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  • Meeting abstract
  • Open Access

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

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  • 1,
  • 1, 2,
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  • 1
EJNMMI Physics20141 (Suppl 1) :A57

  • Published:


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

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.

Authors’ Affiliations

Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
Department of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
Program in Biophysics, Harvard University, Cambridge, MA, USA


  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.460020402View ArticleGoogle Scholar
  2. Muzic RF Jr, Cornelius S: COMKAT: compartment model kinetic analysis tool. J Nucl Med 2001, 42: 636–45.PubMedGoogle Scholar
  3. Burger C, Buck A: Requirements and implementation of a flexible kinetic modeling tool. J Nucl Med 1997, 38: 1818–23.PubMedGoogle 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.2004.07.051PubMedView ArticleGoogle 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;2-4PubMedView ArticleGoogle 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.0b013e3182839fbcPubMed CentralPubMedView ArticleGoogle 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.110.079343PubMed CentralPubMedView ArticleGoogle 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 10.1088/0031-9155/58/20/7081PubMed CentralPubMedView ArticleGoogle 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.109.069112PubMed CentralPubMedView ArticleGoogle Scholar


© Chonde et al; licensee Springer 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.