Online learning in motion modeling for intra-interventional image sequences

TL;DR

This paper introduces a probabilistic motion model for sequential medical images that estimates and forecasts motion, improving image guidance during medical examinations through online learning and patient-specific adaptation.

Contribution

A novel low-dimensional linear Gaussian state-space model for motion estimation and forecasting in medical image sequences with online adaptation capabilities.

Findings

Reliable motion estimates demonstrated on cardiac datasets

Improved forecasting performance with online learning

Effective imputation of missing images

Abstract

Image monitoring and guidance during medical examinations can aid both diagnosis and treatment. However, the sampling frequency is often too low, which creates a need to estimate the missing images. We present a probabilistic motion model for sequential medical images, with the ability to both estimate motion between acquired images and forecast the motion ahead of time. The core is a low-dimensional temporal process based on a linear Gaussian state-space model with analytically tractable solutions for forecasting, simulation, and imputation of missing samples. The results, from two experiments on publicly available cardiac datasets, show reliable motion estimates and an improved forecasting performance using patient-specific adaptation by online learning.