Tracer kinetic models as temporal constraints during brain tumor DCE-MRI reconstruction.

Sajan Goud Lingala,Yi Guo,Meng Law,R Marc Lebel,Krishna S Nayak,Yannick Bliesener,Yinghua Zhu

doi:10.1002/mp.13885

Abstract

PurposeTo apply tracer kinetic models as temporal constraints during reconstruction of under‐sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI).MethodsA library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under‐sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in‐vivo 3T datasets. The performances of the proposed under‐sampled reconstruction scheme and an existing compressed sensing‐based temporal finite‐difference (tFD) under‐sampled reconstruction were compared against the fully sampled inverse Fourier Transform‐based reconstruction.ResultsSimulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts‐Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO‐based experiments showed good fidelity in recovery of kinetic maps from 20‐fold under‐sampled data. In‐vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under‐sampled reduction factors >= 20.ConclusionsTracer kinetic models can be applied as temporal constraints during brain tumor DCE‐MRI reconstruction. The proposed under‐sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters.

Highlights

Dynamic Contrast Enhanced MRI (DCE-MRI) is a powerful technique that provides a quantitative measure of vessel permeability and interstitial volumes
We propose a model-constrained approach for dynamic contrast enhanced (DCE)-MRI, where established contrast-agent kinetic models are used as temporal constraints
Concentration vs. time profiles in the library, and the profiles obtained from qsparse projections onto V at different sparsity levels (q). q-sparse projections of the curves generated from the Patlak and the extended Tofts-Kety (ETK) models are respectively shown in Fig

Summary

Introduction

Dynamic Contrast Enhanced MRI (DCE-MRI) is a powerful technique that provides a quantitative measure of vessel permeability and interstitial volumes In the brain, it characterizes the blood brain barrier (BBB) leakiness, which has proven to be valuable in several applications 1. Acceleration strategies that exploit redundancies along the time dimension have shown significant potential to improve these trade-offs These include early schemes such as view-sharing 121314, highly constrained back projection (HYPR) 15, and more recently compressed sensing 161718192021. Data-driven schemes that learn sparse representations from the data have been proposed 22–25, and have shown to out perform off-the shelf transforms. These are often associated with highly non-convex optimization. Image reconstruction with existing transforms involves tuning one or more regularization parameters, which poses challenges to the standardization of these methods

Methods

Results

Conclusion