On the nature of data collection for soft-tissue image-to-physical organ registration: A noise characterization study Conference Paper


Authors: Collins, J. A.; Heiselman, J. S.; Weis, J. A.; Clements, L. W.; Simpson, A. L.; Jarnagin, W. R.; Miga, M. I.
Editors: Webster, R. J. 3rd; Fei, B.
Title: On the nature of data collection for soft-tissue image-to-physical organ registration: A noise characterization study
Conference Title: Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling
Abstract: In image-guided liver surgery (IGLS), sparse representations of the anterior organ surface may be collected intraoperatively to drive image-to-physical space registration. Soft tissue deformation represents a significant source of error for IGLS techniques. This work investigates the impact of surface data quality on current surface based IGLS registration methods. In this work, we characterize the robustness of our IGLS registration methods to noise in organ surface digitization. We study this within a novel human-to-phantom data framework that allows a rapid evaluation of clinically realistic data and noise patterns on a fully characterized hepatic deformation phantom. Additionally, we implement a surface data resampling strategy that is designed to decrease the impact of differences in surface acquisition. For this analysis, n=5 cases of clinical intraoperative data consisting of organ surface and salient feature digitizations from open liver resection were collected and analyzed within our human-to-phantom validation framework. As expected, results indicate that increasing levels of noise in surface acquisition cause registration fidelity to deteriorate. With respect to rigid registration using the raw and resampled data at clinically realistic levels of noise (i.e. a magnitude of 1.5 mm), resampling improved TRE by 21%. In terms of nonrigid registration, registrations using resampled data outperformed the raw data result by 14% at clinically realistic levels and were less susceptible to noise across the range of noise investigated. These results demonstrate the types of analyses our novel human-to-phantom validation framework can provide and indicate the considerable benefits of resampling strategies. © 2017 SPIE.
Keywords: registration; medical imaging; surgery; robotics; tissue; deformation; soft tissue deformation; hepatic; image-guidance; digital storage; igls; image guidances; image to physical space registration; noise characterization; nonrigid registration
Journal Title Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume: 10135
Conference Dates: 2017 Feb 14-16
Conference Location: Orlando, FL
ISBN: 1605-7422
Publisher: SPIE  
Date Published: 2017-03-03
Start Page: 101351Y
Language: English
DOI: 10.1117/12.2255844
PROVIDER: scopus
DOI/URL:
Notes: Conference Paper -- Export Date: 3 July 2017 -- Source: Scopus
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
  1. William R Jarnagin
    911 Jarnagin
  2. Amber L Simpson
    64 Simpson