Automated Correction of Spin-History Related Motion Artefacts in fMRI: Simulated and Phantom Data

Muresan, Lucian; Renken, Remco; Roerdink, Jos B. T. M.; Duifhuis, Hendrikus

doi:10.1109/tbme.2005.851484

Cited by 39 publications

(39 citation statements)

References 23 publications

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“…Both the noise and motion provide some confounding signal changes for initial testing of the algorithm. Other possible confounds such as spin history effects [Friston et al, 1996;Muresan et al, 2005] and correlated physiological noise [Raj et al, 2001;Windischberger et al, 2002] were not addressed with this phantom but will be present in in vivo images.…”

Section: Simulationsmentioning

confidence: 99%

Development of 2dTCA for the detection of irregular, transient bold activity

Morgan¹,

Li²,

Abou‐Khalil³

et al. 2007

Human Brain Mapping

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The temporal clustering algorithm (TCA) has been developed in order to detect irregular, transient functional MRI (fMRI) activation signals when the timings of the stimuli are unknown. Unfortunately, such methods are also sensitive to signal changes caused by motion and physiological noise. We have developed a modified TCA technique, 2dTCA, which can detect multiple different timing patterns within a dataset so that signals of interest can be separated from artifacts and those of no interest. The objective of this work was to further develop the 2dTCA methods and evaluate their performance in simulated functional MRI datasets. Comparisons were made with TCA and a freely-distributed independent component analysis algorithm (ICA). We created two different sets of six computer-generated phantoms with one and two different simulated activation time courses present in 10 regions of interest. The phantoms also contained real subject rigid and nonrigid body motion and noise. Sensitivity of detection, defined as the true-positive activation rate at false-positive activation rates varying between 0.0001 and 0.01, was compared between methods. Additionally, specificity of detection of the irregular, transient signal of interest was assessed by comparing the number of signal time courses detected by each algorithm. The results suggest that the increased sensitivity of 2dTCA over TCA in detecting this particular signal of interest is comparable to the detection with ICA, but with fewer other signals detected. A few examples of the successful application of 2dTCA to the localization of interictal activity in preliminary studies of temporal lobe epilepsy are also described.

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Section: Simulationsmentioning

confidence: 99%

Development of 2dTCA for the detection of irregular, transient bold activity

Morgan¹,

Li²,

Abou‐Khalil³

et al. 2007

Human Brain Mapping

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“…Some of them have been considered as artifacts arising from subject movement (16,17), but for transients that cannot be attributed to motion, the generation mechanisms have remained elusive. For example, the spatial distribution of the transients is unexpected: after sustained visual stimulation, the transients can be bilateral while the conventional sustained BOLD response is contralateral (18), or the transients at block transitions can be broadly distributed and sometimes occur independently of the type of the task (14).…”

Section: Discussionmentioning

confidence: 99%

Transients may occur in functional magnetic resonance imaging without physiological basis

Renvall¹,

Hari²

2009

Proc. Natl. Acad. Sci. U.S.A.

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Functional magnetic resonance imaging (fMRI) has revolutionized the study of human brain activity, in both basic and clinical research. The commonly used blood oxygen level dependent (BOLD) signal in fMRI derives from changes in oxygen saturation of cerebral blood flow as a result of brain activity. Beyond the traditional spatial mapping of stimulus-activation correspondences, the detailed waveforms of BOLD responses are of high interest. Especially intriguing are the transient overshoots and undershoots, often, although inconclusively, attributed to the interplay between changes in cerebral blood flow and volume after neuronal activation. While physically simulating the BOLD response in fMRI phantoms, we encountered prominent transient deflections, although the magnetic field inside the phantom varied in a square-wave manner. Detailed analysis and modeling indicated that the transients arise from activation-related partial misalignment of the imaging slices and depend heavily on measurement parameters, such as the time between successive excitations. The results suggest that some transients encountered in normal fMRI recordings may be spurious, potentially compromising the physiological interpretation of BOLD signal overshoots and undershoots.fMRI phantom ͉ overshoot ͉ undershoot ͉ BOLD ͉ fMRI S ince the advent of the blood oxygen level dependent (BOLD) imaging method (1-4), functional magnetic resonance imaging (fMRI) of human brain function has spread so rapidly that currently as many as eight new fMRI papers appear each day. The proper analysis of fMRI data (5) and the relationship between the fMRI signal and the underlying brain activity (6) continues to receive much attention, whereas the basic physics of the signal acquisition is considered to be sufficiently understood. Accordingly, computer simulations are used to successfully model the effects of different imaging parameters on the measured signals (7-9).Such computational methods can, however, oversimplify factors affecting the signal acquisition and the MR signal. To scrutinize the behavior of the BOLD response in more detail, we recently introduced physical ''fMRI phantoms'' (10, 11) in which the BOLD signals can be physically simulated by accurately controlling current flow within a conducting medium.The MRI signals of the brain arise from protons that are excited with radio frequency pulses applied at the protons' Larmor frequency in a static magnetic field (B 0 ). With brief gradient fields, a slice of protons (e.g., perpendicular to the magnet's main axis z) can be selected. In a typical fMRI experiment, several (up to 50) slices are repeatedly excited while a subject performs a task or perceives stimuli. Soon after an excitation, the protons start to precess at slightly different frequencies because of local magnetic field inhomogeneities that arise from tissue properties, especially from a blood oxygenation level that varies according to neuronal activity. The field inhomogeneities result in dephasing, or transverse relaxation, of the original...

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“…These motion correction techniques assume that motion affects the entire volume in a similar fashion and attempt to address first-order motion effects; however, they do not address potential sliceto-slice out of plane motion effects or other second order motion effects. Such motion effects may arise from primarily spin history effects (out of plane motion (Friston et al, 1996;Muresan et al, 2005;Yancey et al, 2011), but also B1 inhomogeneity, and B0 inhomogeneity. Semi-prospective (e.g., SimPACE, (Beall and Lowe, 2014)) and retrospective (e.g., SLOMOCO, (Beall and Lowe, 2014) correction methods exist which attempt to address second order motion effects.…”

Section: Motion Correction During Data Processingmentioning

confidence: 99%