Automated multiple trajectory planning algorithm for the placement of stereo-electroencephalography (SEEG) electrodes in epilepsy treatment.

PURPOSE: About one-third of individuals with focal epilepsy continue to have seizures despite optimal medical management. These patients are potentially curable with neurosurgery if the epileptogenic zone (EZ) can be identified and resected. Stereo-electroencephalography (SEEG) to record epileptic activity with intracranial depth electrodes may be required to identify the EZ. Each SEEG electrode trajectory, the path between the entry on the skull and the cerebral target, must be planned carefully to avoid trauma to blood vessels and conflicts between electrodes. In current clinical practice trajectories are determined manually, typically taking 2-3 h per patient (15 min per electrode). Manual planning (MP) aims to achieve an implantation plan with good coverage of the putative EZ, an optimal spatial resolution, and 3D distribution of electrodes. Computer-assisted planning tools can reduce planning time by quantifying trajectory suitability. METHODS: We present an automated multiple trajectory planning (MTP) algorithm to compute implantation plans. MTP uses dynamic programming to determine a set of plans. From this set a depth-first search algorithm finds a suitable plan. We compared our MTP algorithm to (a) MP and (b) an automated single trajectory planning (STP) algorithm on 18 patient plans containing 165 electrodes. RESULTS: MTP changed all 165 trajectories compared to MP. Changes resulted in lower risk (122), increased grey matter sampling (99), shorter length (92), and surgically preferred entry angles (113). MTP changed 42 % (69/165) trajectories compared to STP. Every plan had between 1 to 8 (median 3.5) trajectories changed to resolve electrode conflicts, resulting in surgically preferred plans. CONCLUSION: MTP is computationally efficient, determining implantation plans containing 7-12 electrodes within 1 min, compared to 2-3 h for MP

3D CBIR with sparse coding for image-guided neurosurgery

This research takes an application-specific approach to investigate, extend and implement the state of the art in the fields of both visual information retrieval and machine learning, bridging the gap between theoretical models and real world applications. During an image-guided neurosurgery, path planning remains the foremost and hence the most important step to perform an operation and ensures the maximum resection of an intended target and minimum sacrifice of health tissues. In this investigation, the technique of content-based image retrieval (CBIR) coupled with machine learning algorithms are exploited in designing a computer aided path planning system (CAP) to assist junior doctors in planning surgical paths while sustaining the highest precision. Specifically, after evaluation of approaches of sparse coding and K-means in constructing a codebook, the model of sparse codes of 3D SIFT has been furthered and thereafter employed for retrieving, The novelty of this work lies in the fact that not only the existing algorithms for 2D images have been successfully extended into 3D space, leading to promising results, but also the application of CBIR, that is mainly in a research realm, to a clinical sector can be achieved by the integration with machine learning techniques. Comparison with the other four popular existing methods is also conducted, which demonstrates that with the implementation of sparse coding, all methods give better retrieval results than without while constituting the codebook, implying the significant contribution of machine learning techniques

Towards development of automatic path planning system in image-guided neurosurgery

With the advent of advanced computer technology, many computer-aided systems have evolved to assist in medical related work including treatment, diagnosis, and even surgery. In modern neurosurgery, Magnetic Resonance Image guided stereotactic surgery exactly complies with this trend. It is a minimally invasive operation being much safer than the traditional open-skull surgery, and offers higher precision and more effective operating procedures compared to conventional craniotomy. However, such operations still face significant challenges of planning the optimal neurosurgical path in order to reach the ideal position without damage to important internal structures. This research aims to address this major challenge. The work begins with an investigation of the problem of distortion induced by MR images. It then goes on to build a template of the Circle of Wills brain vessels, realized from a collection of Magnetic Resonance Angiography images, which is needed to maintain operating standards when, as in many cases, Magnetic Resonance Angiography images are not available for patients. Demographic data of brain tumours are also studied to obtain further understanding of diseased human brains through the development of an effect classifier. The developed system allows the internal brain structure to be ‘seen’ clearly before the surgery, giving surgeons a clear picture and thereby makes a significant contribution to the eventual development of a fully automatic path planning system

Intraoperative Visualisierung multimodaler Daten in der Neurochirurgie

Die Neurochirurgie als medizinisches Fachgebiet befasst sich mit der Erkennung und der (operativen) Behandlung von Pathologien des zentralen und peripheren Nervensystems. Dazu gehören unter anderem die operative Entfernung (Resektion) von Gehirntumoren und das Einsetzen von Neurostimulatoren bei Parkinson-patienten. In dieser Arbeit werden Beiträge zur computergestützten Behandlung von zerebralen Erkrankungen – Tumoren, Aneurysmen und Bewegungsstörungen – geleistet. Bei operativen Eingriffen zur Behandlung dieser zerebralen Erkrankungen muss eine exakte Planung vor der Operation erfolgen. Für die Volumen-bestimmung von zerebralen Erkrankungen wurde im Rahmen dieser Arbeit ein graphbasierter Segmentierungsalgorithmus für kugelförmige und elliptische Objekte entwickelt. Außerdem ist ein effizienter geometrischer Ansatz für die präoperative Planung von Zugangswegen bei der tiefen Hirnstimulation ausgearbeitet worden. Weiterhin wurde der Workflow zur multimodalen Integration von Stoffwechselvorgängen – erzeugt mit Hilfe der 3 Tesla Protonen MR-Spektroskopie (1H-MRS) – in ein neurochirurgisches Navigationssystem realisiert. Alle Verfahren werden in der vorliegenden Arbeit im Detail vorgestellt und anhand von Patientendaten evaluiert. Außerdem werden die klinischen Prototypen präsentiert, die auf den Verfahren aufbauen

Catalog, University of Missouri--Columbia, undergraduate (January 1985)

"January 1985