A Deep Learning-Based Approach to Characterize Skull Physical Properties: A Phantom Study

Abstract

Transcranial ultrasound imaging is a popular method to study cerebral functionality and diagnose brain injuries. However, the detected ultrasound signal is greatly distorted due to the aberration caused by the skull bone. The aberration mechanism mainly depends on thickness and porosity, two important skull physical characteristics. Although skull bone thickness and porosity can be estimated from CT or MRI scans, there is significant value in developing methods for obtaining thickness and porosity information from ultrasound itself. Here, we extracted various features from ultrasound signals using physical skull-mimicking phantoms of a range of thicknesses with embedded porosity-mimicking acoustic mismatches and analyzed them using machine learning (ML) and deep learning (DL) models. The performance evaluation demonstrated that both ML- and DL-trained models could predict the physical characteristics of a variety of skull phantoms with reasonable accuracy. The proposed approach could be expanded upon and utilized for the development of effective skull aberration correction methods.

Keywords: characterization; deep learning; photoacoustic; porosity; skull; thickness.

FIGURE 1

Skull phantoms and signal acquisition from same. (a) Top view of samples with different concentrations of poppy seeds (10%–22.5%). (b) Side view of samples showing samples of different thicknesses. (c) Schematic of the experimental setup used to obtain the signals. (d) An example of the acoustic signal obtained from the samples. (e) Thickness and calculated porosity of 180 phantoms. (f) Relationship between poppy seed concentration and sample porosity as empirically measured.

FIGURE 2

A schematic overview of the FFN and CNN architectures used in this study. (a) FFN architecture; DL#1 (8 units) and DL#2 (4 units) are both dense layers. (b) CNN architecture; CL #1 represents a convolutional layer (8 filters), FL represents a Flatten layer, and DL represents a dense layer (4 units). In (b), the dotted lines illustrate how each part of the input is processed by different filters in the convolutional layers, visually representing the learning process and the interaction of various filters with different regions of the input data.

FIGURE 3

Evaluation of the performance of thickness prediction on the test data. (a) best ML architecture (MLR: multiple LR) and (b) best DL architecture (FFN‐F). Percent contributions of the most significant features are shown for the ML architecture. Multiple LR: multiple linear regression; FFN‐F: feed‐forward network (features as input).

FIGURE 4

Evaluation of the performance of porosity prediction on the test data. (a) best ML architecture (RLR: Ridge LR), and (b) best DL architecture (CNN‐E). Percent contributions of the most significant features are shown for the ML architecture. Ridge LR: ridge linear regression. CNN‐E: convolutional neural network (envelope as input). SATT: SA‐Total‐T, FSPT: FWHM‐SP‐T, SATF: SA‐Total‐F, SAPF: SA‐SP‐F, FSPF: FWHM‐SP‐F.