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

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

Assessment of metal implant induced artefacts using photon counting spectral CT

The aim is to perform qualitative and quantitative assessment of metal induced artefacts of small titanium biomaterials using photon counting spectral CT. The energy binning feature of some photon counting detectors enables the measured spectrum to be segmented into low, mid and high energy bins in a single exposure. In this study, solid and porous titanium implants submerged in different concentrations of calcium solution were scanned using the small animal MARS photon counting spectral scanner equipped with a polyenergetic X-ray source operated at 118 kVp. Five narrow energy bins (7-45 keV, 45-55 keV, 55-65 keV, 65-75 keV and 75-118 keV) in charge summing mode were utilised. Images were evaluated in the energy domain (spectroscopic images) as well as material domain (material segmentation and quantification). Results show that calcium solution outside titanium implants can be accurately quantified. However, there was an overestimation of calcium within the pores of the scaffold. This information is critical as it can severely limit the assessment of bone ingrowth within metal structures. The energy binning feature of the spectral scanner was exploited and a correction factor, based on calcium concentrations adjacent to and within metal structures, was used to minimise the variation. Qualitative and quantitative evaluation of bone density and morphology with and without titanium screw shows that photon counting spectral CT can assess bone-metal interface with less pronounced artefacts. Quantification of bone growth in and around the implants would help in orthopaedic applications to determine the effectiveness of implant treatment and assessment of fracture healing

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

Medipix3RX neutron camera for ambient radiation measurements in the CMS cavern

We describe a CMS-Medipix3RX neutron camera developed by adapting and modifying detector readout electronics developed at the University of Canterbury. The readout electronics are part of the MARS x-ray scanner used for imaging applications [1]. The neutron cameras will be used for the precise evaluation of complex radiation fields in and around the Compact Muon Solenoid (CMS) detector on the Large Hadron Collider (LHC) at CERN. This evaluation will help to ascertain the performance of various sub-systems installed in the cavern as well as to predict their useful lifetimes. Medipix3RX detector can deliver real-time images of the flux and spectral composition of different particles, including slow and fast neutrons. In this neutron camera, slow neutrons are detected using a lithium fluoride conversion layer and fast neutrons by a polypropylene layer. These produce charged particles, which are then detected by a semiconductor sensor material, silicon. We modelled the mixed-field radiation at seven Medipix detector locations in the cavern by scoring the particle travelling through the detector location using FOCUS, a Monte-Carlo simulation tool, analysing the energy as well as their angular distributions of neutrons from the result of simulations.A good agreement was observed between the average flux predicted by standard FLUKA methods and those obtained from FOCUS output data integrated over time. Also, the response function of the Medipix detectors was modelled and simulated for different thicknesses of the neutron conversion layer. An algorithm was developed for track reconstruction and recognition using cluster analysis techniques. This labels and determines the density of clusters formed by groups of particles. The CMS-Medipix detectors with their conversion layers were calibrated in the CERN neutron facility and installed in the CMS cavern at the beginning of 2018. This paper discusses the calibration of the detector installation and presents early results of radiation measurements from 2018 run