Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast

Abstract

Purpose A method is presented to select the optimal time points at which to measure DCE‐MRI signal intensities, leaving time in the MR exam for high‐spatial resolution image acquisition. Theory Simplicial complexes are generated from the Kety‐Tofts model pharmacokinetic parameters Ktrans and ve. A geometric search selects optimal time points for accurate estimation of perfusion parameters. Methods The DCE‐MRI data acquired in women with invasive breast cancer (N = 27) were used to retrospectively compare parameter maps fit to full and subsampled time courses. Simplicial complexes were generated for a fixed range of Kety‐Tofts model parameters and for the parameter ranges weighted by estimates from the fully sampled data. The largest‐area manifolds determined the optimal three time points for each case. Simulations were performed along with retrospectively subsampled data fits. The agreement was computed between the model parameters fit to three points and those fit to all points. Results The optimal three‐point sample times were from the data‐informed simplicial complex analysis and determined to be 65, 204, and 393 s after arrival of the contrast agent to breast tissue. In the patient data, tumor‐median parameter values fit using all points and the three selected time points agreed with concordance correlation coefficients of 0.97 for Ktrans and 0.67 for ve. Conclusion It is possible to accurately estimate pharmacokinetic parameters from three properly selected time points inserted into a clinical DCE‐MRI breast exam. This technique can provide guidance on when to capture images for quantitative data between high‐spatial‐resolution DCE‐MRI images.

Structural and practical identifiability of contrast transport models for DCE-MRI

“…Contrast agent transport models have been developed and used for decades in the analysis of concentration time course data derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and they are essential elements of many computational clinical applications [1][2][3][4][5][6][7][8][9]. DCE-MRI transport models are based on linear ordinary differential equations with compartments describing the concentration of the contrast agent (CA) in tissue and vasculature.…”

Structural and practical identifiability of contrast transport models for DCE-MRI

“…Contrast agent transport models have been developed and used for decades in the analysis of concentration time course data derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and are essential elements of many computational biology approaches [1, 2, 3, 4, 5, 6]. DCE-MRI transport models are linear ordinary differential equations with compartments describing the concentration of the contrast agent (CA) in tissue and vasculature.…”