Mice are used for models of almost all human diseases and are routinely scanned by micro-CT scanners. Mouse phantoms are often used for image-quality assessment. With recent developments in deep-learning-based preclinical imaging, there is a major need for large micro-CT datasets in which ground truth is known. In this study, we investigate the feasibility of making cost-effective deformable and reconfigurable mouse phantoms to generate real micro-CT datasets that reflect realistic underlying physical characteristics. Such datasets are highly desirable; for example, complicated photon-counting micro-CT datasets are needed for deep-learning-based material decomposition. In our scheme, mouse body parts are 3Dprinted with high precision using rigid or flexible materials. Liquid tissue surrogates (LTSs) or bioinks/cell lines could be used to emulate mouse organs and physiological fluid in the animals. LTSs provide realistic x-ray properties of their biological counterparts. The LTS organs could be contained in not only 3D-printed chambers, but also dialysis tubing, which emulates the cell membrane. Furthermore, through bioprinting and tissue engineering, organs and tissues can be made even more realistic for micro-CT and other types of tomographic scanning.
X-ray photon-counting detectors (PCDs) become increasingly popular with applications in medical imaging, material science, and other areas. In this paper, we propose a non-uniformity data correction method for photon-counting detectors based on the first and second moment correction. Using three measure datasets, we demonstrate the method’s efficacy in reducing spatial variance of pixel counts. The results demonstrate that both open beam and projection data can be corrected to nearly perfect Poisson counting behavior in both time and space when photon flux is in the detector’s linear response range.
Recent advances in X-ray imaging technologies have paved the way for use of energy-discriminating photon-counting
detector arrays. These detectors show promise in clinical and preclinical applications. Multi-energy or spectral CT images
can be visualized in multi-colors. Despite the advantages offered by the spectral dimension of acquired data, higher image
resolution is still desirable, especially in challenging tasks such as on-site studies of resected pathological tissues. Here we
propose to enhance image resolution of a spectral X-ray imaging system by partially blocking each detector element with
an absorption grating (for reduced aperture), commonly used for Talbot-Lau interferometry. After acquiring X-ray data at
an initial grating-detector configuration, the grating is shifted to expose previously blocked portions so that each
measurement contains new information. All the acquired data are then combined into an augmented system matrix and
subsequently reconstructed using an iterative algorithm. Our proof of concept simulations are performed with MCNP6.1
code and the experiment was performed using a Hamamatsu microfocus X-ray source, an absorption grating, and an Xray
camera. Our results demonstrate that the gratings commonly used for x-ray phase-contrast imaging have a utility for
super-resolution imaging performance.
Noninvasive determination of plaque vulnerability has been a holy grail of medical imaging. Despite advances in
tomographic technologies , there is currently no effective way to identify vulnerable atherosclerotic plaques with high
sensitivity and specificity. Computed tomography (CT) and magnetic resonance imaging (MRI) are widely used, but
neither provides sufficient information of plaque properties. Thus, we are motivated to combine CT and MRI imaging to
determine if the composite information can better reflect the histological determination of plaque vulnerability. Two
human endarterectomy specimens (1 symptomatic carotid and 1 stable femoral) were imaged using Scanco Medical Viva
CT40 and Bruker Pharmascan 16cm 7T Horizontal MRI / MRS systems. μCT scans were done at 55 kVp and tube
current of 70 mA. Samples underwent RARE-VTR and MSME pulse sequences to measure T1, T2 values, and proton
density. The specimens were processed for histology and scored for vulnerability using the American Heart Association
criteria. Single modality-based analyses were performed through segmentation of key imaging biomarkers (i.e.
calcification and lumen), image registration, measurement of fibrous capsule, and multi-component T1 and T2 decay
modeling. Feature differences were analyzed between the unstable and stable controls, symptomatic carotid and femoral
plaque, respectively. By building on the techniques used in this study, synergistic CT+MRI analysis may provide a
promising solution for plaque characterization <i>in vivo</i>.