Pharmacokinetic modeling of dynamic contrast‐enhanced MRI using a reference region and input function tail

Abstract

Purpose Quantitative analysis of dynamic contrast‐enhanced MRI (DCE‐MRI) requires an arterial input function (AIF) which is difficult to measure. We propose the reference region and input function tail (RRIFT) approach which uses a reference tissue and the washout portion of the AIF. Methods RRIFT was evaluated in simulations with 100 parameter combinations at various temporal resolutions (5‐30 s) and noise levels (σ = 0.01‐0.05 mM). RRIFT was compared against the extended Tofts model (ETM) in 8 studies from patients with glioblastoma multiforme. Two versions of RRIFT were evaluated: one using measured patient‐specific AIF tails, and another assuming a literature‐based AIF tail. Results RRIFT estimated the transfer constant Ktrans and interstitial volume ve with median errors within 20% across all simulations. RRIFT was more accurate and precise than the ETM at temporal resolutions slower than 10 s. The percentage error of Ktrans had a median and interquartile range of −9 ± 45% with the ETM and −2 ± 17% with RRIFT at a temporal resolution of 30 s under noiseless conditions. RRIFT was in excellent agreement with the ETM in vivo, with concordance correlation coefficients (CCC) of 0.95 for Ktrans, 0.96 for ve, and 0.73 for the plasma volume vp using a measured AIF tail. With the literature‐based AIF tail, the CCC was 0.89 for Ktrans, 0.93 for ve and 0.78 for vp. Conclusions Quantitative DCE‐MRI analysis using the input function tail and a reference tissue yields absolute kinetic parameters with the RRIFT method. This approach was viable in simulation and in vivo for temporal resolutions as low as 30 s.

Reproducible Research Practices in Magnetic Resonance Neuroimaging: A Review Informed by Advanced Language Models

“…Transparent reconstruction and analysis pipelines are also prominently featured in the reproducible research insights, including methods for real-time MRI, 21 parallel imaging, 22 large-scale volumetric dynamic imaging, 23 pharmacokinetic modeling of dynamic contrast-enhanced MRI (DCE-MRI), 24 phase unwrapping, 25 hyperpolarized MRI, 26 Dixon imaging, 27 and X-nuclei imaging. 28 Deep learning is increasingly present in the reproducibility conversation, as MRI researchers are trying to shine a light on AI-driven workflows for phase-focused applications, 29 CEST, 14 diffusion-weighted imaging, 30 myelin water imaging, 18 B 1 estimation, 31 and tissue segmentation.…”