPOT23 algorithm was applied to obtain the T2 map (also Figure 1). The IDEAL algorithm5 was applied to the data (four TEs, first TI) to estimate water and fat signals together with the B0 field map and T2* map (Figure 2). FreeSurfer9,10 was applied to the RMS combined volume across TEs and TIs. The surfaces and segmentation are shown in Figure 3. AFNI11,12 was used to explore the BOLD data in the vNav time series and extract any apparent resting state networks. Figure 4 shows the frontoparietal control network13. Figure 5 shows the detected motion and tSNR.


The MEMPxRAGE accommodates multiple TIs without affecting acquisition time by using inner-loop GRAPPA acceleration and external reference lines. The multiple TIs provide information for T1 estimation, and this informs the T2 estimate using the DESPOT2 approach. In previous work we compared the results with DESPOT11. T2* maps can be estimated as part of the PD/T1 estimation and are also generated by the IDEAL algorithm. In both cases the T2* maps are very noisy because the TEs are very short compared to the expected T2* values. The incorrect assumption of a single exponential contributor in each voxel further confounds the T2* fit, especially because additional short T2* components are expected. Generally, parameter mapping is limited by the model, although the T1 maps are relatively stable, and the parameter maps can be used to synthesize arbitrary contrasts. The simplest combination, the RMS average, provided a combined high-SNR volume for FreeSurfer segmentation. Although the vNavs are far from optimal for BOLD imaging (TE and resolution are low), we nevertheless observed fMRI signal and possibly the frontoparietal control network. tSNR maps indicate a combination of thermal and physiologic noise. The vNav protocol and supporting MEMPxRAGE could be modified to further improve BOLD contrast and resolution. Real-time motion correction is implemented in the MEMPxRAGE sequence and could be extended to TrueFISP14. Additional acceleration could shorten the protocol. This protocol may be useful in clinical populations such as pediatric patients and Alzheimer’s patients requiring morphometry data in a rapid scan session. The fat, water and brain segmentation may be useful in MRI-PET scanners for attenuation correction15 and partial volume correction of the PET data16.


Multiparameter (PD, T1, T2) maps, fat/water separation, B0 mapping, FreeSurfer segmentation and rudimentary functional imaging are possible with a two-sequence protocol.


This work was supported by NIH NIA R21-AG046657, NICHD R01-HD071664 and R00-HD074649, NIBIB K01-EB011498 and P41-EB015896, and NIBIB R01-EB019437, the Swiss National Science Foundation P2ELP2_159891, the Bertarelli Foundation and the Simon’s Foundation, the Athinoula A. Martinos Center for Biomedical Imaging, and made possible by NIH NCRR Shared Instrumentation Grant S10-RR023401.


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Figure 1: PD and T1 maps derived from MEMPxRAGE data using Bloch simulation. T2 maps derived from T1 maps and two-flip-angle TrueFISP data using DESPOT2 algorithm.

Figure 2: Estimated water and fat signal maps, B0 field map and T2* map, derived from MEMPxRAGE data using IDEAL algorithm.

Figure 3: Cortical surface models and subcortical segmentation illustrated on RMS average of MEMPxRAGE data.

Figure 4: Ostensible frontoparietal control network derived using AFNI with seeds in intraparietal region (left MNI -40,-48,56 and right MNI 46,-33,48).

Figure 5: Motion tracking data and tSNR maps derived from vNav time course acquired during MEMPxRAGE acquisition.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)