Purpose: To evaluate the performance of an experimental X-ray dark-field radiography system for chest imaging in humans and to compare with conventional diagnostic imaging. Materials and Methods: The study was institutional review board (IRB) approved. A single human cadaver (52 years, female, height: 173 cm, weight: 84 kg, chest circumference: 97 cm) was imaged within 24 hours post mortem on the experimental x-ray dark-field system. In addition, the cadaver was imaged on a clinical CT system to obtain a reference scan. The grating-based dark-field radiography setup was equipped with a set of three gratings to enable grating-based dark-field contrast x-ray imaging. The prototype operates at an acceleration voltage of up to 70 kVp and with a field-of-view large enough for clinical chest x-ray (>35 x 35 cm2). Results: It was feasible to extract x-ray dark-field signal of the whole human thorax, clearly demonstrating that human x-ray dark-field chest radiography is feasible. Lung tissue produced strong scattering, reflected in a pronounced x-ray dark-field signal. The ribcage and the backbone are less prominent than the lung but are also distinguishable. Finally, the soft tissue is not present in the dark-field radiography. The regions of the lungs affected by edema, as verified by CT, showed less dark-field signal compared to healthy lung tissue. Conclusion: Our results reveal the current status of translating dark-field imaging from a micro (small animal) scale to a macro (patient) scale. The performance of the experimental x-ray dark-field radiography setup offers, for the first time, obtaining multi-contrast chest x-ray images (attenuation and dark-field signal) from a human cadaver.
In x-ray imaging, contrast information content varies with photon energy. It is therefore possible to improve image quality by weighting photons according to energy. We have implemented and evaluated so-called energy weighting on a commercially available spectral photon-counting mammography system. A practical formula for calculating the optimal weight from pixel values was derived. Computer simulations and phantom measurements revealed that the contrast-tonoise ratio was improved by 3%–5%, and automatic image analysis showed that the improvement was detectable in a set of screening mammograms.
Signal and noise transfer properties of x-ray detectors are described by the detective quantum efficiency DQE. The DQE
is a precise analysis tool, however, it is not meant to identify the various noise sources.
The noise decomposition method is based on measured noise power spectra, following previous work by Mackenzie.
Noise is distinguished by its variations with dose and spatial frequency: Quantum noise, fixed pattern noise, Lubberts
noise, noise aliasing, and others.
By determining all major noise sources, DQE results can be extrapolated within a precision of approximately 2% to other
clinical relevant dose values that have not been measured. This precision shows an improvement to the method proposed
by Mackenzie. The major noise sources are further sub-divided. For the calculation of noise sub-components a precision
of 4% is achieved. The decomposition allows a detailed analysis of the dominant noise component in a certain dose or
spatial frequency range, in particular the determination of spectral noise equivalent dose, the impact on DQE by different
gain and offset correction schemes, and the influence of different scintillators on Lubberts noise.
A processing method is described which allows to present images with equalized detail contrast, i.e., contrast that is the same in all parts of the image, independent of the chosen look-up table (LUT) or the local signal level. Basically, a multiresolution algorithm is used which splits the image in a number of bandpass images. Only the lowest band (low-pass image) is transmitted through the intensity LUT, while all higher frequency subimages are nonlinearly enhanced and added to the LUT-transformed low-pass image. Nonlinear enhancement is used in order to improve the visibility of weakly contrasting details while minimizing artifacts at high contrast edges. Unfavorable noise enhancement can be avoided by limiting the enhancement in the low-dose areas of the highest frequency subimages. The resulting images show good detail visibility in all parts. Detail rendition in images with equalized contrast is independent of image latitude and of slight variations in overall image brightness or density. Preliminary experience with clinical images show that the display does not need individual parameter tuning for different images, yet allows producing artifact-free, naturally looking images with improved detail visibility for a wide variety of input images. Improvements compared to standard (intensity) equalization processing are evident especially for the mediastinum and subdiaphragmal regions of chest images and in lateral spine examinations that have an inherently large dynamic range.
Five different X-ray imaging systems were evaluated comparatively with respect to low-contrast detail deductibility. The systems included in this study were two screen-film systems (speed classes 200 and 400), a computed radiography system, a digital selenium-based system with electrometer scanning and an indirect-type flat-panel detector system. Images of a contrast-detail phantom were acquired with all systems at a set of exactly matched exposures. The digital images were processed in a way to approximate the density and contrast appearance of the conventional film images when printed on laser film. Six observers evaluated a total number of 46 films. With respect to the threshold contrast for each detail size. Correct observation ratios and threshold contrasts were determined for all sizes and conditions. The overall results show that the low-contrast deductibility with all digital imaging systems is equal to or better than that with the conventional film-screen systems. The advantage is more evident for the newer digital systems (selenium detector and flat-panel detector) whereas the CR images are more on a par with the conventional films. The results can be understood assuming that low-contrast detection is limited mainly by quantum noise in the images and taking into account the different levels of detective quantum efficiency of these imaging systems.
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