Spectral analysis of ultrasound radiofrequency backscatter for the identification of epicardial adipose tissue

Abstract

Purpose: The coronary arteries are embedded in a layer of fat known as epicardial adipose tissue (EAT). The EAT influences the development of coronary artery disease (CAD), and increased EAT volume can be indicative of the presence and type of CAD. Identification of EAT using echocardiography is challenging and only sometimes feasible on the free wall of the right ventricle. We investigated the use of spectral analysis of the ultrasound radiofrequency (RF) backscatter for its potential to provide a more complete characterization of the EAT. Approach: Autoregressive (AR) models facilitated analysis of the short-time signals and allowed tuning of the optimal order of the spectral estimation process. The spectra were normalized using a reference phantom and spectral features were computed from both normalized and non-normalized data. The features were used to train random forests for classification of EAT, myocardium, and blood. Results: Using an AR order of 15 with the normalized data, a Monte Carlo cross validation yielded accuracies of 87.9% for EAT, 84.8% for myocardium, and 93.3% for blood in a database of 805 regions-of-interest. Youden's index, the sum of sensitivity, and specificity minus 1 were 0.799, 0.755, and 0.933, respectively. Conclusions: We demonstrated that spectral analysis of the raw RF signals may facilitate identification of the EAT when it may not otherwise be visible in traditional B-mode images.

Keywords: epicardial adipose tissue; radiofrequency signals; spectral analysis; tissue characterization; ultrasound.

Fig. 1

A screen capture of custom Python-based program for ROI selection and review is shown. Seven example ROIs are illustrated in both the rectangular format image created from the raw signals (left) and the scan-converted image (right). Red is used for ROIs labeled as fat, blue is used for muscle, and green is used for blood. The dashed line represents the location of the A-line shown in Fig. 2.

Fig. 2

An example RF signal is shown, with color-coded vertical lines indicating the starting and stopping sample for three of the example ROIs shown in Fig. 1. The location of this A-line in the B-mode image in Fig. 1 is indicated with the white dashed line.

Fig. 3

Plots of the AR order range assessment. (a) MSE. (b) AIC. (c) FPE. (d) MDL. The MSE is significantly reduced by order 3 and the penalty associated with order in AIC, FPE, and MDL only modestly increases to order 35, the upper limit based on the smallest ROI. Based on this data, an order range of 5 to 35 was chosen for assessment.

Fig. 4

Diagram illustrating normalized and unnormalized spectra with spectral parameters indicated where possible. The unnormalized spectra is shown in gray, phantom spectra in dashed-gray, and normalized spectra in black. The line fit to the PSD data and used for calculation of the slope and $Y$-intercept is also shown, as indicated in the legend. Example spectral parameters are indicated where possible: (1) maximum power (dB), (2) frequency of maximum power (MHz), (3) mean power (dB), (4) slope in dB/MHz, (5) $y$-intercept (dB), (6) mid-band power (dB), (7) mid-band frequency (MHz), (8) the depth of the ROI (cm) (not shown), and (9) integrated backscatter (dB) (not shown).

Fig. 5

Example plot of OOB error versus number of trees. The beginning of the plateau is reached by $\sim 30$ classification trees in the random forest. Therefore, 30 trees were used in the random forests presented in this study.

Fig. 6

Plots of mean ($\pm \text{standard deviation}$) accuracy and Youden’s Index (YI) versus AR order. YI is shown in (a) and accuracy is shown in (b). The accuracy and YI suggest an AR order of 15-20 may be most appropriate for this application and an order of 15 was chosen.

Fig. 7

Plots of accuracy and YI for the FFT-based approach, Welch’s periodogram, and the AR model with order 15. The AR model had better accuracy and YI in (a) fat, (b) blood, and (c) muscle classification.

Fig. 8

Relative predictive importance for one example random forest using the 20-dB bandwidth and spectral parameters from the normalized PSD. The difference between OOB error before and after permuting the individual feature is used to compute the relative importance, providing indication of the relative value of the specific parameter in the classification.

Fig. 9

(a) Example short axis cardiac magnetic resonance image and (b) corresponding parasternal short axis echocardiogram. The images are from the same volunteer subject and have been cropped and rotated for approximate alignment. The yellow arrow shows fat on the free wall of the right ventricle, which can often be seen in the ultrasound. The orange arrow shows fat in the interventricular sulci, which is generally not distinguishable.