ProstateAtlas SimDCE: A simulation tool for dynamic contrast enhanced imaging of prostate

Abstract

T1-weighted Dynamic Contrast Enhanced (DCE) MR imaging is emerging as a method with the potential of predicting pharmacodynamic response of biomarkers for cancer. It is becoming an important tool for the detection, staging, and monitoring of highly vascularized tumors. Pixel-wise model-based methods are used for analysis purposes through the computation of quantitative parameters maps. These methods are multi-steps algorithms and their technical and clinical implementations remain difficult. In this study, we describe a dedicated simulation tool to create dynamic images of prostate. Ground truths of the pharmacokinetics parameters maps are well known allowing an effective platform for the validation.

On the Effect of DCE MRI Slice Thickness and Noise on Estimated Pharmacokinetic Biomarkers – A Simulation Study

“…Other models include population-based models, that are obtained by averaging of CA blood plasma contrast measurements of a group of subjects. A common population-based model is the Tofts biexponential model [25,12], used for DCE simulation by [3]. The temporal resolution of the data that were used to fit the Tofts model, however, was several minutes [26].…”

“…Finally, scanner gain k and the density of protons in the tissue (P D) weigh the signal amplitude. Altogether, signal amplitude S(t), in the commonly used spoiled gradient echo acquisition sequence that we adapt, is given by Equation ( 4), which we modified from [27,3].…”

“…Similarly, [7] simulated CA-based enhancement in a DCE sequence of the abdomen acquired without CA administration, using a mathematical model. In [3], prostate cancer DCE was simulated using an anatomical atlas of the prostate. These approaches used thick-slice images of the abdomen or prostate and thus resulted in low resolution (LR) simulated DCE images that are not suitable for evaluation of super-resolution DCE images.…”

“…Simulation of higher resolution images was proposed by [4], which used a digital phantom of the brain, but did not model the partial volume effect (PVE), responsible for blurring and loss of spatial resolution. In this paper, we simulate DCE images by using the DCE signal modelling methods similar to [2,7,3] with a high resolution (HR) brain phantom published on BrainWeb: Simulated Brain Database [5], similarly as [4]. The BrainWeb phantom, however, has an important property that was not exploited in [4]: apart from having relatively high, isotropic spatial resolution, it is a fuzzy model with knowledge about the partial volume effect within each voxel.…”