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

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.

Contrast-Enhanced Imaging of Histotripsy Bubble Clouds Using Chirp-Coded Excitation and Volterra Filtering.

Histotripsy is a focused ultrasound therapy that ablates tissue via bubble cloud activity. Real-time ultrasound image guidance is used to ensure safe and effective treatment. Plane-wave imaging enables tracking of histotripsy bubble clouds at a high frame rate but lacks adequate contrast. Furthermore, bubble cloud hyperechogenicity is reduced in abdominal targets, making the development of contrast-specific sequences for deep-seated targets an active area of research. Chirp-coded subharmonic imaging was reported previously to enhance histotripsy bubble cloud detection by a modest 4-6 dB compared to the conventional sequence. Incorporating additional steps into the signal processing pipeline could enhance bubble cloud detection and tracking. In this study, we evaluated the feasibility of combining chirp-coded subharmonic imaging with Volterra filtering for enhancing bubble cloud detection in vitro. Chirped imaging pulses were used to track bubble clouds generated in scattering phantoms at a 1-kHz frame rate. Fundamental and subharmonic matched filters were applied to the received radio frequency signals, followed by a tuned Volterra filter to extract bubble-specific signatures. For subharmonic imaging, the application of the quadratic Volterra filter improved the contrast-to-tissue ratio from 5.18 ± 1.29 to 10.90 ± 3.76 dB, relative to the application of the subharmonic matched filter. These findings demonstrate the utility of the Volterra filter for histotripsy image guidance.