Entropy-Minimizing Active Sensing for Model-Based Anatomical Target Tracking

This study presents an active sensing framework for information-optimal, model-based tracking of de formable anatomy to track the three-dimensional (3D) surface of the heart ventricles using a time sequence of two-dimensional (2D) image slices.

A low-order deformable model parameterizes the cardiac surface, while cardiac motion dynamics are modeled by a recursive adaptive filter and tracked using a particle filter. Measurements of the system state are obtained from a magnetic resonance imaging (MRI) system whose slice selection is governed by an entropy-minimizing active sensing method that chooses the sensing action maximizing expected information gain. Performance is compared with cases where the image slice is fixed or randomly selected. The framework operates on general 2D image streams and is validated on multi-slice cine MRI data to enable comparison against ground-truth slice locations. A variable temporal sampling strategy reduces computational load by executing tracking updates at intervals defined by a temporal sampling factor.

The active sensing-based tracking method captured left ventricular (LV) surface points-of-interest within 3 pixels of accuracy at a mean root-mean-square error (RMSE) of 2.93mm, right ventricular (RV) tracking was 4.27mm, for an overall mean RMSE of 3.25mm. For a downsampling factor of 4, the framework maintains an overall RMSE of 3.57mm, a modest degradation relative to fully sampled tracking.

The proposed frame work enables a flexible trade-off between computational efficiency and tracking performance.

This work demonstrates that information-driven measurement selection can enhance de formable anatomical tracking under sensing constraints.