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BACKGROUND: Iodine quantification using dual-energy computed tomography (DECT) is helpful in characterizing, and follow-up after treatment of tumors. Some malignant masses, for instance papillary renal cell carcinomas (p-RCC), are hard to differentiate from benign lesions because of very low contrast enhancement. In these cases, iodine concentrations might be very low, and it is therefore important that iodine quantification is reliable even at low concentrations if this technique is used. PURPOSE: To examine the accuracy of iodine quantification and to determine whether it is also accurate for low iodine concentrations. MATERIAL AND METHODS: Twenty-six syringes with different iodine concentrations (0-30 mg I/mL) were scanned in a phantom model using a DECT scanner with two different kilovoltage and image reconstruction settings. Iodine concentrations were measured and compared to known concentration. Absolute and relative errors were calculated. RESULTS: For concentrations of 1 mg I/mL or higher, there was an excellent correlation between true and measured iodine concentrations for all settings (R = 0.999-1.000; P < 0.001). For concentrations <1.0 mg I/mL, the relative error was greater. Absolute and relative errors were smaller using tube voltages of 80/Sn140 kV than 100/Sn140 kV (P < 0.01). Reconstructions using a 3.0-mm slice thickness had less variance between repeated acquisitions versus 0.6 mm (P < 0.001). CONCLUSION: Iodine quantification using DECT was in general very accurate, but for concentrations < 1.0 mg I/mL the technique was less reliable. Using a tube voltage with larger spectral separation was more accurate and the result was more reproducible using thicker image reconstructions.

A penalized ED-optimal design with a discrete parameter distribution was used to find an optimal experimental design for assessment of enzyme kinetics in a screening environment. A data set for enzyme kinetic data (Vmax and Km) was collected from previously reported studies and every Vmax/Km pair (n=76) was taken to represent a unique drug compound. The design was restricted to 15 samples, an incubation time of up to 40 min and starting concentrations (C0) for the incubation between 0.01 and 100 µM. The optimization was performed by finding the sample times and C0 returning the lowest uncertainty (SE) of the model parameter estimates. Individual optimal designs (I-OD), one general optimal design (G-OD) and one for laboratory practice pragmatically modified design (OD) were obtained. In addition, a standard design (STD-D), representing a commonly applied approach for metabolic stability investigations, was constructed. Simulations were performed for OD and STD-D using the Michaelis-Menten (MM) equation and enzyme kinetic parameters were estimated both with MM and a mono exponential (EXP) decay. OD generated a better result (RSE) for 99% of the compounds and an equal or better result (RMSE) for 78% of the compounds. Furthermore, high-quality estimates (RMSE <30%) of both Vmax and Km could be obtained for a considerable number (26%) of the investigated compounds. The results presented in this study demonstrate that the output could generally be improved when compared to that obtained from the standard approaches used today.