A dual echo approach to removing motion artefacts in fMRI time series

In fMRI, subject motion can severely affect data quality. This is a particular problem when movement is correlated with the experimental paradigm as this potentially causes artefactual activation. A method is presented that uses linear regression, to utilise the time course of an image acquired at very short echo time (TE) as a voxel‐wise regressor for a second image in the same echo train, that is acquired with high BOLD sensitivity. The value of this approach is demonstrated using task‐locked motion combined with visual stimulation. Results obtained at both 1.5 and 3 T show improvements in functional activation maps for individual subjects. The method is straightforward to implement, does not require extra scan time and can easily be embedded in a multi‐echo acquisition framework. Copyright © 2009 John Wiley & Sons, Ltd.     

基于一种改进的相位校正算法消除MRI运动伪影

背景：近年来,MRI由于具有高的空间分辨率和软组织对比度,在临床上的运用越来越广泛。但是其成像时间较长,所以容易受到患者身体运动的影响,产生运动伪影。 目的：去除MRI图像成像时产生的伪影,改善图像质量。 方法：使用改进的相位矫正算法,并结合水平集算法去除图像伪影。去除伪影后使用模糊增强改善处理后图像的质量。 结果与结论：实验证明使用改进的相位矫正算法得到的图像比使用原始的相位矫正算法得到的图像效果更加理想。     

Evaluation of linear diaphragm-chest expansion models for magnetic resonance imaging motion artifact correction.

The efficacy of Fourier analysis, autoregressive with exogenous input (ARX) and adaptive models to estimate diaphragm position from respiratory belt signal (a measure of chest expansion) was evaluated for the purpose of correcting respiratory motion artifacts in magnetic resonance imaging (MRI). Respiratory belt signal and diaphragm position data were obtained simultaneously during one-dimensional MRI scans with sampling intervals of 100 ms for 128 s (1280 samples). The models were trained using the first 512 data samples for the Fourier method and the first 640 samples for the ARX and adaptive methods. The remaining samples were used as a test set for evaluating the models. Both ARX and adaptive methods produced more accurate results than the Fourier method as reflected by the normalized mean square error (NMSE) and correlation coefficient (R) between the estimated and actual diaphragm position during normal breathing (P < 0.05). However, all three models had difficulty modeling diaphragm positions during breathing plateaus.     

Digital subtraction angiogram registration method with local distortion vectors to decrease motion artifact.

We have been investigating registration methods for improving digital subtraction angiography (DSA) images to extract blood vessels by reducing artifacts due to body motion, such as rotation, contraction, and dilation. In this paper, we propose a new and simple DSA registration algorithm with local distortion vectors to reduce artifacts. According to the results, the proposed method works well for vascular system around the nasal cavity and the orbit of the head and neck DSA images, which cannot be observed clearly by conventional methods. Additionally, we have applied the proposed method to abdominal and leg DSA images.     

A robust algorithm for heart rate variability time series artefact correction using novel beat classification

Abstract

Purpose: Heart rate variability is a commonly used measurement to evaluate functioning of autonomic nervous system, psychophysiological stress, and exercise intensity and recovery. HRV measurements contain artefacts such as extra, missed or misaligned beat detections, which can produce significant distortion on HRV parameters. In this paper, a robust automatic method for artefact detection from HRV time series is proposed.

Methods: The proposed detection method is based on time-varying thresholds estimated from distribution of successive RR-interval differences combined with a novel beat classification scheme. The method is validated using simulated extra, missed and misaligned beat detections as well as real artefacts such as atrial and ventricular ectopic beats.

Results: The sensitivity of the algorithm to detect simulated missed/extra beats was 100%. The sensitivity to detect real atrial and ventricular ectopic beats was 96.96%, the corresponding specificity being 99.94%. The mean error in HRV parameters after correction was <2% for missed and extra beats as well as for misaligned beats generated with large displacement factors. Misaligned beats with smallest displacement factor were the most difficult to detect and resulted in largest HRV parameter errors after correction, largest errors being <8%.

Conclusions: The HRV artefact correction algorithm presented in this study provided comparable specificity and better sensitivity to detect ectopic beats as compared to state-of-the-art algorithms. The proposed algorithm detects abnormal beats with high accuracy, is relatively easy to implement, and secures reliable HRV analysis by reducing the effect of possible artefacts to tolerable level.     

Performance assessment of an algorithm for the alignment of fMRI time series

This paper reports on performance assessment of an algorithm developed to align functional Magnetic Resonance Image (fMRI) time series. The algorithm is based on the assumption that the human brain is subject to rigid-body motion and has been devised by pipelining fiducial markers and tensor based registration methodologies. Feature extraction is performed on each fMRI volume to determine tensors of inertia and gradient image of the brain. A head coordinate system is determined on the basis of three fiducial markers found automatically at the head boundary by means of the tensors and is used to compute a point-based rigid matching transformation. Intensity correction is performed with sub-voxel accuracy by trilinear interpolation. Performance of the algorithm was preliminarily assessed by fMR brain images in which controlled motion has been simulated. Further experimentation has been conducted with real fMRI time series. Rigid-body transformations were retrieved automatically and the value of motion parameters compared to those obtained with the Statistical Parametric Mapping (SPM99) and the Automatic Image Registration (AIR 3.08). Results indicate that the algorithm offers sub-voxel accuracy in performing both misalignment and intensity correction of fMRI time series.     

A unified canonical correlation analysis-based framework for removing gradient artifact in concurrent EEG/fMRI recording and motion artifact in walking recording from EEG signal

Artifacts cause distortion and fuzziness in electroencephalographic (EEG) signal and hamper EEG analysis, so it is necessary to remove them prior to the analysis. Particularly, artifact removal becomes a critical issue in experimental protocols with significant inherent recording noise, such as mobile EEG recordings and concurrent EEG–fMRI acquisitions. In this paper, we proposed a unified framework based on canonical correlation analysis for artifact removal. Raw signals were reorganized to construct a pair of matrices, based on which sources were sought through maximizing autocorrelation. Those sources related to artifacts were then removed by setting them as zeros, and the remaining sources were used to reconstruct artifact-free EEG. Both simulated and real recorded data were utilized to assess the proposed framework. Qualitative and quantitative results showed that the proposed framework was effective to remove artifacts from EEG signal. Specifically, the proposed method outperformed independent component analysis method for mitigating motion-related artifacts and had advantages for removing gradient artifact compared to the classical method (average artifacts subtraction) and the state-of-the-art method (optimal basis set) in terms of the combination of performance and computational complexity.     

Regional homogeneity of fMRI time series in autism spectrum disorders

Functional magnetic resonance imaging (fMRI) and functional connectivity MRI (fcMRI) studies of autism spectrum disorders (ASD) have suggested atypical patterns of activation and long-distance connectivity for diverse tasks and networks in ASD. We explored the regional homogeneity (ReHo) approach in ASD, which is analogous to conventional fcMRI, but focuses on local connectivity. FMRI data of 26 children with ASD and 29 typically developing (TD) children were acquired during continuous task performance (visual search). Effects of motion and task were removed and Kendall's coefficient of concordance (KCC) was computed, based on the correlation of the blood oxygen level dependent (BOLD) time series for each voxel and its six nearest neighbors. ReHo was lower in the ASD than the TD group in superior parietal and anterior prefrontal regions. Inverse effects of greater ReHo in the ASD group were detected in lateral and medial temporal regions, predominantly in the right hemisphere. Our findings suggest that ReHo is a sensitive measure for detecting cortical abnormalities in autism. However, impact of methodological factors (such as spatial resolution) on ReHo require further investigation.     

Development and characterization of an automatic technique for the alignment of fMRI time series

An automatic technique for the registration of fMRI time series has been developed, implemented and tested. The method assumes the human brain to be a rigid body and computes a head coordinate system on the basis of three reference points that lies on the directions corresponding to two of the principal axes of the volume at the intersections with the head boundary. Such directions are found computing the eigenvectors of the symmetric inertia matrix of the image. The inertia components were extracted weighting pixels' coordinates with their intensity values. The three reference points were found in the same position, relative to the head, in both the test and the reference images. The technique has been tested using T2*-weighted Magnetic Resonance (MR) images in which known rigid body transformations have been applied. The results obtained indicate that the method offers subvoxel accuracy in correcting misalignment among time points in fMRI time series     

4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling

Four-dimensional computed tomography (4D-CT) imaging technology has been developed for radiation therapy to provide tumor and organ images at the different breathing phases. In this work, a procedure is proposed for estimating and modeling the respiratory motion field from acquired 4D-CT imaging data and predicting tissue motion at the different breathing phases. The 4D-CT image data consist of series of multislice CT volume segments acquired in ciné mode. A modified optical flow deformable image registration algorithm is used to compute the image motion from the CT segments to a common full volume 3D-CT reference. This reference volume is reconstructed using the acquired 4D-CT data at the end-of-exhalation phase. The segments are optimally aligned to the reference volume according to a proposed a priori alignment procedure. The registration is applied using a multigrid approach and a feature-preserving image downsampling maxfilter to achieve better computational speed and higher registration accuracy. The registration accuracy is about 1.1 +/- 0.8 mm for the lung region according to our verification using manually selected landmarks and artificially deformed CT volumes. The estimated motion fields are fitted to two 5D (spatial 3D+tidal volume+airflow rate) motion models: forward model and inverse model. The forward model predicts tissue movements and the inverse model predicts CT density changes as a function of tidal volume and airflow rate. A leave-one-out procedure is used to validate these motion models. The estimated modeling prediction errors are about 0.3 mm for the forward model and 0.4 mm for the inverse model.     

List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations

Respiratory motion in emission tomography leads to reduced image quality. Developed correction methodology has been concentrating on the use of respiratory synchronized acquisitions leading to gated frames. Such frames, however, are of low signal-to-noise ratio as a result of containing reduced statistics. In this work, we describe the implementation of an elastic transformation within a list-mode-based reconstruction for the correction of respiratory motion over the thorax, allowing the use of all data available throughout a respiratory motion average acquisition. The developed algorithm was evaluated using datasets of the NCAT phantom generated at different points throughout the respiratory cycle. List-mode-data-based PET-simulated frames were subsequently produced by combining the NCAT datasets with Monte Carlo simulation. A non-rigid registration algorithm based on B-spline basis functions was employed to derive transformation parameters accounting for the respiratory motion using the NCAT dynamic CT images. The displacement matrices derived were subsequently applied during the image reconstruction of the original emission list mode data. Two different implementations for the incorporation of the elastic transformations within the one-pass list mode EM (OPL-EM) algorithm were developed and evaluated. The corrected images were compared with those produced using an affine transformation of list mode data prior to reconstruction, as well as with uncorrected respiratory motion average images. Results demonstrate that although both correction techniques considered lead to significant improvements in accounting for respiratory motion artefacts in the lung fields, the elastic-transformation-based correction leads to a more uniform improvement across the lungs for different lesion sizes and locations.     

Motion correction for three-dimensional chemical exchange saturation transfer imaging without direct water saturation artifacts

In chemical exchange saturation transfer (CEST) MRI, motion correction is compromised by the drastically changing image contrast at different frequency offsets, particularly at the direct water saturation. In this study, a simple extension for conventional image registration algorithms is proposed, enabling robust and accurate motion correction of CEST-MRI data. The proposed method uses weighted averaging of motion parameters from a conventional rigid image registration to identify and mitigate erroneously misaligned images. Functionality of the proposed method was verified by ground truth datasets generated from 10 three-dimensional in vivo measurements at 3 T with simulated realistic random rigid motion patterns and noise. Performance was assessed using two different criteria: the maximum image misalignment as a measure for the robustness against direct water saturation artifacts, and the spectral error as a measure of the overall accuracy. For both criteria, the proposed method achieved the best scores compared with two motion-correction algorithms specifically developed to handle the varying contrasts in CEST-MRI. Compared with a straightforward linear interpolation of the motion parameters at frequency offsets close to the direct water saturation, the proposed method offers better performance in the absence of artifacts. The proposed method for motion correction in CEST-MRI allows identification and mitigation of direct water saturation artifacts that occur with conventional image registration algorithms. The resulting improved robustness and accuracy enable reliable motion correction, which is particularly crucial for an automated and carefree evaluation of spectral CEST-MRI data, e.g., for large patient cohorts or in clinical routines.     

Respiratory motion correction for PET oncology applications using affine transformation of list mode data

Respiratory motion is a source of artefacts and reduced image quality in PET. Proposed methodology for correction of respiratory effects involves the use of gated frames, which are however of low signal-to-noise ratio. Therefore a method accounting for respiratory motion effects without affecting the statistical quality of the reconstructed images is necessary. We have implemented an affine transformation of list mode data for the correction of respiratory motion over the thorax. The study was performed using datasets of the NCAT phantom at different points throughout the respiratory cycle. List mode data based PET simulated frames were produced by combining the NCAT datasets with a Monte Carlo simulation. Transformation parameters accounting for respiratory motion were estimated according to an affine registration and were subsequently applied on the original list mode data. The corrected and uncorrected list mode datasets were subsequently reconstructed using the one-pass list mode EM (OPL-EM) algorithm. Comparison of corrected and uncorrected respiratory motion average frames suggests that an affine transformation in the list mode data prior to reconstruction can produce significant improvements in accounting for respiratory motion artefacts in the lungs and heart. However, the application of a common set of transformation parameters across the imaging field of view does not significantly correct the respiratory effects on organs such as the stomach, liver or spleen.     

Objective evaluation of the correction by non-rigid registration of abdominal organ motion in low-dose 4D dynamic contrast-enhanced CT

We objectively evaluate a straightforward registration method for correcting respiration-induced movement of abdominal organs in CT perfusion studies by measuring the distributions of alignment errors between corresponding landmark pairs. We introduce the concept and describe the advantages of using the surface-normal component of distance between pairs of corresponding landmarks selected so that their surface normal is in one of the three coordinate axis directions, and show that such landmarks can be precisely placed with respect to the surface normal. Using a large population of landmark pairs on a substantial quantity of 4D dynamic contrast-enhanced CT volume data, we quantify the average alignment errors of abdominal organs that remain uncorrected by registration.