Automated quality control of ultrasound based on in-air reverberation patterns

Abstract

Ultrasound image degradation originates primarily from transducer defects and potentially undermines reliable image interpretation. Systematic quantitative quality control is often neglected due to the limited resources available for this task. We propose a quantitative quality control based on in-air reverberation images. These images serve as an initial indication of image degradation. They are easily generated for any (curvi-)linear transducer independent of the level of expertise of the operator. Automated analysis is presented to extract quality parameters based on the in-air reverberation pattern. Static images acquired by the clinical user are transferred to a server where analysis is performed. The results are available to the sonographer prior to clinical use and transducer status can be remotely monitored with trend analysis over time. The method was evaluated for normal functioning and defect transducers. A pilot study was performed over a period of three weeks to assess reproducibility and practical feasibility. All reverberation images were successfully analysed for different transducer types and vendor-specific image presentation. The proposed quality parameters are sensitive to signal loss and allow differentiation of type and severity of image degradation. The pilot study was well received by the sonographers for the simplicity of the method and the measurements were consistent over time. The proposed automated analysis method of ultrasound quality control can monitor (curvi-)linear transducer status in the entire hospital, overcoming previous limitations for periodic quality control. Implementation of the method can reduce the number of defective transducers routinely used in clinical practice.

Image Analysis for Ultrasound Quality Assurance

“…These artifacts were, however, not substantial to change the U skew values. 4 The defective transducers showed values of −1.81%, −1.37% and −2.15% respectively. As expected, these were large negative values, that indicated that the transducers were defective.…”

“…26,27 Since ultrasound image quality, either good or bad, it is primarily due to transducer defects, which compromises image interpretation’s reliability. 4,19 -21 However, as noted in a study by Mårtensson et al, 2 the frequency of quality control should be determined by operators based on equipment use, as the transducer’s function is not yet fully understood. Therefore, the ultrasound equipment manufacturer provides a list of recommended quality assurance procedures in the operator’s manual for each machine.…”

“…The three uniformity parameters as assessed statistically from the horizontal intensity profile were 4 :…”

“…Once this correlation is known, reverberation patterns can be used to detect transducer defects, sequentially increasing the diagnostic power of the ultrasound image. 4,10,14,16…”

“…The current study will consider the most recent and appropriate methodology by van Horssen et al, 4 which offers a new automated analysis method using air reverberation images. As part of their research, van Horssen et al, 4 created a dedicated ultrasound module in Python that was configured to receive and process quality control data for ultrasound equipment. Python 17 is an interpreted, high-level, general-purpose programming language that allows various applications and research domains to operate more quickly, as wells as effectively integrate specific systems and programs.…”