We have developed a 3D dynamic contrast-enhanced (DCE) multislice CT protocol that covers the complete prostate. The DCE-CT data are subsequently analysed using the adiabatic approximation of the tissue homogeneity model resulting in five 3D quantitative maps of blood flow, mean transit time, extraction fraction, extracellular extravascular space and delay time. The purpose of this study was to establish the feasibility of determining these parameters in the prostate with a spatial resolution as high as approximately 0.1 cc as well as a good measurement precision. The precision of the parameter estimation as a function of noise level is determined by a Monte Carlo-based method that simulates the effect of noise present in the data. We find that the precision depends on the value of the flow and transit time, where a higher value is favourable. At a noise level of 4 HU in combination with a peak enhancement in the iliac arteries of approximately 300 HU the 95% confidence intervals are sufficiently small to discriminate whether a parameter value is above or below a given threshold. We have collected and analysed the noise level in the DCE-data of five patients. A noise level of 3.8 HU on average can be obtained by averaging to a voxel volume of 4.5 x 4.5 x 5 mm(3) = 0.1 cc. Analysis of the parameter maps shows that it is feasible to detect both small and large lesions, as well as irregularly shaped lesions.

No abstract

No abstract

No abstract

Transrectal implantation of gold markers in the prostate bed is feasible and safe. Alternatives like cone beam computed tomography (CBCT) should be considered, but the advantages of gold marker implantation for high-precision postprostatectomy RT would seem to outweigh the minor risks involved.

No abstract

No abstract

Purpose: To achieve a higher sensitivity and specificity in the localization of the GTV within the prostate using quantitative perfusion maps obtained from dynamic contrast enhanced CT (DCE‐CT) imaging. Method and Materials: DCE‐CT tracks the passage of a bolus of contrast agent through the prostate in time. With a 40 slice CT scanner we scan a large volume of the prostate with a high temporal (1.5 s) and spatial (0.5×0.5×5 mm) resolution. Moreover, because of the linearity between CT signal enhancement and contrast concentration increase, quantitative analysis is greatly facilitated. We calculate quantitative perfusion maps of the complete prostate by using the tissue homogeneity model, which determines the response of a voxel to the measured input bolus. Furthermore, we have performed an extensive signal‐to‐noise analysis to determine the optimum between voxel size and flow accuracy. We have included 11 patients in our study with biopsy proven prostate cancer (stage T3). The CT exam was performed prior to external beam radiotherapy. Results: For all 11 patients we have successfully calculated perfusion maps with an accuracy of 10% and a resolution of 1.5×1.5×5 mm. We find the average perfusion of the prostate to be 15 ml/100g/min, while the elevated regions have average values of 60–70 ml/100g/min. These hotspots, which are identified as the GTV, can be delineated in three dimensions based on the flow maps and are found to correlate well with the clinically known GTV. Conclusion: Using DCE‐CT we can determine three‐dimensional quantitative perfusion maps of the complete prostate. Elevated flow regions correlate well with the clinically known GTV, and may be of value to improve delineation of the GTV within the prostate.