Model-based reconstruction for quantitative DCE-MRI parameters

In quantitative dynamic contrast-enhanced MRI (DCE-MRI), a fundamental trade-off exists between imaging speed, spatial resolution, and signal-to-noise ratio (SNR), driven by the amount of data acquired per dynamic frame. This work proposes a model-based reconstruction (MBR) framework for directly estimating pharmacokinetic parameters from raw k-space data, eliminating the need for intermediate image reconstruction and potentially mitigating this trade-off.

The extended Tofts model for pharmacokinetic modeling was integrated into an MBR framework-PyQMRI (code is shared). To validate the approach in a controlled setting, a simulated digital phantom of the abdominal region was used. Pharmacokinetic parameters were generated, and corresponding k-space data were calculated based on these values. Additionally, the feasibility of MBR was evaluated in vivo using liver DCE-MRI data from a healthy volunteer. The performance of MBR was compared to a conventional image-based fitting approach using a non-linear least-squares (NLLS) algorithm.

In simulations, MBR showed superior performance, producing more precise pharmacokinetic maps with accuracy comparable to or exceeding that of traditional image-based fitting. Notably, fine anatomical structures, such as blood vessels, were more clearly defined with MBR. This improvement was consistent across different temporal resolutions (1.5-16.5 s/frame). MBR did not show any sign of image degradation for shorter frame rates, and, in fact, performed best with the shortest tested frame rate (1.5 s/frame), highlighting the robustness of MBR image quality to higher frame rates. In vivo, while the improvements offered by MBR were consistent with simulation results, they were less pronounced. Several aspects could have contributed to this discrepancy, including a difference between the simple extended Tofts model and the complex true in vivo pharmacokinetics, and data degradation due to motion combined with a complex landscape of the DCE loss.

The proposed MBR framework offers a promising alternative to traditional DCE-MRI workflows by avoiding intermediate image reconstruction and relaxing the spatio-temporal trade-off. This approach may enable more accurate and robust estimation of pharmacokinetic parameters, particularly in scenarios where imaging constraints are severe.