Molecular Imaging of Pulmonary Tuberculosis in an Ex-Vivo Mouse Model Using Spectral Photon-Counting Computed Tomography and Micro-CT

Assessment of disease burden and drug efficacy is achieved preclinically using high resolution micro computed tomography (CT). However, micro-CT is not applicable to clinical human imaging due to operating at high dose. In addition, the technology differences between micro-CT and standard clinical CT prevent direct translation of preclinical applications. The current proof-of-concept study presents spectral photon-counting CT as a clinically translatable, molecular imaging tool by assessing contrast uptake in an ex-vivo mouse model of pulmonary tuberculosis (TB). Iodine, a common contrast used in clinical CT imaging, was introduced into a murine model of TB. The excised mouse lungs were imaged using a standard micro-CT subsystem (SuperArgus) and the contrast enhanced TB lesions quantified. The same lungs were imaged using a spectral photoncounting CT system (MARS small-bore scanner). Iodine and soft tissues (water and lipid) were materially separated, and iodine uptake quantified. The volume of the TB infection quantified by spectral CT and micro-CT was found to be 2.96 mm(3) and 2.83 mm(3), respectively. This proof-of-concept study showed that spectral photon-counting CT could be used as a predictive preclinical imaging tool for the purpose of facilitating drug discovery and development. Also, as this imaging modality is available for human trials, all applications are translatable to human imaging. In conclusion, spectral photon-counting CT could accelerate a deeper understanding of infectious lung diseases using targeted pharmaceuticals and intrinsic markers, and ultimately improve the efficacy of therapies by measuring drug delivery and response to treatment in animal models and later in humans

Crystal arthritis imaging and in-vivo dosimetry with MARS.

This thesis reports on improvements in crystal arthritis diagnosis that we have obtained using the MARS spectral photon counting imaging system. It also reports on how this system can provide patient-specific dose assessment. We demonstrate how to differentiate gout from Calcium Pyrophosphate Deposition Disease (CPPD) or Basic Calcium Phosphate (BCP) arthritis by comparing the spectral signatures of Monosodium Urate (MSU), Calcium Pyrophosphate (CPP), and calcium Hydroxyapatite (HA) crystals. We show that the MARS system’s high spatial resolution and crystal discrimination ability provide a better diagnosis potential compared to other imaging modalities. Nevertheless, the results show that the crystal discrimination could be improved. The first solution proposed in this thesis is acquisition protocol optimisation. Based on the Material Identification and Quantification (MIQ) algorithm implemented in the MARS systems used in this thesis, the original protocol has been improved to provide additional spectral information, useful for crystal discrimination. The new protocol is compared with the original one using a MIQ-oriented evaluation method. The results show a better crystal discrimination potential with the new protocol than with the original one. The second improvement solution proposed in this thesis is about the MIQ calibration process. A solution to provide a material calibration independent from the background by using the density in the calibration process is presented. This new method offers new possibilities to assess material calibration accuracy. The results show inconsistencies in the CPP, HA, and MSU calibration output, which results in discrimination inaccuracies. A solution to reduce the calibration uncertainties by increasing the concentration range in the phantom is proposed. The MARS system’s ability to provide the energy measurement of each individual photon detected during the acquisition allows the estimation of the radiation dose delivered to the patient during the scan. First, the Total Absorbed Dose (TAD) index is proposed. It represents the averaged dose delivered in the imaged volume, directly estimated from the in-vivo measurements acquired during the scan. The TAD is validated against CTDI measurements. The results show a good agreement between the calculation and measurements when using manual volume segmentation. The automatic volume segmentation proposed in this thesis is yet to be improved to output accurate dose estimation. A dose distribution calculation model is also presented in this thesis. It automatically outputs a dose map in the imaged volume, based on the reconstructed images, the system’s geometry provided in the DICOM metadata, and the TAD. Preliminary testing against TLD measurements in a homogeneous PMMA phantom showed the feasibility of automatically outputting a dose distribution map from the MARS acquisition data. However, further work is required to better consider the impact of scattered radiation on the dose distribution calculation. The work realised during this project could help provide an early and non-invasive diagnosis for crystal arthritis, and contribute to a better patient-specific radiation dose management using the MARS systems

MARS pre-clinical imaging: the benefits of small pixels and good energy data

Images from MARS spectral CT scanners show that there is much diagnostic value from using small pixels and good energy data. MARS scanners use energy-resolving photon-counting CZT Medipix3RX detectors that measure the energy of photons on a five-point scale and with a spatial resolution of 110 microns. The energy information gives good material discrimination and quantification. The 3D reconstruction gives a voxel size of 70 microns. We present images of pre-clinical specimens, including excised atheroma, bone and joint samples, and nanoparticle contrast agents along with images from living humans. Images of excised human plaque tissue show the location and extent of lipid and calcium deposition within the artery wall. The presence of intraplaque haemorrhage, where the blood leaks into the artery wall following a rupture, has also been visualised through the detection of iron. Several clinically important bone and joint problems have been investigated including: site-specific bone mineral density, bone-metal interfaces (spectral CT reduces metal artefacts), cartilage health using ionic contrast media, gout and pseudogout crystals, and microfracture assessment using nanoparticles. Metallic nanoparticles have been investigated as a cellular marker visible in MARS images. Cell lines of different cancer types (Raji and SK-BR3) were incubated with monoclonal antibody-functionalised AuNPs (Herceptin and Rituximab). We identified and quantified the labelled AuNPs demonstrating that Herceptin-functionalised AuNPs bound to SK-BR3 breast cancer cells but not to the Raji lymphoma cells. In vivo human images show the bone microstructure. Fat, water, and calcium concentrations are quantifiable

Interactive Image Segmentation of MARS Datasets Using Bag of Features

In this article, we propose a slice-based interactive segmentation of spectral CT datasets using a bag of features method. The data are acquired from a MARS scanner that divides up the X-ray spectrum into multiple energy bins for imaging. In literature, most existing segmentation methods are limited to performing a specific task or tied to a particular imaging modality. Therefore, when applying generalized methods to MARS datasets, the additional energy information acquired from the scanner cannot be sufficiently utilized. We describe a new approach that circumvents this problem by effectively aggregating the data from multiple channels. Our method solves a classification problem to get the solution for segmentation. Starting with a set of labeled pixels, we partition the data using superpixels. Then, a set of local descriptors, extracted from each superpixel, are encoded into a codebook and pooled together to create a global superpixel-level descriptor (bag of features representation). We propose to use the vector of locally aggregated descriptors as our encoding/pooling strategy, as it is efficient to compute and leads to good results with simple linear classifiers. A linear support vector machine is then used to classify the superpixels into different labels. The proposed method was evaluated on multiple MARS datasets. Experimental results show that our method achieved an average of more than 10% increase in the accuracy over other state-of-the-art methods