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  • HEPATIC-TUMORS  (2)
  • 1
    Keywords: OPTIMIZATION ; tumor ; Germany ; IN-VIVO ; VIVO ; CT ; imaging ; SUPPORT ; SYSTEM ; liver ; TUMORS ; ACCURACY ; computed tomography ; NUCLEAR-MEDICINE ; TIME ; TARGET ; NO ; TRIAL ; TRIALS ; ACQUISITION ; LESIONS ; EXPERIENCE ; RADIOFREQUENCY ABLATION ; REGISTRATION ; tomography ; COMPUTED-TOMOGRAPHY ; MOTION ; TRACKING ; IMAGE REGISTRATION ; nuclear medicine ; ORGAN MOTION ; radiology ; RE ; GUIDANCE ; ABLATION ; radiation therapy ; NUCLEAR ; USA ; SET ; IMPROVEMENT ; navigation ; MEDICINE ; CHALLENGES ; INSERTION ; HEPATIC-TUMORS ; INTERVENTIONS ; tumours ; NEEDLES ; computerised tomography ; needle insertion ; CLINICAL-EVALUATION ; motion compensation ; patient treatment
    Abstract: Computed tomography (CT)-guided percutaneous radiofrequency ablation (RFA) has become a commonly used procedure in the treatment of liver tumors. One of the main challenges related to the method is the exact placement of the instrument within the lesion. To address this issue, a system was developed for computer-assisted needle placement which uses a set of fiducial needles to compensate for organ motion in real time. The purpose of this study was to assess the accuracy of the system in vivo. Two medical experts with experience in CT-guided interventions and two nonexperts used the navigation system to perform 32 needle insertions into contrasted agar nodules injected into the livers of two ventilated swine. Skin-to-target path planning and real-time needle guidance were based on preinterventional 1 mm CT data slices. The lesions were hit in 97% of all trials with a mean user error of 2.4 +/- 2.1 mm, a mean target registration error (TRE) of 2.1 +/- 1.1 mm, and a mean overall targeting error of 3.7 +/- 2.3 mm. The nonexperts achieved significantly better results than the experts with an overall error of 2.8 +/- 1.4 mm (n=16) compared to 4.5 +/- 2.7 mm (n=16). The mean time for performing four needle insertions based on one preinterventional planning CT was 57 +/- 19 min with a mean setup time of 27 min, which includes the steps fiducial insertion (24 +/- 15 min), planning CT acquisition (1 +/- 0 min), and registration (2 +/- 1 min). The mean time for path planning and targeting was 5 +/- 4 and 2 +/- 1 min, respectively. Apart from the fiducial insertion step, experts and nonexperts performed comparably fast. It is concluded that the system allows for accurate needle placement into hepatic tumors based on one planning CT and could thus enable considerable improvement to the clinical treatment standard for RFA procedures and other CT-guided interventions in the liver. To support clinical application of the method, optimization of individual system modules to reduce intervention time is proposed
    Type of Publication: Journal article published
    PubMed ID: 19175098
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  • 2
    Keywords: OPTIMIZATION ; THERAPY ; SIMULATION ; RADIOFREQUENCY ABLATION ; NUCLEAR ; HEPATIC-TUMORS ; image-guided therapy ; interventional radiology ; computer-aided intervention ; IMAGING INTERACTION TOOLKIT ; PRECISE ; trajectory planning
    Abstract: Purpose: Computed tomography (CT) guided minimally invasive interventions such as biopsies or ablation therapies often involve insertion of a needle-shaped instrument into the target organ (e. g., the liver). Today, these interventions still require manual planning of a suitable trajectory to the target (e. g., the tumor) based on the slice data provided by the imaging modality. However, taking into account the critical structures and other parameters crucial to the success of the intervention-such as instrument shape and penetration angle-is challenging and requires a lot of experience. Methods: To overcome these problems, we present a system for the automatic or semiautomatic planning of optimal trajectories to a target, based on 3D reconstructions of all relevant structures. The system determines possible insertion zones based on so-called hard constraints and rates the quality of these zones by so-called soft constraints. The concept of pareto optimality is utilized to allow for a weight-independent proposal of insertion trajectories. In order to demonstrate the benefits of our method, automatic trajectory planning was applied retrospectively to n = 10 data sets from interventions in which complications occurred. Results: The efficient (graphics processing unit-based) implementation of the constraints results in a mean overall planning time of about 9 s. The examined trajectories, originally chosen by the physician, have been rated as follows: in six cases, the insertion point was labeled invalid by the planning system. For two cases, the system would have proposed points with a better rating according to the soft constraints. For the remaining two cases the system would have indicated poor rating with respect to one of the soft constraints. The paths proposed by our system were rated feasible and qualitatively good by experienced interventional radiologists. Conclusions: The proposed computer-assisted trajectory planning system is able to detect unsafe and propose safe insertion trajectories and may especially be helpful for interventional radiologist at the beginning or during their interventional training.
    Type of Publication: Journal article published
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