# Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI

## TL;DR

This study introduces a Bayesian hierarchical modeling approach incorporating spatial information to improve contrast agent quantification in dynamic contrast-enhanced MRI, demonstrating enhanced accuracy in simulations.

## Contribution

The paper presents a novel application of Bayesian hierarchical models with spatial priors for improved contrast agent quantification in MRI, including analysis for both small and large images.

## Key findings

- BHMs outperform existing methods in small images.
- Leroux model outperforms Besag models.
- MAP estimators with Leroux prior improve results for larger images.

## Abstract

The purpose of this study is to investigate a method, using simulations, to improve contrast agent quantification in Dynamic Contrast Enhanced MRI. Bayesian hierarchical models (BHMs) are applied to smaller images ($10\times10\times10$) such that spatial information can be incorporated. Then exploratory analysis is done for larger images ($64\times64\times64$) by using maximum a posteriori (MAP). For smaller images: the estimators of proposed BHMs show improvements in terms of the root mean squared error compared to the estimators in existing method for a noise level equivalent of a 12-channel head coil at 3T. Moreover, Leroux model outperforms Besag models. For larger images: MAP estimators also show improvements by assigning Leroux prior.