Automatic compensation of motion artifacts in MRI

Atkinson, David; Hill, Derek L. G.; Stoyle, P. N. R.; Summers, Paul; Clare, Stuart; Bowtell, Richard; Keevil, Stephen

doi:10.1002/(sici)1522-2594(199901)41:1<163::aid-mrm23>3.0.co;2-9

Cited by 110 publications

(88 citation statements)

References 13 publications

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“…Eqs. [2] or [3] relates the MR signal s to the proton density at the reference position ρ 0 , via an integral equation of kernel K, comprising the downsampling and the coil sensitivity encoding operators used in parallel imaging, and a Fourier encoding term, modified with physiological motion information. The generalized reconstruction problem amounts to inverting Eq.…”

Section: Generalized Reconstruction Frameworkmentioning

confidence: 99%

Generalized MRI reconstruction including elastic physiological motion and coil sensitivity encoding

Odille

Ĉındea

Mandry

et al. 2008

Magnetic Resonance in Med

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This article describes a general framework for multiple coil MRI reconstruction in the presence of elastic physiological motion. On the assumption that motion is known or can be predicted, it is shown that the reconstruction problem is equivalent to solving an integral equation-known in the literature as a Fredholm equation of the first kind-with a generalized kernel comprising Fourier and coil sensitivity encoding, modified by physiological motion information. Numerical solutions are found using an iterative linear system solver. The different steps in the numerical resolution are discussed, in particular it is shown how over-determination can be used to improve the conditioning of the generalized encoding operator. Practical implementation requires prior knowledge of displacement fields, so a model of patient motion is described which allows elastic displacements to be predicted from various input signals (e.g., respiratory belts, ECG, navigator echoes), after a free-breathing calibration scan. Practical implementation was demonstrated with a moving phantom setup and in two free-breathing healthy subjects, with images from the thoracic-abdominal region. Results show that the method effectively suppresses the motion blurring/ghosting artifacts, and that scan repetitions can be used as a source of over-determination to improve the reconstruction.

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Section: Generalized Reconstruction Frameworkmentioning

confidence: 99%

Generalized MRI reconstruction including elastic physiological motion and coil sensitivity encoding

Odille

Ĉındea

Mandry

et al. 2008

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…As a result, in the former algorithm unaccounted-for motion in regions other than the segment could interfere with the correct motion estimation. The proposed center-out method was shown to be superior when compared with several other base and segment selection methods (14,17). For each k-space segment, a two-step search strategy as previously proposed was used to detect 2D translation (17).…”

Section: Autofocusingmentioning

confidence: 99%

“…Among these, the navigator echo technique collects additional data to measure and compensate for translation and rotation (10 -12). A postprocessing technique termed autofocusing (AF) (or autocorrection) (13)(14)(15)(16) has also been shown to be effective in correcting for both translation and rotation but without the need to collect any additional data. The method attempts to deduce and correct for motion by optimizing an image metric, which is a measure of image sharpness.…”

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confidence: 99%

Image metric‐based correction (autofocusing) of motion artifacts in high‐resolution trabecular bone imaging

Wei

Ladinsky

Wehrli

et al. 2007

Magnetic Resonance Imaging

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Purpose:To evaluate the performance of the autofocusing (AF) motion correction technique in high-resolution trabecular bone imaging where image signal-to noise ratio (SNR) is limited. Materials and Methods:Raw data from 26 clinical threedimensional (3D) wrist exams were motion corrected using AF for both in-plane rotation and translation. Changes in image metrics (a measurement of image sharpness) and structural parameters subsequently computed, were used to gauge the performance of the AF algorithm, and comparisons were made with translation-only navigator-corrected results.Results: On average, AF generated images with higher image sharpness compared to the navigator echo technique. The average normalized gradient squared (NGS) metric improved by 0.40%, 0.73%, and 0.84%, respectively, following translation-only navigator, translation-only AF and combined rotation/translation AF. For all structural parameters, the rotation/translation AF resulted in an approximately two-fold greater change compared to the navigator technique. Conclusion:The data provide evidence that errors from subtle translational and rotational motion in the structural parameters in high-resolution trabecular bone images are alleviated by AF and that the resulting improvements are superior to translation-only 2D navigator correction. OSTEOPOROSIS IS the most common degenerative disease in the elderly. There is now strong evidence that the loss of bone mass is accompanied by a decline in the three-dimensional (3D) trabecular bone network's structural integrity (1-3). However, changes in bone mineral density (BMD) in response to treatment with antiresorptive drugs are often incommensurate with the observed reduction in fracture rates (4,5). Recent advances in high-resolution microMRI provide sufficient detection sensitivity to resolve individual trabeculae at peripheral skeletal sites such as the distal radius (wrist) and tibia (ankle), therefore allowing imaging and structural analysis of the 3D trabecular network at these locations (6,7). It has been shown that the disease-related structural changes at these surrogate sites parallel similar structural changes elsewhere in the skeleton and therefore may serve as markers for disease progression or regression (8,9).A major challenge for in vivo high-resolution trabecular bone imaging is subject motion. Due to the small in-plane pixel size (ϳ140 m) and the relatively long scan time (ϳ12 minutes) required to image a 3D volume, involuntary motion could easily cause displacements of several pixels or greater, even with the use of immobilization devices. The resulting image blurring and ghosting may cause errors in the assessment of the 3D trabecular bone network and ultimately limit the usefulness of the method to predict bone strength.Several approaches have been proposed to compensate for motion artifacts in MRI. Among these, the navigator echo technique collects additional data to measure and compensate for translation and rotation (10 -12). A postprocessing technique termed autofocusing (AF) (or...

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“…A comprehensive correction of the inconsistency is very difficult due to the limited amount of data from two orthogonal directions that are supposed to overlap in k-space. Most correction methods used in EPI or PROPELLER (22)(23)(24)(25)(26) only work for fully sampled data or require reference scans and are therefore not applicable here. We are able to correct the inconsistency in k-space rather accurately using a simple model inspired by a PROPELLER correction method used in Ref.…”

Section: Data Coregistrationmentioning

confidence: 99%

Improving GRAPPA using cross‐sampled autocalibration data

Wang

Liang

King

et al. 2011

Magnetic Resonance in Med

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In conventional generalized autocalibrating partially parallel acquisitions, the autocalibration signal (ACS) lines are acquired with a frequency-encoding direction in parallel to other undersampled lines. In this study, a cross sampling method is proposed to acquire the ACS lines orthogonal to the undersampled lines. This cross sampling method increases the amount of calibration data along the direction, where k-space is undersampled, and especially improves the calibration accuracy when a small number of ACS lines are acquired. The cross sampling method is implemented with swapped frequency and phase encoding gradients. In addition, an iterative coregistration method is also developed to correct the inconsistency between the ACS and undersampled data, which are acquired separately in two orthogonal directions. The same calibration and reconstruction procedure as conventional generalized autocalibrating partially parallel acquisitions is then applied to the corrected data to recover the unacquired k-space data and obtain the final image. Reconstruction results from simulations, phantom and in vivo human brain experiments have distinctly demonstrated that the proposed method, named cross-sampled generalized autocalibrating partially parallel acquisitions, can effectively reduce the aliasing artifacts of conventional generalized autocalibrating partially parallel acquisitions when very few ACS lines are acquired, especially at high outer k-space reduction factors. Magn Reson Med 67:1042-1053, 2012. V C 2011 Wiley Periodials, Inc.Key words: GRAPPA; autocalibration; ACS; cross sampling; kspace registrationThe generalized autocalibrating partially parallel acquisitions (GRAPPA) approach (1) has been widely used in parallel imaging to avoid the estimation of coil sensitivities, which is usually necessary for other approaches such as the sensitivity encoding method (2) and simultaneous acquisition of spatial harmonics method (3). GRAPPA uses a linear combination of the acquired k-space data to reconstruct the missing k-space data. The coefficients used for combination are usually calculated by fitting some acquired autocalibration signal (ACS) lines. When the number of ACS lines is insufficient, aliasing artifacts are present in reconstruction along the undersampling direction. A number of reconstruction methods have been proposed to reduce aliasing artifacts and improve image quality, such as multicolumn multiline interpolation (4), regularization (5), reweighted least squares (6), high-pass filtering (7), cross-validation (8), iterative optimization (9), virtual coil using conjugate symmetric signals (10), multislice weighting (11), and an infinite impulse response model (12).The direction along which ACS data are acquired is actually very important in reconstruction quality. Larger amounts of ACS data usually improve calibration, but on the other hand prolong the imaging time. There have been only a few methods that modify the data acquisition procedure to improve GRAPPA. In Ref. 13, it has been noted that the cali...

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Automatic compensation of motion artifacts in MRI

Cited by 110 publications

References 13 publications

Generalized MRI reconstruction including elastic physiological motion and coil sensitivity encoding

Generalized MRI reconstruction including elastic physiological motion and coil sensitivity encoding

Image metric‐based correction (autofocusing) of motion artifacts in high‐resolution trabecular bone imaging

Improving GRAPPA using cross‐sampled autocalibration data

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