Georg Riegler1, Georgios Karanikas2, Ivo Rausch3, Albert Hirtl3, Karem El-Rabadi2, Wolfgang Marik2, Christopher Pivec2, Michael Weber2, Helmut Prosch2, Marius Mayerhoefer2. 1. Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Währingergürtel 18-20, 1090 Vienna, Austria. Electronic address: georg.riegler@meduniwien.ac.at. 2. Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Währingergürtel 18-20, 1090 Vienna, Austria. 3. Medical University of Vienna, Center for Medical Physics and Biomedical Engineering, Währingergürtel 18-20, 1090 Vienna, Austria.
Abstract
PURPOSE: To evaluate the influence of point spread function (PSF)-based reconstruction and matrix size for PET on (1) lung lesion detection and (2) standardized uptake values (SUV). METHODS: This prospective study included oncological patients who underwent [18F]-FDG-PET/CT for staging. PET data were reconstructed with a 2D ordered subset expectation maximization (OSEM) algorithm, and a 2D PSF-based algorithm (TrueX), separately with two matrix sizes (168×168 and 336×336). The four PET reconstructions (TrueX-168; OSEM-168; TrueX-336; and OSEM-336) were read independently by two raters, and PET-positive lung lesions were recorded. Blinded to the PET findings, a third independent rater assessed lung lesions with diameters of >4mm on CT. Subsequently, PET and CT were reviewed side-by side in consensus. Multi-factorial logistic regression analyses and two-way repeated measures analyses of variance (ANOVA) were performed. RESULTS: Thirty-seven patients with 206 lung lesions were included. Lesion-based PET sensitivities differed significantly between reconstruction algorithms (P<0.001) and between reconstruction matrices (P=0.022). Sensitivities were 94.2% and 88.3% for TrueX-336; 88.3% and 85.9% for TrueX-168; 67.8% and 66.3% for OSEM-336; and 67.0% and 67.9% for OSEM-168; for rater 1 and rater 2, respectively. SUVmax and SUVmean were significantly higher for images reconstructed with 336×336 matrices than for those reconstructed with 168×168 matrices (P<0.001). CONCLUSION: Our results demonstrate that PSF-based PET reconstruction, and, to a lesser degree, higher matrix size, improve detection of metabolically active lung lesions. However, PSF-based PET reconstructions and larger matrix sizes lead to higher SUVs, which may be a concern when PET data from different institutions are compared.
PURPOSE: To evaluate the influence of point spread function (PSF)-based reconstruction and matrix size for PET on (1) lung lesion detection and (2) standardized uptake values (SUV). METHODS: This prospective study included oncological patients who underwent [18F]-FDG-PET/CT for staging. PET data were reconstructed with a 2D ordered subset expectation maximization (OSEM) algorithm, and a 2D PSF-based algorithm (TrueX), separately with two matrix sizes (168×168 and 336×336). The four PET reconstructions (TrueX-168; OSEM-168; TrueX-336; and OSEM-336) were read independently by two raters, and PET-positive lung lesions were recorded. Blinded to the PET findings, a third independent rater assessed lung lesions with diameters of >4mm on CT. Subsequently, PET and CT were reviewed side-by side in consensus. Multi-factorial logistic regression analyses and two-way repeated measures analyses of variance (ANOVA) were performed. RESULTS: Thirty-seven patients with 206 lung lesions were included. Lesion-based PET sensitivities differed significantly between reconstruction algorithms (P<0.001) and between reconstruction matrices (P=0.022). Sensitivities were 94.2% and 88.3% for TrueX-336; 88.3% and 85.9% for TrueX-168; 67.8% and 66.3% for OSEM-336; and 67.0% and 67.9% for OSEM-168; for rater 1 and rater 2, respectively. SUVmax and SUVmean were significantly higher for images reconstructed with 336×336 matrices than for those reconstructed with 168×168 matrices (P<0.001). CONCLUSION: Our results demonstrate that PSF-based PET reconstruction, and, to a lesser degree, higher matrix size, improve detection of metabolically active lung lesions. However, PSF-based PET reconstructions and larger matrix sizes lead to higher SUVs, which may be a concern when PET data from different institutions are compared.
Authors: Susan Fernandes; Gareth Williams; Elvira Williams; Katjana Ehrlich; James Stone; Neil Finlayson; Mark Bradley; Robert R Thomson; Ahsan R Akram; Kevin Dhaliwal Journal: Eur Respir J Date: 2021-03-25 Impact factor: 16.671
Authors: Tine N Christensen; Seppo W Langer; Gitte Persson; Klaus Richter Larsen; Annemarie G Amtoft; Sune H Keller; Andreas Kjaer; Barbara Malene Fischer Journal: Diagnostics (Basel) Date: 2021-02-11