Energy-resolving photon counting detectors (PCDs) are being explored for non-destructive spectral x-ray imaging in medical and industrial applications, allowing quantitative material mapping not practical with conventional radiography. However, PCDs suffer inherent detector non-idealities that negatively impact image quality and quantitative accuracy. While analytical methods are being developed for material separation, we leverage machine learning techniques (e.g., principal component analysis and clustering) to increase flexibility by reducing the reliance on prior knowledge of the inspected object or detection properties. Through simulating various acquisition conditions, we evaluate the robustness of these machine learning techniques for material-specific mapping in spectral x-ray imaging.
While conventional computed tomography (CT) is widely used in nondestructive analysis applications, the scanning geometry poses limitations on objects with high aspect ratios, such as printed circuit boards (PCBs), which significantly reduce image quality. Alternatively, computed laminography (CL) techniques are being developed to overcome these challenges by granting additional flexibility of the scan geometry. For this study, the capabilities of a laboratory cabinet x-ray system are extended to perform CL scan acquisitions. Key issues regarding the system geometry are evaluated and calibration techniques are implemented to ensure CL reconstruction accuracy, which is particularly sensitive for high-magnification applications. Results using the CL method are evaluated against the traditional cone-beam CT method which shows improved image quality for the nondestructive inspection of stacked microvias buried within large PCBs. This study also uncovers the particular issues with our system geometry calibration that influence the quality of the reconstructed CL images. Application of these results suggest that cone-beam CL is an effective alternative to conventional CT when inspecting fine features within high aspect ratio objects.
KEYWORDS: Sensors, Iodine, Mammography, Signal attenuation, Breast, Signal to noise ratio, Tissues, Photon counting, Breast imaging, X-rays, Dual energy imaging
Contrast-enhanced spectral mammography (CESM) is being implemented to overcome the limitations of conventional mammography where tumor visualization is obstructed by overlapping glandular tissue. CESM exploits the spectral properties of a contrast agent by subtracting two images one obtained above and other below the K-edge energy. The most common approach requires dual-exposure where two images are obtained with differ- ent incident spectra. However, this comes at the expense of increased patient dose and susceptibility to motion artifacts. We propose the use of photon counting spectral detectors to simultaneously obtain multiple images with single-exposure. This is demonstrated using a wide area CdTe Medipix3RX detector to acquire images of iodine contrast agent in an anthropomorphic breast imaging phantom. The electronic thresholds in the detector replace the traditional physical filters. Our results show single-exposure CESM for the detection of iodine with concentrations as low as 2.5 mg/mL of a 10 mm diameter target in a 5 cm thick heterogeneous background. These results demonstrate the viability of photon counting detectors for low dose contrast-enhanced mammography.
X-ray phase contrast imaging is being investigated with the goal of improving the contrast of soft tissue. Enhanced edges at material boundaries are characteristic of phase contrast images. These allow better retrieval of phase maps and attenuation maps when material properties are very close to each other. Previous observations have shown that the edge contrast of a target material reduces with increasing thickness of the surrounding bulk material. In order to accurately retrieve material properties, it is important to understand the contributions from various factors that may lead to this phase degradation. We investigate this edge degradation dependence due to beam hardening and object scatter that results from the surrounding bulk material. Our results suggest that the large propagation distances used in PB-PCI are effective at reducing the scatter influence. Rather, our results indicate that the phase contrast degradation due to beam hardening is the most critical. The ability to account for these variations may be necessary for more accurate phase retrievals using polychromatic sources and large objects.
X-Ray phase contrast imaging (PCI) is being developed as an alternative to overcome the poor contrast sensitivity of existing attenuation imaging techniques. The “phase sensitivity” can be achieved using a number of phase-enhancing geometries such as free space propagation, grating interferometry and edge illumination (also known as coded aperture) technique. The enhanced contrast in the projected intensities (that combine absorption and phase effect) can vary by object shape, size and its material properties as well as the particular PCI method used. We show a comparison of this signal enhancement for both FSP and coded aperture (CA) PCI. Our data shows that the phase enhancement is significantly higher for CA in comparison to FSP. Our preliminary results indicate that the enhanced phase effect decreases in all PCI techniques with increasing background thickness. Investigations involving signal location and background tissue thickness dependent signal enhancement (and/or loss of this signal) are very important in determining the true benefit of PCI methods in a practical application involving thick organs like breast imaging.
Photon counting spectral detectors are being investigated to allow better discrimination of multiple materials by collecting spectral data for every detector pixel. The process of material decomposition or discrimination starts with an accurate estimation of energy dependent attenuation of the composite object. Photoelectric effect and Compton scattering are two important constituents of the attenuation. Compton scattering while results in a loss of primary photon, also results in an increase in photon counts in the lower ene1rgy bins via multiple orders of scatter. This contribution to each energy bin may change with material properties, thickness and x-ray energies. There has been little investigation into the effect of this increase in counts at lower energies due to presence of these Compton scattered photons using photon counting detectors. Our investigations show that it is important to account for this effect in spectral decomposition problems.
KEYWORDS: Photon counting, Algorithm development, Computer simulations, Calibration, Gold, Signal attenuation, Computed tomography, Data acquisition, Chemical elements, Medical imaging, X-rays
When using a photon counting detector for material decomposition problems, a major issue is the low-count rate per energy bin which may lead to high image-noise with compromised contrast and accuracy. A multi-step algorithmic method of material decomposition is proposed for spectral computed tomography (CT), where the problem is formulated as series of simpler and dose efficient decompositions rather than solved simultaneously. A simple domain of four materials; water, hydroxyapatite, iodine and gold was explored. The results showed an improvement in accuracy with low-noise over a similar method where the materials were decomposed simultaneously. In the multi-step approach, for the same acquired energy bin data, the problem is reformulated in each step with decreasing number of energy bins (resulting in a higher count levels per bin) and unknowns in each step. This offers flexibility in the choice of energy bins for each material type. Our results are very preliminary but show promise and potential to tackle challenging decomposition tasks. Complete work will include detailed analysis of this approach and experimental data with more complex mixtures.
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