Phillip E McClellan1, Lekha Kesavan2,3, Yujing Wen1, Jason Ina4, Derrick M Knapik4, Robert J Gillespie4, Ozan Akkus1,5,4, Victoria A Webster-Wood2,3,6. 1. P. E. McClellan, Y. Wen, O. Akkus, Department of Mechanical Engineering, Case Western Reserve University, Cleveland, OH, USA. 2. L. Kesavan, V. A. Webster-Wood, Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. 3. L. Kesavan, V. A. Webster-Wood, Department of Biomedical Engineering, Mellon University, Pittsburgh, PA, USA. 4. J. Ina, D. M. Knapik, R. J. Gillespie, O. Akkus, Department of Orthopaedic Surgery, University Hospitals of Cleveland, Cleveland, OH, USA. 5. O. Akkus, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. 6. V. A. Webster-Wood, McGowan Institute of Regenerative Medicine, Carnegie Mellon University, Pittsburgh, PA, USA.
Abstract
BACKGROUND: Fatty infiltration of the rotator cuff occurs after injury to the tendon and results in a buildup of adipose in the muscle. Fatty infiltration may be a biomarker for predicting future injuries and mechanical properties after tendon repair. As such, quantifying fatty infiltration accurately could be a relevant metric for determining the success of tendon repairs. Currently, fatty infiltration is quantified by an experienced observer using the Goutallier or Fuchs staging system, but because such score-based quantification systems rely on subjective assessments, newer techniques using semiautomated analyses in CT and MRI were developed and have met with varying degrees of success. However, semiautomated analyses of CT and MRI results remain limited in cases where only a few two-dimensional slices of tissue are examined and applied to the three-dimensional (3-D) tissue structure. We propose that it is feasible to assess fatty infiltration within the 3-D volume of muscle and tendon in a semiautomated fashion by selecting anatomic features and examining descriptive metrics of intensity histograms collected from a cylinder placed within the central volume of the muscle and tendon of interest. QUESTIONS/PURPOSES: (1) Do descriptive metrics (mean and SD) of intensity histograms from microCT images correlate with the percentage of fat present in muscle after rotator cuff repair? (2) Do descriptive metrics of intensity histograms correlate with the maximum load during mechanical testing of rotator cuff repairs? METHODS: We developed a custom semiautomated program to generate intensity histograms based on user-selected anatomic features. MicroCT images were obtained from 12 adult female New Zealand White rabbits (age 8 to 12 months, weight 3.7 kg ± 5 kg) that were randomized to surgical repair or sham repair of an induced infraspinatus defect. Intensity histograms were generated from images of the operative and contralateral intact shoulder in these rabbits which were presented to the user in a random order without identifying information to minimize sources of bias. The mean and SD of the intensity histograms were calculated and compared with the total percentage of the volume threshold as fat. Patterns of fat identified were qualitatively compared with histologic samples to confirm that thresholding was detecting fat. We conducted monotonic tensile strength-to-failure tests of the humeral-infraspinatus bone-tendon-muscle complex, and evaluated associations between histogram mean and SDs and maximum load. RESULTS: The total percentage of fat was negatively correlated with the intensity histogram mean (Pearson correlation coefficient -0.92; p < 0.001) and positively with intensity histogram SD (Pearson correlation coefficient 0.88; p < 0.001), suggesting that the increase in fat leads to a reduction and wider variability in volumetric tissue density. The percentage of fat content was also negatively correlated with the maximum load during mechanical testing (Pearson correlation coefficient -78; p = 0.001), indicating that as the percentage of fat in the volume increases, the mechanical strength of the repair decreases. Furthermore, the intensity histogram mean was positively correlated with maximum load (Pearson correlation coefficient 0.77; p = 0.001) and histogram SD was negatively correlated with maximum load (Pearson correlation coefficient -0.72; p = 0.004). These correlations were strengthened by normalizing maximum load to account for animal size (Pearson correlation coefficient 0.86 and -0.9, respectively), indicating that as histogram mean decreases, the maximum load of the repair decreases and as histogram spread increases, the maximum load decreases. CONCLUSION: In this ex vivo rabbit model, a semiautomated approach to quantifying fat on microCT images was a noninvasive way of quantifying fatty infiltration associated with the strength of tendon healing. CLINICAL RELEVANCE: Histogram-derived variables may be useful as surrogate measures of repair strength after rotator cuff repair. The preclinical results presented here provide a foundation for future studies to translate this technique to patient studies and additional imaging modalities. This semiautomated method provides an accessible approach to quantification of fatty infiltration by users of varying experience and can be easily adapted to any intensity-based imaging approach. To translate this approach to clinical practice, this technique should be calibrated for MRI or conventional CT imaging and applied to patient scans. Further investigations are needed to assess the correlation of volumetric intensity histogram descriptive metrics to clinical mechanical outcomes.
BACKGROUND: Fatty infiltration of the rotator cuff occurs after injury to the tendon and results in a buildup of adipose in the muscle. Fatty infiltration may be a biomarker for predicting future injuries and mechanical properties after tendon repair. As such, quantifying fatty infiltration accurately could be a relevant metric for determining the success of tendon repairs. Currently, fatty infiltration is quantified by an experienced observer using the Goutallier or Fuchs staging system, but because such score-based quantification systems rely on subjective assessments, newer techniques using semiautomated analyses in CT and MRI were developed and have met with varying degrees of success. However, semiautomated analyses of CT and MRI results remain limited in cases where only a few two-dimensional slices of tissue are examined and applied to the three-dimensional (3-D) tissue structure. We propose that it is feasible to assess fatty infiltration within the 3-D volume of muscle and tendon in a semiautomated fashion by selecting anatomic features and examining descriptive metrics of intensity histograms collected from a cylinder placed within the central volume of the muscle and tendon of interest. QUESTIONS/PURPOSES: (1) Do descriptive metrics (mean and SD) of intensity histograms from microCT images correlate with the percentage of fat present in muscle after rotator cuff repair? (2) Do descriptive metrics of intensity histograms correlate with the maximum load during mechanical testing of rotator cuff repairs? METHODS: We developed a custom semiautomated program to generate intensity histograms based on user-selected anatomic features. MicroCT images were obtained from 12 adult female New Zealand White rabbits (age 8 to 12 months, weight 3.7 kg ± 5 kg) that were randomized to surgical repair or sham repair of an induced infraspinatus defect. Intensity histograms were generated from images of the operative and contralateral intact shoulder in these rabbits which were presented to the user in a random order without identifying information to minimize sources of bias. The mean and SD of the intensity histograms were calculated and compared with the total percentage of the volume threshold as fat. Patterns of fat identified were qualitatively compared with histologic samples to confirm that thresholding was detecting fat. We conducted monotonic tensile strength-to-failure tests of the humeral-infraspinatus bone-tendon-muscle complex, and evaluated associations between histogram mean and SDs and maximum load. RESULTS: The total percentage of fat was negatively correlated with the intensity histogram mean (Pearson correlation coefficient -0.92; p < 0.001) and positively with intensity histogram SD (Pearson correlation coefficient 0.88; p < 0.001), suggesting that the increase in fat leads to a reduction and wider variability in volumetric tissue density. The percentage of fat content was also negatively correlated with the maximum load during mechanical testing (Pearson correlation coefficient -78; p = 0.001), indicating that as the percentage of fat in the volume increases, the mechanical strength of the repair decreases. Furthermore, the intensity histogram mean was positively correlated with maximum load (Pearson correlation coefficient 0.77; p = 0.001) and histogram SD was negatively correlated with maximum load (Pearson correlation coefficient -0.72; p = 0.004). These correlations were strengthened by normalizing maximum load to account for animal size (Pearson correlation coefficient 0.86 and -0.9, respectively), indicating that as histogram mean decreases, the maximum load of the repair decreases and as histogram spread increases, the maximum load decreases. CONCLUSION: In this ex vivo rabbit model, a semiautomated approach to quantifying fat on microCT images was a noninvasive way of quantifying fatty infiltration associated with the strength of tendon healing. CLINICAL RELEVANCE: Histogram-derived variables may be useful as surrogate measures of repair strength after rotator cuff repair. The preclinical results presented here provide a foundation for future studies to translate this technique to patient studies and additional imaging modalities. This semiautomated method provides an accessible approach to quantification of fatty infiltration by users of varying experience and can be easily adapted to any intensity-based imaging approach. To translate this approach to clinical practice, this technique should be calibrated for MRI or conventional CT imaging and applied to patient scans. Further investigations are needed to assess the correlation of volumetric intensity histogram descriptive metrics to clinical mechanical outcomes.
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