Beam profile assessment in spectral CT scanners.

In this paper, we present a method that uses a combination of experimental and modeled data to assess properties of x-ray beam measured using a small-animal spectral scanner. The spatial properties of the beam profile are characterized by beam profile shape, the angular offset along the rotational axis, and the photon count difference between experimental and modeled data at the central beam axis. Temporal stability of the beam profile is assessed by measuring intra- and interscan count variations. The beam profile assessment method was evaluated on several spectral CT scanners equipped with Medipix3RX-based detectors. On a well-calibrated spectral CT scanner, we measured an integral count error of 0.5%, intrascan count variation of 0.1%, and an interscan count variation of less than 1%. The angular offset of the beam center ranged from 0.8° to 1.6° for the studied spectral CT scanners. We also demonstrate the capability of this method to identify poor performance of the system through analyzing the deviation of the experimental beam profile from the model. This technique can, therefore, aid in monitoring the system performance to obtain a robust spectral CT; providing the reliable quantitative images. Furthermore, the accurate offset parameters of a spectral scanner provided by this method allow us to incorporate a more realistic form of the photon distribution in the polychromatic-based image reconstruction models. Both improvements of the reliability of the system and accuracy of the volume reconstruction result in a better discrimination and quantification of the imaged materials.

Quantitative imaging of excised osteoarthritic cartilage using spectral CT.

OBJECTIVES: To quantify iodine uptake in articular cartilage as a marker of glycosaminoglycan (GAG) content using multi-energy spectral CT. METHODS: We incubated a 25-mm strip of excised osteoarthritic human tibial plateau in 50 % ionic iodine contrast and imaged it using a small-animal spectral scanner with a cadmium telluride photon-processing detector to quantify the iodine through the thickness of the articular cartilage. We imaged both spectroscopic phantoms and osteoarthritic tibial plateau samples. The iodine distribution as an inverse marker of GAG content was presented in the form of 2D and 3D images after applying a basis material decomposition technique to separate iodine in cartilage from bone. We compared this result with a histological section stained for GAG. RESULTS: The iodine in cartilage could be distinguished from subchondral bone and quantified using multi-energy CT. The articular cartilage showed variation in iodine concentration throughout its thickness which appeared to be inversely related to GAG distribution observed in histological sections. CONCLUSIONS: Multi-energy CT can quantify ionic iodine contrast (as a marker of GAG content) within articular cartilage and distinguish it from bone by exploiting the energy-specific attenuation profiles of the associated materials. KEY POINTS: • Contrast-enhanced articular cartilage and subchondral bone can be distinguished using multi-energy CT. • Iodine as a marker of glycosaminoglycan content is quantifiable with multi-energy CT. • Multi-energy CT could track alterations in GAG content occurring in osteoarthritis.

Discrimination Between Calcium Hydroxyapatite and Calcium Oxalate Using Multienergy Spectral Photon-Counting CT.

OBJECTIVE: We aimed to determine whether multienergy spectral photon-counting CT could distinguish between clinically relevant calcium crystals at clinical x-ray energy ranges. Energy thresholds of 15, 22, 29, and 36 keV and tube voltages of 50, 80, and 110 kVp were selected. Images were analyzed to assess differences in linear attenuation coefficients between various concentrations of calcium hydroxyapatite (54.3, 211.7, 808.5, and 1169.3 mg/cm3) and calcium oxalate (2000 mg/cm3). CONCLUSION: The two lower concentrations of hydroxyapatite were distinguishable from oxalate at all energy thresholds and tube voltages, whereas discrimination at higher concentrations depended primarily on the energy thresholds used. Multienergy spectral photon-counting CT shows promise for distinguishing these calcium crystals.

Assessment of Mixed Sward Using Context Sensitive Convolutional Neural Networks.

Breeding higher yielding forage species is limited by current manual harvesting and visual scoring techniques used for measuring or estimation of biomass. Automation and remote sensing for high throughput phenotyping has been used in recent years as a viable solution to this bottleneck. Here, we focus on using RGB imaging and deep learning for white clover (Trifolium repens L.) and perennial ryegrass (Lolium perenne L.) yield estimation in a mixed sward. We present a new convolutional neural network (CNN) architecture designed for semantic segmentation of dense pasture and canopies with high occlusion to which we have named the local context network (LC-Net). On our testing data set we obtain a mean accuracy of 95.4% and a mean intersection over union of 81.3%, outperforming other methods we have found in the literature for segmenting clover from ryegrass. Comparing the clover/vegetation fraction for visual coverage and harvested dry-matter however showed little improvement from the segmentation accuracy gains. Further gains in biomass estimation accuracy may be achievable through combining RGB with complimentary information such as volumetric data from other sensors, which will form the basis of our future work.