Ultrasound-Scattering Models Based On Quantitative Acoustic Microscopy Of Fresh Samples And Unstained Fixed Sections From Cancerous Human Lymph Nodes

Because of time and cost constraints, current histological methods are unable to detect all clinically significant lymph-node metastases efficiently. Therefore, in previous studies, quantitative ultrasound (QUS) methods were developed to detect metastatic regions in freshly excised sentinel lymph nodes from breast-cancer patients using a 26-MHz transducer. A Gaussian form factor was used to obtain QUS estimates for cancer detection. In this study, quantitative acoustic microscopy (QAM) at 250 MHz was performed to obtain 2D maps of speed of sound (c), attenuation (A) and acoustic impedance (Z) of nodal tissue microstructure with a spatial resolution of 7 mu m. These maps were used to develop new ultrasound-scattering models to improve understanding of ultrasound scattering in lymph nodes. In total, 17 12-mu m thick sections from 12 lymph nodes were scanned using a custom-built QAM system based on an F-1.16, 250-MHz transducer (160-MHz bandwidth). Radiofrequency (RF) signals were digitized at 2.5 GHz with 12-bit accuracy, and 2D QAM maps of Z, A, and c were generated using custom-developed, signal-processing algorithms. Scanned samples were stained using hematoxylin and eosin and imaged by light microscopy. 2D Z maps (2DZMs) were then processed to yield a novel lymph-node-specific scattering model (i.e., form factor), which then was compared to the currently used Gaussian model. The new model was obtained by optimizing an exponential model fit to the Fourier transform of the autocorrelation of the 2DZMs that assumed isotropic scattering. The study illustrates that fine-resolution maps of acoustic properties of lymph nodes can improve the model of ultrasound scattering at 26 MHz. The new lymph-node-specific, ultrasound-scattering model is expected to improve current QUS approaches for detecting metastatic regions in freshly excised sentinel lymph nodes from breast-cancer patients.