X-ray computed tomography (CT) has been a mainstay of clinical neuroimaging since the 1970s. Its quick scans, high spatial resolution, ease of availability, and relatively low cost make it essential for the diagnosis and monitoring of injury and disease. However, low contrast resolution relative to magnetic resonance imaging (MRI) has limited its use in resolving subtle variations between different soft tissue types and between inflamed and healthy tissues. While clinical CT exploits the attenuation of X-rays through the body, phase contrast X-ray imaging (PCXI) additionally uses refraction and diffraction, increasing sensitivity to subtle inhomogeneities in tissue composition. Most PCXI-CT research has focused on lung and breast tissue, where the sparsely-distributed surrounding bone does not significantly obscure soft tissues. However, neuroimaging with X-rays poses unique challenges, since the brain is fully encased within the highly-attenuating skull, making soft-tissue segmentation difficult and creating artifacts that can obscure the underlying anatomy. We have previously demonstrated that PCXI-CT can resolve anatomical structures within the brains of small animals in situ at micron-scale resolution (Croton et al., 2018) and have developed new correction methods to address the key artifacts that appear when capturing these soft-tissue images (Croton et al., 2019). Here, we present recent progress in the detection of brain injury with PCXI-CT, including direct comparisons to MRI of the same injured brains. Our project aims to spatially resolve injuries that elude MRI, including both traumatic and diffuse injury, in order to better understand the progression of cerebral palsy near birth.
Purpose: Propagation-based x-ray imaging (PBI) is a phase-contrast technique that is employed in high-resolution imaging by introducing some distance between sample and detector. PBI causes characteristic intensity fringes that have to be processed with appropriate phase-retrieval algorithms, which has historically been a difficult task for objects composed of several different materials. Spectral x-ray imaging has been introduced to PBI to overcome this issue and to potentially utilize the spectral nature of the data for material-specific imaging. We aim to explore the potential of spectral PBI in three-dimensional computed tomography (CT) imaging in this work.
Approach: We demonstrate phase-retrieval for experimental high-resolution spectral propagation-based CT data of a simple two-component sample, as well as a multimaterial capacitor test sample. Phase-retrieval was performed using an algorithm based on the Alvarez–Macovski model. Virtual monochromatic (VMI) and effective atomic number images were calculated after phase-retrieval.
Results: Phase-retrieval results from the spectral data set show a distinct gray-level for each material with no residual phase-contrast fringes. Several representations of the phase-retrieved data are provided. The VMI is used to display an attenuation-equivalent image at a chosen display energy of 80 keV, to provide good separation of materials with minimal noise. The effective atomic number image shows the material composition of the sample.
Conclusions: Spectral photon-counting detector technology has already been shown to be compatible with spectral PBI, and there is a foreseeable need for robust phase-retrieval in high-resolution, spectral x-ray CT in the future. Our results demonstrate the feasibility of phase-retrieval for spectral PBI CT.
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