Tetsuro Sekine1, Ninon Burgos2, Geoffrey Warnock3, Martin Huellner4, Alfred Buck5, Edwin E G W Ter Voert5, M Jorge Cardoso6, Brian F Hutton7, Sebastien Ourselin6, Patrick Veit-Haibach8, Gaspar Delso9. 1. Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland Department of Radiology, Nippon Medical School, Tokyo, Japan tetsuro.sekine@gmail.com. 2. Translational Imaging Group, Centre for Medical Image Computing, University College London, NW1 2HE, London, United Kingdom. 3. Institute of Pharmacology & Toxicology, University of Zurich, Zurich, Switzerland PMOD Technologies Ltd., Zurich, Switzerland. 4. Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland Department of Neuroradiology, University Hospital Zurich, Zurich, Switzerland. 5. Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland. 6. Translational Imaging Group, Centre for Medical Image Computing, University College London, NW1 2HE, London, United Kingdom Dementia Research Centre, Institute of Neurology, University College London, WC1N 3AR London, United Kingdom. 7. Institute of Nuclear Medicine, University College London, NW1 2BU London, United Kingdom Centre for Medical Radiation Physics, University of Wollongong, NSW 2522 Wollongong, Australia. 8. Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland Division of Diagnostic and Interventional Radiology, Department of Medical Radiology, University Hospital Zurich, Zurich, Switzerland; and. 9. GE Healthcare, Waukesha, Wisconsin.
Abstract
UNLABELLED: In this work, we assessed the feasibility of attenuation correction (AC) based on a multi-atlas-based method (m-Atlas) by comparing it with a clinical AC method (single-atlas-based method [s-Atlas]), on a time-of-flight (TOF) PET/MRI scanner. METHODS: We enrolled 15 patients. The median patient age was 59 y (age range, 31-80). All patients underwent clinically indicated whole-body (18)F-FDG PET/CT for staging, restaging, or follow-up of malignant disease. All patients volunteered for an additional PET/MRI scan of the head (no additional tracer being injected). For each patient, 3 AC maps were generated. Both s-Atlas and m-Atlas AC maps were generated from the same patient-specific LAVA-Flex T1-weighted images being acquired by default on the PET/MRI scanner during the first 18 s of the PET scan. An s-Atlas AC map was extracted by the PET/MRI scanner, and an m-Atlas AC map was created using a Web service tool that automatically generates m-Atlas pseudo-CT images. For comparison, the AC map generated by PET/CT was registered and used as a gold standard. PET images were reconstructed from raw data on the TOF PET/MRI scanner using each AC map. All PET images were normalized to the SPM5 PET template, and (18)F-FDG accumulation was quantified in 67 volumes of interest (VOIs; automated anatomic labeling atlas). Relative (%diff) and absolute differences (|%diff|) between images based on each atlas AC and CT-AC were calculated. (18)F-FDG uptake in all VOIs and generalized merged VOIs were compared using the paired t test and Bland-Altman test. RESULTS: The range of error on m-Atlas in all 1,005 VOIs was -4.99% to 4.09%. The |%diff| on the m-Atlas was improved by about 20% compared with s-Atlas (s-Atlas vs. m-Atlas: 1.49% ± 1.06% vs. 1.21% ± 0.89%, P < 0.01). In generalized VOIs, %diff on m-Atlas in the temporal lobe and cerebellum was significantly smaller (s-Atlas vs. m-Atlas: temporal lobe, 1.49% ± 1.37% vs. -0.37% ± 1.41%, P < 0.01; cerebellum, 1.55% ± 1.97% vs. -1.15% ± 1.72%, P < 0.01). CONCLUSION: The errors introduced using either s-Atlas or m-Atlas did not exceed 5% in any brain region investigated. When compared with the clinical s-Atlas, m-Atlas is more accurate, especially in regions close to the skull base.
UNLABELLED: In this work, we assessed the feasibility of attenuation correction (AC) based on a multi-atlas-based method (m-Atlas) by comparing it with a clinical AC method (single-atlas-based method [s-Atlas]), on a time-of-flight (TOF) PET/MRI scanner. METHODS: We enrolled 15 patients. The median patient age was 59 y (age range, 31-80). All patients underwent clinically indicated whole-body (18)F-FDG PET/CT for staging, restaging, or follow-up of malignant disease. All patients volunteered for an additional PET/MRI scan of the head (no additional tracer being injected). For each patient, 3 AC maps were generated. Both s-Atlas and m-Atlas AC maps were generated from the same patient-specific LAVA-Flex T1-weighted images being acquired by default on the PET/MRI scanner during the first 18 s of the PET scan. An s-Atlas AC map was extracted by the PET/MRI scanner, and an m-Atlas AC map was created using a Web service tool that automatically generates m-Atlas pseudo-CT images. For comparison, the AC map generated by PET/CT was registered and used as a gold standard. PET images were reconstructed from raw data on the TOF PET/MRI scanner using each AC map. All PET images were normalized to the SPM5 PET template, and (18)F-FDG accumulation was quantified in 67 volumes of interest (VOIs; automated anatomic labeling atlas). Relative (%diff) and absolute differences (|%diff|) between images based on each atlas AC and CT-AC were calculated. (18)F-FDG uptake in all VOIs and generalized merged VOIs were compared using the paired t test and Bland-Altman test. RESULTS: The range of error on m-Atlas in all 1,005 VOIs was -4.99% to 4.09%. The |%diff| on the m-Atlas was improved by about 20% compared with s-Atlas (s-Atlas vs. m-Atlas: 1.49% ± 1.06% vs. 1.21% ± 0.89%, P < 0.01). In generalized VOIs, %diff on m-Atlas in the temporal lobe and cerebellum was significantly smaller (s-Atlas vs. m-Atlas: temporal lobe, 1.49% ± 1.37% vs. -0.37% ± 1.41%, P < 0.01; cerebellum, 1.55% ± 1.97% vs. -1.15% ± 1.72%, P < 0.01). CONCLUSION: The errors introduced using either s-Atlas or m-Atlas did not exceed 5% in any brain region investigated. When compared with the clinical s-Atlas, m-Atlas is more accurate, especially in regions close to the skull base.
Authors: Milena Sales Pitombeira; Michel Koole; Kenia R Campanholo; Aline M Souza; Fábio L S Duran; Davi J Fontoura Solla; Maria F Mendes; Samira L Apóstolos Pereira; Carolina M Rimkus; Geraldo Filho Busatto; Dagoberto Callegaro; Carlos A Buchpiguel; Daniele de Paula Faria Journal: Eur J Nucl Med Mol Imaging Date: 2022-07-15 Impact factor: 10.057
Authors: Tetsuro Sekine; Alfred Buck; Gaspar Delso; Bradley Kemp; Edwin E G W Ter Voert; Martin Huellner; Patrick Veit-Haibach; Sandeep Kaushik; Florian Wiesinger; Geoffrey Warnock Journal: PLoS One Date: 2020-06-03 Impact factor: 3.240
Authors: Claes N Ladefoged; Ian Law; Udunna Anazodo; Keith St Lawrence; David Izquierdo-Garcia; Ciprian Catana; Ninon Burgos; M Jorge Cardoso; Sebastien Ourselin; Brian Hutton; Inés Mérida; Nicolas Costes; Alexander Hammers; Didier Benoit; Søren Holm; Meher Juttukonda; Hongyu An; Jorge Cabello; Mathias Lukas; Stephan Nekolla; Sibylle Ziegler; Matthias Fenchel; Bjoern Jakoby; Michael E Casey; Tammie Benzinger; Liselotte Højgaard; Adam E Hansen; Flemming L Andersen Journal: Neuroimage Date: 2016-12-14 Impact factor: 6.556
Authors: Aruki Tanaka; Tetsuro Sekine; Edwin E G W Ter Voert; Konstantinos G Zeimpekis; Gaspar Delso; Felipe de Galiza Barbosa; Geoffrey Warnock; Shin-Ichiro Kumita; Patrick Veit Haibach; Martin Huellner Journal: Front Med (Lausanne) Date: 2022-03-02
Authors: Ana M Franceschi; Valentino Abballe; Roy A Raad; Aaron Nelson; Kimberly Jackson; James Babb; Thomas Vahle; Matthias Fenchel; Yiqiang Zhan; Gerardo Hermosillo Valadez; Timothy M Shepherd; Kent P Friedman Journal: World J Nucl Med Date: 2018 Jul-Sep
Authors: Lucas Rischka; Gregor Gryglewski; Neydher Berroterán-Infante; Ivo Rausch; Gregory Miles James; Manfred Klöbl; Helen Sigurdardottir; Markus Hartenbach; Andreas Hahn; Wolfgang Wadsak; Markus Mitterhauser; Thomas Beyer; Siegfried Kasper; Daniela Prayer; Marcus Hacker; Rupert Lanzenberger Journal: Front Physiol Date: 2019-11-22 Impact factor: 4.566