Paper
16 March 2020 Error analysis for a navigation system using 3D abdominal ultrasound
Author Affiliations +
Abstract
Purpose We present a systematic error analysis for a navigation system which allows image fusion between two arbitrary image modalities. A prototype was built for prostate biopsies merging pre-operative magnetic resonance images (MRI) with interventional 3D free-hand transrectal US (TRUS). Methods The fusion between MRI and TRUS images was performed by replacing the inter-modal MRI-TRUS registration by an intra-modal 3D/3D-US/TRUS registration and a pre-operative image fusion between MRI and 3D(-abdominal)-US using an optical tracking system (OTS). The error of the inter-modal MRI/3D(-abdominal)- US image fusion was evaluated using a multi-cone phantom and a prostate phantom by calculating a target registration error (TRE) and the Dice score of segmented lesions (radius 5 mm) from the prostate phantom. The pre-operative 3D(-abdominal)-US was then registered with the TRUS images and the TRE error was calculated. Finally, a Dice score of segmented lesions between TRUS and MRI was determined. Results The fiducial registration error for the 3D(-abdominal)-US and TRUS calibration resulted in 0:87 mm and 1:18 mm, respectively. The TRE for the 3D abdominal transducer was 1:08 mm. The TRE of the MRI/3D- US image fusion using a multi-cone phantom was 1:81 mm. The Dice-score calculated from test lesions segmented in MRI and TRUS resulted in an error of 0.67. The TRE between the 3D-US and the TRUS was 2:10 mm. The Dice score from segmented lesions in MRI and TRUS was 0:67. Conclusions The intra-modal registration via OTS between MRI and TRUS images showed sufficient accuracy for prostate biopsy.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David Iommi, Alejandra Valladares, Michael Figl, and Johann Hummel "Error analysis for a navigation system using 3D abdominal ultrasound", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151Z (16 March 2020); https://doi.org/10.1117/12.2548866
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KEYWORDS
Magnetic resonance imaging

Image registration

3D image processing

Calibration

Image fusion

Prostate

Image segmentation

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