Subject(s)
Buprenorphine , Opiate Substitution Treatment , Opioid-Related Disorders , Buprenorphine/therapeutic use , Buprenorphine/administration & dosage , Humans , Opioid-Related Disorders/drug therapy , Opiate Substitution Treatment/methods , Narcotic Antagonists/therapeutic use , Narcotic Antagonists/administration & dosageABSTRACT
[This corrects the article DOI: 10.1117/1.JMI.2.3.033502.].
ABSTRACT
Variations among detector channels in computed tomography can lead to ring artifacts in the reconstructed images and biased estimates in projection-based material decomposition. Typically, the ring artifacts are corrected by compensation methods based on flat fielding, where transmission measurements are required for a number of material-thickness combinations. Phantoms used in these methods can be rather complex and require an extensive number of transmission measurements. Moreover, material decomposition needs knowledge of the individual response of each detector channel to account for the detector inhomogeneities. For this purpose, we have developed a spectral response model that binwise predicts the response of a multibin photon-counting detector individually for each detector channel. The spectral response model is performed in two steps. The first step employs a forward model to predict the expected numbers of photon counts, taking into account parameters such as the incident x-ray spectrum, absorption efficiency, and energy response of the detector. The second step utilizes a limited number of transmission measurements with a set of flat slabs of two absorber materials to fine-tune the model predictions, resulting in a good correspondence with the physical measurements. To verify the response model, we apply the model in two cases. First, the model is used in combination with a compensation method which requires an extensive number of transmission measurements to determine the necessary parameters. Our spectral response model successfully replaces these measurements by simulations, saving a significant amount of measurement time. Second, the spectral response model is used as the basis of the maximum likelihood approach for projection-based material decomposition. The reconstructed basis images show a good separation between the calcium-like material and the contrast agents, iodine and gadolinium. The contrast agent concentrations are reconstructed with more than 94% accuracy.
ABSTRACT
Photon-counting detectors are promising candidates for use in the next generation of x-ray computed tomography (CT) scanners. Among the foreseen benefits are higher spatial resolution, better trade-off between noise and dose and energy discriminating capabilities. Silicon is an attractive detector material because of its low cost, mature manufacturing process and high hole mobility. However, it is sometimes overlooked for CT applications because of its low absorption efficiency and high fraction of Compton scatter. The purpose of this work is to demonstrate that silicon is a feasible material for CT detectors by showing energy-resolved CT images acquired with an 80 kVp x-ray tube spectrum using a photon-counting silicon-strip detector with eight energy thresholds developed in our group. We use a single detector module, consisting of a linear array of 50 0.5×0.4 mm detector elements, to image a phantom in a table-top lab setup. The phantom consists of a plastic cylinder with circular inserts containing water, fat and aqueous solutions of calcium, iodine and gadolinium, in different concentrations. By using basis material decomposition we obtain water, calcium, iodine and gadolinium basis images and demonstrate that these basis images can be used to separate the different materials in the inserts. We also show results showing that the detector has potential for quantitative measurements of substance concentrations.
