Clinical boundary conditions for propagation-based X-ray phase contrast imaging: from bio-sample models targeting to clinical applications

BACKGROUND

Typical propagation-based X-ray phase contrast imaging (PB-PCI) experiments using polyenergetic sources are tested in very ideal conditions: low-energy spectrum (mainly characteristic X-rays), small thickness and homogeneous materials considered weakly absorbing objects, large object-to-detector distance, long exposure times and non-clinical detector.

OBJECTIVE

Explore PB-PCI features using boundary conditions imposed by a low power polychromatic X-ray source (X-ray spectrum without characteristic X-rays), thick and heterogenous materials and a small area imaging detector with high low-detection radiation threshold, elements commonly found in a clinical scenario.

METHODS

A PB-PCI setup implemented using a microfocus X-ray source and a dental imaging detector was characterized in terms of different spectra and geometric parameters on the acquired images. Test phantoms containing fibers and homogeneous materials with close attenuation characteristics and animal bone and mixed soft tissues (bio-sample models) were analyzed. Contrast to Noise Ratio (CNR), system spatial resolution and Kerma values were obtained for all images.

RESULTS

Phase contrast images showed CNR up to 15% higher than conventional contact images. Moreover, it is better seen when large magnifications (>3) and object-to-detector distances (>13 cm) were used. The influence of the spectrum was not appreciable due to the low efficiency of the detector (thin scintillator screen) at high energies.

CONCLUSIONS

Despite the clinical boundary condition used in this work, regarding the X-ray spectrum, thick samples, and detection system, it was possible to acquire phase contrast images of biological samples.

An indirect estimation of x-ray spectrum via convolutional neural network and transmission measurement

Objective.In this work, we aim to propose an accurate and robust spectrum estimation method by synergistically combining x-ray imaging physics with a convolutional neural network (CNN).Approach.The approach relies on transmission measurements, and the estimated spectrum is formulated as a convolutional summation of a few model spectra generated using Monte Carlo simulation. The difference between the actual and estimated projections is utilized as the loss function to train the network. We contrasted this approach with the weighted sums of model spectra approach previously proposed. Comprehensive studies were performed to demonstrate the robustness and accuracy of the proposed approach in various scenarios.Main results.The results show the desirable accuracy of the CNN-based method for spectrum estimation. The ME and NRMSE were -0.021 keV and 3.04% for 80 kVp, and 0.006 keV and 4.44% for 100 kVp, superior to the previous approach. The robustness test and experimental study also demonstrated superior performances. The CNN-based approach yielded remarkably consistent results in phantoms with various material combinations, and the CNN-based approach was robust concerning spectrum generators and calibration phantoms.Significance. We proposed a method for estimating the real spectrum by integrating a deep learning model with real imaging physics. The results demonstrated that this method was accurate and robust in estimating the spectrum, and it is potentially helpful for broad x-ray imaging tasks.

X-ray Computed Tomography for Flame-Structure Analysis of Laminar Premixed Flames

Quantitative X-ray computed tomography (XCT) diagnostics for reacting flows are developed and demonstrated in application to premixed flames in open and optically inaccessible geometries. A laboratory X-ray scanner is employed to investigate methane/air flames that were diluted with krypton as an inert radiodense tracer gas. Effects of acquisition rate and tracer gas concentration on the signal-to-noise ratio are examined. It is shown that statistically converged three-dimensional attenuation measurements can be obtained with limited impact from the tracer gas and within an acceptable acquisition time. Specific aspects of the tomographic reconstruction and the experimental procedure are examined, with particular emphasis on the quantification of experimental uncertainties. A method is developed to determine density and temperature from the X-ray attenuation measurements. These experiments are complemented by one- and multi-dimensional calculations to quantify the influence of krypton on the flame behavior. To demonstrate the merit of XCT for optically inaccessible flames, measurements of a complex flame geometry in a tubular confinement are performed. The use of a coflow to provide a uniform tracer-gas concentration is shown to improve the quantitative temperature evaluation. These measurements demonstrate the viability of XCT for flame-structure analysis and multi-dimensional temperature measurements using laboratory X-ray systems. Further opportunities for improving this diagnostic are discussed.

Generation of polychromatic projection for dedicated breast computed tomography simulation using anthropomorphic numerical phantom

Numerical simulations are fundamental to the development of medical imaging systems because they can save time and effort in research and development. In this study, we developed a method of creating the virtual projection images that are necessary to study dedicated breast computed tomography (BCT) systems. Anthropomorphic software breast phantoms of the conventional compression type were synthesized and redesigned to meet the requirements of dedicated BCT systems. The internal structure of the breast was randomly constructed to develop the proposed phantom, enabling the internal structure of a naturally distributed real breast to be simulated. When using the existing monochromatic photon incidence assumption for projection-image generation, it is not possible to simulate various artifacts caused by the X-ray spectrum, such as the beam hardening effect. Consequently, the system performance could be overestimated. Therefore, we considered the polychromatic spectrum in the projection image generation process and verified the results. The proposed method is expected to be useful for the development and optimization of BCT systems.

An indirect transmission measurement-based spectrum estimation method for computed tomography

The characteristics of an x-ray spectrum can greatly influence imaging and related tasks. In practice, due to the pile-up effect of the detector, it's difficult to directly measure the spectrum of a CT scanner using an energy resolved detector. An alternative solution is to estimate the spectrum using transmission measurements with a step phantom or another CT phantom. In this work, we present a new spectrum estimation method based on indirect transmission measurement and a model spectra mixture approach. The estimated x-ray spectrum was expressed as a weighted summation of a set of model spectra, which can significantly reduce the degrees of freedom of the spectrum estimation problem. Next, an estimated projection was calculated with the assumed spectrum. By iteratively updating the unknown weights, we minimized the difference between the estimated projection data and the raw projection data. The final spectrum was calculated with these calibrated weights and the model spectra. Both simulation and experimental data were used to evaluate the proposed method. In the simulation study, the estimated spectra were compared to the raw spectra which were used to generate the raw projection data. For the experimental study, the ground truth measurement of the raw x-ray spectrum was not available. Therefore, the estimated spectrum was compared against the spectra generated using the SpekCalc software with tube configurations provided by the scanner manufacturer. The results show the proposed method has the potential to accurately estimate x-ray spectra using the raw projection data. The difference between the mean energy of the raw spectra and the mean energy of the estimated spectra was less than 0.5 keV for both the simulation and experimental data. Further tests show the method was robust with respect to the model spectra generator.