Application and assessment of a robust elastic motion correction algorithm to dynamic MRI
- First Online:
The purpose of this study was to assess the performance of a new motion correction algorithm. Twenty-five dynamic MR mammography (MRM) data sets and 25 contrast-enhanced three-dimensional peripheral MR angiographic (MRA) data sets which were affected by patient motion of varying severeness were selected retrospectively from routine examinations. Anonymized data were registered by a new experimental elastic motion correction algorithm. The algorithm works by computing a similarity measure for the two volumes that takes into account expected signal changes due to the presence of a contrast agent while penalizing other signal changes caused by patient motion. A conjugate gradient method is used to find the best possible set of motion parameters that maximizes the similarity measures across the entire volume. Images before and after correction were visually evaluated and scored by experienced radiologists with respect to reduction of motion, improvement of image quality, disappearance of existing lesions or creation of artifactual lesions. It was found that the correction improves image quality (76% for MRM and 96% for MRA) and diagnosability (60% for MRM and 96% for MRA).
KeywordsMotion correction MR mammography MR angiography Motion artifacts Registration
- 6.Tanner C, Schnabel JA, Clarkson MJ, Rueckert D, Hill DLG, Hawkes DJ (2000) Volume and shape preservation of enhancing lesions when applying non-rigid registration to a time series of contrast enhancing MR breast images. In: Delp SL, DiGoia AM, Jaramaz B (eds) Lecture Notes in Computer Science, vol 1935 – Third Int. Conf. on Medical Image Computing and Computer-Assisted Intervention. Springer, Berlin Heidelberg New York, pp 327–337Google Scholar
- 10.Press William H, Teukolsky Saul A, Vetterling William T, Flannery Brian P (1988) Numerical recipes in C. Cambridge University Press, New YorkGoogle Scholar
- 11.Hermosillo Gerardo (2002) Variational methods for multimodal image matching. PhD thesis, Institut National de Recherche en Informatique et en Automatique (INRIA), NiceGoogle Scholar