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Updated: Jun 6, 2026
Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System
Published on: March 6, 2019
Targeting error simulator for image-guided prostate needle placement
Andras Lasso1, Shachar Avni, Gabor Fichtinger
1School of Computing, Queen's University, Kingston, Canada K7L3N6. lasso@cs.queensu.ca
Motivation:
Needle-based biopsy and local therapy of prostate cancer depend multimodal imaging for both target planning and needle guidance. The clinical process involves selection of target locations in a pre-operative image volume and registering these to an intra-operative volume. Registration inaccuracies inevitably lead to targeting error, a major clinical concern. The analysis of targeting error requires a large number of images with known ground truth, which has been infeasible even for the largest research centers.
Methods:
We propose to generate realistic prostate imaging data in a controllable way, with known ground truth, by simulation of prostate size, shape, motion and deformation typically encountered in prostatic needle placement. This data is then used to evaluate a given registration algorithm, by testing its ability to reproduce ground truth contours, motions and deformations. The method builds on statistical shape atlas to generate large number of realistic prostate shapes and finite element modeling to generate high-fidelity deformations, while segmentation error is simulated by warping the ground truth data in specific prostate regions. Expected target registration error (TRE) is computed as a vector field.
Results:
The simulator was configured to evaluate the TRE when using a surface-based rigid registration algorithm in a typical prostate biopsy targeting scenario. Simulator parameters, such as segmentation error and deformation, were determined by measurements in clinical images. Turnaround time for the full simulation of one test case was below 3 minutes. The simulator is customizable for testing, comparing, optimizing segmentation and registration methods and is independent of the imaging modalities used.
Insights
This study introduces a novel prostate imaging simulator to generate realistic data for evaluating needle-based biopsy guidance. The simulator accurately models anatomical variations and deformations, enabling robust assessment of registration algorithms and reducing targeting errors in prostate cancer treatment.
Area of Science:
- Medical Imaging
- Computational Anatomy
- Robotics in Medicine
Background:
- Prostate cancer diagnosis and treatment rely on accurate image-guided needle placement.
- Registration inaccuracies between pre-operative and intra-operative imaging lead to significant targeting errors.
- Evaluating these errors requires extensive datasets with known ground truth, which are currently infeasible to obtain.
Purpose of the Study:
- To develop a controllable simulation method for generating realistic prostate imaging data with known ground truth.
- To enable robust evaluation and comparison of image registration algorithms for prostate interventions.
- To reduce targeting errors in needle-based prostate cancer biopsy and therapy.
Main Methods:
- Utilized statistical shape atlases for generating diverse and realistic prostate shapes.
- Employed finite element modeling to simulate high-fidelity prostate motion and deformation during needle placement.
- Incorporated simulated segmentation errors by warping ground truth data to mimic clinical variability.
- Computed expected target registration error (TRE) as a vector field.
Main Results:
- The simulator was successfully configured to evaluate target registration error (TRE) for a surface-based rigid registration algorithm.
- Simulation parameters were informed by clinical image measurements of segmentation error and deformation.
- Full simulation of a test case was completed in under 3 minutes.
- The simulator demonstrated independence from specific imaging modalities.
Conclusions:
- The developed simulator provides a controllable and efficient method for generating realistic prostate imaging data.
- It enables rigorous testing, comparison, and optimization of segmentation and registration algorithms.
- This tool has the potential to significantly improve the accuracy of image-guided prostate interventions.
