Stereotactic Neuro-Navigation Phantom Designs: A Systematic Review

Švaco, Marko; Stiperski, Ivan; Dlaka, Domagoj; Šuligoj, Filip; Jerbić, Bojan; Chudy, Darko; Raguž, Marina

doi:10.3389/fnbot.2020.549603

Cited by 5 publications

(7 citation statements)

References 88 publications

(181 reference statements)

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“…To consider the locations of various lesions located in the sinus space and nasal cavity, we placed the target points densely, as shown in Figure 4a (a total of 90 targets in a 10 × 3 × 3 format). However, this number of targets was too dense compared to the numbers of targets (5, 42 11, 43 and 72% below 10 targets 44 ) of previous studies considered to verify the registration accuracy. Therefore, among these 90 targets, 10 targets were randomly selected and grouped, which can be expected to achieve a similar effect as considering nine lesions in the corresponding area.…”

Section: Discussionmentioning

confidence: 80%

“…The average TRE computed by the conventional method was 2.227 ± 0.193 mm (range 1.851-2.489 mm), which was generally similar to the results reported in the previous research: 2.4 ± 1.7 mm (range 1.0-9.0 mm), 43 2.0-4.5 mm, 11 but with some exceptions: 3.12, 5 5.03 ± 2.30 mm. 44 The exceptions can be explained by the previous research efforts that reported the TRE variance by the difference in the numbers and distribution of the information obtained by CT scan or OTS. Some prior studies have shown better TRE results.…”

Section: Discussionmentioning

confidence: 98%

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Automated machine learning (AutoML)‐based surface registration methodology for image‐guided surgical navigation system

Yoo

Sim

2022

Medical Physics

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Background Although the surface registration technique has the advantage of being relatively safe and the operation time is short, it generally has the disadvantage of low accuracy. Purpose This research proposes automated machine learning (AutoML)‐based surface registration to improve the accuracy of image‐guided surgical navigation systems. Methods The state‐of‐the‐art surface registration concept is that first, using a neural network model, a new point‐cloud that matches the facial information acquired by a passive probe of an optical tracking system (OTS) is extracted from the facial information obtained by computerized tomography. Target registration error (TRE) representing the accuracy of surface registration is then calculated by applying the iterative closest point (ICP) algorithm to the newly extracted point‐cloud and OTS information. In this process, the hyperparameters used in the neural network model and ICP algorithm are automatically optimized using Bayesian optimization with expected improvement to yield improved registration accuracy. Results Using the proposed surface registration methodology, the average TRE for the targets located in the sinus space and nasal cavity of the soft phantoms is 0.939 ± 0.375 mm, which shows 57.8% improvement compared to the average TRE of 2.227 ± 0.193 mm calculated by the conventional surface registration method (p < 0.01). The performance of the proposed methodology is evaluated, and the average TREs computed by the proposed methodology and the conventional method are 0.767 ± 0.132 and 2.615 ± 0.378 mm, respectively. Additionally, for one healthy adult, the clinical applicability of the AutoML‐based surface registration is also presented. Conclusion Our findings showed that the registration accuracy could be improved while maintaining the advantages of the surface registration technique.

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Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 98%

Automated machine learning (AutoML)‐based surface registration methodology for image‐guided surgical navigation system

Yoo

Sim

2022

Medical Physics

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“…After this procedure there is no ambiguity, and the localisation and registration procedure is highly accurate, and the point‐pair correspondences are known. Biopsies performed on real patients are quite demanding and pose new and unpredictable challenges even to the systems and procedures that have been extensively tested on phantoms and in vitro conditions 35 . By definition, biopsies as procedures have their complications and mortality.…”

Section: Discussionmentioning

confidence: 99%

“…Biopsies performed on real patients are quite demanding and pose new and unpredictable challenges even to the systems and procedures that have been extensively tested on phantoms and in vitro conditions. 35 By definition, biopsies as procedures have their complications and mortality.…”

Section: Discussionmentioning

confidence: 99%

Frameless stereotactic brain biopsy: A prospective study on robot‐assisted brain biopsies performed on 32 patients by using the RONNA G4 system

Dlaka

Švaco

Chudy

et al. 2021

Robotics Computer Surgery

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Background We present a novel robotic neuronavigation system (RONNA G4), used for precise preoperative planning and frameless neuronavigation, developed by a research group from the University of Zagreb and neurosurgeons from the University Hospital Dubrava, Zagreb, Croatia. The aim of study is to provide comprehensive error measurement analysis of the system used for the brain biopsy. Methods Frameless stereotactic robot‐assisted biopsies were performed on 32 consecutive patients. Post‐operative CT and MRI scans were assessed to precisely measure and calculate target point error (TPE) and entry point error (EPE). Results The application accuracy of the RONNA system for TPE was 1.95 ± 1.11 mm, while for EPE was 1.42 ± 0.74 mm. The total diagnostic yield was 96.87%. Linear regression showed statistical significance between the TPE and EPE, and the angle of the trajectory on the bone. Conclusion The RONNA G4 robotic system is a precise and highly accurate autonomous neurosurgical assistant for performing frameless brain biopsies.

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“…In addition, an in-depth accuracy system analysis with included errors of measurement should be conducted, as was previously described in the literature ( 2 , 4 , 5 , 8 , 22 ). All other factors that considerably affect the accuracy and diagnostic yield should be investigated, and in vitro studies should be conducted ( 35 ). Different planning software programs and platforms prevented us from comparing the accuracy measurement between robotic frameless and framebased Leksell stereotactic biopsies.…”

Section: Discussionmentioning

confidence: 99%

Clinical application of the RONNA G4 system – preliminary validation of 23 robotic frameless brain biopsies

Dlaka¹,

Švaco²,

Chudy³

et al. 2021

Croat Med J

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Aims To report the outcomes of robot-assisted brain biopsies performed using a novel RONNA G4 system. The system was developed by a research group from the Faculty of Mechanical Engineering and Naval Architecture and a team of neurosurgeons from Dubrava University Hospital, University of Zagreb School of Medicine. Methods This prospective study included 49 biopsies analyzed during one year: 23 robotic frameless and 26 frame-based Leksell stereotactic biopsies. We analyzed the presenting symptoms, tumor range and location, postoperative complications, pathohistological diagnosis, diagnostic yield, as well as operation and hospitalization duration. The target point error was calculated to assess the accuracy of the RONNA system. Results No postoperative mortality, morbidity, or infections were observed. In the frameless robotic biopsy group, only one pathohistological diagnosis was inconclusive. Therefore, the diagnostic yield was 95.6% (22/23), similar to that of the framebased Leksell stereotactic biopsy group (95.1% or 25/26). The average target point error in the frameless robotic biopsy group was 2.15 ± 1.22 mm (range 0.39-5.85). Conclusion The RONNA G4 robotic system is a safe and accurate tool for brain biopsy, although further research warrants a larger patient sample, comparison with other robotic systems, and a systematic analysis of the entry and target point errors.

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Stereotactic Neuro-Navigation Phantom Designs: A Systematic Review

Cited by 5 publications

References 88 publications

Automated machine learning (AutoML)‐based surface registration methodology for image‐guided surgical navigation system

Automated machine learning (AutoML)‐based surface registration methodology for image‐guided surgical navigation system

Frameless stereotactic brain biopsy: A prospective study on robot‐assisted brain biopsies performed on 32 patients by using the RONNA G4 system

Clinical application of the RONNA G4 system – preliminary validation of 23 robotic frameless brain biopsies

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