Pierre Jannin

Validation of Knowledge Acquisition for Surgical Process Models

OBJECTIVEnSurgical Process Models (SPMs) are models of surgical interventions. The objectives of this study are to validate acquisition methods for Surgical Process Models and to assess the performance of different observer populations.nnnDESIGNnThe study examined 180 SPM of simulated Functional Endoscopic Sinus Surgeries (FESS), recorded with observation software. About 150,000 single measurements in total were analyzed.nnnMEASUREMENTSnValidation metrics were used for assessing the granularity, content accuracy, and temporal accuracy of structures of SPMs.nnnRESULTSnDifferences between live observations and video observations are not statistically significant. Observations performed by subjects with medical backgrounds gave better results than observations performed by subjects with technical backgrounds. Granularity was reconstructed correctly by 90%, content by 91%, and the mean temporal accuracy was 1.8 s.nnnCONCLUSIONnThe study shows the validity of video as well as live observations for modeling Surgical Process Models. For routine use, the authors recommend live observations due to their flexibility and effectiveness. If high precision is needed or the SPM parameters are altered during the study, video observations are the preferable approach.

Abnormal functional lateralization and activity of language brain areas in typical specific language impairment (developmental dysphasia)

Atypical functional lateralization and specialization for language have been proposed to account for developmental language disorders, yet results from functional neuroimaging studies are sparse and inconsistent. This functional magnetic resonance imaging study compared children with a specific subtype of specific language impairment affecting structural language (n = 21), to a matched group of typically developing children using a panel of four language tasks neither requiring reading nor metalinguistic skills, including two auditory lexico-semantic tasks (category fluency and responsive naming) and two visual phonological tasks based on picture naming. Data processing involved normalizing the data with respect to a matched pairs paediatric template, groups and between-groups analysis, and laterality indices assessment within regions of interest using single and combined task analysis. Children with specific language impairment exhibited a significant lack of left lateralization in all core language regions (inferior frontal gyrus-opercularis, inferior frontal gyrus-triangularis, supramarginal gyrus and superior temporal gyrus), across single or combined task analysis, but no difference of lateralization for the rest of the brain. Between-group comparisons revealed a left hypoactivation of Wernickes area at the posterior superior temporal/supramarginal junction during the responsive naming task, and a right hyperactivation encompassing the anterior insula with adjacent inferior frontal gyrus and the head of the caudate nucleus during the first phonological task. This study thus provides evidence that this subtype of specific language impairment is associated with atypical lateralization and functioning of core language areas.

Augmented virtuality based on stereoscopic reconstruction in multimodal image-guided neurosurgery: methods and performance evaluation

Displaying anatomical and physiological information derived from preoperative medical images in the operating room is critical in image-guided neurosurgery. This paper presents a new approach referred to as augmented virtuality (AV) for displaying intraoperative views of the operative field over three-dimensional (3-D) multimodal preoperative images onto an external screen during surgery. A calibrated stereovision system was set up between the surgical microscope and the binocular tubes. Three-dimensional surface meshes of the operative field were then generated using stereopsis. These reconstructed 3-D surface meshes were directly displayed without any additional geometrical transform over preoperative images of the patient in the physical space. Performance evaluation was achieved using a physical skull phantom. Accuracy of the reconstruction method itself was shown to be within 1 mm (median: 0.76 mm /spl plusmn/ 0.27), whereas accuracy of the overall approach was shown to be within 3 mm (median: 2.29 mm /spl plusmn/ 0.59), including the image-to-physical space registration error. We report the results of six surgical cases where AV was used in conjunction with augmented reality. AV not only enabled vision beyond the cortical surface but also gave an overview of the surgical area. This approach facilitated understanding of the spatial relationship between the operative field and the preoperative multimodal 3-D images of the patient.

Automatic computation of electrode trajectories for Deep Brain Stimulation: a hybrid symbolic and numerical approach

PurposeThe optimal electrode trajectory is needed to assist surgeons in planning Deep Brain Stimulation (DBS). A method for image-based trajectory planning was developed and tested.MethodsRules governing the DBS surgical procedure were defined with geometric constraints. A formal geometric solver using multimodal brain images and a template built from 15 brain MRI scans were used to identify a space of possible solutions and select the optimal one. For validation, a retrospective study of 30 DBS electrode implantations from 18 patients was performed. A trajectory was computed in each case and compared with the trajectories of the electrodes that were actually implanted.ResultsComputed trajectories had an average difference of 6.45° compared with reference trajectories and achieved a better overall score based on satisfaction of geometric constraints. Trajectories were computed in 2xa0min for each case.ConclusionA rule-based solver using pre-operative MR brain images can automatically compute relevant and accurate patient-specific DBS electrode trajectories.

Chronic and treatment-resistant depression: a study using arterial spin labeling perfusion MRI at 3Tesla.

The aim of the present study was to compare patients displaying chronic and treatment-resistant depression with healthy controls, using the resting-state perfusion with arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) technique at 3T. The study focused on the subgenual anterior cingulate cortex (sACC), which is a key component in the pathophysiology of depression. Six patients with chronic and treatment-resistant depression and six healthy control subjects were included. ASL is an innovative imaging technique which sidesteps the limitations of other functional neuroimaging techniques (functional MRI, positron emission tomography). A statistical analysis of perfusion maps was performed using SPM2 software. Statistically significant hyperperfusion regions were found in the depressed patient group compared with the healthy control group in the following: the bilateral sACC, left prefrontal dorsomedian cortex, left ACC and left subcortical areas (putamen, pallidum and amygdala). This study confirmed the involvement of the sACC in depression, particularly chronic and treatment-resistant depression, using ASL at 3T, a safe perfusion technique that seems to be appropriate for investigating functional abnormalities in psychiatric disorders.

A Surface Registration Method for Quantification of Intraoperative Brain Deformations in Image-Guided Neurosurgery

Intraoperative brain deformations decrease accuracy in image-guided neurosurgery. Approaches to quantify these deformations based on 3-D reconstruction of cortectomy surfaces have been described and have shown promising results regarding the extrapolation to the whole brain volume using additional prior knowledge or sparse volume modalities. Quantification of brain deformations from surface measurement requires the registration of surfaces at different times along the surgical procedure, with different challenges according to the patient and surgical step. In this paper, we propose a new flexible surface registration approach for any textured point cloud computed by stereoscopic or laser range approach. This method includes three terms: the first term is related to image intensities, the second to Euclidean distance, and the third to anatomical landmarks automatically extracted and continuously tracked in the 2-D video flow. Performance evaluation was performed on both phantom and clinical cases. The global method, including textured point cloud reconstruction, had accuracy within 2 mm, which is the usual rigid registration error of neuronavigation systems before deformations. Its main advantage is to consider all the available data, including the microscope video flow with higher temporal resolution than previously published methods.

Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson's disease.

PurposeTemplate-based segmentation techniques have been developed to facilitate the accurate targeting of deep brain structures in patients with movement disorders. Three template-based brain MRI segmentation techniques were compared to determine the best strategy for segmenting the deep brain structures of patients with Parkinson’s disease.MethodsT1-weighted and T2-weighted magnetic resonance (MR) image templates were created by averaging MR images of 57 patients with Parkinson’s disease. Twenty-four deep brain structures were manually segmented on the templates. To validate the template-based segmentation, 14 of the 24 deep brain structures from the templates were manually segmented on 10 MR scans of Parkinson’s patients as a gold standard. We compared the manual segmentations with three methods of automated segmentation: two registration-based approaches, automatic nonlinear image matching and anatomical labeling (ANIMAL) and symmetric image normalization (SyN), and one patch-label fusion technique. The automated labels were then compared with the manual labels using a Dice-kappa metric and center of gravity. A Friedman test was used to compare the Dice-kappa values and paired t tests for the center of gravity.ResultsThe Friedman test showed a significant difference between the three methods for both thalami (pxa0<xa00.05) and not for the subthalamic nuclei. Registration with ANIMAL was better than with SyN for the left thalamus and was better than the patch-based method for the right thalamus.ConclusionAlthough template-based approaches are the most used techniques to segment basal ganglia by warping onto MR images, we found that the patch-based method provided similar results and was less time-consuming. Patch-based method may be preferable for the subthalamic nucleus segmentation in patients with Parkinson’s disease.

Decision Making During Preoperative Surgical Planning

Objective: This study analyzes decision making during preoperative surgical planning through two cognitive indicators: conflict and cognitive control. Background: Planning is a critical stage in naturalistic decision making, and there is some evidence suggesting that this activity depends on the level of expertise and the demands of the task. The specificity of surgery resides in the necessity to cope with (potential) conflicts between the purpose of the surgical intervention and the biological laws governing the patients body. Method: Six neurosurgeons (two board-certified neurosurgeons, two chief residents, and two residents) described the operative procedure envisaged on nine surgical cases of increasing surgical complexity. A detailed analysis of one surgical case described by one expert was performed. Moreover, we measured the number of conflicts and controls reported by each surgeon. Results: Two experts were the only ones for which the report of conflicts increased with surgical complexity (respectively, 75% and 73% of the conflict variance predicted by complexity). The two experts significantly activated a higher proportion of knowledge-based control (respectively, 43% and 38%) than did intermediates and residents. The residents significantly activated more motor skill—based controls (respectively, 40% and 44%) than did intermediates and experts. Conclusion: It seems that expert surgical decision making to cope with task demands is significantly associated with conflict monitoring. Knowledge-based control to regulate conflict is mainly produced by experts. Application: Conflicts and controls analyzed through verbal reports can be used as relevant indicators to highlight critical moments in decision making that potentially require assistance from information systems.

PyDBS: an automated image processing workflow for deep brain stimulation surgery

Purposexa0xa0xa0Deep brain stimulation (DBS) is a surgical procedure for treating motor-related neurological disorders. DBS clinical efficacy hinges on precise surgical planning and accurate electrode placement, which in turn call upon several image processing and visualization tasks, such as image registration, image segmentation, image fusion, and 3D visualization. These tasks are often performed by a heterogeneous set of software tools, which adopt differing formats and geometrical conventions and require patient-specific parameterization or interactive tuning. To overcome these issues, we introduce in this article PyDBS, a fully integrated and automated image processing workflow for DBS surgery.Methodsxa0xa0xa0PyDBS consists of three image processing pipelines and three visualization modules assisting clinicians through the entire DBS surgical workflow, from the preoperative planning of electrode trajectories to the postoperative assessment of electrode placement. The system’s robustness, speed, and accuracy were assessed by means of a retrospective validation, based on 92 clinical cases.Resultsxa0xa0xa0The complete PyDBS workflow achieved satisfactory results in 92xa0% of tested cases, with a median processing time of 28xa0min per patient.Conclusionxa0xa0xa0The results obtained are compatible with the adoption of PyDBS in clinical practice.

Similarity metrics for surgical process models

OBJECTIVEnThe objective of this work is to introduce a set of similarity metrics for comparing surgical process models (SPMs). SPMs are progression models of surgical interventions that support quantitative analyses of surgical activities, supporting systems engineering or process optimization.nnnMETHODS AND MATERIALSnFive different similarity metrics are presented and proven. These metrics deal with several dimensions of process compliance in surgery, including granularity, content, time, order, and frequency of surgical activities. The metrics were experimentally validated using 20 clinical data sets each for cataract interventions, craniotomy interventions, and supratentorial tumor resections. The clinical data sets were controllably modified in simulations, which were iterated ten times, resulting in a total of 600 simulated data sets. The simulated data sets were subsequently compared to the original data sets to empirically assess the predictive validity of the metrics.nnnRESULTSnWe show that the results of the metrics for the surgical process models correlate significantly (p<0.001) with the induced modifications and that all metrics meet predictive validity. The clinical use of the metrics was exemplarily, as demonstrated by assessment of the learning curves of observers during surgical process model acquisition.nnnCONCLUSIONnMeasuring similarity between surgical processes is a complex task. However, metrics for computing the similarity between surgical process models are needed in many uses in the field of medical engineering. These metrics are essential whenever two SPMs need to be compared, such as during the evaluation of technical systems, the education of observers, or the determination of surgical strategies. These metrics are key figures that provide a solid base for medical decisions, such as during validation of sensor systems for use in operating rooms in the future.