Showing papers presented at "Computer Assisted Radiology and Surgery in 2017"

3D intra-operative ultrasound and MR image guidance: pursuing an ultrasound-based management of brainshift to enhance neuronavigation.

Abstract: Brainshift is still a major issue in neuronavigation. Incorporating intra-operative ultrasound (iUS) with advanced registration algorithms within the surgical workflow is regarded as a promising approach for a better understanding and management of brainshift. This work is intended to (1) provide three-dimensional (3D) ultrasound reconstructions specifically for brain imaging in order to detect brainshift observed intra-operatively, (2) evaluate a novel iterative intra-operative ultrasound-based deformation correction framework, and (3) validate the performance of the proposed image-registration-based deformation estimation in a clinical environment. Eight patients with brain tumors undergoing surgical resection are enrolled in this study. For each patient, a 3D freehand iUS system is employed in combination with an intra-operative navigation (iNav) system, and intra-operative ultrasound data are acquired at three timepoints during surgery. On this foundation, we present a novel resolution-preserving 3D ultrasound reconstruction, as well as a framework to detect brainshift through iterative registration of iUS images. To validate the system, the target registration error (TRE) is evaluated for each patient, and both rigid and elastic registration algorithms are analyzed. The mean TRE based on 3D-iUS improves significantly using the proposed brainshift compensation compared to neuronavigation (iNav) before (2.7 vs. 5.9 mm; $$p=0.001$$ ) and after dural opening (4.2 vs. 6.2 mm, $$p=0.049$$ ), but not after resection (6.7 vs. 7.5 mm; $$p=0.426$$ ). iUS depicts a significant ( $$p=0.001$$ ) dynamic spatial brainshift throughout the three timepoints. Accuracy of registration can be improved through rigid and elastic registrations by 29.2 and 33.3%, respectively, after dural opening, and by 5.2 and 0.4%, after resection. 3D-iUS systems can improve the detection of brainshift and significantly increase the accuracy of the navigation in a real scenario. 3D-iUS can thus be regarded as a robust, reliable, and feasible technology to enhance neuronavigation.

Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery

Abstract: Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. ADMTP efficiently compute safe and surgically feasible electrode trajectories.

Objective assessment based on motion-related metrics and technical performance in laparoscopic suturing

Abstract: The aim of this study is to present the construct and concurrent validity of a motion-tracking method of laparoscopic instruments based on an optical pose tracker and determine its feasibility as an objective assessment tool of psychomotor skills during laparoscopic suturing. A group of novice ( $${<}10$$ laparoscopic procedures), intermediate (11–100 laparoscopic procedures) and experienced ( $${>}100$$ laparoscopic procedures) surgeons performed three intracorporeal sutures on an ex vivo porcine stomach. Motion analysis metrics were recorded using the proposed tracking method, which employs an optical pose tracker to determine the laparoscopic instruments’ position. Construct validation was measured for all 10 metrics across the three groups and between pairs of groups. Concurrent validation was measured against a previously validated suturing checklist. Checklists were completed by two independent surgeons over blinded video recordings of the task. Eighteen novices, 15 intermediates and 11 experienced surgeons took part in this study. Execution time and path length travelled by the laparoscopic dissector presented construct validity. Experienced surgeons required significantly less time ( $$p<0.008$$ ), travelled less distance using both laparoscopic instruments ( $$p<0.013$$ ) and made more efficient use of the work space ( $$p<0.018$$ ) compared with novice and intermediate surgeons. Concurrent validation showed strong correlation between both the execution time and path length and the checklist score ( $$r =-0.712$$ and $$r=-0.731$$ , $$p<0.001$$ ). The suturing performance was successfully assessed by the motion analysis method. Construct and concurrent validity of the motion-based assessment method has been demonstrated for the execution time and path length metrics. This study demonstrates the efficacy of the presented method for objective evaluation of psychomotor skills in laparoscopic suturing. However, this method does not take into account the quality of the suture. Thus, future works will focus on developing new methods combining motion analysis and qualitative outcome evaluation to provide a complete performance assessment to trainees.

Statistical shape modelling to aid surgical planning: associations between surgical parameters and head shapes following spring-assisted cranioplasty.

Abstract: Spring-assisted cranioplasty is performed to correct the long and narrow head shape of children with sagittal synostosis. Such corrective surgery involves osteotomies and the placement of spring-like distractors, which gradually expand to widen the skull until removal about 4 months later. Due to its dynamic nature, associations between surgical parameters and post-operative 3D head shape features are difficult to comprehend. The current study aimed at applying population-based statistical shape modelling to gain insight into how the choice of surgical parameters such as craniotomy size and spring positioning affects post-surgical head shape. Twenty consecutive patients with sagittal synostosis who underwent spring-assisted cranioplasty at Great Ormond Street Hospital for Children (London, UK) were prospectively recruited. Using a nonparametric statistical modelling technique based on mathematical currents, a 3D head shape template was computed from surface head scans of sagittal patients after spring removal. Partial least squares (PLS) regression was employed to quantify and visualise trends of localised head shape changes associated with the surgical parameters recorded during spring insertion: anterior–posterior and lateral craniotomy dimensions, anterior spring position and distance between anterior and posterior springs. Bivariate correlations between surgical parameters and corresponding PLS shape vectors demonstrated that anterior–posterior (Pearson’s $$r=0.64, p=0.002$$ ) and lateral craniotomy dimensions (Spearman’s $$\rho =0.67, p<0.001$$ ), as well as the position of the anterior spring ( $$r=0.70, p<0.001$$ ) and the distance between both springs ( $$r=0.67, p=0.002$$ ) on average had significant effects on head shapes at the time of spring removal. Such effects were visualised on 3D models. Population-based analysis of 3D post-operative medical images via computational statistical modelling tools allowed for detection of novel associations between surgical parameters and head shape features achieved following spring-assisted cranioplasty. The techniques described here could be extended to other cranio-maxillofacial procedures in order to assess post-operative outcomes and ultimately facilitate surgical decision making.

Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images: application to fully automatic detection of healthy wall regions

Abstract: Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena. First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic. The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were $$29 \pm 46~\upmu \text {m}$$ for the intima–media interface, $$30 \pm 50~\upmu \text {m}$$ for the media–adventitia interface, and $$50 \pm 64~\upmu \text {m}$$ for the adventitia–periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78–0.98). The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.

Acquisition models in intraoperative positron surface imaging

Abstract: Intraoperative imaging aims at identifying residual tumor during surgery. Positron Surface Imaging (PSI) is one of the solutions to help surgeons in a better detection of resection margins of brain tumor, leading to an improved patient outcome. This system relies on a tracked freehand beta probe, using $$^{18}$$ F-based radiotracer. Some acquisition models have been proposed in the literature in order to enhance image quality, but no comparative validation study has been performed for PSI. In this study, we investigated the performance of different acquisition models by considering validation criteria and normalized metrics. We proposed a reference-based validation framework to perform the comparative study between acquisition models and a basic method. We estimated the performance of several acquisition models in light of four validation criteria: efficiency, computational speed, spatial accuracy and tumor contrast. Selected acquisition models outperformed the basic method, albeit with the real-time aspect compromised. One acquisition model yielded the best performance among all according to the validation criteria: efficiency (1-Spe: 0.1, Se: 0.94), spatial accuracy (max Dice: 0.77) and tumor contrast (max T/B: 5.2). We also found out that above a minimum threshold value of the sampling rate, the reconstruction quality does not vary significantly. Our method allowed the comparison of different acquisition models and highlighted one of them according to our validation criteria. This novel approach can be extended to 3D datasets, for validation of future acquisition models dedicated to intraoperative guidance of brain surgery.

Application of activity semantics and BPMN 2.0 in the generation and modeling of generic surgical process models

Abstract: In this paper a method for the generation of gSPM with ontology-based generalization was presented. The resulting gSPM was modeled with BPMN/BPMNsix in an efficient way and could be executed with BPMN workflow engines. In the next step the implementation of resource concepts, anatomical structures, and transition probabilities for workflow execution will be realized.