Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
G. A. Prenosil, T. Weitzel, M. Hentschel, B. Klaeser, and T. Krause, “Transconvolution and the virtual positron emission tomograph—A new method for cross calibration in quantitative PET/CT imaging,” Med. Phys. 40, 062503 (15pp.) (2013).
C. H. Chen, R. F. Muzic, Jr., A. D. Nelson, and L. P. Adler, “Simultaneous recovery of size and radioactivity concentration of small spheroids with PET data,” J. Nucl. Med. 40, 118130 (1999).
C. Cloquet, F. C. Sureau, M. Defrise, G. V. Simaeys, N. Trotta, and S. Goldman, “Non-Gaussian space-variant resolution modelling for list-mode reconstruction,” Phys. Med. Biol. 55, 50455066 (2010).
F. Hofheinz, S. Dittrich, C. Potzsch, and J. Hoff, “Effects of cold sphere walls in PET phantom measurements on the volume reproducing threshold,” Phys. Med. Biol. 55, 10991113 (2010).
A. P. Samartzis, G. P. Fountos, I. S. Kandarakis, E. P. Kounadi, E. N. Zoros, E. Skoura, I. E. Datseris, and G. H. Nikiforides, “A robust method, based on a novel source, for performance and diagnostic capabilities assessment of the positron emission tomography system,” Hell. J. Nucl. Med. 17, 97105 (2014).
T. Weitzel, G. A. Prenosil, M. Hentschel, B. Klaeser, and T. Krause, “Response to ‘Comment on “Transconvolution and the virtual positron emission tomograph (vPET): A new method for cross calibration in quantitative PET/CT imaging” ’ [Med. Phys. 40, 062503 (15pp.) (2013)],” Med. Phys. 40, 117102 (2013).
M. E. Phelps, E. J. Hoffman, S.-C. Huang, and M. M. Ter-Pogossian, “Effect of positron range on spatial resolution,” J. Nucl. Med. 16, 649652 (1975).
P. Hao and S. L. Craig, “Study of PET intrinsic spatial resolution and contrast recovery improvement for PET/MRI systems,” Phys. Med. Biol. 57, N101N115 (2012).
D. R. Tilley, H. R. Weller, C. M. Cheves, and R. M. Chasteler, “Energy levels of light nuclei A = 18–19,” Nucl. Phys. A 595, 1170 (1995).
E. A. McCutchan, “Nuclear data sheets for A = 68,” Nucl. Data Sheets 113, 17351870 (2012).
L. B. David, T. F. Ryan, E. H. James, J. N. Robert, and J. Robert, “A method for partial volume correction of PET-imaged tumor heterogeneity using expectation maximization with a spatially varying point spread function,” Phys. Med. Biol. 55, 221236 (2010).
N. J. Hoetjes, F. H. van Velden, O. S. Hoekstra, C. J. Hoekstra, N. C. Krak, A. A. Lammertsma, and R. Boellaard, “Partial volume correction strategies for quantitative FDG PET in oncology,” Eur. J. Nucl. Med. Mol. Imaging 37, 16791687 (2010).
A. Rahmim, J. Qi, and V. Sossi, “Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls,” Med. Phys. 40, 064301 (15pp.) (2013).
P. B. Fellgett and E. H. Linfoot, “On the assessment of optical images,” Philos. Trans. R. Soc., A 247, 369407 (1955).
V. Y. Panin, F. Kehren, C. Michel, and M. Casey, “Fully 3-D PET reconstruction with system matrix derived from point source measurements,” IEEE Trans. Med. Imaging 25, 907921 (2006).
T. Weitzel, F. Corminboeuf, B. Klaeser, and T. Krause, “Kreuzkalibrierung von positronen-emissions-tomographen für multizentrische studien: festkörper-phantom und transconvolution,” SGSMP-Bull. 72, 913 (2010).
S. E. Derenzo, Precision measurement of annihilation point spread distributions for medically important positron emitters, 5th International Conference on Positron Annihilation, Lake Yamanaka, Japan, April 8-11, 1979.
A. Wirrwar, H. Vosberg, H. Herzog, H. Halling, S. Weber, and H. W. Muller-Gartner, “4.5 tesla magnetic field reduces range of high-energy positrons-potential implications for positron emission tomography,” IEEE Trans. Nucl. Sci. 44, 184189 (1997).
L. Jødal, C. L. Loirec, and C. Champion, “Positron range in PET imaging: An alternative approach for assessing and correcting the blurring,” Phys. Med. Biol. 57, 39313943 (2012).
J. Cal-González, J. L. Herraiz, S. España, P. M. G. Corzo, J. J. Vaquero, M. Desco, and J. M. Udias, “Positron range estimations with PeneloPET,” Phys. Med. Biol. 58, 51275152 (2013).
C. S. Levin and E. J. Hoffman, “Calculation of positron range and its effect on the fundamental limit of positron emission tomography system spatial resolution,” Phys. Med. Biol. 44, 781799 (1999).
J. Radon, “On the determination of functions from their integral values along certain manifolds,” IEEE Trans. Med. Imaging 5, 170176 (1986).
M. Defrise, P. E. Kinahan, D. W. Townsend, C. Michel, M. Sibomana, and D. F. Newport, “Exact and approximate rebinning algorithms for 3-D PET data,” IEEE Trans. Med. Imaging 16, 145158 (1997).
Siemens, “Biograph™ TruePoint PET-CT,” edited by M. Siemens AG, DE,2008.
National Electrical Manufacturers Association (NEM), NEMA PS3/ISO 12052, Digital Imaging and Communications in Medicine (DICOM) Standard, NEMA, Rosslyn, VA,2016.
W. Lehnert, M.-C. Gregoire, A. Reilhac, and S. R. Meikle, “Characterisation of partial volume effect and region-based correction in small animal positron emission tomography (PET) of the rat brain,” NeuroImage 60, 21442157 (2012).
B. Knäusl, I. F. Rausch, H. Bergmann, R. Dudczak, A. Hirtl, and D. Georg, “Influence of PET reconstruction parameters on the TrueX algorithm. A combined phantom and patient study,” Nuklearmedizin 52, 2835 (2013).
R. Boellaard, M. J. O’Doherty, W. A. Weber, F. M. Mottaghy, M. N. Lonsdale, S. G. Stroobants, W. J. Oyen, J. Kotzerke, O. S. Hoekstra, J. Pruim, P. K. Marsden, K. Tatsch, C. J. Hoekstra, E. P. Visser, B. Arends, F. J. Verzijlbergen, J. M. Zijlstra, E. F. Comans, A. A. Lammertsma, A. M. Paans, A. T. Willemsen, T. Beyer, A. Bockisch, C. Schaefer-Prokop, D. Delbeke, R. P. Baum, A. Chiti, and B. J. Krause, “FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: Version 1.0,” Eur. J. Nucl. Med. Mol. Imaging 37, 181200 (2010).
M. A. Lodge, A. Rahmim, and R. L. Wahl, “A practical, automated quality assurance method for measuring spatial resolution in PET,” J. Nucl. Med. 50, 13071314 (2009).
B.-K. Teo, Y. Seo, S. L. Bacharach, J. A. Carrasquillo, S. K. Libutti, H. Shukla, B. H. Hasegawa, R. A. Hawkins, and B. L. Franc, “Partial-volume correction in PET: Validation of an iterative postreconstruction method with phantom and patient data,” J. Nucl. Med. 48, 802810 (2007).

Data & Media loading...


Article metrics loading...



A PET/CT system’s imaging capabilities are best described by its point spread function (PSF) in the spatial domain or equivalently by its modulation transfer function (MTF) in the spatial frequency domain. Knowing PSFs or MTFs is a prerequisite for many numerical methods attempting to improve resolution and to reduce the partial volume effect. In PET/CT, the observed PSF is a convolution of the system’s intrinsic imaging capabilities including image reconstruction (PSF) and the positron range function (PRF) of the imaged + emitting isotope. A PRF describes the non-Gaussian distribution of + annihilation events around a hypothetical point source. The main aim was to introduce a new method for determining a PET/CT system’s intrinsic MTF (MTF ) from phantom measurements of hot spheres independently of the + emitting isotope used for image acquisition. Secondary aim was to examine non-Gaussian and nonlinear MTFs of a modern iterative reconstruction algorithm.

PET/CT images of seven phantom spheres with volumes ranging from 0.25 to 16 ml and filled either with 18F or with 68Ga were acquired and reconstructed using filtered back projection (FBP). MTFs were modeled with linear splines. The spline fit iteratively minimized the mean squared error between the acquired PET/CT image and a convolution of the thereof derived PSF with a numerical representation of the imaged hot phantom sphere. For determining MTF , the numerical sphere representations were convolved with a PRF, simulating a fill with either 18F or 68Ga. The MTFs determined by this so-called MTF fit method were compared with MTFs derived from point source measurements and also compared with MTFs derived with a previously published PSF fit method. The MTF fit method was additionally applied to images reconstructed by a vendor iterative algorithm with PSF recovery (Siemens TrueX).

The MTF fit method was able to determine 18F and 68Ga dependent MTFs and MTF from FBP reconstructed images. Root-mean-square deviation between fit determined MTFs and point source determined MTFs ranged from 0.023 to 0.039. MTFs from Siemens TrueX reconstructions varied with size of the imaged sphere.

MTF can be determined regardless of the imaged isotope, when using existing PRF models for the MTF fit method presented. The method proves that modern iterative PET/CT reconstruction algorithms have nonlinear imaging properties. This behaviour is not accessible by point source measurements. MTFs resulting from these clinically applied algorithms need to be estimated from objects of similar geometry to those intended for clinical imaging.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd