Autocorrection in MR imaging: adaptive motion correction without navigator echoes.
ABSTRACT 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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ABSTRACT: PurposeTo develop a registration-based autofocusing (RAF) motion correction technique for high-resolution trabecular bone (TB) imaging and to evaluate its performance on in vivo MR data.Materials and Methods The technique combines serial registration with a previously developed motion correction technique — autofocusing — for automatic correction of subject movement degradation of MR images acquired in longitudinal studies. The method was tested on in vivo images of the distal radius to measure improvements in serial reproducibility of parameters in 12 women (ages 50–75 years), and to compare with the navigator echo-based correction and autofocusing. Furthermore, the technique's ability to optimize the sensitivity to detect simulated bone loss was ascertained.ResultsThe new technique yielded superior reproducibility of image-derived structural and mechanical parameters. Average coefficient of variation across all parameters improved by 12.5%, 27.0%, 33.5%, and 37.0%, respectively, following correction by navigator echoes, autofocusing, and the RAF technique (without and with correction for rotational motion); average intra-class correlation coefficient increased by 1.2%, 2.2%, 2.8%, and 3.2%, respectively. Furthermore, simulated bone loss (5%) was well recovered independent of the choice of reference image (4.71% or 4.86% with respect to using either the original or the image subjected to bone loss) in the time series.Conclusion The data suggest that our technique simultaneously corrects for intra-scan motion corruption while improving inter-scan registration. Furthermore, the technique is not biased by small changes in bone architecture between time-points.J. Magn. Reson. Imaging 2014. © 2014 Wiley Periodicals, Inc.Journal of Magnetic Resonance Imaging 05/2014; · 2.57 Impact Factor
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ABSTRACT: To implement a nonrigid autofocus motion correction technique to improve respiratory motion correction of free-breathing whole-heart coronary magnetic resonance angiography acquisitions using an image-navigated 3D cones sequence. 2D image navigators acquired every heartbeat are used to measure superior-inferior, anterior-posterior, and right-left translation of the heart during a free-breathing coronary magnetic resonance angiography scan using a 3D cones readout trajectory. Various tidal respiratory motion patterns are modeled by independently scaling the three measured displacement trajectories. These scaled motion trajectories are used for 3D translational compensation of the acquired data, and a bank of motion-compensated images is reconstructed. From this bank, a gradient entropy focusing metric is used to generate a nonrigid motion-corrected image on a pixel-by-pixel basis. The performance of the autofocus motion correction technique is compared with rigid-body translational correction and no correction in phantom, volunteer, and patient studies. Nonrigid autofocus motion correction yields improved image quality compared to rigid-body-corrected images and uncorrected images. Quantitative vessel sharpness measurements indicate superiority of the proposed technique in 14 out of 15 coronary segments from three patient and two volunteer studies. The proposed technique corrects nonrigid motion artifacts in free-breathing 3D cones acquisitions, improving image quality compared to rigid-body motion correction. Magn Reson Med, 2013. © 2013 Wiley Periodicals, Inc.Magnetic Resonance in Medicine 09/2013; · 3.40 Impact Factor