Autocorrection in MR Imaging: Adaptive Motion Correction without Navigator Echoes1

Department of Diagnostic Radiology, Mayo Clinic and Foundation, Rochester, MN 55905, USA.
Radiology (Impact Factor: 6.87). 07/2000; 215(3):904-9. DOI: 10.1148/radiology.215.3.r00jn19904
Source: PubMed


A technique for automatic retrospective correction of motion artifacts on magnetic resonance (MR) images was developed that uses only the raw (complex) data from the MR imager and requires no knowledge of patient motion during the acquisition. The algorithm was tested on coronal images of the rotator cuff in a series of 144 patients, and the improvements in image quality were similar to those achieved with navigator echoes. The results demonstrate that autocorrection can significantly reduce motion artifacts in a technically demanding MR imaging application.

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    • "Aktinson et al. [2] [3] used the entropy criterion to determine the motions. Lin et al. [15] suggested to use normalized gradient squared (NGS) [4], where several clinical results are shown to be comparable to those from the navigator echo based techniques. More recently, to improve algorithm efficiency and robustness, they proposed the EXTRACT [14] method based on extrapolation of the k-space data and its correlation to prior corrected results. "
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    ABSTRACT: In two-dimensional Fourier transform magnetic resonance imaging (2DFT-MRI), patient/object motion during the image acquisition results in ghosting and blurring. These motion artifacts are commonly considered as a major limitation in the MRI community. To correct these artifacts without resorting to additional navigator echoes, most existing methods perform image quality measure to estimate motion; but they may easily fail when the motion is large. Viewed as a blind image restoration problem where the motion point spread function (PSF) is unknown, state-of-the-art restoration algorithms can not be easily applied because they cannot handle a complex PSF kernel that has the same size as the image. To overcome these challenges, we propose a novel approach that exploits the image structure to segment the kernel into several fragments. Based on this kernel representation, determining a kernel fragment can be formulated as a binary optimization problem, where each binary variable represents whether a segment in MR signals is corrupted by a certain motion or not. We establish a graphical model for these variables and estimate the kernel by minimizing an energy functional associated with the model. Experimental results show that the proposed method can provide satisfactory compensation of motion artifacts even when large motions are involved in the MR images.
    2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA; 06/2009
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    • "Various techniques have been proposed to correct for the effects of within-scan subject movement. Such techniques include the acquisition of navigator echoes (Welch et al., 2002a), acquisition of an additional scan with swapped phase encoding direction (Welch et al., 2002b), autocorrection in k space (Manduca et al., 2000), application of the autofocus technique (Atkinson et al., 1999), or use of multiple receiver coils (Atkinson et al., 2004). Although these techniques are highly effective, a complete correction for subject movement is difficult to achieve, especially in areas where the image quality is considerably degraded. "
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    ABSTRACT: T1-weighted anatomical brain scans are routinely used in neuroimaging studies, for example, as anatomical reference for functional data and in brain morphometry studies. Subject motion can degrade the quality of these images. An additional problem is the occurrence of signal dropouts in the case of long echo times and low receiver bandwidths. These problems are addressed in two different studies. In the first study, it is shown that the high scalp signal, which results from the low T1 value of fat, may cause a typical ringing artefact in the presence of head motion. This problem may be enhanced if phased array coils are used for signal reception due to their increased sensitivity in the peripheral head regions. It is shown that this artefact can be avoided by combining certain fat suppression techniques that reduce the scalp signal. In the second study, it is shown that signal dropout affects mainly the orbitofrontal cortex and the temporal lobes, and that a bandwidth of 100 Hz/pixel should be chosen for the investigation of these areas to avoid signal losses while maintaining an acceptable signal-to-noise ratio. Experimental results are based on the MDEFT sequence but can be applied to other T1-weighted sequences like FLASH and MP-RAGE. Furthermore, the presented methods for improving the image quality can be combined with other artefact reduction techniques.
    NeuroImage 03/2006; 29(3):930-7. DOI:10.1016/j.neuroimage.2005.08.004 · 6.36 Impact Factor
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    • "ATIENT motion during a magnetic resonance imaging (MRI) scan can seriously degrade the quality of the image. Among various motion correction techniques previously proposed , most either require acquisition of additional data [1]–[3], or need computationally intensive iterative postprocessing [4], [5]. In this work, a simple and rapid technique for in-plane motion correction is described, based on the point spread functions (PSFs) measured from one or two point sources, co-imaged with the main object. "
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    ABSTRACT: A technique is proposed for correcting both translational and rotational motion artifacts in magnetic resonance imaging without the need to collect additional navigator data or to perform intensive postprocessing. The method is based on measuring the point spread function (PSF) by attaching one or two point-sized markers to the main imaging object. Following the isolation of a PSF marker from the acquired image, translational motion could be corrected directly from the modulation transfer function, without the need to determine the object's positions during the scan, although the shifts could be extracted if desired. Rotation is detected by analyzing the relative displacements of two such markers. The technique was evaluated with simulations, phantom and in vivo experiments.
    IEEE Transactions on Medical Imaging 10/2005; 24(9):1170-6. DOI:10.1109/TMI.2005.853235 · 3.39 Impact Factor
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