We present the first investigation of propagation-based phase-contrast chest X-ray (CXR) imaging. Conventional and phase-contrast CXR images of virtual patients were generated by applying wave-propagation simulations to XCAT virtual chest phantoms. We then performed a reader study with clinical radiologists to investigate the potential clinical impact of phase-contrast CXR. No significant improvement in lesion (6-20 mm) detection rate was found, however, phase-contrast CXR could visualize small airways that are invisible in conventional CXR. This could have clinical significance for diagnosing diseases presenting a thickening of airway walls, such as in asthma and early-stage chronic obstructive pulmonary disease (COPD).

The purpose of this work is develop a novel multi-contrast chest x-ray radiography (MC-CXR) imaging system to enable the simultaneous generation of three mutually complementary x-ray contrast mechanisms to enhance the diagnostic performance of CXR for respiratory diseases. The developed grating-based MC-CXR system employs a scanning beam image acquisition scheme in which the patient table is translated at a speed of up to 9 cm/s. The system is capable of accomplishing MC-CXR imaging of an anthropomorphic chest phantom in under 4 seconds, with an air kerma and effective dose that are well below that of a conventional CXR exam.

In this study, we fabricated a pixelated unipolar charge sensing detector based on amorphous selenium with a 20-μm pixel pitch using standard lithography process. A pulse-height spectroscopy (PHS) setup with a very low noise front-end electronics was designed, and experiments were performed to investigate the achievable energy resolution with the unipolar detector, as well as with a conventional detector for comparison purposes. PHS measurement results are presented that demonstrate, for the first time, a measured energy resolution of 8.3 keV at 59.5 keV is for the unipolar charge sensing device in contrast to 14.5 keV at 59.5 keV for conventional a-Se devices, indicating its promise for the contrast-enhanced photon counting imaging with an unsurpassed spatial resolution.

CT imaging is one of the primary diagnostic medical imaging modalities. However, there is a long-standing technical limitation associated with conventional CT imaging: anatomical structures with different material compositions may have the same CT number, thereby limiting the ability to differentiate and classify different tissue types and contrast agents. To address this limitation, the currently available strategy is to modify the hardware acquisition systems such that dual energy CT (DECT) data acquisition scheme can be accommodated. In this work, we show that the elemental composition of a material can be directly extracted from a conventional single-kV CT acquisition without invoking DECT acquisition scheme.

Towards providing a “one-stop-shop” solution to anatomical and parenchymal perfusion imaging for pulmonary embolism (PE) evaluation, this work developed a method that uses a deep neural network to estimate effective atomic number (Zeff) information embedded in single-kV pulmonary CT angiography projection data. Based on the estimated Zeff map and the definition of perfusion blood volume (PBV), quantitatively accurate PBV maps can be generated. A multi-center human subject study demonstrates that the proposed single-kV CT and Zeff based PBV method provides a more sensitive and specific biomarker to quantify pulmonary perfusion defects compared with the iodine material image-based perfusion estimation method.

In this work, a unified framework was developed to jointly address scatter artifacts, detector nonuniformity-induced concentric artifacts, and beam hardening artifacts in C-arm photon counting detector (PCD) cone beam CT. By leveraging the energy-resolving capability of PCDs, a better estimation of the scattered photon signal was obtained via a photoelectric-Compton scattering decomposition. Next, detector nonuniformity and beam hardening artifacts were jointly corrected via a second-round projection domain pixel-wise material decomposition. Both phantom and in vivo animal results demonstrated that the proposed correction method generated high-quality and quantitative PCD cone beam CT images for image-guided interventions.

We compared CT x-ray beam-hardening artifacts in a hybrid scanner with energy-integrating detectors (EID) versus photon-counting detectors (PCD) subsystems. EID-CT images had less beam hardening artifacts compared to PCD-CT images for x-ray tube voltages 120 kVp and higher. We further demonstrated that the inherent spectral information of PCDs can be used to effectively eliminate beam-hardening artifacts.

X-ray image segmentation of different anatomical structures or tissue types is essential for diagnosing lesions of various kinds and for the differentiation between contrast agent and bone tissue. However, the complete separation of multiple targets and tissue types remains a challenge. We describe the use a combination of high-dimensional data clustering and material decomposition methods using spectral information from an energy resolving CdTe Medipix3 photon-counting detector. This paper introduces a flexible, iterative semi-supervised algorithm for multi-material decomposition that uses spectral measurements and the K-edge effects to label and classify CT voxel clusters using a Gaussian Mixture Model (GMM). Preliminary results show excellent quantitative accuracy and separation of more than 3 materials. Results are shown with phantom and mouse CT data. Our correction and calibration methods required for these successful decomposition results will also be described.

The purpose of this study was to investigate the use of a Gallium Arsenide (GaAs) photon-counting spectral mammography system to differentiate between Type I and Type II calcifications. Type I calcifications, consisting of calcium oxalate dihydrate (CO) or weddellite compounds are more often associated with benign lesions, and Type II calcifications containing hydroxyapatite (HA) are associated with benign or malignant lesions. The study was carried out on a custom-built laboratory bench-top system using the SANTIS 0804 GaAs detector prototype system from DECTRIS Ltd. Measurements were performed on CIRS (Norfolk, VA) swirl and uniform phantoms mimicking a 50% adipose, 50% fibroglandular breast tissue composition with inserted clusters of synthetic microcalcifications. First, an inverse problem-based approach was used to estimate the full energy x-ray transmission fraction factor using known basis transmission factors of varying thicknesses of Aluminum and PMMA at each pixel. Secon

X-ray phase contrast imaging (PCI) has a great potential for improving the visibility of soft tissues in medical imaging. Single-mask edge-illumination (EI) (also sometime referred to as coded-aperture) phase contrast imaging method has been developed with the ability to obtain differential phase contrast with simpler experimental setup in comparison to grating based or conventional double mask EI PCI. We show results of single-mask PCI and results of differential phase contrast estimation in a single shot. The potential of this single mask PCI to reduce the radiation dose and improved contrast has not been fully investigated yet. In this work we compared the x-ray dose requirement of single-mask method with other methods by analyzing the SNR under different level of detector counts. We also propose and demonstrate a new model based on TIE for differential retrieval from single mask EI PCI with experimental data.