A retrospective physiological noise correction method for oscillating steady‐state imaging

Abstract

Purpose Oscillating steady‐state imaging (OSSI) is an SNR‐efficient steady‐state sequence with T2∗ sensitivity suitable for FMRI. Due to the frequency sensitivity of the signal, respiration‐ and drift‐induced field changes can create unwanted signal fluctuations. This study aims to address this issue by developing retrospective signal correction methods that utilize OSSI signal properties to denoise task‐based OSSI FMRI experiments. Methods A retrospective denoising approach was developed that leverages the unique signal properties of OSSI to perform denoising without a manually specified noise region of interest and works with both voxel timecourses (oscillating steady‐state correction [OSSCOR]) or FID timecourses (F‐OSSCOR). Simulations were performed to estimate the number of principal components optimal for denoising. In vivo experiments at 3 T field strength were conducted to compare the performance of proposed methods against a standard principal component analysis‐based method, measured using mean t score within an region of interest, number of activations, and mean temporal SNR. Results Correction using OSSCOR was significantly better than the standard method in all metrics. Correction using F‐OSSCOR was not significantly different from the standard method using an equal number of principal components. Increasing the number of OSSCOR principal components decreased activation strength and increased the number of suspected false positives. However, increasing the number of principal components in F‐OSSCOR increased activation strength with little to no increase in false activation. Conclusion Both OSSCOR and F‐OSSCOR substantially reduce physiological noise components and increase temporal SNR, improving the functional results of task‐based OSSI functional experiments. F‐OSSCOR demonstrates a proof of concept utilization of coil‐localized FID signal information for physiological noise correction.

Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics

“…Previous studies reported positive effects of physiological noise removal [26,28,44], showing that physiological correction improved rs-fMRI data stability, and produced better functional outcomes such as more specific activation maps, better correlation maps, and functional networks. With respect to stability, for example, a study showed that physiological correction could remove spurious activations in regions outside the gray matter shown in the uncorrected data [45]; another showed that physiological correction substantially reduced physiological noise components and increased temporal SNR [46]. In terms of functional outcomes, some studies showed that physiological noise correction increased the spatial extent, reduced apparent false positives in DMN [47], and increased DMN localization estimated by seed correlation to the seed region [48], leading to improved detection of consistent group differences [49].…”

Off‐resonance artifact correction for MRI: A review

“…For prospective correction, dynamic shimming can be done either in a volumetric approach, where the shim is updated during the scan to optimize homogeneity over the whole region of interest (this will require real time measurement of the field changes), or in a slice‐by‐slice manner, where the optimal shims for each slice are determined, and then the shims are updated during the scan depending on which slice is being acquired (one can measure and dynamically update slice shims in real time, or optimal slice shims are determined prior to the experiment). Prospective correction without spatial information can also be useful to dynamically measure and update the center frequency in real time, for example in applications such as interventional MRI 107 and oscillating steady state imaging (OSSI) fMRI, 108 which has been shown to be very sensitive 109 …”