Hermite-scan imaging for differentiating glioblastoma from normal brain: Simulations and ex vivo studies for applications in intra-operative tumor identification

Abstract

Hermite-scan (H-scan) imaging is a tissue characterization technique based on the analysis of raw ultrasound radio frequency (RF) echoes. It matches the RF echoes to Gaussian-weighted Hermite polynomials of various orders to extract information related to scatterer diameter. It provides a color map of large and small scatterers in the red and blue H-scan image channels, respectively. H-scan has been previously reported for characterizing breast, pancreatic, and thyroid tumors. The present work evaluated H-scan imaging to differentiate glioblastoma tumors from normal brain tissue ex vivo. First, we conducted 2-D numerical simulations using the k-wave toolbox to assess the performance of parameters derived from H-scan images of acoustic scatterers (15–150 μm diameters) and concentrations (0.2%–1% w/v). We found that the parameter intensity-weighted percentage of red (IWPR) was sensitive to changes in scatterer diameters independent of concentration. Next, we assessed the feasibility of using the IWPR parameter for differentiating glioblastoma and normal brain tissues (n = 11 samples per group). The IWPR parameter estimates for normal tissue (44.1% ± 1.4%) were significantly different (p < 0.0001) from those for glioblastoma (36.2% ± 0.65%). These findings advance the development of H-scan imaging for potential use in differentiating glioblastoma tumors from normal brain tissue during resection surgery.