Periprosthetic osteolysis is a disease triggered by the body's response to tiny wear fragments from total hip replacements (THR), which leads to localized bone loss and disappearance of the trabecular bone texture. We have been investigating methods of temporal radiographic texture analysis (tRTA) to help detect periprosthetic osteolysis. One method involves merging feature measurements at multiple time points using an LDA or BANN. The major drawback of this method is that several cases do not meet the inclusion criteria because of missing data, i.e., missing image data at the necessary time intervals. In this research, we investigated imputation methods to fill in missing data points using feature averaging, linear interpolation, and first and second order polynomial fitting. The database consisted of 101 THR cases with full data available from four follow-up intervals. For 200 iterations, missing data were randomly created to simulate a typical THR database, and the missing points were then filled in using the imputation methods. ROC analysis was used to assess the performance of tRTA in distinguishing between osteolysis and normal cases for the full database and each simulated database. The calculated values from the 200 iterations showed that the imputation methods produced negligible bias, and substantially decreased the variance of the AUC estimator, relative to excluding incomplete cases. The best performing imputation methods were those that heavily weighted the data points closest to the missing data. The results suggest that these imputation methods appear to be acceptable means to include cases with missing data for tRTA.
We have been developing radiographic texture analysis (RTA) for assessing osteoporosis and the related risk of fracture.
Currently, analyses are performed on heel images obtained from a digital imaging device, the GE/Lunar PIXI, that yields
both the bone mineral density (BMD) and digital images (0.2-mm pixels; 12-bit quantization). RTA is performed on the
image data in a region-of-interest (ROI) placed just below the talus in order to include the trabecular structure in the
analysis. We have found that variations occur from manually selecting this ROI for RTA. To reduce the variations, we
present an automatic method involving an optimized Canny edge detection technique and parameterized bone
segmentation, to define bone edges for the placement of an ROI within the predominantly calcaneus portion of the
radiographic heel image. The technique was developed using 1158 heel images and then tested on an independent set of
176 heel images. Results from a subjective analysis noted that 87.5% of ROI placements were rated as "good". In
addition, an objective overlap measure showed that 98.3% of images had successful ROI placements as compared to
placement by an experienced observer at an overlap threshold of 0.4. In conclusion, our proposed method for automatic
ROI selection on radiographic heel images yields promising results and the method has the potential to reduce intra- and
inter-observer variations in selecting ROIs for radiographic texture analysis.
Periprosthetic osteolysis is a disease caused by the body's response to submicron polyethylene debris particles from the hip implant in total hip replacement (THR) patients. It leads to resorption of bone surrounding the implant and deterioration of the bone's trabecular texture, but this is difficult to detect until the later stages of disease progression. Radiographic texture analysis methods have shown promise in detecting this disease at an earlier stage; however,
changes in texture over time may be more important than absolute texture measures. In this research, we investigated temporal radiographic texture analysis (tRTA) methods as possible aids in the detection of osteolysis. A database of 48 THR cases with images available from four different follow-up time intervals was used. ROIs were selected within the osteolytic region of the most recent follow-up image (or comparable region for normal cases) and visually matched on all previous images. Texture features were calculated from the ROIs and then trend analysis was performed using a simple linear regression method, an LDA method and a BANN method. The performance of these three methods was evaluated by ROC analysis. Maximum AUC values of 0.68, 0.78, and 0.88 for the task of distinguishing between osteolysis and normal cases were achieved for the respective tRTA features. These performances were superior to those of our prior stationary, non-temporal texture analysis. The results suggest that tRTA may have the potential to help detect osteolysis at an earlier, more treatable stage.