Comprehensive mathematical simulation of functional magnetic resonance imaging time series including motion-related image distortion and spin saturation effect

Kim, Boklye; Bhagalia, Roshni

doi:10.1016/j.mri.2007.05.007

Cited by 9 publications

(9 citation statements)

References 22 publications

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“…Slice-to-volume registration was previously used for motion correction in functional MRI [20], [21] and motion-robust fetal MRI reconstruction [22]–[31]. In particular, Oubel et al [28] presented a method for slice-level motion correction and reconstruction of fetal DW-MRI data in which a group-wise registration and a dual spatio-angular interpolation technique was employed.…”

Section: Discussionmentioning

confidence: 99%

“…All these distortions should be minimized in DWI acquisition and compensated in the analysis based on physics-driven models of distortion [52], [53], especially in the presence of motion [21], [28], [54]–[56]. In this study we focused on tracking and correcting the head motion and did not consider or correct for distortions.…”

Section: Discussionmentioning

confidence: 99%

“…Slice-to-volume registration was previously used for motion correction in functional MRI analysis [20], [21] and motion-robust fetal brain MRI [22]–[31], but is not routinely used for motion-robust DWI reconstruction because of technical challenges. Image registration is an ill-posed problem without a guaranteed optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking

Marami

Scherrer

Afacan

et al. 2016

IEEE Trans. Med. Imaging

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This work proposes a novel approach for motion-robust diffusion-weighted (DW) brain MRI reconstruction through tracking temporal head motion using slice-to-volume registration. The slice-level motion is estimated through a filtering approach that allows tracking the head motion during the scan and correcting for out-of-plane inconsistency in the acquired images. Diffusion-sensitized image slices are registered to a base volume sequentially over time in the acquisition order where an outlier-robust Kalman filter, coupled with slice-to-volume registration, estimates head motion parameters. Diffusion gradient directions are corrected for the aligned DWI slices based on the computed rotation parameters and the diffusion tensors are directly estimated from the corrected data at each voxel using weighted linear least squares. The method was evaluated in DWI scans of adult volunteers who deliberately moved during scans as well as clinical DWI of 28 neonates and children with different types of motion. Experimental results showed marked improvements in DWI reconstruction using the proposed method compared to the state-of-the-art DWI analysis based on volume-to-volume registration. This approach can be readily used to retrieve information from motion-corrupted DW imaging data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking

Marami

Scherrer

Afacan

et al. 2016

IEEE Trans. Med. Imaging

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“…Some models have as few as three sources including baseline, source of interest, and noise (Della-Maggiore et al, 2002; Logan and Rowe, 2004; Marchini and Presanis, 2004; Baumgartner et al, 2000; Lu et al, 2003; Dimitriadou et al, 2004; Baumgartner et al, 1998; Beckmann and Smith, 2004; Bellec et al, 2006; Bullmore et al, 2001; Jahanian et al, 2004). Other fMRI simulation software use first principles of the MR physics (Drobnjak et al, 2006; Xu et al, 2007; Kim et al, 2008), bootstrap from real data (Bellec et al, 2009), or interpolate to introduce motion (Ardekani et al, 2001; Freire and Mangin, 2001; Pickens et al, 2005). Some of these are computationally demanding, requiring hours or days for a single simulation, others rely on existing fMRI subject data, and others are too simple or specialized to be of general use (see Sec.…”

Section: Introductionmentioning

confidence: 99%

SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability

et al. 2012

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We introduce SimTB, a MATLAB toolbox designed to simulate functional magnetic resonance imaging (fMRI) datasets under a model of spatiotemporal separability. The toolbox meets the increasing need of the fMRI community to more comprehensively understand the effects of complex processing strategies by providing a ground truth that estimation methods may be compared against. SimTB captures the fundamental structure of real data, but data generation is fully parameterized and fully controlled by the user, allowing for accurate and precise comparisons. The toolbox offers a wealth of options regarding the number and configuration of spatial sources, implementation of experimental paradigms, inclusion of tissue-specific properties, addition of noise and head movement, and much more. A straightforward data generation method and short computation time (3–10 seconds for each dataset) allow a practitioner to simulate and analyze many datasets to potentially understand a problem from many angles. Beginning MATLAB users can use the SimTB graphical user interface (GUI) to design and execute simulations while experienced users can write batch scripts to automate and customize this process. The toolbox is freely available at http://mialab.mrn.org/software together with sample scripts and tutorials.

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“…More elaborate approaches included motion as part of the data-generating process, either through spatial interpolation [11–13] or using a first-principle model of the MR physics [19,25,26]. Such models gave access to the motion parameters and in some cases to the pulse sequence parameters.…”

Section: Introductionmentioning

confidence: 99%

Bootstrap generation and evaluation of an fMRI simulation database

Bellec

Perlbarg

Evans

2009

Magnetic Resonance Imaging

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Computer simulations have played a critical role in functional magnetic resonance imaging (fMRI) research, notably in the validation of new data analysis methods. Many approaches have been used to generate fMRI simulations, but there is currently no generic framework to assess how realistic each one of these approaches may be. In this paper, a statistical technique called parametric bootstrap was used to generate a simulation database that mimicked the parameters found in a real database, which comprised 40 subjects and 5 tasks. The simulations were evaluated by comparing the distributions of a battery of stastical measures between the real and simulated databases. Two popular simulation models were evaluated for the first time by applying the bootstrap framework. The first model was an additive mixture of multiple components and the second one implemented a non-linear motion process. In both models, the simulated components included the following brain dynamics : a baseline, physiological noise, neural activation and random noise. These models were found to successfully reproduce the relative variance of the components and the temporal autocorrelation of the fMRI time series. By contrast, the level of spatial autocorrelation was found to be drastically low using the additive model. Interestingly, the motion process in the second model intrisically generated some slow time drifts and increased the level of spatial autocorrelations. These experiments demonstrated that the bootstrap framework is a powerful new tool that can pinpoint the respective strengths and limitations of simulation models.

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Comprehensive mathematical simulation of functional magnetic resonance imaging time series including motion-related image distortion and spin saturation effect

Cited by 9 publications

References 22 publications

Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking

Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking

SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability

Bootstrap generation and evaluation of an fMRI simulation database

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