Toward Quantifying the Prevalence, Severity, and Cost Associated With Patient Motion During Clinical MR Examinations

Andre, Jalal B.; Bresnahan, Brian W.; Mossa‐Basha, Mahmud; Hoff, Michael N.; Smith, C. Patrick; Anzai, Yoshimi; Cohen, Wendy A.

doi:10.1016/j.jacr.2015.03.007

Cited by 195 publications

(194 citation statements)

References 19 publications

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“…As few as 7.5% of single MR images exhibited motion artifacts, while up to 19.8% of long scans of multiple contrasts may be affected. 19 Because synthetic imaging reduces the overall scan time, the impact of acquisition issues is expected to be limited in practice.…”

Section: Discussionmentioning

confidence: 99%

Synthetic MRI for Clinical Neuroimaging: Results of the Magnetic Resonance Image Compilation (MAGiC) Prospective, Multicenter, Multireader Trial

Tanenbaum¹,

Tsiouris

Johnson

et al. 2017

AJNR Am J Neuroradiol

208

226

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Section: Discussionmentioning

confidence: 99%

Synthetic MRI for Clinical Neuroimaging: Results of the Magnetic Resonance Image Compilation (MAGiC) Prospective, Multicenter, Multireader Trial

Tanenbaum¹,

Tsiouris

Johnson

et al. 2017

AJNR Am J Neuroradiol

208

226

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“…Direct applications of the method include studies of severely diseased, non-compliant, elderly or pediatric patients. In addition, a high prevalence of motion artifacts has been reported in clinical examinations [2]. Finally, small motion will become a limiting factor for ultra-high resolution images.…”

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confidence: 99%

“…The contributions of our proposal are threefold: (1) we provide a fully data-based generalized reconstruction formulation where a common functional is used to estimate the rigid motion and the structural data in parallel MR using the sensitivity encoding (SENSE) redundancy the coil array provides, (2) we make use of a rigid transformation representation that fully preserves the image resolution, thereby allowing for optimal reconstructions when neglecting the non-rigid motion components 1 , and (3) we study the regime in which these fully rigidly corrected reconstructions are possible in terms of the amount of motion, encoding trajectories, number of shots and use of prior information. In addition, the source code of a MAT-LAB implementation of the reconstruction method proposed in Section II together with the experiments in Section III is freely available at https://github.com/mriphysics/alignedSENSE.…”

mentioning

confidence: 99%

Sensitivity Encoding for Aligned Multishot Magnetic Resonance Reconstruction

Cordero‐Grande

Teixeira

Hughes

et al. 2016

IEEE Trans. Comput. Imaging

155

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This paper introduces a framework for the reconstruction of magnetic resonance images in the presence of rigid motion. The rationale behind our proposal is to make use of the partial k-space information provided by multiple receiver coils in order to estimate the position of the imaged object throughout the shots that contribute to the image. The estimated motion is incorporated into the reconstruction model in an iterative manner to obtain a motion-free image. The method is parameter-free, does not assume any prior model for the image to be reconstructed, avoids blurred images due to resampling, does not make use of external sensors, and does not require modifications in the acquisition sequence. Validation is performed using synthetically corrupted data to study the limits for full motion-recovered reconstruction in terms of the amount of motion, encoding trajectories, number of shots and availability of prior information, and to compare with the state of the art. Quantitative and visual results of its application to a highly challenging volumetric brain imaging cohort of 207 neonates are also presented, showing the ability of the proposed reconstruction to generally improve the quality of reconstructed images, as evaluated by both sparsity and gradient entropy based metrics.

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“…Image quality degradation may result in non-diagnostic data and require repetition of a single scan or the entire session leading to a delay of treatment and rising costs. If unnoticed, potential false positive or negative findings may occur (Andre et al 2015). …”

Section: Introductionmentioning

confidence: 99%

Motion correction in MRI of the brain

et al. 2016

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Subject motion in MRI is a relevant problem in the daily clinical routine as well as in scientific studies. Since the beginning of clinical use of MRI, many research groups have developed methods to suppress or correct motion artefacts. This review focuses on rigid body motion correction of head and brain MRI and its application in diagnosis and research. It explains the sources and types of motion and related artefacts, classifies and describes existing techniques for motion detection, compensation and correction and lists established and experimental approaches. Retrospective motion correction modifies the MR image data during the reconstruction, while prospective motion correction performs an adaptive update of the data acquisition. Differences, benefits and drawbacks of different motion correction methods are discussed.

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Toward Quantifying the Prevalence, Severity, and Cost Associated With Patient Motion During Clinical MR Examinations

Cited by 195 publications

References 19 publications

Synthetic MRI for Clinical Neuroimaging: Results of the Magnetic Resonance Image Compilation (MAGiC) Prospective, Multicenter, Multireader Trial

Synthetic MRI for Clinical Neuroimaging: Results of the Magnetic Resonance Image Compilation (MAGiC) Prospective, Multicenter, Multireader Trial

Sensitivity Encoding for Aligned Multishot Magnetic Resonance Reconstruction

Motion correction in MRI of the brain

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