What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Automatic, brain region-specific generation of arterial input functions for DCE MRI using physics informed neural network

Puntos clave

The proposed method enhances accuracy in pharmacokinetic mapping by generating brain region-specific arterial input functions.
Using simulation-based datasets, the method showed reduced noise and stabilized Ktrans variation, improving visualization of tumor regions.
This approach employs a physics informed neural network with a 1D CNN architecture for automated extraction of local arterial input functions.
The method addresses partial volume effects effectively, allowing for high reproducibility, which could benefit routine clinical DCE MRI studies.

Resumen

Motivation: AIF in DCE MRI is often compromised by partial volume effects and lacks reproducibility across different radiologists. Also, different brain regions may require different optimal AIFs, but one global AIF is used for most DCE MRI. Goal(s): To develop a method to automatically generate optimal, brain region-specific AIFs to achieve more accurate pharmacokinetic mapping in DCE-MRI. Approach: We utilized physics informed neural network (PINN) using a 1D CNN architecture and trained it using only simulation-based datasets. Results: The proposed method reduced noise, stabilized Ktrans variation, and enhanced highlighting of suspected tumor regions, as MCA influence decreased toward peripheral slices. Impact: In this study, we proposed a PINN method for end-to-end automated extraction of local AIFs from multiple tissue response functions. The proposed method can also overcome partial volume effects with high reproducibility, potentially improving routine clinical DCE studies.

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