Assessing imaging performance of ultrasound systems using a random hypoechoic sphere phantom with freehand scanning

Diagnostic ultrasound is rapidly evolving, and the increasing complexity of ultrasound systems underscores the importance of robust quality assurance (QA) and quality control (QC) methods. Current methods typically rely on manual image acquisition and subjective evaluation, making them operator-dependent, poorly reproducible, and challenging for longitudinal tracking. Efforts have been made to develop automated ultrasound QA/QC methods for more quantitative and reproducible outcomes. One such approach utilized the Random Hypoechoic Sphere Phantom (RHSP) to evaluate the detectability of ultrasound systems by measuring the human observer-related Lesion Signal-to-Noise Ratio (LSNR). A practical manual scanning method was developed for the RHSP, which eliminated the need for a mechanical guide to control transducer translation. However, this approach requires maintaining a uniform translation speed and a small translation distance between successive images. Broader adoption of the RHSP also requires easily accessible automated analysis software.

To improve the practicality of using the RHSP for ultrasound performance evaluation in clinical settings, we developed an automated analysis method tailored for freehand scanning. We validated the method by evaluating ultrasound system performance under various acquisition settings, identifying sources of variability, and analyzing their impact on LSNR measurements.

The RHSP consisted of 2 mm diameter spheres at 20% volume fraction. It was scanned by moving the transducer by freehand during a cine-loop acquisition. Volumetric images were generated by stacking the elevational frames. After denoising and homogeneity correction, the spheres were segmented in 3D using depth-adaptive thresholds from a Gaussian-Mixture Model. LSNR was computed for each segmented sphere, and LSNRs at similar depth were averaged to generate an LSNR vs. depth curve. The algorithm was validated by computing the LSNR vs. depth and sphere-count vs. depth curves for various acquisition settings, with the following factors changing one at a time: nominal frequency, imaging modes and compound techniques, transmit power, gain, dynamic range, and transmit focal depth. Variability of the algorithm was assessed using repeated scans performed by different operators and at various transducer translation speeds.

All LSNR results closely matched visual assessments and followed expectations when changing acquisition settings. LSNR values improved with higher nominal frequency, harmonic imaging with compound, higher transmit power, and decreased dynamic range; gain had minimal effect. The best LSNR also aligned with the transmit focal depths set by the operator. The variabilities of LSNR were from 2.5% to 6.9% intra-operator, 5.2% inter-operator, and 5.5% due to transducer translation speed. These variabilities of the LSNR were much smaller than those of the corresponding sphere counts, demonstrating the proposed algorithms' robustness against potential variations produced by freehand scanning.

The automated analysis method for the RHSP with freehand scanning provides an accurate and stable quantitative assessment of lesion detectability for evaluating ultrasound performance, and is feasible in clinical settings. It demonstrated robustness by achieving approximately 5% variability in LSNR across various transducer translation speeds and different operators. With such robustness, this approach simplifies ultrasound performance evaluation and holds promise for integration into routine ultrasound QA/QC in a clinical environment.