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Fast generation of 4D PET-MR data from real dynamic MR acquisitions

(2011) PHYSICS IN MEDICINE AND BIOLOGY. 56(20). p.6597-6613
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Abstract
We have implemented and evaluated a framework for simulating simultaneous dynamic PET-MR data using the anatomic and dynamic information from real MR acquisitions. PET radiotracer distribution is simulated by assigning typical FDG uptake values to segmented MR images with manually inserted additional virtual lesions. PET projection data and images are simulated using analytic forward projections (including attenuation and Poisson statistics) implemented within the image reconstruction package STIR. PET image reconstructions are also performed with STIR. The simulation is validated with numerical simulation based on Monte Carlo (GATE) which uses more accurate physical modelling, but has 150x slower computation time compared to the analytic method for ten respiratory positions and is 7000x slower when performing multiple realizations. Results are validated in terms of region of interest mean values and coefficients of variation for 65 million coincidences including scattered events. Although some discrepancy is observed, agreement between the two different simulation methods is good given the statistical noise in the data. In particular, the percentage difference of the mean values is 3.1% for tissue, 17% for the lungs and 18% for a small lesion. The utility of the procedure is demonstrated by simulating realistic PET-MR datasets from multiple volunteers with different breathing patterns. The usefulness of the toolkit will be shown for performance investigations of the reconstruction, motion correction and attenuation correction algorithms for dynamic PET-MR data.
Keywords
RESPIRATORY MOTION, SIMSET, IMAGES, HUMAN ANATOMY, SIMULATION, RECONSTRUCTION, VALIDATION, ALGORITHM, SCANNER, PATH

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Chicago
Tsoumpas, C, C Buerger, AP King, Pieter Mollet, Vincent Keereman, Stefaan Vandenberghe, V Schulz, P Schleyer, T Schaeffter, and P K Marsden. 2011. “Fast Generation of 4D PET-MR Data from Real Dynamic MR Acquisitions.” Physics in Medicine and Biology 56 (20): 6597–6613.
APA
Tsoumpas, C, Buerger, C., King, A., Mollet, P., Keereman, V., Vandenberghe, S., Schulz, V., et al. (2011). Fast generation of 4D PET-MR data from real dynamic MR acquisitions. PHYSICS IN MEDICINE AND BIOLOGY, 56(20), 6597–6613.
Vancouver
1.
Tsoumpas C, Buerger C, King A, Mollet P, Keereman V, Vandenberghe S, et al. Fast generation of 4D PET-MR data from real dynamic MR acquisitions. PHYSICS IN MEDICINE AND BIOLOGY. 2011;56(20):6597–613.
MLA
Tsoumpas, C, C Buerger, AP King, et al. “Fast Generation of 4D PET-MR Data from Real Dynamic MR Acquisitions.” PHYSICS IN MEDICINE AND BIOLOGY 56.20 (2011): 6597–6613. Print.
@article{1957300,
  abstract     = {We have implemented and evaluated a framework for simulating simultaneous dynamic PET-MR data using the anatomic and dynamic information from real MR acquisitions. PET radiotracer distribution is simulated by assigning typical FDG uptake values to segmented MR images with manually inserted additional virtual lesions. PET projection data and images are simulated using analytic forward projections (including attenuation and Poisson statistics) implemented within the image reconstruction package STIR. PET image reconstructions are also performed with STIR. The simulation is validated with numerical simulation based on Monte Carlo (GATE) which uses more accurate physical modelling, but has 150x slower computation time compared to the analytic method for ten respiratory positions and is 7000x slower when performing multiple realizations. Results are validated in terms of region of interest mean values and coefficients of variation for 65 million coincidences including scattered events. Although some discrepancy is observed, agreement between the two different simulation methods is good given the statistical noise in the data. In particular, the percentage difference of the mean values is 3.1\% for tissue, 17\% for the lungs and 18\% for a small lesion. The utility of the procedure is demonstrated by simulating realistic PET-MR datasets from multiple volunteers with different breathing patterns. The usefulness of the toolkit will be shown for performance investigations of the reconstruction, motion correction and attenuation correction algorithms for dynamic PET-MR data.},
  author       = {Tsoumpas, C and Buerger, C and King, AP  and Mollet, Pieter and Keereman, Vincent and Vandenberghe, Stefaan and Schulz, V and Schleyer, P and Schaeffter, T and Marsden, P K},
  issn         = {0031-9155},
  journal      = {PHYSICS IN MEDICINE AND BIOLOGY},
  keyword      = {RESPIRATORY MOTION,SIMSET,IMAGES,HUMAN ANATOMY,SIMULATION,RECONSTRUCTION,VALIDATION,ALGORITHM,SCANNER,PATH},
  language     = {eng},
  number       = {20},
  pages        = {6597--6613},
  title        = {Fast generation of 4D PET-MR data from real dynamic MR acquisitions},
  url          = {http://dx.doi.org/10.1088/0031-9155/56/20/005},
  volume       = {56},
  year         = {2011},
}

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