Hybrid prospective and retrospective head motion correction to mitigate cross‐calibration errors

Aksoy, Murat; Forman, Christoph; Straka, Matús; Çukur, Tolga; Hornegger, Joachim; Bammer, Roland

doi:10.1002/mrm.23101

Cited by 66 publications

(88 citation statements)

References 26 publications

(55 reference statements)

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“…While most of the previous investigations performed the RMC method for rigid motion correction in k -space and some in spatial domain [29][30][51][52], this work demonstrated that the RMC method can be successfully utilized for non-rigid motion correction of bladder MR imaging in the spatial domain. Since the motion correction in the k -space can only effectively compensate for rigid motions, such as: translation, rotation, expansion, and general affine, it is not desired to use direct correction method for non-rigid motions in the k -space.…”

Section: Conclusion and Discussionmentioning

confidence: 89%

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Motion Correction for MR Cystography by an Image Processing Approach

Lin

Zhang

Duan

et al. 2013

IEEE Trans. Biomed. Eng.

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Magnetic resonance (MR) cystography or MR-based virtual cystoscopy is a promising new technology to evaluate the entire bladder in a fully non-invasive manner. It requires the anatomical bladder images be acquired at high spatial resolution and with adequate signal-to-noise ratio (SNR). This often leads to a long-time scan (> 5 minutes) and results in image artifacts due to involuntary bladder motion and deformation. In this paper, we investigated an image-processing approach to mitigate the problem of motion and deformation. Instead of a traditional single long-time scan, six repeated short-time scans (each of approximately 1 minute) were acquired for the purpose of shifting bladder motion from intra-scan into inter-scans. Then the inter-scan motions were addressed by registering the short-time scans to a selected reference and finally forming a single average motion-corrected image. To evaluate the presented approach, three types of images were generated: (1) the motion-corrected image by registration and average of the short-time scans; (2) the directly-averaged image of the short-time scans (without motion correction); and (3) the single image of the corresponding long-time scan. Six experts were asked to blindly score these images in terms of two important aspects: (i) the definition of the bladder wall and (ii) the overall expression on the image quality. Statistical analysis on the scores suggested that the best result in both the aspects is achieved by the presented motion-corrected average. Furthermore the superiority of the motion-corrected average over the other two is statistically significant by the measure of a linear mixed-effect model with p-values<0.05. Our findings may facilitate the detection of bladder abnormality in MR cystography by mitigating the motion challenge. The effectiveness of this approach depends on the noise level of acquired short-time scans and the robustness of image registration and future effort on these two aspects is needed.

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Section: Conclusion and Discussionmentioning

confidence: 89%

“…al. , [30] divided the k-space into several segments and considered the motion by individually correcting for the rotation and translation of each segment via minimizing an entropy-based auto-focusing criterion. Sharper edges in the final image were observed after the motion correction.…”

Section: Introductionmentioning

confidence: 99%

Motion Correction for MR Cystography by an Image Processing Approach

Lin

Zhang

Duan

et al. 2013

IEEE Trans. Biomed. Eng.

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“…In this regard, we should recall that our technique admits a straightforward combination with these sort of approaches. Namely, if we assume motion can be well approximated by using external sensors, we would be in a more favorable regime to perform further small-motion adjustments (for instance to mitigate cross-calibration errors [25]). Also, provided motion can be accurately estimated and k-space sampling can be assumed homogeneous enough, our retrospective reconstruction would have a negligible impact in the image quality, which would reduce the need to perform real time modifications to the acquisition sequence.…”

Section: Discussionmentioning

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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“…For rigid body motion these are described according to the Fourier theorems: a translation of the object leads to a phase ramp in the acquired k-space, an object rotation corresponds to a rotation of k-space (113). While translations are relatively easy to correct by applying a phase change to the acquired data, the correction of rotations requires the use of non-Cartesian reconstruction methods (114,115) and includes some sophisticated algorithms (116-119) which are computationally intensive.…”

Section: Artefact Mitigation Strategiesmentioning

confidence: 99%

Motion artifacts in MRI: A complex problem with many partial solutions

Zaitsev

Maclaren

Herbst

2015

Magnetic Resonance Imaging

522

481

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Subject motion during magnetic resonance imaging (MRI) has been problematic since its introduction as a clinical imaging modality. While sensitivity to particle motion or blood flow can be used to provide useful image contrast, bulk motion presents a considerable problem in the majority of clinical applications. It is one of the most frequent sources of artefacts. Over 30 years of research have produced numerous methods to mitigate or correct for motion artefacts, but no single method can be applied in all imaging situations. Instead, a ‘toolbox’ of methods exists, where each tool is suitable for some tasks, but not for others. This article reviews the origins of motion artefacts and presents current mitigation and correction methods. In some imaging situations, the currently available motion correction tools are highly effective; in other cases, appropriate tools still need to be developed. It seems likely that this multifaceted approach will be what eventually solves the motion sensitivity problem in MRI, rather than a single solution that is effective in all situations. This review places a strong emphasis on explaining the physics behind the occurrence of such artefacts, with the aim of aiding artefact detection and mitigation in particular clinical situations.

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Hybrid prospective and retrospective head motion correction to mitigate cross‐calibration errors

Cited by 66 publications

References 26 publications

Motion Correction for MR Cystography by an Image Processing Approach

Motion Correction for MR Cystography by an Image Processing Approach

Sensitivity Encoding for Aligned Multishot Magnetic Resonance Reconstruction

Motion artifacts in MRI: A complex problem with many partial solutions

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