Transcript Simulation and the Monte Carlo method

fNIRS Image reconstruction.
Analysis of the vascular
compartamentalization
Dr. Felipe Orihuela-Espina
Initial proposition: 2013
Current version: 2016
What is image reconstruction?
 Image reconstruction and the inverse
problem
Figure from: [OrihuelaEspina, F. (2005) PhD thesis]
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What is image reconstruction?
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Research Question and Hypothesis
 RQ: From which head compartment does
the haemodynamic signal comes from?
 Hypothesis: After removal of the
extracranial systemic hemodynamic
component, the intracranial hemodynamic
signal comes from the microvasculature of
the cortex, but sensing on top of a big vein
or artery sensibly distorts the remitted
spectra.
© 2013-6. Dr. Felipe Orihuela-Espina
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Aims and goals
 Characterization of the spectral response
associated to the vascular
compartmentalization
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Contributions
 Computing:
 Enhanced reconstruction algorithms.
 Neuroimaging:
 Enhanced understanding of the
haemodynamic signal origin.
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Associated topics
 Computing:
 Inverse problems,
 Regularization
 Monte Carlo methods
 Multidisciplinary:
 Neuroscience and functional neuroimaging
 Histophysiology
 Physics and Optics
 Diffusion theory
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A few challenges
 Convincing the academy that this is computer
science and not “maths”
 Inverse ill-problems and ill-posed problems
require a steep learning curve
 Simulations are computationally burdensome,
…and a lot of them are required.
 The complexity of the anatomical and optical
model
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Other considerations
 Possible co-direction: N/A
 Previous and/or current work in the group:
 Felipe Orihuela’s thesis [2005]
 Javier Herrera’s thesis [2013-2017]
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Suggested Workflow
 PENDING
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Monte Carlo software
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mcml –quizás el estándar de oro-
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Lihong Wang, Steven L. Jacques, and Liqiong Zheng. Mcml - monte carlo modeling of light transport in multi-layered tissues. Computer Methods
and Programs in Biomedicine, 47:131–146, 1995.
MCX,
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Qianqian Fang and David A. Boas. Monte carlo simulation of photon migration in 3d turbid media accelerated by graphics processing units.
Optics Express, 17(22):20178–20190, 2009
penMesh
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Andreu Badal, Iacovos Kyprianou, Diem Phuc Banh, Aldo Badano, and Josep Sempau. Penmesh A monte carlo radiation transport simulation in
a triangle mesh geometry. IEEE Transactions on Medical Imaging, 28(12):1894–1901, DEC 2009
CUDAMCML,
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Erik Alerstam, William Chun Yip Lo, Tianyi David Han, Jonathan Rose, Stefan Andersson-Engels, and Lothar Lilge. Next-generation acceleration
and code optimization for light transport in turbid media using gpus. Biomedical Optics Express, 1(2):658–675, SEP 2010.
O3MC,
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Alexander Doronin and Igor Meglinski. Online object oriented monte carlo computational tool for the needs of biomedical optics. Biomedical
Optics Express, 2(9):2461–2469, SEP 2011.
GEANT
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S. Agostinelli and J. et al Allison. Geant4- a simulation toolkit. Nuclear Instruments and Methods in Physics Research Section A: Accelerators,
Spectrometers, Detectors and Associated Equipment, 506(3):250–303, JUL 2003.
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y su subproyecto GATE
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D. Sarrut, M. Bardiés, N. Boussion, N. Freud, S. Jan, J. M. Létang, G. Loudos, L. Maigne, S. Marcatili, T. Mauxion, P. Papadimitroulas, Y. Perrot, U.
Pietrzyk, C. Robert, D. Schaart, D. Visvikis, and I. Buvat. A review of the use and potential of the gate monte carlo code for radiation therapy and
dosimetry applications. Medical Physics, 41(6):064301, 2014
MOCARTS
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Bibiana Cuervo-Soto, Javier Herrera-Vega, J. Alfonso del C. Garcés-Báez Carlos Treviño-Palacios Felipe Orihuela-Espina. MOCARTS: A
lightweight radiation transport simulator for easy handling of complex sensing geometries. In 13th International Symposium on Biomedical
Imaging (ISBI’16), pages 377–380, 2016.
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Starting literature
 About… fNIRS:
 [Villringer 1997] Villringer, A. & Chance, B. Non-invasive optical spectroscopy and
imaging of human brain function Trends in Neuroscience, 1997, 20, 435-442
 [Strangman 2002] Strangman, G.; Boas, D. A. & Sutton, J. P. Non-Invasive
Neuroimaging Using Near-Infrared Light Biological Psychiatry, 2002, 52, 679-693
 [OrihuelaEspina 2010] Orihuela-Espina, F.; Leff, D. R.; James, D. R. C.; Darzi, A. W. &
Yang, G.-Z. Quality control and assurance in functional near infrared spectroscopy
(fNIRS) experimentation Physics in Medicine and Biology, 2010, 55, 3701-3724
 [Ferrari 2012] Ferrari, M. & Quaresima, V. A brief review on the history of human
functional near-infrared spectroscopy (fNIRS) development and fields of application
Neuroimage, 2012, 63, 921-935
 [Boas 2014] Boas, D. A.; Elwell, C. E.; Ferrari, M. & Taga, G. Twenty years of
functional near-infrared spectroscopy: introduction for the special issue Neuroimage,
2014, 85, 1-5
 [Scholkmann 2014] Scholkmann, F.; Kleiser, S.; Metz, A. J.; Zimmermann, R.; Pavia, J.
M.; Wolf, U. & Wolf, M. A review on continuous wave functional near-infrared
spectroscopy and imaging instrumentation and methodology Neuroimage, 2014, 85,
6-27
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Starting literature
 About… Image reconstruction:
 [Arridge 1997] Arridge, S. R.; Hebden, J. C. & Delpy, D. T. Optical imaging in medicine:
II. Modelling and reconstruction Physics in Medicine and Biology, 1997, 42, 841-853
 [Arridge 2011] Arridge, S. R. Methods in diffuse optical imaging Philosophical
Transactions of the Royal Society A - Mathematical, Physical and Engineering
Sciences, 2011, 369, 4558-4576
 [Deghani 2009] Dehghani, H.; White, B. R.; Zeff, B. W.; Tizzard, A. & Culver, J. P.
Depth sensitivity and image reconstruction analysis of dense imaging arrays for
mapping brain function with diffuse optical tomography Applied Optics, 2009, 48,
D137-D143
 [Deghani 2009b] Dehghani, H.; Srinivasan, S.; Pogue, B. W. & Gibson, A. P. Numerical
modelling and image reconstruction in diffuse optical tomography Philosophical
Transactions of the Royal Society of London. Serie A, 2009, 367, 3073-3093
 [Wu 2015] Wu, X.; Eggebrecht, A. T.; Ferradal, S. L.; Culver, J. P. & Dehghani, H. Fast
and efficient image reconstruction for high density diffuse optical imaging of the human
brain Biomedical Optics Express, 2015, 6, 4567-4584
 [Tesis de Javier?]
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Starting literature
 About… Monte Carlo:
 [Reuven 2008] Reuven Y. Rubinstein and Dirk
P. Kroese. Simulation and the Monte Carlo
method. Wiley-Interscience, 2nd ed. edition,
2008
 [Mackay 1998] D. J. C. Mackay. Introduction
to monte carlo methods, 1998
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