Technical Note: Error metrics for estimating the accuracy of needle/instrument placement during transperineal magnetic resonance/ultrasound-guided prostate interventions
journal contributionposted on 22.10.2018 by Ester Bonmati, Yipeng Hu, Barbara Villarini, Rachael Rodell, Paul Martin, Lianghao Han, Ian Donaldson, Hashim U. Ahmed, Caroline M. Moore, Mark Emberton, Dean C. Barratt
Any type of content formally published in an academic journal, usually following a peer-review process.
Purpose Image‐guided systems that fuse magnetic resonance imaging (MRI) with three‐dimensional (3D) ultrasound (US) images for performing targeted prostate needle biopsy and minimally invasive treatments for prostate cancer are of increasing clinical interest. To date, a wide range of different accuracy estimation procedures and error metrics have been reported, which makes comparing the performance of different systems difficult. Methods A set of nine measures are presented to assess the accuracy of MRI‐US image registration, needle positioning, needle guidance, and overall system error, with the aim of providing a methodology for estimating the accuracy of instrument placement using a MR/US‐guided transperineal approach. Results Using the SmartTarget fusion system, an MRI‐US image alignment error was determined to be 2.0 ± 1.0 mm (mean ± SD), and an overall system instrument targeting error of 3.0 ± 1.2 mm. Three needle deployments for each target phantom lesion was found to result in a 100% lesion hit rate and a median predicted cancer core length of 5.2 mm. Conclusions The application of a comprehensive, unbiased validation assessment for MR/US guided systems can provide useful information on system performance for quality assurance and system comparison. Furthermore, such an analysis can be helpful in identifying relationships between these errors, providing insight into the technical behavior of these systems.
This publication presents independent research supported by the Health Innovation Challenge (HIC) Fund (Grant Ref. HICF-T4-310), a parallel funding partnership between the Department of Health and the Wellcome Trust. The research was undertaken at UCL/ULCH who received a proportion of funding from the Department of Health’s NIHR Biomedical Research Centres funding scheme.
- Computer Science