PURPOSE: Reconstruction of DaTSCAN brain studies using OS-EM iterative reconstruction offers better image quality and more accurate quantification than filtered back-projection. However, reconstruction must proceed for a sufficient number of iterations to achieve stable and accurate data. This study assessed the impact of the number of iterations on the image quantification, comparing the results of the iterative reconstruction with filtered back-projection data. METHODS: A striatal phantom filled with (123)I using striatal to background ratios between 2:1 and 10:1 was imaged on five different gamma camera systems. Data from each system were reconstructed using OS-EM (which included depth-independent resolution recovery) with various combinations of iterations and subsets to achieve up to 200 EM-equivalent iterations and with filtered back-projection. Using volume of interest analysis, the relationships between image reconstruction strategy and quantification of striatal uptake were assessed. RESULTS: For phantom filling ratios of 5:1 or less, significant convergence of measured ratios occurred close to 100 EM-equivalent iterations, whereas for higher filling ratios, measured uptake ratios did not display a convergence pattern. Assessment of the count concentrations used to derive the measured uptake ratio showed that nonconvergence of low background count concentrations caused peaking in higher measured uptake ratios. Compared to filtered back-projection, OS-EM displayed larger uptake ratios because of the resolution recovery applied in the iterative algorithm. CONCLUSION: The number of EM-equivalent iterations used in OS-EM reconstruction influences the quantification of DaTSCAN studies because of incomplete convergence and possible bias in areas of low activity due to the nonnegativity constraint in OS-EM reconstruction. Nevertheless, OS-EM using 100 EM-equivalent iterations provides the best linear discriminatory measure to quantify the uptake in DaTSCAN studies.
PURPOSE: Reconstruction of DaTSCAN brain studies using OS-EM iterative reconstruction offers better image quality and more accurate quantification than filtered back-projection. However, reconstruction must proceed for a sufficient number of iterations to achieve stable and accurate data. This study assessed the impact of the number of iterations on the image quantification, comparing the results of the iterative reconstruction with filtered back-projection data. METHODS: A striatal phantom filled with (123)I using striatal to background ratios between 2:1 and 10:1 was imaged on five different gamma camera systems. Data from each system were reconstructed using OS-EM (which included depth-independent resolution recovery) with various combinations of iterations and subsets to achieve up to 200 EM-equivalent iterations and with filtered back-projection. Using volume of interest analysis, the relationships between image reconstruction strategy and quantification of striatal uptake were assessed. RESULTS: For phantom filling ratios of 5:1 or less, significant convergence of measured ratios occurred close to 100 EM-equivalent iterations, whereas for higher filling ratios, measured uptake ratios did not display a convergence pattern. Assessment of the count concentrations used to derive the measured uptake ratio showed that nonconvergence of low background count concentrations caused peaking in higher measured uptake ratios. Compared to filtered back-projection, OS-EM displayed larger uptake ratios because of the resolution recovery applied in the iterative algorithm. CONCLUSION: The number of EM-equivalent iterations used in OS-EM reconstruction influences the quantification of DaTSCAN studies because of incomplete convergence and possible bias in areas of low activity due to the nonnegativity constraint in OS-EM reconstruction. Nevertheless, OS-EM using 100 EM-equivalent iterations provides the best linear discriminatory measure to quantify the uptake in DaTSCAN studies.
Authors: T S Benamer; J Patterson; D G Grosset; J Booij; K de Bruin; E van Royen; J D Speelman; M H Horstink; H J Sips; R A Dierckx; J Versijpt; D Decoo; C Van Der Linden; D M Hadley; M Doder; A J Lees; D C Costa; S Gacinovic; W H Oertel; O Pogarell; H Hoeffken; K Joseph; K Tatsch; J Schwarz; V Ries Journal: Mov Disord Date: 2000-05 Impact factor: 10.338
Authors: Albert Cot; Carles Falcón; Cristina Crespo; Josep Sempau; Deborah Pareto; Santiago Bullich; Francisco Lomeña; Francisco Calviño; Javier Pavía; Domènec Ros Journal: J Nucl Med Date: 2005-09 Impact factor: 10.057
Authors: J Booij; G Tissingh; G J Boer; J D Speelman; J C Stoof; A G Janssen; E C Wolters; E A van Royen Journal: J Neurol Neurosurg Psychiatry Date: 1997-02 Impact factor: 10.154
Authors: Livia Tossici-Bolt; John C Dickson; Terez Sera; Robin de Nijs; Maria Claudia Bagnara; Catherine Jonsson; Egon Scheepers; Felicia Zito; Anita Seese; Pierre Malick Koulibaly; Ozlem L Kapucu; Michel Koole; Maria Raith; Jean George; Markus Nowak Lonsdale; Wolfgang Münzing; Klaus Tatsch; Andrea Varrone Journal: Eur J Nucl Med Mol Imaging Date: 2011-04-06 Impact factor: 9.236
Authors: Andrea Varrone; John C Dickson; Livia Tossici-Bolt; Terez Sera; Susanne Asenbaum; Jan Booij; Ozlem L Kapucu; Andreas Kluge; Gitte M Knudsen; Pierre Malick Koulibaly; Flavio Nobili; Marco Pagani; Osama Sabri; Thierry Vander Borght; Koen Van Laere; Klaus Tatsch Journal: Eur J Nucl Med Mol Imaging Date: 2012-11-16 Impact factor: 9.236
Authors: Michael A King; Joyeeta M Mukherjee; Arda Könik; I George Zubal; Joyoni Dey; Robert Licho Journal: IEEE Trans Nucl Sci Date: 2016-02-03 Impact factor: 1.679
Authors: Navid Zeraatkar; Kesava S Kalluri; Benjamin Auer; Arda Konik; Timothy J Fromme; Lars R Furenlid; Phillip H Kuo; Michael A King Journal: IEEE Trans Med Imaging Date: 2020-11-30 Impact factor: 10.048