Authors:
Pouyan Mohajerani
1
;
Reinhard Meier
2
;
Ernst Rummeny
2
and
Vasilis Ntziachristos
1
Affiliations:
1
Institute for Biological and Medical Imaging and Technische Universität München and Helmholtz Zentrum München, Germany
;
2
Department of Radiology, Klinikum Rechts der Isar and Technische Universität München, Germany
Keyword(s):
Optical Imaging, Fluorescence, Rheumatoid Arthritis (RA), Inflammation, Indocynine Green (ICG), Planar Illumination, Near-Infrared Dyes, Spatiotemporal Analysis, Principal Component Analysis (PCA).
Related
Ontology
Subjects/Areas/Topics:
Bioimaging
;
Biomedical Engineering
;
Biophotonics
;
Image Processing Methods
;
Medical Imaging and Diagnosis
;
Ultrasound and Optical Imaging
Abstract:
Successful detection of rheumatoid arthritis (RA) at the early stages of development can significantly
enhance the chances of effective therapy. The early onset of RA is often marked with inflammation of the
synovial lining of the joint, a condition known as synovitis. Effective imaging of synovitis is therefore of
critical importance. While dynamic, contrast-enhanced magnetic resonance imaging (MRI) is capable of
effective imaging of synovitis, it is a costly modality. As an alternative, inexpensive approach, optical
imaging post injection of the near-infrared fluorescent dye indocynine green (ICG) has been recently
proposed for imaging RA. Evaluation of the obtained optical images is performed via examination by
trained human readers. However, optical imaging has yet to achieve the diagnostic accuracy of MRI. In this
paper we present a method for automatic evaluation of the fluorescence images and compare its
performance with the human-based evaluation. Our method relies on our pre
vious work on spatiotemporal
analysis of image sequence with principal component analysis (PCA) to seek synovitis signal components
with the help of a segmentation method. The results for a group of 600 joints, obtained from 20 patients,
suggest improved diagnostic performance using the automatic approach in comparison to human-based
evaluation.
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