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Early experience with automated B-mode quality assurance tests.

N J Dudley¹, N M Gibson²

¹Medical Physics Department, United Lincolnshire Hospitals NHS Trust, Lincoln LN2 5QY.

Ultrasound (Leeds, England)

|July 20, 2016

Summary

Automated image quality analysis effectively detects scanner performance changes in quality assurance programs. This method demonstrates efficacy in identifying faults, though further optimization is needed for routine fault detection.

Keywords:

Quality assurance image processing test object

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Area of Science:

Medical Imaging
Quality Assurance
Diagnostic Ultrasound

Background:

Quality assurance (QA) programs are crucial for medical imaging but lack robust evidence of efficacy.
Automated image analysis offers a potential solution for objective and consistent QA.

Purpose of the Study:

To assess the efficacy of an automated image quality analysis method in a QA program.
To determine if automated analysis can detect changes in scanner performance.

Main Methods:

Analysis of test object images measuring lateral resolution, low contrast penetration, slice thickness, and grey-scale contrast/visibility.
Investigation of known/suspected scanner faults and review of routine QA results.

Main Results:

Automated analysis detected changes in performance variables corresponding to known/suspected scanner faults.
Image shadowing affected resolution and grey-scale visibility; user-reported quality deterioration correlated with changes in slice thickness, lateral resolution, and grey-scale contrast.
A probe failure was identified due to an unrecorded change in default settings.

Conclusions:

The automated image quality analysis method provides evidence of efficacy for scanner performance testing.
Further research is needed to optimize the method for prospective fault detection and reduce confounding factors.