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.

Cone-beam CT reconstruction can be performed at lower integral dose, by using a non-uniform beam filter between the x-ray source and the patient to obtain good image quality within an ROI with minimal artifacts. To evaluate the method, a human head phantom was placed on a rotary stage. Cone-beam projection images of the phantom were obtained with and without an ROI filter (dose reduction factor ~7). A mapping function was established to equalize the intensity outside the ROI (to compensate for the attenuation by the filter) to the intensity inside by assuming that those features lying both inside and outside very close to the edge of the ROI are the same. Reconstructed images were obtained using equalized projection images for 2 cases: one in which the outside region was smoothed using an averaging filter and the other with no smoothing outside. In addition, a third case was simulated by calculating the average pixel value inside the ROI for each image and assigning this value to all pixels outside the ROI for that image. The images were then back projected using a Feldkamp algorithm. We found that the three cases yield results inside the ROI comparable to those obtained using FFOV projections. In addition, the ROI filter reconstruction with smoothing provides image information outside the ROI comparable to the FFOV reconstruction. CT using an ROI filter provides a means to reconstruct reliable 3D for a volume of interest with greatly reduced integral dose compared to FFOV projections and with minimal artifacts.

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