Automated phase correction via maximization of the real signal

Hardy, Edme H.; Hoferer, Jürgen; Mertens, Dirk; Kasper, Gerhard

doi:10.1016/j.mri.2008.07.009

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…In Fig. 3, we show the approximate density the use of score function atanh implies (given by exp(− u * atanh u )), which exactly corresponds to the data that results from the fMRI preprocessing we have described [28]. In the second row of the same figure, we show estimation results of joint approximate diagonalization of eigenmatrices (JADE) [32] and ML with atanh as the score function.…”

Section: Phase Ambiguity Correctionmentioning

confidence: 93%

“…A common pre-processing technique used in the after the acquisition of complex-valued fMRI data is to re-normalize the data such that most of the power is concentrated in the real part of the complex-valued fMRI signal [28]. This reconstruction technique (second block in Fig.…”

Section: Phase Ambiguity Correctionmentioning

confidence: 99%

“…The phase ambiguity correction schemes introduced in this paper are similar to this pre-processing technique, but they are applied to correct the phase ambiguity in the estimated ICs using complex-valued ICA (phase ambiguity correction block in Fig. 1) and not to correct the raw fMRI data phase errors as is done in [28]. …”

Section: Phase Ambiguity Correctionmentioning

confidence: 99%

“…Here, we present an example of the selection of the nonlinear functions in applications where the fMRI data are preprocessed in the scanner so that most of the power is in the real component [28]. In [23], it has been shown that a number of trigonometric functions and their hyperbolic counterparts can be effectively used for performing ICA, and in [31] it has been noted that by selecting the appropriate function, we can effectively alleviate the phase ambiguity.…”

Section: Phase Ambiguity Correctionmentioning

confidence: 99%

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De-noising, phase ambiguity correction and visualization techniques for complex-valued ICA of group fMRI data

Rodríguez

Calhoun

2012

Pattern Recognition

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Analysis of functional magnetic resonance imaging (fMRI) data in its native, complex form has been shown to increase the sensitivity both for data-driven techniques, such as independent component analysis (ICA), and for model-driven techniques. The promise of an increase in sensitivity and specificity in clinical studies, provides a powerful motivation for utilizing both the phase and magnitude data; however, the unknown and noisy nature of the phase poses a challenge. In addition, many complex-valued analysis algorithms, such as ICA, suffer from an inherent phase ambiguity, which introduces additional difficulty for group analysis. We present solutions for these issues, which have been among the main reasons phase information has been traditionally discarded, and show their effectiveness when used as part of a complex-valued group ICA algorithm application. The methods we present thus allow the development of new fully complex data-driven and semi-blind methods to process, analyze, and visualize fMRI data. We first introduce a phase ambiguity correction scheme that can be either applied subsequent to ICA of fMRI data or can be incorporated into the ICA algorithm in the form of prior information to eliminate the need for further processing for phase correction. We also present a Mahalanobis distance-based thresholding method, which incorporates both magnitude and phase information into a single threshold, that can be used to increase the sensitivity in the identification of voxels of interest. This method shows particular promise for identifying voxels with significant susceptibility changes but that are located in low magnitude (i.e. activation) areas. We demonstrate the performance gain of the introduced methods on actual fMRI data.

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Section: Phase Ambiguity Correctionmentioning

confidence: 93%

Section: Phase Ambiguity Correctionmentioning

confidence: 99%

Section: Phase Ambiguity Correctionmentioning

confidence: 99%

Section: Phase Ambiguity Correctionmentioning

confidence: 99%

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De-noising, phase ambiguity correction and visualization techniques for complex-valued ICA of group fMRI data

Rodríguez

Calhoun

2012

Pattern Recognition

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“…Here we have used the prior knowledge of fMRI data where the real part of the signal of interest is dominant over the imaginary part [38]. The value for each voxel in the bivariate t-map tells us how the voxel values are distributed across different subjects.…”

Section: Resultsmentioning

confidence: 99%

Application of Independent Component Analysis With Adaptive Density Model to Complex-Valued fMRI Data

Correa

Rodríguez

et al. 2011

IEEE Trans. Biomed. Eng.

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Independent component analysis (ICA) has proven quite useful for the analysis of functional resonance magnetic imaging (fMRI) data, especially when the underlying nature of the data is hard to model. It is especially attractive for the analysis of fMRI data in its native complex form since very little is known about the nature of phase, which is typically discarded in most analyses. In this paper, we show that a complex ICA approach using a flexible nonlinearity that adapts to the source density is the more desirable one for performing ICA of complex fMRI data compared to the those that use fixed nonlinearity. We show that by adaptively matching the underlying fMRI density model, the analysis performance can be improved in terms of both the estimation of the spatial maps and the task-related time courses. We also define a procedure for analysis and visualization of complex-valued fMRI results, which includes the construction of bivariate t-maps for multiple subjects and a complex-valued ICASSO [1] scheme for evaluating the consistency of ICA algorithms.

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Introduction

Hardy¹

2011

NMR Methods for the Investigation of Structure and Transport

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Automated phase correction via maximization of the real signal

Cited by 9 publications

References 17 publications

De-noising, phase ambiguity correction and visualization techniques for complex-valued ICA of group fMRI data

De-noising, phase ambiguity correction and visualization techniques for complex-valued ICA of group fMRI data

Application of Independent Component Analysis With Adaptive Density Model to Complex-Valued fMRI Data

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