Dataset Information

TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization.

ABSTRACT: We introduce a data consistency based retrospective motion correction method, TArgeted Motion Estimation and Reduction (TAMER), to correct for patient motion in Magnetic Resonance Imaging (MRI). Specifically, a motion free image and motion trajectory are jointly estimated by minimizing the data consistency error of a SENSE forward model including rigid-body subject motion. In order to efficiently solve this large non-linear optimization problem, we employ reduced modeling in the parallel imaging formulation by assessing only a subset of target voxels at each step of the motion search. With this strategy we are able to effectively capture the tight coupling between the image voxel values and motion parameters. We demonstrate in simulations TAMER's ability to find similar search directions compared to a full model, with an average error of 22%, vs. 73% error when using previously proposed alternating methods. The reduced model decreased the computation time fold compared to a full image volume evaluation. In phantom experiments, our method successfully mitigates both translation and rotation artifacts, reducing image RMSE compared to a motion-free gold standard from 21% to 14% in a translating phantom, and from 17% to 10% in a rotating phantom. Qualitative image improvements are seen in human imaging of moving subjects compared to conventional reconstruction. Finally, we compare in vivo image results of our method to the state-of-the-art.

SUBMITTER: Haskell MW

PROVIDER: S-EPMC6633918 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Publications

TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization.

Haskell Melissa W MW Cauley Stephen F SF Wald Lawrence L LL

IEEE transactions on medical imaging 20180501 5

We introduce a data consistency based retrospective motion correction method, TArgeted Motion Estimation and Reduction (TAMER), to correct for patient motion in Magnetic Resonance Imaging (MRI). Specifically, a motion free image and motion trajectory are jointly estimated by minimizing the data consistency error of a SENSE forward model including rigid-body subject motion. In order to efficiently solve this large non-linear optimization problem, we employ reduced modeling in the parallel imaging ...[more]

PMID: 29727288

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Project description:BackgroundFunctional magnetic resonance imaging (fMRI) provides critical information about the neurophysiology of the central nervous systems (CNS), posing clinical significance for the understanding of neuropathologies and advancement of rehabilitation. Typical fMRI study designs include subjects performing designed motor tasks within specific time frames, in which fMRI data are then analyzed by assuming that observed functional brain activations correspond to the designed tasks. Therefore, developing MRI-compatible sensors that enable real-time monitoring of subjects' task performances would allow for highly accurate fMRI studies. While several MRI-compatible sensors have been developed, none have demonstrated the ability to measure individual muscle fascicle length during fMRI, which could help uncover the complexities of the peripheral and central nervous systems. Furthermore, previous MRI-compatible sensors have been focused on biologically intact populations, limiting accessibility to populations such as those who have undergone amputation.MethodsWe propose a lightweight, low-cost, skin impedance-insensitive pressure-based muscular motion sensor (pMMS) that provides reliable estimates of muscle fascicle length and joint angle. The muscular motions are captured through measured pressure changes in an air pocket wrapped around the muscle of interest, corresponding to its muscular motion. The muscle fascicle length and joint angle are then estimated from the measured pressure changes based on the proposed muscle-skin-sensor interaction dynamics. Furthermore, we explore an integration method of multiple pMMS systems to expand the sensor capacity of estimating muscle fascicle length and joint angle. Ultrasound imaging paired with joint encoder measurements are utilized to assess pMMS estimation accuracy of muscle fascicle length in the tibialis anterior (TA) and ankle joint angle, respectively, of five biologically intact subjects.ResultsWe found that a single pMMS sufficiently provides robust and accurate estimations of TA muscle fascicle length and ankle joint angle during dorsiflexion at various speeds and amplitudes. Further, differential pressure readings from two pMMSs, in which each pMMS were proximally and distally placed, were able to mitigate errors due to perturbations, expanding pMMS capacity for muscle fascicle length and ankle joint angle estimation during the full range of plantar flexion and dorsiflexion.ConclusionsOur results from this study demonstrate the feasibility of the pMMS system to further be incorporated in fMRI settings for real-time monitoring of subjects' task performances, allowing sophisticated fMRI study designs.

Dataset Information

TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization.

Publications

TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization.

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