# Brightness-Invariant Tracking Estimation in Tagged MRI

arXiv: https://arxiv.org/abs/2505.18365 · HTML: https://mlsanity.com/paper/2505.18365/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 6 of 6)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Compressed Sensing using Generative Models](https://mlsanity.com/paper/1703.03208/) (2017)
- [DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images](https://mlsanity.com/paper/2103.02772/) (2021)
- [New starting point registration method for tagged MRI tongue motion estimation](https://mlsanity.com/paper/2302.04330/) (2023)
- [Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?](https://mlsanity.com/paper/2401.17571/) (2024)

## Built on by (most cited first, 1 of 1)

- [Solving a Nonlinear Blind Inverse Problem for Tagged MRI with Physics and Deep Generative Priors](https://mlsanity.com/paper/2603.00882/) (2026)

## Abstract (arXiv)

Magnetic resonance (MR) tagging is an imaging technique for noninvasively tracking tissue motion in vivo by creating a visible pattern of magnetization saturation (tags) that deforms with the tissue. Due to longitudinal relaxation and progression to steady-state, the tags and tissue brightnesses change over time, which makes tracking with optical flow methods error-prone. Although Fourier methods can alleviate these problems, they are also sensitive to brightness changes as well as spectral spreading due to motion. To address these problems, we introduce the brightness-invariant tracking estimation (BRITE) technique for tagged MRI. BRITE disentangles the anatomy from the tag pattern in the observed tagged image sequence and simultaneously estimates the Lagrangian motion. The inherent ill-posedness of this problem is addressed by leveraging the expressive power of denoising diffusion probabilistic models to represent the probabilistic distribution of the underlying anatomy and the flexibility of physics-informed neural networks to estimate biologically-plausible motion. A set of tagged MR images of a gel phantom was acquired with various tag periods and imaging flip angles to demonstrate the impact of brightness variations and to validate our method. The results show that BRITE achieves more accurate motion and strain estimates as compared to other state of the art methods, while also being resistant to tag fading.