3D ultrasound biomicroscopy for assessment of cartilage repair tissue: volumetric characterisation and correlation to established classification systems.

Objective and sensitive assessment of cartilage repair outcomes lacks suitable methods. This study investigated the feasibility of 3D ultrasound biomicroscopy (UBM) to quantify cartilage repair outcomes volumetrically and their correlation with established classification systems. 32 sheep underwent bilateral treatment of a focal cartilage defect. One or two years post-operatively the repair outcomes were assessed and scored macroscopically (Outerbridge, ICRS-CRA), by magnetic resonance imaging (MRI, MOCART), and histopathology (O'Driscoll, ICRS-I and ICRS-II). The UBM data were acquired after MRI and used to reconstruct the shape of the initial cartilage layer, enabling the estimation of the initial cartilage thickness and defect volume as well as volumetric parameters for defect filling, repair tissue, bone loss and bone overgrowth. The quantification of the repair outcomes revealed high variations in the initial thickness of the cartilage layer, indicating the need for cartilage thickness estimation before creating a defect. Furthermore, highly significant correlations were found for the defect filling estimated from UBM to the established classification systems. 3D visualisation of the repair regions showed highly variable morphology within single samples. This raises the question as to whether macroscopic, MRI and histopathological scoring provide sufficient reliability. The biases of the individual methods will be discussed within this context. UBM was shown to be a feasible tool to evaluate cartilage repair outcomes, whereby the most important objective parameter is the defect filling. Translation of UBM into arthroscopic or transcutaneous ultrasound examinations would allow non-destructive and objective follow-up of individual patients and better comparison between the results of clinical trials.

Articular cartilage degeneration classification by means of high-frequency ultrasound.

CONTEXT: To date only single ultrasound parameters were regarded in statistical analyses to characterize osteoarthritic changes in articular cartilage and the potential benefit of using parameter combinations for characterization remains unclear. OBJECTIVE: Therefore, the aim of this work was to utilize feature selection and classification of a Mankin subset score (i.e., cartilage surface and cell sub-scores) using ultrasound-based parameter pairs and investigate both classification accuracy and the sensitivity towards different degeneration stages. DESIGN: 40 punch biopsies of human cartilage were previously scanned ex vivo with a 40-MHz transducer. Ultrasound-based surface parameters, as well as backscatter and envelope statistics parameters were available. Logistic regression was performed with each unique US parameter pair as predictor and different degeneration stages as response variables. The best ultrasound-based parameter pair for each Mankin subset score value was assessed by highest classification accuracy and utilized in receiver operating characteristics (ROC) analysis. RESULTS: The classifications discriminating between early degenerations yielded area under the ROC curve (AUC) values of 0.94-0.99 (mean ± SD: 0.97 ± 0.03). In contrast, classifications among higher Mankin subset scores resulted in lower AUC values: 0.75-0.91 (mean ± SD: 0.84 ± 0.08). Variable sensitivities of the different ultrasound features were observed with respect to different degeneration stages. CONCLUSIONS: Our results strongly suggest that combinations of high-frequency ultrasound-based parameters exhibit potential to characterize different, particularly very early, degeneration stages of hyaline cartilage. Variable sensitivities towards different degeneration stages suggest that a concurrent estimation of multiple ultrasound-based parameters is diagnostically valuable. In-vivo application of the present findings is conceivable in both minimally invasive arthroscopic ultrasound and high-frequency transcutaneous ultrasound.

3-d high-frequency ultrasound improves the estimation of surface properties in degenerated cartilage.

High-frequency ultrasound (US) surface parameters are well known to be sensitive to degenerative changes in cartilage tissue, but estimates deteriorate if the sample is inclined. We propose 3-D US to precisely estimate the local surface and inclination. For this purpose, the most common ultrasonic surface parameters ultrasound roughness index and integrated reflection coefficient were extended to 2-D surface measurements. Tissue-mimicking phantoms and human cartilage samples with varying degrees of degeneration were measured using a 40-MHz transducer. Characteristic inclination dependencies of the parameters aided in the distinction between specular reflected or backscattered signal origins and allowed a restriction to suitable local inclinations. In the application to cartilage, comparisons with histologic grading (structural Mankin-score) depicted a statistically significant (p < 0.05) increase of US roughness index for scores larger than 0 and decrease of integrated reflection coefficient for scores larger than 1. The presented findings will increase the reliability of ultrasonic surface parameters and can in principal be applied in vivo.

Prediction of the intramuscular fat content in loin muscle of pig carcasses by quantitative time-resolved ultrasound.

A novel method for non-destructive intramuscular fat (IMF) estimation via spectral ultrasound backscatter analysis of signals obtained from pig carcasses early post mortem is described. A commercial hand-held ultrasound device (center frequency: 2.7 MHz) was modified to focus the sound beam to the longissimus muscle at the 2nd/3rd last rib. Time-resolved ultrasound backscatter signals of loin muscle were recorded 45 min p.m. on 82 pig carcass sides. Backfat width (d(BF)=18.9±3.8 mm) and muscle attenuation (α(muscle)=.77±.15 dB MHz(-1) cm(-1)) were assessed from the measured pulse echo data. Other propagation properties of skin, backfat and muscle tissue obtained in a previous investigation were incorporated into the signal pre-processing to minimize parameter estimation artifacts. Spectral and cepstral parameters were derived from time-gated backscattered signals measured in the central muscle region. The range of intramuscular fat (IMF) determined by ether extraction was representative for German pig populations (.7%≤IMF(chem)≤3.6%, coefficient of variation CV(IMF(chem))=44.8%). Variations of IMF were associated with variations of backfat width (CV(d(BF))=20.2%), muscle attenuation (CV(α(muscle))=19.3%), and slope of the backscattered amplitude spectrum (CV(m)=28.8%). A full cross validated multiple linear regression model using these parameters resulted in good predictability of IMF(chem) (R(2)=.76, RMSEP=.34%). Among all tested carcasses, 73% could be correctly classified into one of three IMF classes (LOW: <1%, MID: 1-2%, HIGH: >2%). Using a single threshold (2% IMF), about 92% of all carcasses were correctly classified. With respect to the inherent variability of IMF within a single muscle and the different tissue volumes used for the chemical and ultrasound based IMF estimations the remaining prediction errors are acceptable. Compared to previous ultrasound based studies, the number of acoustic parameters used for the IMF prediction could be reduced. Moreover, the used parameters are based on time-of-flight and spectral slope estimations, which are i) more robust with respect to measurement artifacts and ii) have a causal link to structural variations associated with IMF variations in pork loin.