Brightness-Invariant Tracking Estimation in Tagged MRI

2025-05-23

AI Summary

In arterial spin labeling (ASL) MRI, time-varying label intensity due to longitudinal relaxation induces substantial errors in optical flow–based motion tracking, while Fourier-based methods suffer from sensitivity to intensity variations and motion spectral broadening. To address these limitations, we propose Brightness-Invariant Tracking Estimation (BRITE), the first framework integrating denoising diffusion probabilistic models (DDPMs) with physics-informed neural networks (PINNs). DDPMs encode anatomical priors, while PINNs embed biomechanical constraints; their joint optimization decouples label patterns from anatomical background and enables end-to-end Lagrangian tissue motion estimation. BRITE exhibits exceptional robustness to intensity fluctuations and label decay. Quantitative evaluation on gel phantom experiments demonstrates significantly improved accuracy in displacement and strain estimation compared to state-of-the-art optical flow and Fourier-based approaches.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Bio-inspired Learning
• Intelligent Robots: State Estimation

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Tracking tissue motion in MRI with brightness changes
• Overcoming tag fading and spectral spreading in MRI
• Estimating Lagrangian motion despite brightness variations

Innovation

• BRITE disentangles anatomy from tag pattern
• Uses denoising diffusion probabilistic models
• Employs physics-informed neural networks