Temporal SNR optimization through RF coil combination in fMRI: The more, the better?

For functional MRI with a multi-channel receiver RF coil, images are often reconstructed channel by channel, resulting into multiple images per time frame. The final image to analyze usually is the result of the covariance Sum-of-Squares (covSoS) combination across these channels. Although this reconstruction is quasi-optimal in SNR, it is not necessarily the case in terms of temporal SNR (tSNR) of the time series, which is yet a more relevant metric for fMRI data quality. In this work, we investigated tSNR optimality through voxel-wise RF coil combination and its effects on BOLD sensitivity. An analytical solution for an optimal RF coil combination is described, which is somewhat tied to the extended Krueger-Glover model involving both thermal and physiological noise covariance matrices. Compared experimentally to covSOS on four volunteers at 7T, the method yielded great improvement of tSNR but, surprisingly, did not result into higher BOLD sensitivity. Solutions to improve the method such as for example the t-score for the mean recently proposed are also explored, but result into similar observations once the statistics are corrected properly. Overall, the work shows that data-driven RF coil combinations based on tSNR considerations alone should be avoided unless additional and unbiased assumptions can be made.

Improving the Breath-Holding CVR Measurement Using the Multiband Multi-Echo EPI Sequence

Blood oxygen level-dependent (BOLD) functional MRI (fMRI) is commonly used to measure cerebrovascular reactivity (CVR), which can convey insightful information about neurovascular health. Breath-holding (BH) has been shown to be a practical vasodilatory stimulus for measuring CVR in clinical settings. The conventional BOLD fMRI approach has some limitations, however, such as susceptibility-induced signal dropout at air tissue interfaces and low BOLD sensitivity especially in areas of low T2*. These drawbacks can potentially be mitigated with multi-echo sequences, which acquire several images at different echo times in one shot. When combined with multiband techniques, high temporal resolution images can be acquired. This study compared an advanced multiband multi-echo (MBME) echo planar imaging (EPI) sequence with an existing multiband single-echo (MB) sequence to evaluate the repeatability and sensitivity of BH activation and CVR mapping. Images were acquired from 28 healthy volunteers, of which 18 returned for repeat imaging. Both MBME and MB data were pre-processed using both standard and advanced denoising techniques. The MBME data was further processed by combining echoes using a T2*-weighted approach and denoising using multi-echo independent component analysis. BH activation was calculated using a general linear model and the respiration response function. CVR was computed as the percent change related to the activation. To account for differences in CVR related to TE, relative CVR (rCVR) was computed and normalized to the mean gray matter CVR. Test–retest metrics were assessed with the Dice coefficient, rCVR difference, within subject coefficient of variation, and the intraclass correlation coefficient. Our findings demonstrate that rCVR for MBME scans were significantly higher than for MB scans across most of the gray matter. In areas of high susceptibility-induced signal dropout, however, MBME rCVR was significantly less than MB rCVR due to artifactually high rCVR for MB scans in these regions. MBME rCVR showed improved test–retest metrics compared with MB. Overall, the MBME sequence displayed superior BOLD sensitivity, improved specificity in areas of signal dropout on MBME scans, enhanced reliability, and reduced variability across subjects compared with MB acquisitions. Our results suggest that the MBME EPI sequence is a promising tool for imaging CVR.

The effects of multi-echo fMRI combination and rapid T2*-mapping on offline and real-time BOLD sensitivity

A variety of strategies are used to combine multi-echo functional magnetic resonance imaging (fMRI) data, yet recent literature lacks a systematic comparison of the available options. Here we compare six different approaches derived from multi-echo data and evaluate their influences on BOLD sensitivity for offline and in particular real-time use cases: a single-echo time series (based on Echo 2), the real-time T2*-mapped time series (T2*FIT) and four combined time series (T2*-weighted, tSNR-weighted, TE-weighted, and a new combination scheme termed T2*FIT-weighted). We compare the influences of these six multi-echo derived time series on BOLD sensitivity using a healthy participant dataset (N=28) with four task-based fMRI runs and two resting state runs. We show that the T2*FIT-weighted combination yields the largest increase in temporal signal-to-noise ratio across task and resting state runs. We demonstrate additionally for all tasks that the T2*FIT time series consistently yields the largest offline effect size measures and real-time region-of-interest based functional contrasts. These improvements show the possible utility of multi-echo fMRI for studies employing real-time paradigms, while caution is still advised due to decreased tSNR of the T2*FIT time series. We recommend the use and continued exploration of T2*FIT for offline task-based and real-time fMRI analysis. Supporting information includes: a data repository (https://dataverse.nl/dataverse/rt-me-fmri), an interactive web-based application to explore the data (https://rt-me-fmri.herokuapp.com/), and further materials and code for reproducibility (https://github.com/jsheunis/rt-me-fMRI).