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MR-based PET Motion Correction using the PET Reconstruction Software Toolkit (PRESTO)

PET/MR and SPECT/MR: New Paradigms for Combined Modalities in Molecular Imaging 26-30 May 2012, La Biodola, Isola d‘Elba. MR-based PET Motion Correction using the PET Reconstruction Software Toolkit (PRESTO). J. Scheins , M. Ullisch, L. Tellmann,

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MR-based PET Motion Correction using the PET Reconstruction Software Toolkit (PRESTO)

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  1. PET/MR and SPECT/MR: New Paradigms for Combined Modalities in Molecular Imaging 26-30 May 2012, La Biodola, Isola d‘Elba MR-based PET Motion Correction using the PET Reconstruction Software Toolkit (PRESTO) J. Scheins, M. Ullisch, L. Tellmann, C. Weirich, E. Rota Kops, H. Herzog, N.J. Shah Institute of Neuroscience and Medicine Medical Imaging Physics (INM-4)

  2. PET/MR and SPECT/MR: New Paradigms for Combined Modalities in Molecular Imaging 26-30 May 2012, La Biodola, Isola d‘Elba MR-based PET Motion Correction using the PET Reconstruction Software Toolkit (PRESTO) • Overview • Motion Correction of LORs (Line-of-Response) • LOR-based Reconstruction with PRESTO • Results

  3. Overview • Acquisition time of (dynamic) PET scans between approx. 2 - 60 minutes •  patient head motion can significantly degrade image quality & dynamics • Motion correction desireable for high resolution PET brain images • (resolution about 3 mm) • current application: • use functional Magnetic Resonance Imaging (fMRI) in • simultaneous MR/PET for tracking of head  more details in presentation [49] of M.Ullisch: MR-guided Data Framing for PET Motion Correction in Simultaneous MR-PET • other options for (external) motion tracking in preparation: • MR navigators, optical tracking device

  4. Our Hardware Siemens BrainPET in 3T-Tim-Trio MR MR Head Coil BrainPET

  5. 3D Data Acquisition 144 crystals / block 6 blocks / modul 32 moduls  27648 crystals 1 HEAD  227 million measured LORs 19 OPPOSITE HEADS

  6. Influence of Motion LOR = LineOfResponse specific LOR through object

  7. Influence of Motion object moved specific LOR through object same LOR through object after motion different detectors observe same object LOR

  8. Motion Compensation • regard object in fixed • (initial) position: •  complete scanner • is transformed for • any relative change • of positions • object position • becomes stationary

  9. Motion Compensation • transformation of detector • positions for each object • position • superposition of • identical object LORs true detector position relative to moved object motion compensating detector position

  10. Projection Space of PRESTO*) *) J.Scheins et. al., IEEE Trans. Med. Img., Vol. 30, 2011 Generic Cylinder free Parameters of Generic Cylinder 1) Dimensions - system diameter - axial field-of-view 2) Granularity - number of rings - number of crystals per ring cylinder diameter •  segmentation of cylinder into virtual crystals •  highly regular structure •  avoiding drawbacks of using • sinograms axial acceptance

  11. Angle Number 1 2 Detector A 3 4 5 6 Ring Number 1 4 3 5 2 Detector B calculate both intersection points on cylinder surface LOR data Mapping by PRESTO

  12. MLEM/OSEM reconstruction with PRESTO • System Matrix • matching volume-of-intersection • projectors for any generic TOR • compression using • multiple rotation symmetries • pre-calculated, memory-resident • system matrix • processing 480 million LORs • with 2 subsets •  55 sec per iteration (5 nodes) generic Tube-of-Response (TOR) IEEE Medical Imaging Conf. 2006, San Diego, J. Scheins, Enhanced System Matrix Compression for Iterative PET Re- construction Using Rotationally Symmetric Voxel Formations

  13. Listmode Data Sorting Transformation of whole Scanner coordinates to compensate motion Mapping of all physical LORs to the Generic Cylinder and assigning to specific projection data bin Mapping separately done for prompts, randoms & sensitivity apply statistical weight for each position (including observation time & decay correction)  repeat steps I. – IV. for each position and merge all frames  220 million LORs per position for mapping; approx. 1 minute calculation time for any position  merged data reconstructed with Ordinary Poisson Algorithm (no data precorrection to avoid quantitative bias)

  14. Projection Data of the BrainPET Without Motion Correction (precorrected data) With Motion Correction (precorrected data) projection angle radial coordinate missing data due to physical detector gaps gaps are (partially) filled in case of motion correction

  15. Sensitivity Correction merged Prompts (not normalised) normalised merged Prompts “integrated“ Sensitivity

  16. Motion Corrected Phantom Data (9 Positions) Motion Correction uncorrected data

  17. FDG Patient Data uncorrected Motion Parameters from EPI corrected MR

  18. FDG Patient Data motion corrected uncorrected • Arrows mark • the uptake in • Culliculi Superiores • small structures • become visible after • motion correction MR

  19. FDG Human Brain Data no motion correction with motion correction with motion correction + resolution modelling

  20. Conclusions • simultaneous measurements of PET & fMRI can be used • for rigid PET motion correction (Siemens BrainPET) • EPI sequences provide motion parameters 2-3 seconds • with high accuracy • computational burden of motion correction of Listmode data • is moderate (approx. 1 minute per position) • concept of Generic Cylinder (PRESTO) allows efficient • implementation and high resolution image reconstruction • MR navigators & optical tracking device for higher flexibility • of simultaneous MR/PET in preparation Thank you !

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