Groupwise Elastic Registration by a New Sparsity-Promoting Metric: Application to the Alignment of Cardiac Magnetic Resonance Perfusion Images

• Published in: IEEE transactions on pattern analysis and machine intelligence 2013-11, Vol.35 (11), p.2638-2650
• Main Authors: Cordero-Grande, L., Merino-Caviedes, S., Aja-Fernandez, S., Alberola-Lopez, C.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Biological and medical sciences; cardiac magnetic resonance; Cardiovascular system; Computer science; control theory; systems; Data processing. List processing. Character string processing; Detection, estimation, filtering, equalization, prediction; Entropy; Exact sciences and technology; Feature extraction; Groupwise elastic registration; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image resolution; Information, signal and communications theory; Investigative techniques, diagnostic techniques (general aspects); Magnetic resonance; Magnetic Resonance Angiography - methods; Magnetic Resonance Imaging, Cine - methods; Measurement; Medical sciences; Memory organisation. Data processing; myocardial perfusion; Myocardial Perfusion Imaging - methods; Myocardium; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Radiodiagnosis. Nmr imagery. Nmr spectrometry; registration metric; Reproducibility of Results; Sensitivity and Specificity; Signal and communications theory; Signal, noise; Software; sparseness; Subtraction Technique; Telecommunications and information theory; Vectors
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2013.74

This paper proposes a methodology for the joint alignment of a sequence of images based on a groupwise registration procedure by using a new family of metrics that exploit the expected sparseness of the temporal intensity curves corresponding to the aligned points. Therefore, this methodology is able to tackle the alignment of temporal sequences of images in which the represented phenomenon varies in time. Specifically, we have applied it to the correction of motion in contrast-enhanced first-pass perfusion cardiac magnetic resonance images. The time sequence is elastically registered as a whole by using the aforementioned family of multi-image metrics and jointly optimizing the parameters of the transformations involved. The proposed metrics are able to cope with dynamic changes in the intensity content of corresponding points in the sequence guided by the assumption that these changes allow for a sparse representation in a properly selected frame. Results have shown the statistically significant improvement in the performance of the proposed metric with respect to previous groupwise registration metrics for the problem at hand, which is especially relevant to correct for elastic deformations.