报告时间：3月29日 14:00-16:00
报告地点：屏峰校区A1区块计算机大楼A411

报 告 人：Prof. Alejandro Frangi（英国利兹大学教授）

报告题目：

Image-based Cerebrovascular Modeling for Advanced Diagnosis and Interventional Planning

报告人简介：

Prof. Alejandro Frangi, IEEE Fellow (2014), EAMBES Fellow (2015), Chair of the Fellows Committee of the IEEE EMBS (2017). He is a Diamond Jubilee Chair in Computational Medicine at the University of Leeds, and the director of the Center for Computational Imaging and Simulation Technologies in Biomedicine of University of Leeds. Prof. Frangi has main research interests at the crossroad of medical image analysis and modeling with emphasis on machine learning (phenomenological models) and computational physiology (mechanistic models). He has particular interest in statistical methods applied to population imaging and in silico clinical trials. His highly interdisciplinary work has been translated to the areas of cardiovascular, musculoskeletal and neuro sciences. He been principal investigator or scientific coordinator of over 25 national and European projects, both funded by public and private bodies. Prof Frangi has edited several books, published 7 editorial articles and over 200 journal papers in key international journals of his research field and more than over 200 book chapters and international conference papers with an h-index 53 and over 19,400 citations according to Google Scholar. He has been three times Guest Editor of special issues of IEEE Trans Med Imaging, one on IEEE Trans Biomed Eng, and one of Medical Image Analysis journal. Prof Frangi is Chair of the Editorial Board of the MICCAI-Elsevier Book Series (2017-2020), and serves as Associate Editor of IEEE Trans on Medical Imaging, Medical Image Analysis, SIAM Journal Imaging Sciences, Computer Vision and Image Understanding journals.  Prof. Frangi is a recipient of the IEEE Engineering in Medicine and Biology Early Career Award in 2006, the ICT Knowledge Transfer Prize (2008), he was awarded the ICREA-Academia Prize by the Institució Catalana de Recerca i Estudis Avançats (ICREA) in 2008.

内容摘要：

Current technological progress in multidimensional and multimodal acquisition of biomedical data enables detailed investigation of the individual health status that should underpin improved patient diagnosis and treatment outcome. However, the abundance of biomedical information has not always been translated directly in improved healthcare. It rather increases the current information deluge and desperately calls for more holistic ways to analyse and assimilate patient data in an effective manner. The Virtual Physiological Human aims at developing the framework and tools that would ultimately enable such integrated investigation of the human body and rendering methods for personalized and predictive medicine.

This lecture will focus on and illustrate two specific aspects: a) how the integration of biomedical imaging and sensing, signal and image computing and computational physiology are essential components in addressing this personalized, predictive and integrative healthcare challenge, and b) how such principles could be put at work to address specific clinical questions in the cardiovascular domain. Finally, this lecture will also underline the important role of model validation as a key to translational success and how such validations span from technical validation of specific modeling components to clinical assessment of the effectiveness of the proposed tools. To conclude, the talk will outline some of the areas where current research efforts fall short in the VPH domain and that will possibly receive further investigation in the upcoming years.

Selected References

1.    Frangi AF, Taylor ZA, Gooya A. Precision Imaging: more descriptive, predictive and integrative imaging. Med Image Anal. 2016 Oct, 33:27-32.

2.    Geers AJ, Morales HG, Larrabide I, Butakoff C, Bijlenga P, Frangi AF. Wall shear stress at the initiation site of cerebral aneurysms. Biomech Model Mechanobiol. 2017 Feb,16(1):97-115

3.    Larrabide I, Aguilar ML, Morales HG, Geers AJ, Kulcsár Z, Rüfenacht D, Frangi AF. Intra-aneurysmal pressure and flow changes induced by flow diverters: relation to aneurysm size and shape. AJNR Am J Neuroradiol. 2013 Apr, 34(4):816-22.

4.    Larrabide I, Kim M, Augsburger L, Villa-Uriol MC, Rüfenacht D, Frangi AF. Fast virtual deployment of self-expandable stents: method and in vitro evaluation for intracranial aneurysmal stenting. Med Image Anal. 2012 Apr, 16(3):721-30.

5.    Larrabide I, Villa-Uriol MC, Cárdenes R, Barbarito V, Carotenuto L, Geers AJ, Morales HG, Pozo JM, Mazzeo MD, Bogunović H, Omedas P, Riccobene C, Macho JM, Frangi AF.AngioLab--a software tool for morphological analysis and endovascular treatment planning of intracranial aneurysms. Comput Methods Programs Biomed. 2012 Nov, 108(2):806-19

6.    Morales HG, Larrabide I, Geers AJ, San Román L, Blasco J, Macho JM, Frangi AF. A virtual coiling technique for image-based aneurysm models by dynamic path planning. IEEE Trans Med Imaging. 2013 Jan; 32(1):119-29.

7.    Villa-Uriol MC, Berti G, Hose DR, Marzo A, Chiarini A, Penrose J, Pozo J, Schmidt JG, Singh P, Lycett R, Larrabide I, Frangi AF. @neurIST complex information processing toolchain for the integrated management of cerebral aneurysms. Interface Focus. 2011 Jun 6, 1(3):308-19.