Cardiac-induced physiological noise in 3D gradient echo brain imaging: effect of k-space sampling scheme

A characterization of cardiac-induced noise in R2 * maps of the brain

PURPOSE

Cardiac pulsation increases the noise level in brain maps of the transverse relaxation rate R2 *. Cardiac-induced noise is challenging to mitigate during the acquisition of R2 * mapping data because its characteristics are unknown. In this work, we aim to characterize cardiac-induced noise in brain maps of the MRI parameter R2 *.

METHODS

We designed a sampling strategy to acquire multi-echo 3D data in 12 intervals of the cardiac cycle, monitored with a fingertip pulse-oximeter. We measured the amplitude of cardiac-induced noise in this data and assessed the effect of cardiac pulsation on R2 * maps computed across echoes. The area of k-space that contains most of the cardiac-induced noise in R2 * maps was then identified. Based on these characteristics, we introduced a tentative sampling strategy that aims to mitigate cardiac-induced noise in R2 * maps of the brain.

RESULTS

In inferior brain regions, cardiac pulsation accounts for R2 * variations of up to 3 s-1 across the cardiac cycle (i.e., ∼35% of the overall variability). Cardiac-induced fluctuations occur throughout the cardiac cycle, with a reduced intensity during the first quarter of the cycle. A total of 50% to 60% of the overall cardiac-induced noise is localized near the k-space center (k < 0.074 mm-1 ). The tentative cardiac noise mitigation strategy reduced the variability of R2 * maps across repetitions by 11% in the brainstem and 6% across the whole brain.

CONCLUSION

We provide a characterization of cardiac-induced noise in brain R2 * maps that can be used as a basis for the design of mitigation strategies during data acquisition.

Effect of Task-Correlated Physiological Fluctuations and Motion in 2D and 3D Echo-Planar Imaging in a Higher Cognitive Level fMRI Paradigm

Purpose: To compare 2D and 3D echo-planar imaging (EPI) in a higher cognitive level fMRI paradigm. In particular, to study the link between the presence of task-correlated physiological fluctuations and motion and the fMRI contrast estimates from either 2D EPI or 3D EPI datasets, with and without adding nuisance regressors to the model. A signal model in the presence of partly task-correlated fluctuations is derived, and predictions for contrast estimates with and without nuisance regressors are made.

Materials and Methods: Thirty-one healthy volunteers were scanned using 2D EPI and 3D EPI during a virtual environmental learning paradigm. In a subgroup of 7 subjects, heart rate and respiration were logged, and the correlation with the paradigm was evaluated. FMRI analysis was performed using models with and without nuisance regressors. Differences in the mean contrast estimates were investigated by analysis-of-variance using Subject, Sequence, Day, and Run as factors. The distributions of group level contrast estimates were compared.

Results: Partially task-correlated fluctuations in respiration, heart rate and motion were observed. Statistically significant differences were found in the mean contrast estimates between the 2D EPI and 3D EPI when using a model without nuisance regressors. The inclusion of nuisance regressors for cardiorespiratory effects and motion reduced the difference to a statistically non-significant level. Furthermore, the contrast estimate values shifted more when including nuisance regressors for 3D EPI compared to 2D EPI.

Conclusion: The results are consistent with 3D EPI having a higher sensitivity to fluctuations compared to 2D EPI. In the presence partially task-correlated physiological fluctuations or motion, proper correction is necessary to get expectation correct contrast estimates when using 3D EPI. As such task-correlated physiological fluctuations or motion is difficult to avoid in paradigms exploring higher cognitive functions, 2D EPI seems to be the preferred choice for higher cognitive level fMRI paradigms.

Retrospective correction of physiological noise in DTI using an extended tensor model and peripheral measurements

Diffusion tensor imaging is widely used in research and clinical applications, but this modality is highly sensitive to artefacts. We developed an easy-to-implement extension of the original diffusion tensor model to account for physiological noise in diffusion tensor imaging using measures of peripheral physiology (pulse and respiration), the so-called extended tensor model. Within the framework of the extended tensor model two types of regressors, which respectively modeled small (linear) and strong (nonlinear) variations in the diffusion signal, were derived from peripheral measures. We tested the performance of four extended tensor models with different physiological noise regressors on nongated and gated diffusion tensor imaging data, and compared it to an established data-driven robust fitting method. In the brainstem and cerebellum the extended tensor models reduced the noise in the tensor-fit by up to 23% in accordance with previous studies on physiological noise. The extended tensor model addresses both large-amplitude outliers and small-amplitude signal-changes. The framework of the extended tensor model also facilitates further investigation into physiological noise in diffusion tensor imaging. The proposed extended tensor model can be readily combined with other artefact correction methods such as robust fitting and eddy current correction.

Advances in High-Field BOLD fMRI

This review article examines the current state of BOLD fMRI at a high magnetic field strength of 7 Tesla. The following aspects are covered: a short description of the BOLD contrast, spatial and temporal resolution, BOLD sensitivity, localization and spatial specificity, technical challenges as well as an outlook on future developments are given. It is shown that the main technical challenges of performing BOLD fMRI at high magnetic field strengths-namely development of array coils, imaging sequences and parallel imaging reconstruction-have been solved successfully. The combination of these developments has lead to the availability of high-resolution BOLD fMRI protocols that are able to cover the whole brain with a repetition time (TR) shorter than 3 s. The structural information available from these high-resolution fMRI images itself is already very detailed, which helps to co-localize structure and function. Potential future applications include whole-brain connectivity analysis on a laminar resolution and single subject examinations.