The aim of this study is to use a simulation environment to evaluate the potential of using photoncounting CT (PCCT) against dual-energy CT (DECT) in the context of quantitative contrastenhanced CT for radiotherapy. An adaptation of Bayesian eigentissue decomposition by Lalonde et al (2017 Med. Phys. 44 5293-302) that incorporates the estimation of contrast agent fractions and virtual non-contrast (VNC) parameters is proposed, and its performance is validated against conventional maximum likelihood material decomposition methods for single and multiple contrast agents. PCCT and DECT are compared using two simulation frameworks: one including ideal CT numbers with image-based Gaussian noise and another defined as a virtual patient with projectionbased Poisson noise and beam hardening artifacts, with both scenarios considering spectral distortion for PCCT. The modalities are compared for their accuracy in estimating four key physical parameters: (1) the contrast agent fraction, as well as VNC parameters relevant to radiotherapy such as the (2) electron density, (3) proton stopping power and (4) photon linear attenuation coefficient. Considering both simulation frameworks, a reduction of root mean square (RMS) errors with PCCT is noted for all physical parameters evaluated, with the exception of the error on the contrast agent fraction being about constant through modalities in the virtual patient. Notably, for the virtual patient, RMS errors on VNC electron density and stopping power are respectively reduced from 2.0% to 1.4% and 2.7% to 1.4% when going from DECT to PCCT with four energy bins. The increase in accuracy is comparable to the differences between contrast-enhanced and non-contrast DECT. This study suggests that in a realistic simulation environment, the overall accuracy of radiotherapyrelated parameters can be increased when using PCCT with four energy bins instead of DECT. This confirms the potential of PCCT to provide robust and quantitative tissue parameters for contrast-enhanced CT required in radiotherapy applications.

A hyperspectral microscopy system based on a reflected light method for plasmonic nanoparticle (NP) imaging was designed and compared with a conventional darkfield method for spatial localization and spectroscopic identification of single Au, Ag and Au/Ag alloy NPs incubated with fixed human cancer cell preparations. A new synthesis protocol based on co-reduction of Au and Ag salts combined with the seeded growth technique was used for the fabrication of monodispersed alloy NPs with sizes ranging from 30 to 100 nm in diameter. We validated theoretically and experimentally the performance of 60 nm Au, Ag and Au/Ag (50 : 50) NPs as multiplexed biological chromatic markers for biomedical diagnostics and optical biosensing. The advantages of the proposed reflected light microscopy method are presented for NP imaging in a complex and highly diffusing medium such as a cellular environment. The obtained information is essential for the development of a high throughput, selective and efficient strategy for cancer detection and treatment.

The purpose of this work is to evaluate the impact of single-, dual- and multi-energy CT (SECT, DECT and MECT) on proton range uncertainties in a patient like geometry and a full Monte Carlo environment. A virtual patient is generated from a real patient pelvis CT scan, where known mass densities and elemental compositions are overwritten in each voxel. Simulated CT images for SECT, DECT and MECT are generated for two limiting cases: (1) theoretical and idealistic CT numbers only affected by Gaussian noise (case A, the best scenario) and (2) reconstructed polyenergetic sinograms containing beam hardening, projection-based Poisson noise, and reconstruction artifacts (case B, the worst scenario). Conversion of the simulated SECT images into Monte Carlo inputs is done following the stoichiometric calibration method. For DECT and MECT, the Bayesian eigentissue decomposition method of Lalonde (2017 Med. Phys. 44 5293-302) is used. Pencil beams from seven different angles around the virtual patient are simulated using TOPAS to assess the performance of each method. Percentage depth doses curves (PDD) are compared to ground truth in order to determine the accuracy of range prediction of each imaging modality. For the idealistic images of case A, MECT and DECT slightly outperforms SECT. Root mean square (RMS) errors or 0.78 mm, 0.49 mm and 0.42 mm on R mm, are observed for SECT, DECT and MECT respectively. In case B, PDD calculated in the MECT derived Monte Carlo inputs generally shows the best agreement with ground truth in both shape and position, with RMS errors of 2.03 mm, 1.38 mm and 0.86 mm for SECT, DECT and MECT respectively. Overall, the Bayesian eigentissue decomposition used with DECT systematically predicts proton ranges more accurately than the gold standard SECT-based approach. When CT numbers are severely affected by imaging artifacts, MECT with four energy bins becomes more reliable than both DECT and SECT.