Spectral capabilities of photon counting detectors (PCDs) can allow material decomposition. We recently showed multi-material decomposition using high resolution photon counting detectors (Medipix3). High-resolution photon counting spectral detectors have unique advantages and challenges. Some of the challenges arise from noise properties as well as spectral distortions. We show benefits of an empirical correction method to obtain accurate attenuation values in a spectral CT even with high resolution detectors and a combination of spectral distortions. Aided with accurate spectral correction, we show that a Gaussian mixture model assisted iterative decomposition can separate multiple materials at once. Our group has been investigating the role of image texture features in signal detection performance in tomographic images. Here we will explore utilizing variations in image texture features in spectral CT material decomposition. Along with attenuation variations for each energy bin, second order statistical texture feature variations associated with spectral data will be used to reduce the number of energy bins and imaging dose to perform multi-material decomposition. With promising preliminary results, we will show a more thorough investigation of image texture variations in spectral data to assist efficient and low dose material decomposition in spectral CT.
X-ray phase contrast imaging (XPCI) holds immense promise for enhancing contrast and visibility in medical imaging, as it harnesses the phase information within X-ray wavefront to reveal intricate structures within soft tissues. Among the diverse techniques available for x-ray phase contrast imaging, the single-mask phase contrast imaging method stands out for its notable benefits: heightened contrast levels, minimal system complexity demands, and the capacity to extract multi-modal information within a single shot. In this study, we introduce an X-ray phase contrast tomography system designed to deliver exceptional contrast for soft materials, along with the unique capability to retrieve absorption, differential phase, and phase images in a single shot per projection angle. Notably, our imaging setup circumvents the necessity for a highly coherent x-ray source, an ultra-high-resolution detector, or intricately fabricated x-ray gratings. Moreover, it exhibits substantial resilience towards alignment discrepancies and mechanical vibrations, contributing to its robust performance. While the methods would translate to all CT systems with the mask design available, we demonstrate our results in a benchtop system suitable for micro CT type imaging.
We explore the utility of photon counting spectral detectors in projection-based material decomposition for applications such as in mammographic imaging. These detectors, with their ability to count individual x-ray photons and measure their energy and time of arrival offer significant advantages. Material decomposition using photon counting detectors (PCDs) could result in artifacts, particularly at the transitions between different materials, especially of high attenuation like metals or bone when embedded in soft tissue. This paper presents a novel approach to eliminate these artifacts at material boundaries in projection domain. We perform pixel classification or clustering based on the estimated mass attenuation for two energy windows to reduce the number of material bases in decomposition. Furthermore, we introduce an iterative method that allows significant reduction of artifacts, especially at material edges. Our findings reveal the potential of Timepix3 in biomedical imaging and offer a robust technique for improved material decomposition for improved diagnostic interpretations in pre-clinical and clinical spectral CT.
Functional near-infrared spectroscopy (fNIRS) is a non-invasive method of imaging brain function, and it is portable and much cheaper than other functional imaging modalities, such as functional MRI. It is beneficial for users to validate their fNIRS systems on a controlled phantom, but for such a phantom to be worthwhile, it must have well-characterized optical properties and be able to simulate the hemodynamic response (HDR). In this paper, we describe a dynamic phantom capable of producing reproducible fNIRS data, and we describe methods for characterizing the optical properties of this phantom. This phantom has already produced fNIRS data with spatially accurate simulated HDR signals. Additionally, we propose modifications for this phantom to produce accurate X-ray and computed tomography (CT) data to make it viable for multimodal imaging systems. CT brain imaging allows high-resolution structural imaging, but with contrast agents some functional information can also be mapped. The combination of CT and fNIRS may be desirable for easily and more accurately estimating differential pathlength factor (DPF) values across a patient’s head, and it may be beneficial for diagnosing and treating patients suffering from head trauma and other neurological conditions.
Photon counting detectors (PCDs) offer significant advantages in quantitative material decomposition applications due to their spectroscopic capabilities. However, various challenges, including charge sharing, partial charge collection, fluorescence photons from the detector’s material, and crystal defects introduced during fabrication, negatively impact image quality and energy resolution. Consequently, conventional flat-field correction proves insufficient for accurate spectral correction. A common correction technique is the signal-to-thickness calibration (STC) method, which correlates photon counts with material thickness. In biological applications, polymethyl methacrylate (PMMA) is often used as a calibration material due to its similar attenuation properties to soft tissue. However, single-material calibration struggles to yield accurate results for samples with higher atomic numbers, such as bone. In this study, we present a deep neural network model trained using two calibration materials (PMMA and Aluminum) that not only corrects the measured mass attenuation of complex materials but also restores photon counts, leading to improved accuracy in energy spectral measurements. When tested using the Medipix3 CdTe photon counting detector, this method successfully removed the intrinsic x-ray fluorescence of CdTe. Additionally, our approach offers the advantage of not requiring a detailed formulation of the detector’s response function. Our experimental results demonstrate that this approach reduces noise and enhances mass attenuation accuracy compared to single-material calibration. By incorporating both calibration datasets during training, we developed a single model capable of handling a wide range of potential attenuation values. The dual-material model method addresses the limitations of conventional techniques and holds promise for improving the performance of photon-counting detectors across various applications.
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 coherent nature of X-ray scattering at small angles produce scattering interference which generates a unique energy spectrum characteristic of the intra-molecular structure of the irradiated sample. The phase difference introduced by the scattering centers will depend upon the wavelength of the radiation and the scattering angle. The resulting interference effects can be studied very effectively using a mono energetic source and scanning an angular range. However, when using a poly energetic source the interference happen at different energies simultaneously, which generates a much more complex and obscured interference pattern due to the spread of wavelength. The spectral pattern still contains valuable information about the electron density distribution of materials of interest. In this study we propose the use of spectral xray measurements with an advanced photon counting detector Timepix3 (300μm Si) as well as an X-123 (1mm CdTe) Amptek spectrometer to measure the small angle x-ray scattering generated by samples illuminated by a polychromatic X-ray source. We show that the resultant scatter interference can be effectively characterized with this setting. In this study, we also examine the photon counting profiles obtained from different metals and plastics as well as normal and malignant breast tissue. The measured photon counting spectrum for different materials shows reproducible features. We were able to characterize benign and malignant breast tissue using spectral scatter signature. Momentum transfer when scattered from benign breast tissue is observed to be lower in comparison to that from carcinoma. These features could be used for reliable tissue discrimination and material identification. Future work will assess the potential and limitations of this method in improving mammographic diagnostic capabilities.
Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false). As such the computer program should be able to "predict" with reasonable level of confidence if a given particle belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a logistic regression equation as they proved to have a relatively high predictive value on their own.
We report the experimental measurement of the relationship between the size of particles being moved by optically patterned dielectrophoresis in an Optoelectronic Tweezers (OET) device and the force that they experience. The OET device turns an optical pattern into a pattern of electrical fields through the selective illumination of a photoconductive material. In this work we use a data projector to create the structured illumination which gives a relatively flat optical profile with steep optical gradients and hence steep electrical gradients at the edges of the light patterns created. For a small particle in a constant electrical gradient it would be expected that the force due to dielectrophoresis would scale with the cube of the particle’s radius whereas the forces needed to move it against the viscous fluid scale with the radius so that there would be a an increase of the velocity at which we can move particles with a relationship of the radius squared. As the particles in an OET device are often larger than the area over which the electrical gradients are produced it is not obvious how their forces scale with size. In this paper we show that there is a small size regime where the particle size relationship with force is well described by a linear fit and a regime where it is not. We show that the magnitude of the force is dependent on the light pattern used and that with larger particles and optimized light patterns velocities of around 1mms-1 can be achieved.
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