1 / 20

National Coordinator: A. Chincarini ( Genova ) Participant INFN Sections (local coordinators):

2015 - 2017. A dvancing Magnetic Resonance Imaging and Data Analysis. National Coordinator: A. Chincarini ( Genova ) Participant INFN Sections (local coordinators): Pisa – (A. Retico) Trieste – (R. Longo) L’Aquila – (M. Alecci ) Catania – (M. Marrale ) Bari – (S. Tangaro )

manning
Télécharger la présentation

National Coordinator: A. Chincarini ( Genova ) Participant INFN Sections (local coordinators):

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. 2015-2017 Advancing Magnetic Resonance Imaging and Data Analysis National Coordinator: A. Chincarini (Genova) Participant INFN Sections (local coordinators): Pisa – (A. Retico) Trieste – (R. Longo) L’Aquila – (M. Alecci) Catania – (M. Marrale) Bari– (S. Tangaro) Lecce – (G. De Nunzio) • Status report June 2015 • Plans for 2016

  2. A. Retico - nextMR Objectives and milestones 2015 PI: MS.1.1 [02/2015] Review critico della letteratura sui requisiti clinici delle bobine doppio tunate 1H-23Na. PI: MS.1.2 [09/2015] Costruzione del modelloelettromagneticodellebobineRF doppio-tunata. Report di simulazione e dell’ottimizzazionegeometricaedelettricadellabobinadi grandediametro. PI: MS.1.3 [12/2015] Ordine per l’acquistodeicomponenti per la produzione di unabobinadi grandediametro. PI: MS.3.1 [03/2015] Selezione delle patologie di riferimento e acquisizione dei dataset pubblici (ADNI, ABIDE, ADHD200, ...) e non pubblici necessari.

  3. A. Retico - nextMR Obj 1 - 23Na MRI @ 7T • Focus: advanced imaging techniques to assess brain tumors • R&D: non-proton nuclei MRI at Ultra High Filed (UHF) • New dedicated hardware • New dedicated acquisition sequences Possible design for the head coil, with and without RF shielding, on double-coated substrate material to generate “printed” capacitors. • Synergic funding applications: • RicercaFinalizzata 2013 (Ministerodella Salute) Ultra-high-field (UHF) sodium (23Na) MRI imaging as noninvasive biomarker for measuring structural changes and assessing prognosis of pathology in pediatric brain tumors • PI: M. Tosetti, IRCCS Stella Maris • UOs: Meyer (A. Rosati), INFN (A.Retico) 23Na can provide important information for diagnostic and prognostic evaluations. Complementary information (anatomical, metabolic and from different nuclei) accessible by 23Na MRI can enable personalized medicine using UHF MR equipment.

  4. A. Retico - nextMR T/R switch • A switch board have been designed at protonfrequency (298 MHz). • The same board can be used for sodium (78.8 MHz), retuning the components. • Two stages of blocking using quarter wavelenght networks and shunt diodes • Very good results have been obtained for proton frequency. The switch before (a) and after (b) the connection of the preamp.

  5. A. Retico - nextMR Birdcage coil (sodium frequency) • A standard 8-rung birdcage of diameter 26 cm and height 23 cm tuned at the sodium frequency (78.8 MHz) was simulated using FEKO and ADS. • The needed capacitors values are in the range 50 pF - 200 pF for such frequency and dimensions

  6. A. Retico - nextMR Birdcage coil (sodium frequency) • 3D printing of the mechanics • High voltage capacitors: • Range 1-30 pF (proton) • Range 50-200 pF (sodium) • Cables and Connectors • PCB materials and PCB printing

  7. A. Retico - nextMR 7 T degenerate birdcage PhD Thesis of Riccardo Stara Defended

  8. A. Retico - nextMR In vivo acquisition of a human knee bSSFP (slice thickness 600 um, TE 3.5 ms, TR 9.0 ms, FOV 13 cm x 13 cm Matrix 512 x 512, interpolated to 1024 x1024 COSMIC 3D (slice thickness 600 um, TE 1.8 ms, TR 6 ms. Field of view (FOV) 14 cm x 14 cm, Matrix 512 x 512, interpolated to 1024 x1024 • Potential clinical research applications: • collaboration with AOUP (Pisa)

  9. 11 Applications • MR imaging: • manipulation of contrast to highlight different tissue types • morphologic assessment of cartilage and bone • quantification of thickness and volume • evaluation of biochemical composition • MRI morphologic: spin-echo (SE) and gradient-recalled echo (GRE) sequences, fast SE sequences, isotropic 3D SE and GRE sequences • MRI compositional (cartilage: 70% water by weight, type 2 collagen fibers and proteoglycans): T2 mapping, delayed gadolinium-enhanced MR imaging of cartilage (dGEMRIC), T1ρ imaging, sodium imaging, diffusion-weighted imaging • Cartilage (traumatic injuries, degenerative changes, osteoarthritis): • Morphology • Compositional quality • Trabecular bone (susceptibility to fractures, osteoporosis): • Quantification of bone architecture (average diameter of the trabeculae =100-150mm, trabecular spacing =300-800 mm) Cartilage and bone MRI @ IMAGO 7: work in progress

  10. Rossella, in vivo image Knee of an healthy volunteer 3D MERGE,TR=30ms, TE=12.2 ms, Thickness 0.5mm, In-plane resolution: ~ 270 mm Separation between different layers of cartilage bSSFP, TR=10.6 ms, TE=3 ms, Thickness 0.5mm In-plane resolution: ~ 270 mm Trabecular structure of the patella

  11. 13 T2 map Free water molecules slow down the loss of transverse magnetization The collagen matrix of healthy cartilage traps and immobilizes water protons, so signal intensity on T2w images is low. In the earliest stages of osteoarthritis, the matrix breaks down and becomes more permeable to water, causing an elevation in T2 relaxation times

  12. Cartilage segmentation Fiesta 3D sag FA=20o, TR=8.3ms TE=3.1ms Slicethickness=1mm In-plane res=300mm

  13. A. Retico - nextMR Searching for external co-funding Pending evaluation • Italian Ministry of Health: • GR project: Advanced structural and functional Magnetic Resonance at 1,5T, 3T and UHF (7T) in distal Neuromuscular Disorders(PI: Dr. G. Astrea, Stella Maris;R. Staracoordinator of Operative Unit under the Physics Department of Pisa University). • RF project: Ultra-high-field (UHF) sodium (23Na) MRI imaging as noninvasive biomarker for measuring structural changes and assessing prognosis of pathology in pediatric brain tumors (PI: M. Tosetti, IRCCS Stella Maris; UOs: Meyer, A. Rosati, INFN A.Retico) • It is fundamental to have a synergic clinical project approved to scan human subjects (ethical committee, medical staff, scan costs, …).

  14. A. Retico - nextMR Publications & disseminations 2015 StaraR, Tiberi G, Gabrieli M, Buonincontri G, Fontana N, Monorchio A, Costagli M, Symms M, Retico A, Tosetti M, Quadrature birdcage coil with distributed capacitors for 7.0 T magnetic resonance data acquisition of small animals, Concepts in Magnetic Resonance Part B: Magnetic Resonance Engineering 44 (4), pp. 83-88, 2015 M.E. Fantacci, G. Astrea, R. Battini, A. Retico, C. Sottocornola and M. Tosetti, Quantitative Scoring of Muscle Involvement in MRI of Neuromuscular Diseases Proc. BIOIMAGING 2015, Lisbon Jan 12-15 2015 G. Tiberi, N. Fontana, M. Costagli, R. Stara, L. Biagi, M.R. Symms, A. Monorchio, A. Retico, M. Cosottini, M. Tosetti Investigation of the maximum local Specific Absorption Rate in 7.0T Magnetic Resonance with respect to load size by the use of Electromagnetic simulations Bioelectromagnetics36 (5), pp. 358-366,2015 Tiberi G, Fontana N, Monorchio A, Retico A, TosettiM Evaluation of 3D radio-frequency electromagnetic fields for any matching and coupling conditions by the use of basis functions submitted to Concepts in Magnetic Resonance Part B • IEEE Conference Records of Medical Measurements and Applications (MeMeA 2015), May 07-09, 2015, Torino, Italy: • A. Retico, R. Stara, G. Tiberi, M.E. Fantacci, A. Toncelli, M. Costagli, A. Galante, T. Florio, M. Alecci, M. Cosottini, G. Astrea, R. Battini, M. Tosetti, Non-Invasive Assessment of Neuromuscular Disorders by 7 Tesla Magnetic Resonance Imaging and Spectroscopy: Dedicated Radio-Frequency Coil Development • A. Retico, A. Vitacolonna, A. Galante, T. Florio, A. Cimini, R. Stara, G. Tiberi, M. Tosetti, N. Fontana, G, Manara, A. Monorchio, M. Alecci, A 7T double-tuned (1H/31P) microstrip surface RF coil for the Imago7 MR scanner • M.E. Fantacci, C. Sottocornola, A. RETICO, G. Astrea, R. Battini, M. Tosetti • A Non-Invasive Method for a Quantitative Evaluation of Muscle Involvement in MRI of Neuromuscular Diseases • Annual Meeting of International Society of Magnetic Resonance in Medicine (ISMRM 2015), 30 May- 5 May 2015 Toronto, CA: • G. Tiberi, N. Fontana, R. Stara, A. RETICO, A. Monorchio and M. Tosetti, The basis functions: a novel approach for electromagnetic fields evaluations for any matching and coupling conditions • A. Toncelli, R. Noeske, M. Cosottini, M. Tosetti, Improving Robustness for Voxel Based Transmit Gain Calibration using Bloch-Siegert Shift Method for MR Spectroscopy at 7T.

  15. The Autism Brain Imaging Data Exchange (ABIDE) • storing and sharing rs-fMRI and sMRI datasets collected in 17 international sites • includes 1112 exams and phenotypic information common across all sites. • age: 7 to 64 years This data sharing initiative has already provided important contribution to the ASD research by unraveling inconsistent results about the strength of connectivity in ASD brains. http://fcon_1000.projects.nitrc.org/indi/abide A. Retico - nextMR nextMR Obj.3 DATA ANALYSIS PI will develop and test new algorithms to investigate brain functional connectivity, working on the following publicly available large datasets of rs-fMRI.

  16. A. Retico - nextMR Schölkopfet al., NeuralComput 13 (2001) 1443 – 71 One-class SVM Tax & Duin, PatternRecogLetter 20 (1999) 1191-1199 Mouraõ-Miranda et al., NeuroImage58 (2011) 793 – 804 Sato et al., Frontiers in Neuroscience 6 (2012) 178 Patient classification as an outlier detection problem Sato et al., 2012, PloSone, 7(9), p. e45671 Unsupervised learning Non-linear kernel SVM: • Gaussian (Radial Basis Function, RBF) kernel maps the data into the feature space Finding the smallest hypersphere enclosing data corresponds to finding the most distant hyperplane from the origin.

  17. A. Retico - nextMR Implementation of one-class SVM • In contrast to two-class classification, the One-Class Classification (OCC) or Data Description makes a description of a training class of objects • Radial basis function kernel MRI and clinical data acquired at IRCCS Stella Maris Foundation Freesurfer provides 5 features for 62 anatomical brain regions OCC-SVM implemented in RapidMiner http://rapidminer.com/ advanced analytics platform version 5.3, which includes the single-class SVM as a part of the LibSVMoperator. http://surfer.nmr.mgh.harvard.edu/ • The control is not homogeneous enough to allowrecognizing ASD as outliers. • Conversely, there is a common structure among the ASD patients that the single-class SVM can capture.

  18. A. Retico - nextMR People & budget 2016 Preliminary Richiestatotale: 23 kE totale: 4.3 FTE a Pisa su circa 18 in nextMR

  19. A. Retico - nextMR Milestones proposte 2016 • Giugno2016: • O1. Realizzazioneprototipo birdcage sodio– workbench tests • Dicembre 2016: • O1 realizzazioneprototipo birdcage protone-sodio, integrazione e disaccoppiamento– workbench tests • O3. Mappe di functional connectivity sul database ABIDE

  20. A. Retico - nextMR Richieste sui servizi di sezione • Progettazionemeccanica e Officinameccanica: • Realizzazionesupportimeccanici • Stampe 3D • Servizio di elettronica

More Related