A practical exposure-equivalent metric for instrumentation noise in x-ray imaging systems

Abstract

The performance of high-sensitivity x-ray imagers may be limited by additive instrumentation noise rather than by quantum noise when operated at the low exposure rates used in fluoroscopic procedures. The equipment-invasive instrumentation noise measures (in terms of electrons) are generally difficult to make and are potentially not as helpful in clinical practice as would be a direct radiological representation of such noise that may be determined in the field. In this work, we define a clinically relevant representation for instrumentation noise in terms of noise-equivalent detector entrance exposure, termed the instrumentation noise-equivalent exposure (INEE), which can be determined through experimental measurements of noise-variance or signal-to-noise ratio (SNR). The INEE was measured for various detectors, thus demonstrating its usefulness in terms of providing information about the effective operating range of the various detectors. A simulation study is presented to demonstrate the robustness of this metric against post-processing, and its dependence on inherent detector blur. These studies suggest that the INEE may be a practical gauge to determine and compare the range of quantum-limited performance for clinical x-ray detectors of different design, with the implication that detector performance at exposures below the INEE will be instrumentation-noise limited rather than quantum-noise limited.

Figure 1

(a) Variance-versus-exposure data and the least-squares regression fit of equation (1) for the determination of INEE for the MA detector. (b) SNR²-versus-exposure data and the least-squares regression fit of equation (3) for the determination of INEE for the MA detector.

Figure 2

Variance-versus-exposure data and the least-squares regression fit of equation (1) for the determination of INEE using the data for the (a) MAF, (b) HSMAF and (c) XII at low exposures.

Figure 3

Square root of INEE versus FWHM of the Gaussian point spread function for the simulation case where constant additive noise was added after the filtering process (detector blurring).

Figure 4

Square root of INEE versus FWHM of the Gaussian point spread function for the simulation case where constant additive noise was added before the Gaussian filtering (postprocess-smoothing).

Figure 5

Plot showing the increase in SNR due to image post-process-smoothing using Gaussian point spread functions of increasing FWHM in the simulation.