Technical Note: Independent component analysis for quality assurance in functional MRI

Real-time and Recursive Estimators for Functional MRI Quality Assessment

Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates.

A multidimensional artefact-reduction approach to increase robustness of first-level fMRI analyses: Censoring vs. interpolating

BACKGROUND

This manuscript describes a new, multidimensional and data-driven approach to identify outlying datapoints from a first-level fMRI dataset.

NEW METHOD

Using three different indicators of data corruption (the fast variance component of DVARS [Δ%D-var], scan-to-scan total displacement [STS], and each scan's overall explained variance [R²]), it identifies outlying datapoints while being balanced using Akaike'c corrected criterion (AIC _C) to avoid overcorrection. We then explore the impact of censoring, interpolating, or both, to remove a bad scan's contribution to the final timeseries.

RESULTS AND COMPARISON WITH EXISTING METHODS

Our results (using three real-life datasets and extensive simulations) show that motion-corrupted datapoints as well as non-motion related image artefacts are detected reliably. Using several indicators is shown to be an advantage over existing single-indicator solutions in different settings. As a result of using our algorithm, stronger activation (as detected by both T-value and number of activated voxels) and an increase in the temporal signal-to-noise ratio can be seen. The effects of censoring and interpolation are distinct and complex.

CONCLUSIONS

The multidimensional approach described here is able to identify outlying datapoints in fMRI timeseries, with demonstrable positive effects on several outcome measures. While censoring datapoints may be preferable in many settings, the ultimate choice on which approach to choose may depend on the data at hand. Recommendations are provided for different scenarios.

Technical Note: Independent component analysis for quality assurance in functional MRI

PURPOSE

Independent component analysis (ICA) is an established method of analyzing human functional MRI (fMRI) data. Here, an ICA-based fMRI quality control (QC) tool was developed and used.

METHODS

ICA-based fMRI QC tool to be used with a commercial phantom was developed. In an attempt to assess the performance of the tool relative to preexisting alternative tools, it was used seven weeks before and eight weeks after repair of a faulty gradient amplifier of a non-state-of-the-art MRI unit. More specifically, its performance was compared with the AAPM 100 acceptance testing and quality assurance protocol and two fMRI QC protocols, proposed by Freidman et al. ["Report on a multicenter fMRI quality assurance protocol," J. Magn. Reson. Imaging 23, 827-839 (2006)] and Stocker et al. ["Automated quality assurance routines for fMRI data applied to a multicenter study," Hum. Brain Mapp. 25, 237-246 (2005)], respectively.

RESULTS

The easily developed and applied ICA-based QC protocol provided fMRI QC indices and maps equally sensitive to fMRI instabilities with the indices and maps of other established protocols. The ICA fMRI QC indices were highly correlated with indices of other fMRI QC protocols and in some cases theoretically related to them. Three or four independent components with slow varying time series are detected under normal conditions.

CONCLUSIONS

ICA applied on phantom measurements is an easy and efficient tool for fMRI QC. Additionally, it can protect against misinterpretations of artifact components as human brain activations. Evaluating fMRI QC indices in the central region of a phantom is not always the optimal choice.