Quantitative measurement of cartilage volume is possible using two-dimensional magnetic resonance imaging data sets.

PURPOSE: 3D Magnetic resonance imaging (MRI) scans are generally used for quantitative cartilage measurements in knee osteoarthritis. However, a great deal of MRI data is from 2D scans, often thought to be unsuitable for quantitative cartilage assessment. The goal of our study was to demonstrate that mLACS, a modified version of the Local Area Cartilage Segmentation (LACS) method, could be used to measure cartilage volume on 2D MRI images. METHODS: We studied 301 randomly selected subjects from the OA Biomarkers Consortium FNIH Study, a nested case-control study within the Osteoarthritis Initiative (OAI). The study comprised four subgroups based on radiographic and pain progression. We compared mLACS applied to 2D TSE scans to LACS on 3D DESS data. The Pearson's correlation coefficient was used to establish agreement between LACS and mLACS, standardized response means (SRMs) for responsiveness, and intra-class correlation coefficients (ICCs) to measure reader precision. Logistic regression in a case/control analysis was used to compare the clinical validity between the two methods. RESULTS: We found R2 = 0.76 for the correlation between LACS and mLACs. For LACS, the responsiveness was SRM = 0.49 compared to 0.39 for mLACS. The odds ratios (OR) for the primary case/control analyses were 1.62 for LACS and 1.78 for mLACS. The intra and inter reader reproducibility values for mLACS were ICC = 0.90 and 0.86, respectively. CONCLUSION: This study has demonstrated that a reproducible, responsive, and clinically valid quantitative measurement of cartilage volume can be made using 2D TSE scans with a modest loss of responsiveness compared to 3D scans.

Quantitative measurement of medial femoral knee cartilage volume - analysis of the OA Biomarkers Consortium FNIH Study cohort.

OBJECTIVE: Large studies of knee osteoarthritis (KOA) require well-characterized efficient methods to assess progression. We previously developed the local-area cartilage segmentation (LACS) software method, to measure cartilage volume on magnetic resonance imaging (MRI) scans. The present study further validates this method in a larger patient cohort and assesses predictive validity in a case-control study. METHOD: The OA Biomarkers Consortium FNIH Project, a case-control study of KOA progression nested within the Osteoarthritis Initiative (OAI), includes 600 subjects in four subgroups based on radiographic and pain progression. Our software tool measured change in medial femoral cartilage volume in a central weight-bearing region. Different sized regions of cartilage were assessed to explore their sensitivity to change. The readings were performed on MRI scans at the baseline and 24-month visits. We used standardized response means (SRMs) for responsiveness and logistic regression for predictive validity. RESULTS: Cartilage volume change was associated strongly with radiographic progression (odds ratios (OR) = 4.66; 95% confidence intervals (CI) = 2.85-7.62). OR were significant but of lesser magnitude for the combined radiographic and pain progression outcome (OR = 1.70; 95% CI = 1.40-2.07). For the full 600 subjects, theSRM was -0.51 for the largest segmented area. Smaller areas of cartilage segmentation were also able to predict the case-control status. The average reader time for the largest area was less than 20 min per scan. Smaller areas could be assessed with less reader time. CONCLUSION: We demonstrated that the LACS method is fast, responsive, and associated with radiographic and pain progression, and is appropriate for existing and future large studies of KOA.

Integration of accelerated MRI and post-processing software: a promising method for studies of knee osteoarthritis.

OBJECTIVE: Magnetic resonance imaging (MRI) is a widely used imaging modality for studies of knee osteoarthritis (OA). Compared to radiography, MRI offers exceptional soft tissue imaging and true three-dimensional (3D) visualization. However, MRI is expensive both due to the cost of acquisition and evaluation of the images. The goal of our study is to develop a new method to address the cost of MRI by combining innovative acquisition methods and automated post-processing software. METHODS: Ten healthy volunteers were scanned with three different MRI protocols: A standard 3D dual-echo steady state (DESS) pulse sequence, an accelerated DESS (DESSAcc), acquired at approximately half the time compared to DESS, and a multi-echo time DESS (DESSMTE), which is capable of producing measurements of T2 relaxation time. A software tool was used to measure cartilage volume. Accuracy was quantified by comparing DESS to DESSAcc and DESSMTE and precision was measured using repeat readings and acquisitions. T2 precision was determined using duplicate DESSMTE acquisitions. Intra-class correlation coefficients (ICCs), root-mean square standard deviation (RMSSD), and the coefficient of variation (CoV) were used to quantify accuracy and precision. RESULTS: The accuracies of DESSAcc and DESSMTE were CoV = 3.7% and CoV = 6.6% respectively, while precision was 3.8%, 3.0%, and 3.1% for DESS, DESSAcc and DESSMTE. T2 repositioning precision was 5.8%. CONCLUSION: The results demonstrate that accurate and precise quantification of cartilage volume is possible using a combination of substantially faster MRI acquisition and post-processing software. Precise measurements of cartilage T2 and volume can be made using the same acquisition.

Time-lapse interferometry and contouring using television systems.

A novel approach to time-lapse interferometry and contouring is introduced that uses television rather than photographic techniques. A first interferogram is stored on a video storage device such as a magnetic disk. After a change in the stress of the object, a second interferogram is electronically compared to the stored pattern to form a signal representative of the fringe pattern caused by the deformation. This signal may be directly displayed on a cathode-ray tube or employed by some automatic inspection device.