In CT imaging, a standard set of bench testing performances, including modulation transfer function (MTF) and noise power spectrum (NPS), is considered essential to ensure that patient images from CT systems have sufficient image quality. These performances are measured using CT quality assurance (QA) phantoms commonly of a uniform background. However, for deep learning (DL)-based CT image denoising models that are often overwhelmingly trained with patient CT images, it is unclear whether bench testing performances measured with a uniform background reflect the performance in anatomic patient backgrounds. In this work, we insert test objects into a uniform phantom and a patient slice and simulate their CT images to facilitate the measurement of contrast-dependent MTF (cdMTF) and NPS, which are indicators for image sharpness and noise properties. We compare the cdMTF and NPS behaviors of three DL denoising models (REDCNN, REDCNN-tv and DnCNN) measured with images of uniform and patient backgrounds. We observed that cdMTF performance appears consistent between backgrounds, but noise reduction and texture performances can vary substantially. We suggest that caution should be taken when conclusions regarding noise-related performances of a DL-based CT image denoiser are made purely based on measurements from a uniform phantom.
X-ray dark-field measured on laboratory sources with large focal spots and detector apertures is sensitive to intra-pixel phase gradients abundant in the lungs due to its hierarchical structure of subdividing airways terminating in thin-walled alveoli. This work leverages this sensitivity to exploit complementary information from x-ray dark-field and attenuation computed tomography (CT) images to improve quantification of morphology in pulmonary fibrosis. Specifically, a darkfield enhanced attenuation technique is developed to restore edges and small features in the attenuation image lost to blurring by appropriately scaling and subtracting the dark-field image. An intratracheally treated bleomycin mouse model of pulmonary fibrosis was used to evaluate the impact of the proposed dark-field enhanced attenuation technique on quantifying fibrosis extent. The mouse model was fixated ex vivo to be imaged with a Talbot-Lau grating interferometer micro-CT to generate x-ray dark field and attenuation volumes of 60 µm voxels. Then the specimen was imaged with a reference micro-CT scanner at 5 μm voxel resolution to get a ground truth approximation of local structure. The volumes were co-registered for visual and pixelwise comparisons. Qualitative image comparisons were used to assess visual sharpness while Bland-Altman plots were used to assess agreement with the reference scan at quantifying fibrosis in terms of tissue area fraction measured in 80 randomly sampled nonoverlapping 2 mm square patches. Visual comparisons demonstrated enhanced sharpness and retention of small lung structures while BlandAltman analysis revealed an improved agreement ratio of 0.544 compared to 0.374 in the original attenuation image with a reduction in variance. These results demonstrate that dark-field and attenuation images can be used together to improve resolution of small structures and aid in quantification of pulmonary fibrosis in a mouse model.
We present a proof-of-principle demonstration of material decomposition using a single X-ray tube potential (38 kVp + 0.2 mm Sn, for an effective energy around 27 keV) with a Talbot-Lau grating-based phase-contrast computed tomography (CT) system. We show good material separation of water and fat and an accurate quantitative measurement of isopropyl alcohol. This method utilizes the distinctiveness of both components of the refractive index, δ and β, and is promising for separating soft tissue materials that have similar attenuation values such as water and fat.
Talbot-Lau grating interferometry enables the use of clinical x-ray tubes for phase contrast imaging, greatly broadening its utility for both laboratory and preclinical applications. However, phase contrast measurements made in porous or highly heterogeneous media are negatively impacted by low visibility, the interferometer signal amplitude used to calculate relative phase shifts. While this loss in visibility is the source of dark field contrast it presents an additional source of noise in phase images. In this work, we develop a method to use normalized visibility images as the weighting matrix for denoising the corresponding phase contrast images. By using the visibility to guide filtering, the resulting denoised images are locally smoothed in regions of low visibility while maintaining spatial detail in regions of high visibility. This work demonstrates how the complementary properties of the dark field signal in grating interferometry can be leveraged to improve image quality in phase contrast images and presents an application in preclinical lung micro-CT.
X-ray phase-contrast imaging (XPCI) overcomes the problem of low contrast between different soft tissues achieved in conventional x-ray imaging by introducing x-ray phase as an additional contrast mechanism. This work describes a compact x-ray light source (CXLS) and compares, via simulations, the high quality XPCI results that can be produced from this source to those produced using a microfocus x-ray source. The simulation framework is first validated using an image acquired with a microfocus-source, propagation-based XPCI (PB-XPCI) system. The phase contrast for a water sphere simulating a simple cyst submersed in muscle is evaluated and the evolution of PB-XPCI signal as the object to detector distance is increased is demonstrated. The proposed design of a PB-XPCI system using the CXLS is described and simulated images of a coronary artery compared between CXLS and microfocus source PB-XPCI systems. To generate images with similar noise levels, a microfocus source would require a 3000 times longer exposure than would the CXLS. We conclude that CXLS technology has the potential to provide high-quality XPCI in a medical environment using extremely short exposure times relative to microfocus source approaches.
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