In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, prospective study, uncompressed envelope data (RF data) were collected from 100 patients with focal renal masses using an RS80A ultrasound scanner with B-mode and CEUS. By summing and averaging the Nakagami images formed using sliding windows, we use the average ‘m’ to stratify manually segmented masses, using data from both the B-mode and CEUS scans. Wilcoxon rank sum test using an alpha value of 0.05 was used detect differences between the groups. Logistic regression was used for classification and the area under the receiver operator curve (AUC) was used to assess performance. Among the 100 masses, 40 were benign, 37 were malignant based on histopathology, and 23 were radiologically and clinically presumed malignant but with no pathological proof at the time of data analysis. Univariate analyses showed significant (p<0.01) differences between the benign and non-benign masses on both B-mode and CEUS, with non-benign masses having smaller ‘m’. Predictive models constructed using Nakagami parameters extracted from Bmode and CEUS-based RF scans showed an AUC of 0.67 95% CI: (0.56, 0.78) and 0.61 95% CI: (0.5, 0.73), respectively for discriminating benign from non-benign renal masses. The concordance between the two assessments was 95%. We present a framework for characterizing images using speckle textural properties, for example Nakagami analysis, to aid in objective tissue characterization using ultrasound.
Purpose: Evaluate the feasibility of using a Nakagami model to create an accurate parametric image from ultrasound imaging data for the differentiation of homogenous and heterogeneous texture phantoms. Analysis was done on the raw data i.e., radiofrequency (RF) data collected before any post processing that can affect the images. Materials and methods: The Nakagami parametric image was constructed on demodulated RF data with the sliding window technique to create a map of local parameters. The Nakagami parameter (m) for the entire image was found by averaging all values. By design, when m is greater than 1, the distribution is post-Rayleigh. When m is equal to 1, the distribution is Rayleigh. To test the technique, two agar phantoms were constructed, using varying amounts of flour as the scatterer. The higher amount of flour scatterer was meant to mimic heterogeneous texture and the lesser amount meant to mimic homogeneous texture. Scans were done on each phantom and analyzed for differences in the Nakagami parameter. Results: Phantom 1 displayed a post-Rayleigh distribution (m = 36.1±7.0), while phantom 2 did so, to a lesser extent (m = 1.64±0.12). As the distribution transitions from Rayleigh to post Rayleigh, the scatterers in the sample go from being periodically located/randomly distributed to large numbers of randomly distributed scatterers. Conclusion: Our study suggests that Nakagami parametric based metrics may be used to increase robustness of texture analysis, considering the analysis is done on the raw data before any post processing that can affect the images is introduced.
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