AngioLab - A software tool for morphological analysis and endovascular treatment planning of intracranial aneurysms

Determining whether and how an intracranial aneurysm should be treated is a tough decision that clinicians face everyday. Emerging computational tools could help clinicians analyze clinical data and make these decisions. AngioLab is a single graphical user interface, developed on top of the open source framework GIMIAS, that integrates some of the latest image analysis and computational modeling tools for intracranial aneurysms. Two workflows are available: Advanced Morphological Analysis (AMA) and Endovascular Treatment Planning (ETP). AngioLab has been evaluated by a total of 62 clinicians, who considered the information provided by AngioLab relevant and meaningful. They acknowledged the emerging need of these type of tools and the potential impact they might have on the clinical decision-making process.

[1]  Rubén Cárdenes,et al.  Three-dimensional morphological analysis of intracranial aneurysms: a fully automated method for aneurysm sac isolation and quantification. , 2011, Medical physics.

[2]  C. Slump,et al.  Relation between aneurysm volume, packing, and compaction in 145 cerebral aneurysms treated with coils. , 2004, Radiology.

[3]  C. Putman,et al.  Quantitative Characterization of the Hemodynamic Environment in Ruptured and Unruptured Brain Aneurysms , 2010, American Journal of Neuroradiology.

[4]  Gavin W Britz,et al.  Prevalence of asymptomatic incidental aneurysms: review of 4568 arteriograms. , 2002, Journal of neurosurgery.

[5]  Alejandro F. Frangi,et al.  Influence of different computational approaches for stent deployment on cerebral aneurysm haemodynamics , 2011, Interface Focus.

[6]  J. Mocco,et al.  Hemodynamic–Morphologic Discriminants for Intracranial Aneurysm Rupture , 2011, Stroke.

[7]  F. Kajiya,et al.  Effects of size and shape (aspect ratio) on the hemodynamics of saccular aneurysms: a possible index for surgical treatment of intracranial aneurysms. , 1999, Neurosurgery.

[8]  Alejandro F. Frangi,et al.  How Do Coil Configuration and Packing Density Influence Intra-Aneurysmal Hemodynamics? , 2011, American Journal of Neuroradiology.

[9]  Alejandro F. Frangi,et al.  Automatic Aneurysm Neck Detection Using Surface Voronoi Diagrams , 2011, IEEE Transactions on Medical Imaging.

[10]  Alejandro F Frangi,et al.  Non-parametric geodesic active regions: Method and evaluation for cerebral aneurysms segmentation in 3DRA and CTA , 2007, Medical Image Anal..

[11]  Alejandro F. Frangi,et al.  Fast virtual deployment of self-expandable stents: Method and in vitro evaluation for intracranial aneurysmal stenting , 2012, Medical Image Anal..

[12]  Alejandro F. Frangi,et al.  GIMIAS: An Open Source Framework for Efficient Development of Research Tools and Clinical Prototypes , 2009, FIMH.

[13]  Alejandro F. Frangi,et al.  Efficient 3D Geometric and Zernike Moments Computation from Unstructured Surface Meshes , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Isabel Wanke,et al.  Treatment of wide-necked intracranial aneurysms with a self-expanding stent system: initial clinical experience. , 2003, AJNR. American journal of neuroradiology.

[15]  Rainald Löhner,et al.  Efficient simulation of blood flow past complex endovascular devices using an adaptive embedding technique , 2005, IEEE Transactions on Medical Imaging.

[16]  Alejandro F. Frangi,et al.  Automated segmentation of cerebral vasculature with aneurysms in 3DRA and TOF-MRA using geodesic active regions: an evaluation study. , 2010, Medical physics.

[17]  D. Nichols,et al.  Unruptured intracranial aneurysms: natural history, clinical outcome, and risks of surgical and endovascular treatment , 2003, The Lancet.

[18]  Alejandro F Frangi,et al.  Reproducibility of haemodynamical simulations in a subject-specific stented aneurysm model--a report on the Virtual Intracranial Stenting Challenge 2007. , 2008, Journal of biomechanics.

[19]  M. L. Raghavan,et al.  Quantified aneurysm shape and rupture risk. , 2005, Journal of neurosurgery.

[20]  Alejandro F Frangi,et al.  The @neurIST project: towards understanding cerebral aneurysms , 2007 .

[21]  Alejandro F. Frangi,et al.  Morphological Characterization of Intracranial Aneurysms Using 3-D Moment Invariants , 2007, IEEE Transactions on Medical Imaging.

[22]  Yiannis Ventikos,et al.  The Haemodynamics of Endovascular Aneurysm Treatment: A Computational Modelling Approach for Estimating the Influence of Multiple Coil Deployment , 2008, IEEE Transactions on Medical Imaging.

[23]  W. Eric L. Grimson,et al.  An Integrated Visualization System for Surgical Planning and Guidance Using Image Fusion and Interventional Imaging , 1999, MICCAI.

[24]  I. Larrabide,et al.  Comparison of steady-state and transient blood flow simulations of intracranial aneurysms , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[25]  Osman Ratib,et al.  OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images , 2004, Journal of Digital Imaging.

[26]  Örjan Smedby,et al.  An interactive software module for visualizing coronary arteries in CT angiography , 2008, International Journal of Computer Assisted Radiology and Surgery.

[27]  Alejandro F. Frangi,et al.  Virtual Coiling of Intracranial Aneurysms Based on Dynamic Path Planning , 2011, MICCAI.

[28]  Alejandro F Frangi,et al.  Deployment of self-expandable stents in aneurysmatic cerebral vessels: comparison of different computational approaches for interventional planning , 2012, Computer methods in biomechanics and biomedical engineering.

[29]  F. Mut,et al.  Association of Hemodynamic Characteristics and Cerebral Aneurysm Rupture , 2011, American Journal of Neuroradiology.

[30]  David A. Steinman,et al.  Virtual angiography for visualization and validation of computational models of aneurysm hemodynamics , 2005, IEEE Transactions on Medical Imaging.