A computer-aided detection system for rheumatoid arthritis MRI data interpretation and quantification of synovial activity

Disease assessment and follow-up of rheumatoid arthritis (RA) patients require objective evaluation and quantification. Magnetic resonance imaging (MRI) has a large potential to supplement such information for the clinician, however, time spent on data reading and interpretation slow down developmen...

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Bibliographic Details
Published in:European journal of radiology Vol. 74; no. 3; pp. e67 - e72
Main Authors: Kubassova, Olga, Boesen, Mikael, Cimmino, Marco A., Bliddal, Henning
Format: Journal Article
Language:English
Published: Ireland Elsevier Ireland Ltd 01.06.2010
Subjects:
Algorithms
Arthritis, Rheumatoid - diagnosis
Arthritis, Rheumatoid - etiology
Artificial Intelligence
Computer-aided detection (CAD) tools
Dynamic MRI
Female
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Male
Middle Aged
Musculoskeletal radiology
Pattern Recognition, Automated - methods
Radiology
Reproducibility of Results
Rheumatoid arthritis
Sensitivity and Specificity
Synovial Membrane - pathology
Synovitis - complications
Synovitis - diagnosis
Computer-aided detection (CAD) tools
Musculoskeletal radiology
Dynamic MRI
Rheumatoid arthritis
ISSN:0720-048X, 1872-7727, 1872-7727
Online Access:Get full text
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Summary:Disease assessment and follow-up of rheumatoid arthritis (RA) patients require objective evaluation and quantification. Magnetic resonance imaging (MRI) has a large potential to supplement such information for the clinician, however, time spent on data reading and interpretation slow down development in this area. Existing scoring systems of especially synovitis are too rigid and insensitive to measure early treatment response and quantify inflammation. This study tested a novel automated, computer system for analysis of dynamic MRI data acquired from patients with RA, Dynamika-RA, which incorporates efficient data processing and analysis techniques. 140 MRI scans from hands and wrists of 135 active RA patients and 5 healthy controls were processed using Dynamika-RA and evaluated with RAMRIS. To reduce patient motion artefacts, MRI data were processed using Dynamika-RA, which removed motion in 2D and 3D planes. Then synovial enhancement was visualised and qualified using a novel fully automated voxel-by-voxel analysis based algorithm. This algorithm was used to replace traditional region-of-interest (ROI) and subtraction methods, yielding observer independent quantitative results. Conventional scoring performed by an observer took 30–45 min per dataset. Dynamika-RA reduced motion artefacts, visualised inflammation and quantified disease activity in less than 3 min. Data processing allowed increasing signal to noise ratio by a factor 3. Due to fully automated procedure of data processing, there was no intertest variation in the results. Algorithms incorporated into Dynamika-RA allow for the significant enhancement of data quality through eliminating motion artefacts and reduction of time for evaluation of synovial inflammation.
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ISSN:0720-048X
1872-7727
1872-7727
DOI:10.1016/j.ejrad.2009.04.010