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Simon David

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Simon David

  1. 1. Multi-tracer analysis for patient’s following using multi-observation statistical image fusion : a feasibility study S. David1 , M. Hatt1 , P. Fernandez2 , M. Allard2 , O. Barrett2 , D. Visvikis1 1. LaTIM, INSERM U650, Brest, France1. LaTIM, INSERM U650, Brest, France 2. Department2. Department ofof nuclear medicinenuclear medicine HospitalHospital Pellegrin – CHU BordeauxPellegrin – CHU Bordeaux
  2. 2. Outline Context of oncology • Positron Emission Tomography (PET) • External radiotherapy Use of PET in clinical application • Multi-tracer analysis for dose-painting • Patient monitoring in PET Multi-observation fusion • Multiband segmentation of a spectroscopic line data cube • Developed method • Preliminary results Further work
  3. 3. Context of oncology Cancer • In 2002 : 11 millions new cases and 7 millions deaths • Foresee in 2030 : 11 millions deaths Diagnosis • Computed tomography (CT) • Magnetic resonance imaging (MRI) • Emission imaging (PET, SPECT) Treatment • Surgery • Chemotherapy • Radiotherapy Focus on the PET imaging and its application : • Radiotherapy planning • Therapy response assessment Since 2000 : PET / CT combined Gold standard for the diagnosis Usually combined
  4. 4. Context and motivations Positron Emission Tomography (PET) : • Functional imaging : visualization of physiological processes • Mainly used for cancer diagnosis and staging Principle : • Injection of a radiotracer o Biological tracer targets the tumor o Radionuclide : β- emitter • Detection of the 2 γ rays • Image reconstruction by tomography Drawbacks of the PET imaging : • Blur (spatial resolution) • High noise (acquisition variability) • Low resolution ( >5 mm)
  5. 5. Context and motivations PET/CT imaging : • Since 2000, acquisition of PET and CT in the same bed • Gold standard for the diagnosis PET imageCT image PET / CT fusion + Combination of anatomical and functional information + Allow to anatomically locate the tracer uptake in the PET image - Registration of the CT and PET scans - Difference in the image resolution ( CT <1mm, PET >5 mm)  PET / CT used in the radiotherapy planning
  6. 6. Principle of radiotherapy : • Use the ionizing radiation to kill the malignant cells Context and motivations External radiotherapy (most widely used) : • Photon or electron beams produced by a linear accelerator • Shape of the beam defined by the collimator Definition of the target volumes : • Gross tumor volume (GTV) o Defined with conventional imaging modalities • Clinical target volume (CTV) > GTV o Volume computed considering the anatomical information • Planning target volume (PTV) > CTV o Take into account the physiological variations
  7. 7. IMRT planning Biological image-guided dose escalation • Development of Intensity-modulated radiation therapy (IMRT) • Administration of a non-uniform dose o Adapt the treatment to the patient o Reduce the irradiation of organ at risk (OAR) and surrounding healthy tissues • 18 F-FDG : Measure the glucose consumption (tumor highly glucose consumer) o Radiotracer the most widely use in PET o Not tumor specific (other physiological processes need glucoses) o Uptake in inflammatory tissues Use of PET in clinical application PET / CT : Target volume definition  Improvement of the tumor volume definition with the multi-tracer analysis Multi-tracer analysis
  8. 8. Development of specific radio-marker measuring ≠ tumor features : • FMISO : measure of the tumor hypoxia (lack of oxygen) o Hypoxia induces resilience to the radiotherapy Use of PET in clinical application FDG coronal PET scan FLT coronal PET scan • 18 F-FLT : measure of the tumor proliferation oTumor specific radio-marker : ≠ FDG o Avoid inflammatory tissues o Lower uptake in the tumor than FDG Y. Yamamoto et al, European Journal of Nuclear Medicine and Molecular Imaging, 2008  Hypoxic tumors require a dose boosting • Merging all features measured by the tracers • Each radiotracer is measuring a specific biologic process o Scans are quantitatively not comparable
  9. 9. Use of PET in clinical application Patient follow-up with PET : • Patients underwent chemo / radio-therapy o Early assessment of the response to the treatment o Adapt the therapy to the response o Avoid toxic and costly cares for non-responding patients PET1 pre-treatment PET2 post-treatment Tumors • PET acquisition during the course of the therapy o Estimation of prognosis
  10. 10. Goal of our work Multi-sensor observation of an objet • Many clinical data available with the multi-modality imaging Goal : • Fusing all the scans obtained : o with the ≠ radiotracers  Should improve the tumor volume definition o at different time of the treatment  A more accurate assessment of therapy response Approach : • With the statistical segmentation framework • Model the information of multi-tracer and/or follow-up scans Analogy with the astronomical framework
  11. 11. Multiband segmentation of a spectroscopic line data cube • EM algorithm on Gaussian mixture model o fit the spectrum : assessment of mean and variance F. Flitti et al, Multiband segmentation of a spectroscopic line data cube : application to the H1 data cube of the spiral galaxy NGC 4254 Segmentation process based on Bayesian inference • Observation with radio interferometers o 3D data cubes (astronomical coordinates and frequency third axis) • Choice of the 6 most pertinent Gaussian o K-means • Computation of weight associated to each class o Levenberg-Marquardt algorithm Reduction of the dimension (42 channels) of the cube Maps of the weights of the 6 Gaussian with the NGC 4254 Cube
  12. 12. Bayesian classifier • Hierarchical Markovian Model o Models the spatial dependencies between neighbors Results on the NGC 4254 Cube • Creation of a label map Multiband segmentation of a spectroscopic line data cube Steps of the segmentation process • Initialization : K-mean algorithm • Parameter estimation step o Unsupervised with the Iterative Conditional Estimation (ICE) • Segmentation step o Criteria of maximum a posteriori (MAP) F. Flitti et al, Multiband segmentation of a spectroscopic line data cube : application to the H1 data cube of the spiral galaxy NGC 4254
  13. 13. Local prior model (spatial or contextual) Blind, contextual, adaptive… ( , ) ( | ) ( )p x y p y x p x= Observation model (noise) Gaussian, generalized gaussian Iterative estimation of the parameters deterministic (EM) stochastic (SEM) hybrid (ICE) Segmentation MAP, MPM criteria Multi-observation method Statistical image segmentation : • Estimation of (hidden) with (measurements) • No determinist link between and ( )t t TX x ∈= ( )t t TY y ∈= X Y o Probabilistic approach (Bayesian inference) Global Markovian model (field, quadtree…)
  14. 14. • X : labels field • Y : measurements field Multi-observation method Estimation of the (X,Y) distribution • Mixture defined by θ=(α,β) o αi : priors of X o βi=(mi,Гi) : distribution of Y conditional to X PET Tracer N Label X … … Label X Measures Y time PET MISO ... PET FDG … … … … … … … ... ... … … … … Multi-tracer analysis Patient follow-up
  15. 15. Multi-observation method : preliminary work Estimation of the (X,Y) distribution • Maximization of the log-likelihood • Implementation of EM and SEM algorithm o Blind and adaptive (AEM,ASEM) version Initialization : • K-means • Fuzzy K-means Test on data set : • Synthetic images • Simulated tumors Decision step • MAP criteria o Creation of a segmented map Fusion process :
  16. 16. Preliminary results Synthetic images : • Map of labels X o 2 or 3 labels per image • Measurements Y o Mean discrimination (MD) o Variance discrimination (VD) Map of labels X • N = 2 spectral bands • K = 3 labels • Random initialization AEM segmentation ASEM segmentationVDMD Segmentation results : • N = 2 spectral bands • K = 2 labels • Random initialization VD AEM segmentation ASEM segmentation Measurement Y MD Segmentation map • Fusion of measurement with the same map of labels o Number of labels in segmentation map = Number of labels per image
  17. 17. Preliminary results Synthetic images : • 2 or 3 labels per image • Fusion of measurement with the different map of labels o Additional labels in the segmented map AEM segmentation ASEM segmentation • N = 2 spectral bands • K = 3 labels per scan • 5 classes in segmentation map • Fuzzy K-means initialization AEM segmentation ASEM segmentation Measurement Y Segmentation mapSegmentation results : • N = 2 spectral bands • K = 2 and 3 labels • 4 classes in segmentation map • Fuzzy K-means initialization
  18. 18. Preliminary results Simulated tumors : • N = 2 tracer scans • K = 2 levels of tracer uptake per scan • Fuzzy-K mean initialization • N = 3 tracer scans • K = 2 levels of tracer uptake per scan • Fuzzy-K mean initialization • N = 2 tracer scans • K = 3 levels of tracer uptake per scan • Fuzzy-K mean initialization  The ground truth of each tumor scan will be different  The segmentation process should identify the new labels • Each band : a tracer scan • Label in the scan : uptake of the tracer 3 cases :
  19. 19. Preliminary results Segmentation results : AEM segmentation ASEM segmentation AEM segmentation ASEM segmentation AEM segmentation ASEM segmentation • N images = 2 , K tracer uptake =2 • N images = 3 , K tracer uptake =2 • N images = 2 , K tracer uptake =3
  20. 20. Fusion of the synthetic images • In the different situations (N Bands, K classes) o Supervised, semi-supervised and unsupervised segmentation o Satisfactory classification Fusion of simulated tumors • Segmentation error depends on : o The fuzzy K-means initialization o Noise level in the scans o The number of classes in the label map Limitations : • Segmentation not totally unsupervised : o Number of labels has to be defined by the user • Fusion of few spectral bands Preliminary results
  21. 21. Further work • Fusion process with more data o Other tracers images and / or follow-up scans • Segmentation totally unsupervised o Estimation of the classes number in the label map • Test the method on simulated data with GATE o More realistic simulated tumors o Computation of classification error • Application of our method in a radiotherapy planning station
  22. 22. Thank you for your attention

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