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Frangi, A.F.; Taylor, Z.A.; Gooya, A. (2016)
Publisher: Elsevier
Languages: English
Types: Article

Classified by OpenAIRE into

Medical image analysis has grown into a matured field challenged by progress made across all medical\ud imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation\ud between medical image analysis, biomedical imaging physics and technology, and domain knowledge\ud from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original\ud boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies.\ud Consideration on how the field has evolved and the experience of the work carried out over the last\ud 15 years in our centre, has led us to envision a future emphasis of medical imaging in Precision Imaging.\ud Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne\ud at the cross-roads between, and unifying the efforts behind mechanistic and phenomenological modelbased\ud imaging. It captures three main directions in the effort to deal with the information deluge in\ud imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is\ud finally characterised by being descriptive, predictive and integrative about the imaged object. This paper\ud provides a brief and personal perspective on how the field has evolved, summarises and formalises our\ud vision of Precision Imaging for Precision Medicine, and highlights some connections with past research\ud and current trends in the field.
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    • Ackoff, R., 1989. From data to wisdom. Journal of Applied Systems Analysis 16, 3-9.
    • Bamberg, F., Kauczor, H. U., Weckbach, S., Schlett, C. L., Forsting, M., Ladd, S. C., Greiser, K. H., Weber, M. A., Schulz-Menger, J., Niendorf, T., Pischon, T., Caspers, S., Amunts, K., Berger, K., Bulow, R., Hosten, N., Hegenscheid, K., Kroncke, T., Linseisen, J., Gunther, M., Hirsch, J. G., Kohn, A., Hendel, T., Wichmann, H. E., Schmidt, B., Jockel, K. H., Hoffmann, W., Kaaks, R., Reiser, M. F., Vlzke, H., German National Cohort MRI Study Investigators, 2015. Whole-Body MR Imaging in the German National Cohort: Rationale, Design, and Technical Background. Radiology 277 (1), 206-220.
    • Castro-Mateos, I., Pozo, J. M., Cootes, T. F., Wilkinson, J. M., Eastell, R., Frangi, A. F., 2014. Statistical shape and appearance models in osteoporosis. Curr Osteoporos Rep 12 (2), 163-73.
    • Center for Devices and Radiological Health, 2014. Reporting of computational modeling studies in medical device submissions. Draft Guidance Federal Register Number 2014-00874, Food and Drug Administration.
    • Clayden, J. D., Nagy, Z., Weiskopf, N., Alexander, D. C., Clark, C. A., 2016. Microstructural parameter estimation in vivo using diffusion mri and structured prior information. Magn Reson Med 75 (4), 1787-96.
    • Collins, F. S., Varmus, H., 2015. A new initiative on precision medicine. New England Journal of Medicine 372 (9), 793-795.
    • Committee on a Framework for Development a New Taxonomy of Disease; Board on Life Sciences; Division on Earth and Life Studies; National Research Council, 2011. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. The National Academies Press.
    • Coskun, A. F., Ozcan, A., 2014. Computational imaging, sensing and diagnostics for global health applications. Curr Opin Biotechnol 25, 8-16.
    • Duchateau, N., De Craene, M., Piella, G., Frangi, A. F., 2012. Constrained manifold learning for the characterization of pathological deviations from normality. Med Image Anal 16 (8), 1532-49.
    • Frangi, A. F., Coatrieux, J. L., Peng, G. C., D'Argenio, D. Z., Marmarelis, V. Z., Michailova, A., 2011. Multiscale modeling and analysis in computational biology and medicine. IEEE Trans Biomed Eng 58 (10), 2936-42.
    • Frangi, A. F., Hose, D., Hunter, P., Ayache, N., Brooks, D., 2013. Medical imaging and image computing in computational physiology. IEEE Trans Med Imaging 32 (1), 1-7.
    • Hu, Y., Ahmed, H. U., Taylor, Z. A., Allen, C., Emberton, M., Hawkes, D., Barratt, D., 2012. Mr to ultrasound registration for image-guided prostate interventions. Medical Image Analysis 16 (3), 687-703.
    • Ikram, M. A., van der Lugt, A., Niessen, W. J., Koudstaal, P. J., Krestin, G. P., Hofman, A., Bos, D., Vernooij, M. W., 2015. The Rotterdam Scan Study: design update 2016 and main findings. Eur J Epidemiol 30 (12), 1299-315.
    • Kansagra, A. P., Yu, J. P., Chatterjee, A., Lenchik, L., Chow, D. S., Prater, A., Yeh, J., Doshi, A., Hawkins, C. M., Heilbrun, M., Smith, S., Oselkin, M., Gupta, P. amd Ali, S., 2016. Big data and the future of radiology informatics. Acad Radiol 23 (1), 30-4249.
    • Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J., Dekker, A., Fenstermacher, D., Goldgof, D. B., Hall, L. O., Lambin, P., Balagurunathan, Y., Gatenby, R. A., Gillies, R. J., 2012. Radiomics: the process and the challenges. Magn Reson Imaging 30 (9), 1234-48.
    • Larrabide, I., Aguilar, M. L., Morales, H. G., Geers, A. J., Kulcsar, Z., R u¨fenacht, D., Frangi, A. F., 2013. Intra-aneurysmal pressure and flow changes induced by flow diverters: relation to aneurysm size and shape. AJNR Am J Neuroradiol 34 (4), 816-22.
    • Larrabide, I., Kim, M., Augsburger, L., Villa-Uriol, M. C., R u¨fenacht, D., Frangi, A. F., 2012. Fast virtual deployment of self-expandable stents: method and in vitro evaluation for intracranial aneurysmal stenting. Med Image Anal 16 (3), 721-30.
    • Lekadir, K., Hazrati-Marangalou, J., Hoogendoorn, C., Taylor, Z. A., van Rietbergen, B., Frangi, A. F., 2015. Statistical estimation of femur micro-architecture using optimal shape and density predictors. J Biomech 48 (4), 598-603.
    • Lekadir, K., Noble, C., Hazrati-Marangalou, J., Hoogendoorn, C., van Rietbergen, B., Taylor, Z. A., Frangi, A. F., 2016. Patient-specific biomechanical modeling of bone strength using statisticallyderived fabric tensors. Ann Biomed Eng 44 (1), 234-46.
    • Lekadir, K., Pashaei, A., Hoogendoorn, C., Pereanez, M., Alba, X., Frangi, A. F., 2014. Effect of statistically derived fiber models on the estimation of cardiac electrical activation. IEEE Trans Biomed Eng 61 (11), 2740-8.
    • Margolies, L. R., Pandey, G., Horowitz, E. R., Mendelson, D. S., 2016. Breast imaging in the era of big data: Structured reporting and data mining. AJR Am J Roentgenol 206 (2), 259-64.
    • Mattila, J., Koikkalainen, J., Virkki, A., Simonsen, A., van Gils, M., Waldemar, G., Soininen, H., Ltjnen, J., Alzheimers Disease Neuroimaging Initiative, 2011. A disease state fingerprint for evaluation of Alzheimer's disease. J Alzheimers Dis 27 (1), 163-76.
    • Medrano-Gracia, P., Cowan, B. R., Suinesiaputra, A., Young, A. A., 2015. Challenges of cardiac image analysis in large-scale population-based studies. Curr Cardiol Rep 17 (3), 563.
    • Miller, M. I., Trouv, A., Younes, L., 2015. Hamiltonian systems and optimal control in computational anatomy: 100 years since d'arcy thompson. Annu Rev Biomed Eng 17, 447-509.
    • Morales, H. G., Kim, M., Vivas, E. E., Villa-Uriol, M. C., Larrabide, I., Sola, T., Guimaraens, L., Frangi, A. F., 2011. How do coil configuration and packing density influence intra-aneurysmal hemodynamics? AJNR Am J Neuroradiol 32 (10), 1935-41.
    • Morales, H. G., Larrabide, I., Geers, A. J., San Romn, L., Blasco, J., Macho, J. M., Frangi, A. F., 2013. A virtual coiling technique for image-based aneurysm models by dynamic path planning. IEEE Trans Med Imaging 32 (1), 119-29.
    • Nørgaard, B. L., Leipsic, J., Koo, B. K., Zarins, C. K., Jensen, J. M., Sand, N. P., Taylor, C., 2016. Coronary computed tomography angiography derived fractional flow reserve and plaque stress. Curr Cardiovasc Imaging Rep 9 (2), in press.
    • Petersen, S. E., Matthews, P. M., Bamberg, F., Bluemke, D. A. ., Francis, J. M., Friedrich, M. G., Leeson, P., Nagel, E., Plein, S., Rademakers, F. E., Young, A. A., Garratt, S., Peakman, T., Sellors, J., Collins, R., Neubauer, S., 2013. Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches. J Cardiovasc Magn Reson 28, 15-46.
    • Rowley, J., 2007. The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science 33 (2), 163-180.
    • Sarvazyan, A. P., Lizzi, F. L., Wells, P. N., 1991. A new philosophy of medical imaging. Med Hypotheses 36 (4), 327-35.
    • Schmidt-Richberg, A., Ledig, C., Guerrero, R., Molina-Abril, H., Frangi, A. F., Rueckert, D., Alzheimers Disease Neuroimaging Initiative., 2016. Learning biomarker models for progression estimation of alzheimer's disease. PLoS One 11 (4), e0153040.
    • Sharpe, J., 2011. Two ways to use imaging: focusing directly on mechanism, or indirectly via behaviour? Curr Opin Genet Dev 21 (5), 523-29.
    • Smith, N., de Vecchi, A., McCormick, M., Nordsletten, D., Camara, O., Frangi, A. F., Delingette, H., Sermesant, M., Relan, J., Ayache, N., Krueger, M. W., Schulze, W., Hose, R. D., Valverde, I., Beerbaum, P., Staicu, C., Siebes, M., Spaan, J., Hunter, P. J., Weese, J., Lehmann, H., Chapelle, D., Rezavi, R., 2011. euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling. Interface Focus 1 (3), 349-64.
    • Viceconti, M., Henney, A., Morley-Fletcher, E., 2016. in silico Clinical Trials: How Computer Simulation will Transform the Biomedical Industry. Research and Technological Development Roadmap, Avicenna Consortium, http://avicenna-isct.org/roadmap.
    • Villa-Uriol, M. C., Berti, G., Hose, D. R., Marzo, A., Chiarini, A., Penrose, J., Pozo, J. M., Schmidt, J. G. Singh, P., Lycett, R., Larrabide, I., Frangi, A. F., 2011. @neurIST complex information processing toolchain for the integrated management of cerebral aneurysms. Interface Focus 1 (3), 308-19.
    • Villa-Uriol, M. C., Larrabide, I., Pozo, J., Kim, M., Camara, O., De Craene, M., Zhang, C., Geers, A., Morales, H., Bogunovi, H., Cardenes, R., Frangi, A. F., 2010. Toward integrated management of cerebral aneurysms. Philos Trans A Math Phys Eng Sci 368 (1921), 2961-82.
    • Volzke, H., Schmidt, C. O., Hegenscheid, K., Kuhn, J.-P., Bamberg, F., Lieb, W., Kroemer, H. K., Hosten, N., Puls, R., 2012. Population imaging as valuable tool for personalized medicine. Clinical Pharmacology & Therapeutics 92 (4), 422-424.
    • York, T., McCann, H., Ozanyan, K. B., 2011. Agile sensing systems for tomography. IEEE Sensors Journal 11 (12), 3086-105.
    • Young, A. A., Frangi, A. F., 2009. Computational cardiac atlases: from patient to population and back. Exp Physiol 94 (5), 578-96.
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