Towards 3D ultrasound image based soft tissue tracking: a transrectal ultrasound prostate image alignment system

TL;DR

This paper introduces a fast, robust 3D ultrasound-based system for tracking prostate movement during biopsy, improving targeting accuracy and enabling better diagnosis and treatment monitoring.

Contribution

It presents a novel multi-resolution registration approach combining global and local optimization for accurate prostate tracking in real-time 3D ultrasound imaging.

Findings

Achieved 96.7% convergence rate in 6.5s for 3D-3D registration.

Attained 88.9% success rate in 2.3s for 3D to slices registration.

Provided sub-2mm accuracy in prostate position estimation.

Abstract

The emergence of real-time 3D ultrasound (US) makes it possible to consider image-based tracking of subcutaneous soft tissue targets for computer guided diagnosis and therapy. We propose a 3D transrectal US based tracking system for precise prostate biopsy sample localisation. The aim is to improve sample distribution, to enable targeting of unsampled regions for repeated biopsies, and to make post-interventional quality controls possible. Since the patient is not immobilized, since the prostate is mobile and due to the fact that probe movements are only constrained by the rectum during biopsy acquisition, the tracking system must be able to estimate rigid transformations that are beyond the capture range of common image similarity measures. We propose a fast and robust multi-resolution attribute-vector registration approach that combines global and local optimization methods to solve this problem. Global optimization is performed on a probe movement model that reduces the dimensionality of the search space and thus renders optimization efficient. The method was tested on 237 prostate volumes acquired from 14 different patients for 3D to 3D and 3D to orthogonal 2D slices registration. The 3D-3D version of the algorithm converged correctly in 96.7% of all cases in 6.5s with an accuracy of 1.41mm (r.m.s.) and 3.84mm (max). The 3D to slices method yielded a success rate of 88.9% in 2.3s with an accuracy of 1.37mm (r.m.s.) and 4.3mm (max).