Automatic motion correction of clinical shoulder MR images

Armando Manduca; Kiaran P. McGee; Edward B. Welch; Joel P. Felmlee; Richard L. Ehman

doi:10.1117/12.348591

21 May 1999 Automatic motion correction of clinical shoulder MR images

Armando Manduca, Kiaran P. McGee, Edward B. Welch, Joel P. Felmlee, Richard L. Ehman

Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348591
Event: Medical Imaging '99, 1999, San Diego, CA, United States

Abstract

A technique for the automatic correction of motion artifacts in MR images was developed. The algorithm uses only the raw (complex) data from the MR scanner, and requires no knowledge of the patient motion during the acquisition. It operates by searching over the space of possible patient motions and determining the motion which, when used to correct the image, optimizes the image quality. The performance of this algorithm was tested in coronal images of the rotator cuff in a series of 144 patients. A four observer comparison of the autocorrelated images with the uncorrected images demonstrated that motion artifacts were significantly reduced in 48% of the cases. The improvements in image quality were similar to those achieved with a previously reported navigator echo-based adaptive motion correction. The results demonstrate that autocorrelation is a practical technique for retrospectively reducing motion artifacts in a demanding clinical MRI application. It achieves performance comparable to a navigator based correction technique, which is significant because autocorrection does not require an imaging sequence that has been modified to explicitly track motion during acquisition. The approach is flexible and should be readily extensible to other types of MR acquisitions that are corrupted by global motion.

Citation Download Citation

Armando Manduca, Kiaran P. McGee, Edward B. Welch, Joel P. Felmlee, and Richard L. Ehman "Automatic motion correction of clinical shoulder MR images", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348591

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available