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In multi-echo fMRI (ME-fMRI), two metrics have been widely used to measure the performance of various acquisition and analysis approaches. These are temporal SNR (tSNR) and differential contrast-to-noise ratio (dCNR). A key step in ME-fMRI is the weighted combination of the data from multiple echoes, and prior work has examined the dependence of tSNR and dCNR on the choice of weights. However, most studies have focused on only one of these two metrics, and the relationship between the two metrics has not been examined. In this work, we present a geometric view that offers greater insight into the relation between the two metrics and their weight dependence. We identify three major regimes: (1) a tSNR robust regime in which tSNR is robust to the weight selection with most weight variants achieving close to optimal performance, whereas dCNR shows a pronounced dependence on the weights with most variants achieving suboptimal performance; (2) a dCNR robust regime in which dCNR is robust to the weight selection with most weight variants achieving close to optimal performance, while tSNR exhibits a strong dependence on the weights with most variants achieving significantly lower than optimal performance; and (3) a within-type robust regime in which both tSNR and dCNR achieve nearly optimal performance when the form of the weights are variants of their respective optimal weights and exhibit a moderate decrease in performance for other weight variants. Insight into the behavior observed in the different regimes is gained by considering spherical representations of the weight dependence of the components used to form each metric. For multi-echo acquisitions, dCNR is shown to be more directly related than tSNR to measures of CNR and signal-to-noise ratio (SNR) for task-based and resting-state fMRI scans, respectively.

Abstract Objectives To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI. Material and methods Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (r s) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations). Results Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute r s values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute r s values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness. Conclusion A dynamic “one-stop shop” procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.

In blood-oxygen-level-dependent (BOLD)-based resting-state functional (RS-fMRI) studies, usage of multi-echo echo-planar-imaging (ME-EPI) is limited due to unacceptable late echo times when high spatial resolution is used. Equipped with high-performance gradients, the compact 3T MRI system (C3T) enables a three-echo whole-brain ME-EPI protocol with smaller than 2.5 mm isotropic voxel and shorter than 1 s repetition time, as required in landmark fMRI studies. The performance of the ME-EPI was comprehensively evaluated with signal variance reduction and region-of-interest-, seed- and independent-component-analysis-based functional connectivity analyses and compared with a counterpart of single-echo EPI with the shortest TR possible. Through the multi-echo combination, the thermal noise level is reduced. Functional connectivity, as well as signal intensity, are recovered in the medial orbital sulcus and anterior transverse collateral sulcus in ME-EPI. It is demonstrated that ME-EPI provides superior sensitivity and accuracy for detecting functional connectivity and/or brain networks in comparison with single-echo EPI. In conclusion, the high-performance gradient enabled high-spatial-temporal resolution ME-EPI would be the method of choice for RS-fMRI study on the C3T.

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.

Recent advances in functional MRI techniques include multiband (MB) imaging and multi-echo (ME) imaging. In MB imaging multiple slices are acquired simultaneously leading to significant increases in temporal and spatial resolution. Multi-echo imaging enables multiple echoes to be acquired in one shot, where the ME images can be used to denoise the BOLD time series and increase BOLD sensitivity. In this study, resting state fMRI (rs-fMRI) data were collected using a combined MBME sequence and compared to an MB single echo sequence. In total, 29 subjects were imaged, and 18 of them returned within two weeks for repeat imaging. Participants underwent one MBME scan with three echoes and one MB scan with one echo. Both datasets were processed using standard denoising and advanced denoising. Advanced denoising included multi-echo independent component analysis (ME-ICA) for the MBME data and ICA-AROMA for the MB data. Resting state functional connectivity (RSFC) was evaluated using both selective seed-based and whole grey matter (GM) region-of-interest (ROI) based approaches. The reproducibility of connectivity metrics was also analyzed in the repeat subjects. In addition, functional connectivity density (FCD), a data-driven approach that counts the number of significant connections, both within a local cluster and globally, with each voxel was analyzed. Regardless of the standard or advanced denoising technique, all seed-based RSFC was significantly higher for MBME compared to MB. Much more GM ROI combinations showed significantly higher RSFC for MBME vs. MB. Reproducibility, evaluated using the dice coefficient was significantly higher for MBME relative to MB data. Finally, FCD was also higher for MBME vs. MB data. This study showed higher RSFC for MBME vs. MB data using selected seed-based, whole GM ROI-based, and data-driven approaches. Reproducibility found also higher for MBME data. Taken together, these results indicate that MBME is a promising technique for rs-fMRI.

In brain PET/MR applications, accurate attenuation maps are required for accurate PET image quantification. An implemented attenuation correction (AC) method for brain imaging is the single-atlas approach that estimates an AC map from an averaged CT template. As an alternative, we propose to use a zero echo time (ZTE) pulse sequence to segment bone, air and soft tissue. A linear relationship between histogram normalized ZTE intensity and measured CT density in Hounsfield units () in bone has been established thanks to a CT-MR database of 16 patients. Continuous AC maps were computed based on the segmented ZTE by setting a fixed linear attenuation coefficient (LAC) to air and soft tissue and by using the linear relationship to generate continuous μ values for the bone. Additionally, for the purpose of comparison, four other AC maps were generated: a ZTE derived AC map with a fixed LAC for the bone, an AC map based on the single-atlas approach as provided by the PET/MR manufacturer, a soft-tissue only AC map and, finally, the CT derived attenuation map used as the gold standard (CTAC). All these AC maps were used with different levels of smoothing for PET image reconstruction with and without time-of-flight (TOF). The subject-specific AC map generated by combining ZTE-based segmentation and linear scaling of the normalized ZTE signal into was found to be a good substitute for the measured CTAC map in brain PET/MR when used with a Gaussian smoothing kernel of corresponding to the PET scanner intrinsic resolution. As expected TOF reduces AC error regardless of the AC method. The continuous ZTE-AC performed better than the other alternative MR derived AC methods, reducing the quantification error between the MRAC corrected PET image and the reference CTAC corrected PET image. [ABSTRACT FROM AUTHOR]