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SU‐D‐213CD‐01: 4D Ultrasound Calibration for Radiotherapy Guidance Using Automatic Intramodality Image Registration
Author(s) -
Schlosser J,
Kirmizibayrak C,
Shamdasani V,
Metz S,
Hristov D
Publication year - 2012
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4734685
Subject(s) - fiducial marker , imaging phantom , centroid , computer vision , calibration , artificial intelligence , computer science , image registration , volume (thermodynamics) , 3d ultrasound , medical imaging , tracking (education) , image guided radiation therapy , orientation (vector space) , ultrasound , nuclear medicine , image (mathematics) , mathematics , physics , medicine , acoustics , psychology , pedagogy , statistics , geometry , quantum mechanics
Purpose: In prior work we developed a robotic system providing real‐time soft‐tissue ultrasound (US) volumes during radiotherapy beam delivery. for image guidance, the US volumes must be transformed to the linear accelerator reference frame. In this work we propose and characterize a new method of calibrating 4D US volumes based on automatic intramodality image registration. Methods: A dynamic navigation link was used to port 3D US volumes from a Philips iU22 xMatrix machine to a PC in real‐time. Sixty volumetric (3D) US images of a pelvic phantom were collected from various probe positions while the transducer's pose was monitored by an optical tracking system. US volumes were automatically registered to the first US volume using normalized mutual information. A system of equations was formulated and solved for the US probe‐to‐image transformation using the registration transformations and the optical tracking information. Accuracy of the US calibration was assessed on eight additional US volumes with two separate methods. In the first method, a set of three fiducial markers implanted in the phantom was manually selected in each volume by three individual readers. Selected marker locations were reconstructed in the stationary camera frame, and for each marker, mean distance to the reconstructed centroid was measured. In the second method, a bladder structure was semi‐automatically segmented in each image volume. Mean distance between bladders segmented in a reference volume and the other seven volumes was computed. Calibration accuracy was also investigated as a function of the number of calibration images used. Results: Mean error for the fiducial marker reconstruction was 2.3 mm. Mean distance error between segmented structures was 1.1 mm. The proposed calibration method typically converged with less than 20 images. Conclusion: Automatic image registration facilitates fast and simple US spatial calibration with accuracy under 2.3 mm using any US phantom. This work is supported in part by the Stanford University BioX program and by Philips Medical. Two of the authors of the abstract are employed by Philips Medical.