Quantitative nuclear medicine imaging is an increasingly important frontier. In order to achieve quantitative imaging, various interactions of photons with matter have to be modeled and compensated. Although correction for photon attenuation has been addressed by including x-ray CT scans (accurate), correction for Compton scatter remains an open issue. The inclusion of scattered photons within the energy window used for planar or SPECT data acquisition decreases the contrast of the image. While a number of methods for scatter correction have been proposed in the past, in this work, we propose and assess a novel, user-independent framework applying factor analysis (FA). Extensive Monte Carlo simulations for planar and tomographic imaging were performed using the SIMIND software. Furthermore, planar acquisition of two Petri dishes filled with 99mTc solutions and a Jaszczak phantom study (Data Spectrum Corporation, Durham, NC, USA) using a dual head gamma camera were performed. In order to use FA for scatter correction, we subdivided the applied energy window into a number of sub-windows, serving as input data. FA results in two factor images (photo-peak, scatter) and two corresponding factor curves (energy spectra). Planar and tomographic Jaszczak phantom gamma camera measurements were recorded. The tomographic data (simulations and measurements) were processed for each angular position resulting in a photo-peak and a scatter data set. The reconstructed transaxial slices of the Jaszczak phantom were quantified using an ImageJ plugin. The data obtained by FA showed good agreement with the energy spectra, photo-peak, and scatter images obtained in all Monte Carlo simulated data sets. For comparison, the standard dual-energy window (DEW) approach was additionally applied for scatter correction. FA in comparison with the DEW method results in significant improvements in image accuracy for both planar and tomographic data sets. FA can be used as a user-independent approach for scatter correction in nuclear medicine.

Carl E Ravin Advanced Imaging Laboratories Department of Radiology Duke University Medical Center Durham North Carolina 27705 USA

Department of Nuclear Medicine 1st Faculty of Medicine Charles University Prague Praha Czech Republic

Department of Nuclear Medicine with PET Center Wilhelminenspital Vienna Austria

Department of Radiation Physics Lund University Lund Sweden

Department of Telecommunications and Teleinformatics Wroclaw University of Science and Technology Wroclaw Poland

Division of Nuclear Medicine and Molecular Imaging Department of Radiology Johns Hopkins University Baltimore Maryland 21287 USA

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Ritt P., Vija H., Hornegger J. et al., “Absolute quantification in SPECT,” Eur. J. Nucl. Med. Mol. Imaging 38, 69 (2011).10.1007/s00259-011-1770-8 PubMed DOI

Bailey D. L. and Willowson K. P., “Quantitative SPECT/CT: SPECT joins PET as quantitative imaging modality,” Eur. J. Nucl. Med. Mol. Imaging 41, 17 (2014).10.1007/s00259-013-2542-4 PubMed DOI

Patton J. A. and Turkington T. G., “SPECT/CT physical principles and attenuation correction,” J. Nucl. Med. Technol. 36, 1 (2008).10.2967/jnmt.107.046839 PubMed DOI

Rahmim A. and Zaidi H., “PET versus SPECT: Strengths, limitations and challenges,” Nucl. Med. Commun. 29(3), 193 (2008).10.1097/mnm.0b013e3282f3a515 PubMed DOI

Zaidi H. and Koral K. F., “Scatter correction strategies in emission tomography,” in Quantitative Analysis in Nuclear Medicine Imaging (Springer Verlag, New York, 2006).

Hutton B. F., Buvat I., and Beekman F. J., “Review and current status of SPECT scatter correction,” Phys. Med. Biol. 56(14), R85 (2011).10.1088/0031-9155/56/14/r01 PubMed DOI

Bethge K., Kernphysik (Springer Verlag, Berlin, Heidelberg, 2008).

Buvat I., Rodriguez-Villafuerte M., Todd-Prokopek A. et al., “Comparative assessment of nine scatter correction methods based on spectral analysis using Monte Carlo simulations,” J. Nucl. Med. 36, 1476 (1995). PubMed

Ogawa K. and Nishizaki N., “Accurate scatter compensation using neural networks in radionuclide imaging,” IEEE Trans. Nucl. Sci. 40, 1020 (1993).10.1109/23.256705 DOI

Maksud P., Fertil B., Rica C. et al., “Artifical neural network as a tool to compensate for scatter and attenuation in radionuclide imaging,” J. Nucl. Med. 39, 735 (1998). PubMed

El Fakhri G., Moore S. C., and Maksud P., “A new scatter compensation method for Ga-67 imaging using artificial neural networks,” IEEE Trans. Nucl. Sci. 48(3), 799 (2011).10.1109/23.940166 DOI

Jaszczak R. J., Floyd C. E., and Coleman R. E., “Scatter compensation techniques for SPECT,” IEEE Trans. Nucl. Sci. 32(1), 786 (1985).10.1109/tns.1985.4336941 DOI

Bergmann H., Dworak E., König B. et al., “Improved automatic separation of renal parenchyma and pelvis in dynamic renal scintigraphy using fuzzy regions of interest,” Eur. J. Nucl. Med. 26(8), 837 (1999).10.1007/s002590050457 PubMed DOI

Murthy V. L., Lee B. C., Sitek A. et al., “Comparison and prognostic validation of multiple methods of quantification of myocardial blood flow with 82Rb PET,” J. Nucl. Med. 55(12), 1952 (2014).10.2967/jnumed.114.145342 PubMed DOI

Knoll P., Krotla G., Bastati B. et al., “Improved quantification of salivary gland scintigraphy by means of factor analysis,” Iran. J. Nucl. Med. 20, 5 (2012).

Mas J., Hannequin P., Ben Younes R. et al., “Scatter correction in planar imaging and SPECT by constrained factor analysis of dynamic structures (FADS),” Phys. Med. Biol. 35(11), 1451 (1990).10.1088/0031-9155/35/11/002 PubMed DOI

Buvat I., Benali H., Frouin F. et al., “Target apex-seeking in factor analysis of medical imaging sequences (FAMIS),” Phys. Med. Biol. 38(1), 123 (1993).10.1088/0031-9155/38/1/009 PubMed DOI

Gagnon D., Todd-Pokropek A., Arsenault A. et al., “Introduction to holospectral imaging in nuclear medicine for scatter subtraction,” IEEE Trans. Med. Imaging 8(3), 245 (1989).10.1109/42.34713 PubMed DOI

Jouan A., Laperriere L., and Gagnon D., “Nonlinear holospectral imaging: Scatter removal from curvilinear data in multidimensioonal energy space,” in Conference Record of the 1992 IEEE Nuclear Science Symposium and Medical Imaging Conference (IEEE, 1992).

Benali H., Buvat I., Frouing F. et al., “Foundations of factor analysis of medical image sequences: A unified approach and some practical implications,” in Information Processing in Medical Imaging, edited by Barrett H. H. and Gmitro A. F. (Springer-Verlag, Berlin, 1993), pp. 401–421.

Šámal M., Kárný M., Sůrová H., Maříková E., and Dienstbier Z., “Rotation to simple structure in factor analysis of dynamic radionuclide studies,” Phys. Med. Biol. 32, 371 (1987).10.1088/0031-9155/32/3/007 PubMed DOI

Šámal M., Sůrová H., Kárný M. et al., “The reality and meaning of physiological factors,” in Information Processing in Medical Imaging, edited by de Graaf C. N. and Viergever M. A. (Plenum, New York, 1988), pp. 499–519.

Buvat I., Hapdey S., Benali H. et al., “Information processing in medical imaging,” in Spectral Factor Analysis for Multi-Isotope Imaging in Nuclear Medicine, edited by Kuba A., Samal M., and Todd-Pokropek A. (Springer, Berlin, 1999), pp. 442–447.

See https://de.mathworks.com/help/stats/factoran.html?s_tid=gn_loc_drop 2017 for information about the factor analysis implementation in Matlab (accessed February 15, 2017).

Jaszczak R. J., Greer K. L., Floyd C. E. et al., “Improved SPECT quantification using compensation for scattered photons,” J. Nucl. Med. 25, 893–900 (1984). PubMed

Zaidi H., Quantitative Analysis in Nuclear Medicine Imaging (Springer Verlag, 2006).

Ljungberg M. and Strand S. E., “A Monte Carlo program for the simulation of scintillation camera characteristics,” Comput. Methods Prog. Biomed. 29(4) 257 (1989).10.1016/0169-2607(89)90111-9 PubMed DOI

Ljungberg M., “Simulation techniques and phantoms,” in Emission Tomography: The Fundamentals of PET and SPECT, edited by Wernick M. and Aarsvold J. (Academic Press, Inc., New York, USA, 2000).

Zubal I. G., Harrell C. R., Smith E. O. et al., “Computerized three-dimensional segmented human anatomy,” Med. Phys. 21(2), 299 (1994).10.1118/1.597290 PubMed DOI

Hirtl A., Knaeusl B., Knoll P. et al., “A dedicated fully automated software for SPECT quality control using Jaszczak phantoms,” Eur. J. Nucl. Med. Mol. Imaging 39, 2 (2012).

Holstensson M., Hindorf C., Ljungberg M. et al., “Optimization of energy window settings for scatter correction in quantitative 111In imaging: Comparison of measurements and Monte Carlo simulations,” Cancer Biother. Radiopharm. 22(1), 136 (2007).10.1089/cbr.2007.307 PubMed DOI

Wernick M. and Aarsvold J., Emission Tomography: The Fundamentals of PET and SPECT (Elsevier, Inc., London, 2004).

Ljungberg M., Frey E., Sjögreen K. et al., “3D absorbed dose calculations based on SPECT: Evaluation of 111In/90Y therapy using Monte Carlo simulations,” Cancer Biother. Radiopharm. 18(1), 99 (2003).10.1089/108497803321269377 PubMed DOI

Noori Asa M., Sadremomtaz A., and Bitarafan-Rajabi A., “Evaluation of six scatter correction methods on spectral analysis in 99mTc SPECT imaging using SIMIND Monte Carlo simulation,” J. Med. Phys. 38, 4 (2013).10.4103/0971-6203.121197 PubMed DOI PMC

Xiao J., de Wit T. C., Zbijewski W. et al., “Evaluation of 3D Monte Carlo–based scatter correction for 201Tl cardiac perfusion SPECT,” J. Nucl. Med. 48(4), 637 (2007).10.2967/jnumed.106.037259 PubMed DOI