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Masamune: a tool for automatic dynamic PET data processing, image reconstruction and integrated PET/MRI data analysis

We describe a novel semi-automated pipeline which integrates advanced data analysis tools for MR and PET with advanced PET reconstruction correction methods (partial volume effect correction [PVC], motion correction [MC], attenuation correction [AC]) in a user-friendly Matlab graphical user interface (GUI).

The reconstruction and analysis GUI is written in Matlab. Computationally intensive tasks in the pipeline are automatically transferred to a high-performance computing cluster and retrieved.

Descriptions of the commercial packages used can be found in their corresponding references. SPM8 [1] is used in MC and AC processing. Comkat [2] and PMOD [3] are used for kinetic modeling. FSL [4] and SPM8 are used for group analysis. Freesurfer [5] is used for regions-of-interest (ROI) definition and smoothing.

Data preprocessing: Head-motion is derived from a number of sources: echo-planar MR images, MR-based motion navigators, and directly from the PET data when MR data is unavailable (e.g. during shimming). Subsequently, the ME-MPRAGE is reoriented to the reference position. Cortical and subcortical ROIs are labeled using FreeSurfer; similarly, the MPRAGE is registered to MNI-space for generating subject-specific atlases.

Image reconstruction: An OP-OSEM algorithm is used for PET reconstruction [6]. MC [7] and PVC [8] can be performed using the results from data preprocessing. AC can be imported directly from CT, using MR-images [9], or through atlas-based methods.

Automated Bolus Arrival Time (BAT) & Image-Derived Input Function: The singles count rate is recorded during PET acquisition. The BAT is determined by fitting a trilinear piecewise function and used as the reference time. Time-of-Flight MR can then be used to segment the arteries of the head and an image-derived input function can be determined using short frames.

We presented a novel pipeline which interfaces with a number of different commercial software to provide improved PET data quantification.

References

  1. Friston KJ, Holmes AP, Worsley KJ, et al.: Statistical parametric maps in functional imaging: A general linear approach. Hum Brain Mapp 1994, 2: 189–210. doi:10.1002/hbm.460020402 10.1002/hbm.460020402

    Article  Google Scholar 

  2. Muzic RF Jr, Cornelius S: COMKAT: compartment model kinetic analysis tool. J Nucl Med 2001, 42: 636–45.

    CAS  PubMed  Google Scholar 

  3. Burger C, Buck A: Requirements and implementation of a flexible kinetic modeling tool. J Nucl Med 1997, 38: 1818–23.

    CAS  PubMed  Google Scholar 

  4. Smith SM, Jenkinson M, Woolrich MW, et al.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004,23(Suppl 1):S208–19. doi:10.1016/j.neuroimage.2004.07.051

    PubMed  Article  Google Scholar 

  5. Fischl B, Sereno MI, Tootell RBH, et al.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp 1999, 8: 272–84. 10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4

    CAS  PubMed  Article  Google Scholar 

  6. Chonde DB, Abolmaali N, Arabasz G, et al.: Effect of MRI acoustic noise on cerebral fludeoxyglucose uptake in simultaneous MR-PET imaging. Invest Radiol 2013, 48: 302–12. doi:10.1097/RLI.0b013e3182839fbc 10.1097/RLI.0b013e3182839fbc

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  7. Catana C, Benner T, van der Kouwe A, et al.: MRI-assisted PET motion correction for neurologic studies in an integrated MR-PET scanner. J Nucl Med 2011, 52: 154–61. doi:10.2967/jnumed.110.079343 10.2967/jnumed.110.079343

    PubMed Central  PubMed  Article  Google Scholar 

  8. Bowen SL, Byars LG, Michel CJ, et al.: Influence of the partial volume correction method on (18)F-fluorodeoxyglucose brain kinetic modelling from dynamic PET images reconstructed with resolution model based OSEM. Phys Med Biol 2013, 58: 7081–106. doi:10.1088/0031–9155/58/20/7081 10.1088/0031-9155/58/20/7081

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  9. Catana C, van der Kouwe A, Benner T, et al.: Toward implementing an MRI-based PET attenuation-correction method for neurologic studies on the MR-PET brain prototype. J Nucl Med 2010, 51: 1431–8. doi: 10.2967/jnumed.109.069112 10.2967/jnumed.109.069112

    CAS  PubMed Central  PubMed  Article  Google Scholar 

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Chonde, D.B., Izquierdo-Garcia, D., Chen, K. et al. Masamune: a tool for automatic dynamic PET data processing, image reconstruction and integrated PET/MRI data analysis. EJNMMI Phys 1, A57 (2014). https://doi.org/10.1186/2197-7364-1-S1-A57

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  • DOI: https://doi.org/10.1186/2197-7364-1-S1-A57

Keywords

  • Partial Volume Effect Correction
  • Automate Bolus
  • Advanced Data Analysis
  • Bolus Arrival Time
  • Single Count Rate