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We are developing automatic systems to track, and exploit the morphology of the Intracranial Pressure Signal to improve the understanding and treatment of various pathophysiologies related to Traumatic Brain Injuries (TBI).
Bayesian Tracking of Intracranial Pressure Signal Morphology.
F. Scalzo, S. Asgari, S. Kim, M. Bergsneider, and X. Hu. Artif Intell Med, 2011.

Robust Peak Recognition in Intracranial Pressure Signals.
F. Scalzo, S. Asgari, S. Kim, M. Bergsneider, and X. Hu. BioMed Eng OnLine, 2010.

Regression Analysis for Peak Designation in Pulsatile Pressure Signals.
F. Scalzo, P. Xu, S. Asgari, M. Bergsneider, X. Hu. Med & Biol Eng & Comp, 2009.

Morphological Clustering and Analysis of Continuous Intracranial Pressure.
X. Hu, P. Xu, F. Scalzo, P. Vespa, M. Bergsneider. IEEE TBME, 2009.

Computer Vision and Computational Fluid Dynamics (CFD) can be used to reconstruct 3D models of the brain vessels and simulate blood flow.
CFD Modeling of Symptomatic Intracranial Atherosclerosis May Predict Risk of Stroke Recurrence. X. Leng, F. Scalzo, H. Ip, M. Johnson, A. Fong, F. Fan, X. Chen, Y. Soo, Z. Miao, L. Liu, E. Feldmann, T. Leung, D. Liebeskind, K. Wong. Plos One. in press, 2014.

Noninvasive fractional flow on MRA predicts stroke risk of intracranial stenosis in SONIA/WASID. D. Liebeskind, A. Kosinski, M. Lynn, F. Scalzo, A. Fong, P. Fariborz. Stroke. 44, 2013.

Computational Hemodynamics in Intracranial Vessels Reconstructed from Biplane Angiograms. F. Scalzo, Q. Hao, A. Walczak, X. Hu, Y. Hoi, K. Hoffmann, D. Liebeskind. ISVC, 2010.

We develop Machine Learning models to personalize treatment and predict the outcome of stroke patients.
Multi-Center Prediction of Hemorrhagic Transformation in Acute Ischemic Stroke using Permeability Imaging Features. F. Scalzo, J. Alger, X. Hu, J. Saver, K. Dani, K. Muir, A. Demchuk, S. Coutts, M. Luby, S. Warach, D. Liebeskind. Magn Reson Imaging. 1(6):961-9, 2013.

Regional Prediction of Tissue Fate in Acute Ischemic Stroke.
F. Scalzo, Q. Hao, X. Hu, and D. Liebeskind. Annals of Biomedical Engineering. in press, 2012.

Our research on morphological analysis and noninvasive ICP is the central technology used in a novel portable TCD-based ICP estimation device; see Neural Analytics.
  • We recently obtained the 1st price at the CASIS award and are going to use the International Space Station (ISS) as a controlled environment for improvement for our portable ultrasound device that measures ICP. Spaceflight effects on intracranial pressure in astronauts are well known; thus, the experiment will compare measurements from astronauts with data from TBI patients with the goal of refining data measurements.
  • Noninvasive Intracranial Pressure Assessment based on Data Mining Approach using Nonlinear Mapping Function.
    S. Kim, F. Scalzo, M. Bergsneider, P. Vespa, N. Martin, and X. Hu. IEEE Trans Biomed Eng, November, 2010.

    During my PhD, I developed a hierarchical probabilistic graphical model to perform visual inference, which could be seen as an implementation of Lee and Mumford's model.
    Adaptive patch features for object class recognition with learned hierarchical models. F. Scalzo, J. Piater. CVPR, Beyond Patches Workshop, 2007.

    Task-Driven Learning of Spatial Combinations of Visual Features.
    S. Jodogne, F. Scalzo, J. Piater. CVPR, Workshop on Learning, 2005.

    Statistical learning of visual feature hierarchies.
    F. Scalzo, J. Piater. CVPR, Workshop on Learning, 2005.