Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion

Abstract

We present the use of an entropy focus criterion to enable automatic focusing of motion corrupted magnetic resonance images. We demonstrate the principle using illustrative examples from cooperative volunteers. Our technique can determine unknown patient motion or use knowledge of motion from other measures as a starting estimate. The motion estimate is used to compensate the acquired data and is iteratively refined using the image entropy. Entropy focuses the whole image principally by favoring the removal of motion induced ghosts and blurring from otherwise dark regions of the image. Using only the image data, and no special hardware or pulse sequences, we demonstrate correction for arbitrary rigid-body translational motion in the imaging plane and for a single rotation. Extension to three-dimensional (3-D) and more general motion should be possible. The algorithm is able to determine volunteer motion well. The mean absolute deviation between algorithm and navigator-echo-determined motion is comparable to the displacement step size used in the algorithm. Local deviations from the recorded motion or navigator-determined motion are explained and we indicate how enhanced focus criteria may be derived. In all cases we were able to compensate images for patient motion, reducing blurring and ghosting.

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

“…Note that in the proposed method, the expanding base only includes the initial base and motion-corrected segments, while the outer k-space regions are zero-filled prior to image reconstruction and metric evaluation. In contrast, the entire k-space, including uncorrected regions, was used for image reconstruction in the original AF method (13,16). As a result, in the former algorithm unaccounted-for motion in regions other than the segment could interfere with the correct motion estimation.…”

“…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.…”

Unbiased Atlas Formation Via Large Deformations Metric Mapping

“…To address this question, we build atlases of increasing population size and analyze their stability with respect to image intensity entropy. Entropy has often been proposed as a good measure of image quality [14,15] where sharp images have relatively low entropy. Let X be a random variable associated with the intensities for a given image and let p X be the probability mass function associated with X. Discrete entropy is defined as the expected uncertainty in X,…”

“…In the context of in MR images, entropy is often used to assess the degree to which an image differs from an ideal where an ideal image intensity histogram consists of a small number of modes representing tissue classes [14,15]. We study the stability of atlases produced by our method by building atlases, of increasing population size, using multiple permutations of images from a database of images.…”

Wireless Accelerometer for Neonatal MRI Motion Artifact Correction

“…The non-wireless based methods have some advantages but also suffer from increased acquisition time, limited visual access and extended reconstruction times, respectively [1][2][3][4][5][6][7][8][9][10][11]. This feasibility study measured 3D motion in real time using a wireless accelerometer to provide correction data for single angle, in-plane rotations which are common in neonatal imaging and difficult to correct with MR based navigator techniques.…”