Integration of sulcal and functional information for multimodal neuronavigation

Jannin, Pierre; Morandi, Xavier; Fleig, Oliver J.; Rumeur, Élisabeth Le; Toulouse, P.; Gibaud, Bernard; Scarabin, Jean-Marie

doi:10.3171/jns.2002.96.4.0713

Cited by 74 publications

(43 citation statements)

References 43 publications

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“…118 Only these combined imaging technologies will allow to maximize the spatial extent of tumor treatment and to simultaneously preserve functional relevant tissue. 119 Moreover, metabolic or molecular information derived from PET or SPECT studies is being used in some institutions for the exact planning for radio- 105 and gene therapy. 120,121 This is of special importance because tumor volume as depicted by contrast-enhanced MRI is always smaller than the tumor volume as depicted by [ 11 C]MET-PET.…”

Section: Imaging For Planning Resection and Radiation Therapy Includimentioning

confidence: 99%

Imaging in neurooncology

et al. 2005

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Section: Imaging For Planning Resection and Radiation Therapy Includimentioning

confidence: 99%

Imaging in neurooncology

et al. 2005

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“…This augmented reality technology, called a ''head-up display'' (HUD) (commonly used by air force pilots), allowed guidance of the surgical approach without the surgeon having to leave the operative field in order to follow the progress on the workstation. The technique has already been applied to cerebral tumor resection in areas around eloquent cortex [43]. The authors of this latter study have also suggested the potential usefulness of this technique for quick location and emergency clipping of the aneurysm neck in cases of ultra-early rupture, which is the most unfavorable situation in IA surgery.…”

Section: Neuronavigation-guided Aneurysm Clippingmentioning

confidence: 92%

Recent advances in cerebrovascular simulation and neuronavigation for the optimization of intracranial aneurysm clipping

Marinho

Thines

Verscheure

et al. 2012

Computer Aided Surgery

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Endovascular treatment of intracranial aneurysms (IAs) has improved to the extent that in some instances such an approach has now become safer than surgery. This has dramatically changed clinical practice by reducing the volume and increasing the complexity of IAs referred for open surgical treatment. We review the simulation techniques and dedicated vascular neuronavigation systems that have been developed to maintain the quality of aneurysm clipping in this context. Simulation of surgical approaches was made possible by the introduction of high-resolution 3D imaging techniques such as three-dimensional CT angiography (3D-CTA) and three-dimensional digital subtraction angiography (3D-DSA), enabling reproduction of the craniotomy and rotation of the vascular tree according to the orientation of the operative microscope. A virtual simulator for compiling such data, the Dextroscope Õ , is now available for this purpose. Simulation of final clipping has been investigated through virtual or physical models, enabling anticipation of aneurysm deformation during clip application and selection of the appropriate clip(s) in terms of number, size, shape and orientation. To improve surgical dissection guidance, specific cerebrovascular neuronavigation procedures have been developed based on 3D-CTA or 3D-DSA. These help make the operation secure by accurately predicting the location and orientation of an aneurysm within its parenchymal and vascular environment. Future simulators dedicated to cerebrovascular procedures will need to integrate representation of the brain surface and biomechanical modeling of brain and aneurysm wall deformation under retraction or during clipping. They should contribute to training and maintenance of surgical skills, thereby optimizing the quality of surgical treatment in this field.

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“…Our primary application field is the preparation of surgical procedures in neurosurgery. Actually, the precise identification of gyri and sulci around the lesional area is of primary importance because they provide useful landmarks for the surgeon during surgery, especially in eloquent cortex [3]. The annotation of images, in our case anatomical MRI images, has two objectives: 1) to help to identify precisely the gyri and sulci around the lesion, and to provide the detailed annotation data relating anatomical conceptual entities to graphical primitives extracted from the images; 2) to use these metadata for cases retrieval in an images database.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid System for the Semantic Annotation of Sulco-Gyral Anatomy in MRI Images

Mechouche

Morandi

Golbreich

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. This paper presents an interactive system for the annotation of brain anatomical structures in Magnetic Resonance Images. The system is based on hybrid knowledge and techniques. First, it exploits both numerical knowledge from atlases and symbolic knowledge from a ruleextended ontology represented in OWL, the Web ontology language, and combines them with graphical data about cortical sulci, automatically extracted from the images. Second, the annotations of the parts of gyri and of sulci located in a region of interest are obtained with different reasoning techniques: Constraint Satisfaction Solving and Description Logics techniques. Preliminary experiments have been achieved on normal and also pathological data. The results obtained so far are very promising.

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Integration of sulcal and functional information for multimodal neuronavigation

Abstract: This method, in which both sulcal and functional mapping are used for surgery planning and neuronavigation, provides helpful information. It is a promising procedure for the treatment of patients who harbor lesions in areas around the eloquent cortex.

Cited by 74 publications

References 43 publications

Imaging in neurooncology

Imaging in neurooncology

Recent advances in cerebrovascular simulation and neuronavigation for the optimization of intracranial aneurysm clipping

A Hybrid System for the Semantic Annotation of Sulco-Gyral Anatomy in MRI Images

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