# A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images

## TL;DR

This paper introduces a generative statistical model for brain image analysis that simultaneously estimates templates, corrects bias fields, and registers images, improving robustness to variability in MRI data.

## Contribution

It presents a novel joint inference framework for template estimation, bias correction, and registration, addressing limitations of existing methods that rely on bias-invariant measures.

## Key findings

- Model accurately captures intensity heterogeneity in MRI images.
- Provides reliable template estimation from registration.
- Demonstrates effectiveness on synthetic and real data.

## Abstract

Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population variability. While building models for template estimation, variability in sites and image acquisition protocols need to be accounted for. To account for such variability, we propose a generative template estimation model that makes simultaneous inference of both bias fields in individual images, deformations for image registration, and variance hyperparameters. In contrast, existing maximum a posterori based methods need to rely on either bias-invariant similarity measures or robust image normalization. Results on synthetic and real brain MRI images demonstrate the capability of the model to capture heterogeneity in intensities and provide a reliable template estimation from registration.