Anatomical DCE-MRI phantoms generated from glioma patient data

Several digital reference objects (DROs) for DCE-MRI have been created to test the accuracy of pharmacokinetic modeling software under a variety of different noise conditions. However, there are few DROs that mimic the anatomical distribution of voxels found in real data, and similarly few DROs that are based on both malignant and normal tissue. We propose a series of DROs for modeling K_trans and V_e derived from a publically-available RIDER DCEMRI dataset of 19 patients with gliomas. For each patient's DCE-MRI data, we generate K_trans and V_e parameter maps using an algorithm validated on the QIBA Tofts model phantoms. These parameter maps are denoised, and then used to generate noiseless time-intensity curves for each of the original voxels. This is accomplished by reversing the Tofts model to generate concentration-times curves from K_trans and V_e inputs, and subsequently converting those curves into intensity values by normalizing to each patient's average pre-bolus image intensity. The result is a noiseless DRO in the shape of the original patient data with known ground-truth K_trans and V_e values. We make this dataset publically available for download for all 19 patients of the original RIDER dataset.