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Partner 3: FORTH Contribution Fast Inversion Methods (WP3). Jorge Ripoll, Athanasios Zacharopoulos, Giannis Zacharakis, Rosy Favicchio IESL – FORTH Greece. Outline. Main Achievements in 2009/2010: User Friendly Inversion Software (Deliverable 3.5)
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Partner 3: FORTH ContributionFast Inversion Methods (WP3) Jorge Ripoll, Athanasios Zacharopoulos, Giannis Zacharakis, Rosy Favicchio IESL – FORTH Greece
Outline Main Achievements in 2009/2010: • User Friendly Inversion Software (Deliverable 3.5) • Spectral Unmixing Algorithm (Deliverable 3.4) • Fast Inversion Method: Matrix Free Method (Deliverables 3.1 and 3.3) – Milestone 3.3
Collaborations CEA-LIME UCL Matrix-Free Algorithm FMT test Data Generation FORTH User Friendly software testing FMT-XCT data inplemetation Basic FMT principles ETH HGGM
I. User Friendly Software • One-bottom Inversion software • Software for Fast raw-data analysis • Automatic report generation • Export to NIfTI format
I. User Friendly Software REFLECTION TRANSMISSION
I. User Friendly Software Running the FMT experiment
I. User Friendly Software RAW image analysis
I. User Friendly Software Batch inverting data ONE BOTTON INVERSION
I. User Friendly Software Automated Report in Word Format
II. Spectral Unmixing Algorithm M. Simantiraki, R. Favicchio, S. Psycharakis, G. Zacharakis and J. Ripoll, “Multispectral unmixing of fluorescence molecular tomography data”, J. of Inn. Opt. Health Sci. Vol. 2(4), 353–364 (2009).
II. Fast Inversion Algorithms II. Fast Inversion Algorithms Athanasios Zacharopoulos & Simon Arridge (UCL <> FORTH collaboration)
FUTURE WORK FUTURE WORK: • Implementation of XCT data into user-friendly software • Multi-Spectral Matrix-Free code • Matrix-Free & Data Compression Approach (UCL) • Implementation of Matrix-Free code in User-friendly environment • User-Friendly Implementation of Priors from XCT data for FMT-XCT data. • Test experimental ihmonogeneous FMT-XCT phantoms
FMT-XCT FastMatrix Free Method Athanasios Zacharopoulos March 2010
Improve resolution of FMT reconstructions Deal with large number of data Reduce memory requirements Reduce Computational Time In-Vivo Reconstructions Good Quantification properties Realistic Geometries (XCT-MRI)
Forward Model Step1: Excitation Wavelength Kx .φx = q Step2: Fluorescence Wavelength : Kf .φf = h.φx Forward Model F(h) = A.h = M.[ Kf -1 .h .Kx-1.q] TOAST FEM code http://web4.cs.ucl.ac.uk/research/vis/toast/ φx Fluorochrome Concentration φf h
Inverse Problem Find concentration for fluorochrome h’ so that: h’= min ||gmeas-F (h’)||2 Using a Gauss Newton scheme: (ATA+λI ).h’ = AT gmeas Where the Jacobian (weighting matrix) is given by: A =φxxφf+ Kf φf+ = M Kx φx= q NoS x NoD A n n : number of nodes <10.000 NoS : number of sources ~ 36 NoD : number of detectors ~ 2000 NoS x NoD = 72000
Matrix Free (ATA+λI ).h’ = AT gmeas Remove Matrix MultiplicationsATA . h Replace Matrix times Vector products with Vector times Vector products Solve in respect to Sources rather than Detectors NoS x NoD NoS x NoD AT y
Matrix Free Use GMRES solver iteratively
Matrix Free Multispectral Reconstructions
Matrix Free Multispectral Reconstructions
Matrix Free Multispectral Reconstructions
Matrix Free 2. In-Vivo Reconstructions and quantification
Matrix Free 2. In-Vivo Reconstructions and Quantification
Matrix Free 2. In-Vivo Reconstructions and quantification
Matrix Free 2. In-Vivo Reconstructions and Quantification
Matrix Free 3. Realistic Geometries Prior Information
Matrix Free 3. Realistic Geometries Prior Information 5% Gaussian Noise Target Reconstruction
Future Reconstruction with XCT geometry. Need Data!! Parametric Surfaces. Spherical Harmonics for Prior information. Thank you