Influence of ultrasound machine settings on quantitative measures derived from spatial frequency analysis of muscle tissue

Abstract

Background: Ultrasound is a powerful tool for diagnostic purposes and provides insight into both normal and pathologic tissue structure. Spatial frequency analysis (SFA) methods characterize musculoskeletal tissue organization from ultrasound images. Both sonographers in clinical imaging and researchers may alter a minimized range of ultrasound settings to optimize image quality, and it is important to know how these small adjustments of these settings affect SFA parameters. The purpose of this study was to investigate the effects of making small adjustments in a typical default ultrasound machine setting on extracted spatial frequency parameters (peak spatial frequency radius (PSFR), Mmax, Mmax%, and Sum) in the biceps femoris muscle.

Methods: Longitudinal B-mode images were collected from the biceps femoris muscle in 36 participants. The window depth, foci locations, and gain were systematically adjusted consistent with clinical imaging procedures for a total of 27 images per participant. Images were analyzed by identifying a region of interest (ROI) in the middle portion of the muscle belly in a template image and using a normalized two-dimensional cross-correlation technique between the template image and subsequent images. The ROI was analyzed in the frequency domain using conventional SFA methods. Separate linear mixed effects models were run for each extracted parameter.

Results: PSFR was affected by modifications in focus location only (p < 0.001) with differences noted between all locations. Mmax% was influenced by the interaction of gain and focus location (p < 0.001) but was also independently affected by increasing window depth (p < 0.001). Both Mmax and Sum parameters were sensitive to small changes in machine settings with the interaction of focus location and window depth (p < 0.001 for both parameters) as well as window depth and gain (p < 0.001 for both) influencing the extracted values.

Conclusions: Frequently adjusted imaging settings influence some SFA statistics. PSFR and Mmax% appear to be most robust to small changes in image settings, making them best suited for comparison across individuals and between studies, which is appealing for the clinical utility of the SFA method.

Keywords: Hamstring muscles; Imaging; Musculoskeletal; Spatial frequency; Ultrasonography.

Fig. 1

Experimental setup. A) The transducer was placed in the fixation mold at the mid-belly of the biceps femoris muscle. B) Side view of the transducer in the fixation mold

Fig. 2

Representative B-mode images of 5 cm window depth from one participant. The rows correspond to the superficial (top), mid-belly (middle), and deep (bottom) focus locations. The columns correspond to 46% (left), 48% (middle), and 50% (right) gain settings. The yellow boxes correspond to the parent ROI of interest from which SFA parameters were extracted. The ROI was drawn using one representative image (for example, image E) and a normalized two-dimensional cross-correlation technique was used to position the parent ROI for each image (A-I) for subsequent analysis

Fig. 3

Data analysis procedure for extracting spatial frequency analysis (SFA) parameters across images. (A) Once all ultrasound images were collected, all images were reviewed to identify the image to be used as a template. (B) The parent region of interest (ROI) was then drawn on the template image. (C) A normalized two-dimensional cross correlation technique was used to ensure SFA parameters were extracted from the same ROI. (D) All kernels within the image were analyzed. (E) A Fast Fourier Transform (FFT) was applied to each kerenel and SFA parameters were extracted. Steps A-C were repeated for each window depth (5.0, 6.5, and 8.0 cm) and steps D and E were then performed for all image setting combinations across all participants

Fig. 4

Least square mean estimates (squares) with 95% confidence intervals (whiskers) for peak spatial frequency radius. Estimates decreased only with respect to deeper focus locations

Fig. 5

Least square mean estimates (squares) with 95% confidence intervals (whiskers) for Mmax. Estimates in Mmax were influenced in combination with increasing gain and decreasing window depth and in combination with increasing gain and focus location. a.u. = arbitrary units

Fig. 6

Least square mean estimates (squares) with 95% confidence intervals (whiskers) for Sum. Estimates in Sum were influenced in combination with increasing gain and decreasing window depth and in combination with increasing gain and focus location. a.u. = arbitrary units

Fig. 7

Least square mean estimates (squares) with 95% confidence intervals (whiskers) for Mmax%. Estimates decreased with increasing window depth and in combination with increasing gain and focus location