naa a22 4500 510807364 CHVBK 20180411083424.0 cr unu---uuuuu 180411e20130501xx s 000 0 eng 10.1007/s11548-012-0788-0 doi (NATIONALLICENCE)springer-10.1007/s11548-012-0788-0 MR image overlay guidance: system evaluation for preclinical use [Elektronische Daten] [Paweena U-Thainual, Jan Fritz, Choladawan Moonjaita, Tamas Ungi, Aaron Flammang, John Carrino, Gabor Fichtinger, Iulian Iordachita] Purpose: A clinical augmented reality guidance system was developed for MRI-guided musculoskeletal interventions Magnetic Resonance Image Overlay System (MR-IOS). The purpose of this study was to assess MRI compatibility, system accuracy, technical efficacy, and operator performance of the MR-IOS. Methods and materials: The impact of the MR-IOS on the MR environment was assessed by measuring image quality with signal-to-noise ratio (SNR) and signal intensity uniformity with the system in various on/off states. The system accuracy was assessed with an in-room preclinical experiment by performing 62 needle insertions on a spine phantom by an expert operator measuring entry, depth, angle, and target errors. Technical efficacy and operator performance were tested in laboratory by running an experiment with 40 novice operators (20 using freehand technique versus 20 MR-IOS-guided) with each operator inserting 10 needles into a geometric phantom. Technical efficacy was measured by comparing the success rates of needle insertions between the two operator groups. Operator performance was assessed by comparing total procedure times, total needle path distance, presumed tissue damage, and speed of individual insertions between the two operator groups. Results: The MR-IOS maximally altered SNR by 2% with no perceptible change in image quality or uniformity. Accuracy assessment showed mean entry error of 1.6 ± 0.6 mm, depth error of 0.7 ± 0.5 mm, angle error of 1.5 ± 1.1°, and target error of 1.9 ± 0.8 mm. Technical efficacy showed a statistically significant difference (p=0.031) between success rates (freehand 35.0% vs. MR-IOS 80.95%). Operator performance showed: mean total procedure time of 40.3 ± 4.4 (s) for freehand and 37.0 ± 3.7 (s) for MR-IOS (p=0.584), needle path distances of 152.6 ± 15.0 mm for freehand and 116.9 ± 8.7 mm for MR-IOS (p =0.074), presumed tissue damage of 7,417.2 ± 955.6 mm2 for freehand and 6062.2 ± 678.5 mm2 for MR-IOS (p=0.347), and speed of insertion 5.9 ± 0.4 mm/s for freehand and 4.3 ± 0.3 mm/s for MR-IOS (p=0.003). Conclusion: The MR-IOS is compatible within a clinical MR imaging environment, accurate for needle placement, technically efficacious, and improves operator performance over the unassisted insertion technique. The MR-IOS was found to be suitable for further testing in a clinical setting. CARS, 2012 Augmented reality nationallicence Percutaneous interventions nationallicence Needle guidance nationallicence Image overlay nationallicence U-Thainual Paweena Department of Mechanical and Materials Engineering, Queen's University, Kingston, ON, Canada aut Fritz Jan Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA aut Moonjaita Choladawan Center for Biomedical and Robotics Technology (BART LAB), Mahidol University, Salaya, Nakorn Pathom, Thailand aut Ungi Tamas School of Computing, Queen's University, Kingston, ON, Canada aut Flammang Aaron Healthcare Sector, Imaging and Therapy, Siemens Corporation, Center for Applied Medical Imaging, Erlangen, Germany aut Carrino John Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA aut Fichtinger Gabor Engineering Research Center (ERC)/Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD, USA aut Iordachita Iulian Engineering Research Center (ERC)/Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD, USA aut International Journal of Computer Assisted Radiology and Surgery Springer-Verlag 8/3(2013-05-01), 365-378 1861-6410 8:3<365 2013 8 11548 https://doi.org/10.1007/s11548-012-0788-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-012-0788-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- U-Thainual Paweena Department of Mechanical and Materials Engineering, Queen's University, Kingston, ON, Canada aut NATIONALLICENCE 700 1- Fritz Jan Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA aut NATIONALLICENCE 700 1- Moonjaita Choladawan Center for Biomedical and Robotics Technology (BART LAB), Mahidol University, Salaya, Nakorn Pathom, Thailand aut NATIONALLICENCE 700 1- Ungi Tamas School of Computing, Queen's University, Kingston, ON, Canada aut NATIONALLICENCE 700 1- Flammang Aaron Healthcare Sector, Imaging and Therapy, Siemens Corporation, Center for Applied Medical Imaging, Erlangen, Germany aut NATIONALLICENCE 700 1- Carrino John Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA aut NATIONALLICENCE 700 1- Fichtinger Gabor Engineering Research Center (ERC)/Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD, USA aut NATIONALLICENCE 700 1- Iordachita Iulian Engineering Research Center (ERC)/Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD, USA aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer-Verlag 8/3(2013-05-01), 365-378 1861-6410 8:3<365 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer