Compound speckle model detects anti‐angiogenic tumor response in preclinical nonlinear contrast‐enhanced ultrasonography. Issue 1 (19th January 2017) - Lowerison, Matthew R.; Tse, Justin J.; Hague, M. Nicole; Chambers, Ann F.; Holdsworth, David W.; Lacefield, James C.
Abstract : Purpose: This paper proposes a method for analyzing the first‐order speckle statistics of nonlinear contrast‐enhanced ultrasound images from tumors. Methods: Contrast signal intensity is modeled as a compound distribution of exponential probability density functions with a gamma weighting function. The gamma probability weighting function serves as an approximation for log‐normally distributed flow velocities in a vascular network. The model was applied to sub‐harmonic bolus‐injection images acquired from a mouse breast cancer xenograft model treated with murine version bevacizumab. Results: The area under curve produced using the compound statistical model could more accurately discriminate anti‐VEGF‐treated tumors from untreated tumors than conventional contrast‐enhanced ultrasound image processing. This result was validated with gold standard histological measures of microvascular density. Fractal vessel geometry was estimated using the gamma weighting function and tested against micro‐CT perfusion casting. Treated tumors had a significantly lower vascular fractal dimension than control tumors. Vascular complexity estimated using the ultrasound compound statistical model performed similarly to micro‐CT fractal dimension for discriminating treated from control tumors. Conclusion: The proposed technique can quantify tumor perfusion and provide an index of vascular complexity, making it a potentially useful addition for clinical detection of vascular normalization in anti‐angiogenic trials.