Francisco J López-González1, Jesús Silva-Rodríguez2, José Paredes-Pacheco1, Aida Niñerola-Baizán3, Nikos Efthimiou4, Carmen Martín-Martín5, Alexis Moscoso6, Álvaro Ruibal6, Núria Roé-Vellvé7, Pablo Aguiar8. 1. Molecular Imaging Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain; Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, General Foundation of the University of Málaga, Málaga, Spain. 2. R&D Department, Qubiotech Health Intelligence, SL., Rúa Real n° 24, Planta 1, A Coruña, Galicia, Spain; Nuclear Medicine Department & Molecular Imaging Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Travesía da Choupana S/N 15706, Santiago de Compostela, Galicia, Spain. Electronic address: jesus@qubiotech.com. 3. Nuclear Medicine Department, Hospital Clínic, Barcelona, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain. 4. Positron Emission Tomography Research Centre, University of Hull, Hull HU6 7RX, United Kingdom. 5. Nuclear Medicine Department, Hospital Clínic, Barcelona, Spain. 6. Molecular Imaging Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain; Nuclear Medicine Department & Molecular Imaging Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Travesía da Choupana S/N 15706, Santiago de Compostela, Galicia, Spain. 7. Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain. 8. Molecular Imaging Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain; Nuclear Medicine Department & Molecular Imaging Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Travesía da Choupana S/N 15706, Santiago de Compostela, Galicia, Spain. Electronic address: pablo.aguiar.fernandez@sergas.es.
Abstract
BACKGROUND: The lack of standardization of intensity normalization methods and its unknown effect on the quantification output is recognized as a major drawback for the harmonization of brain FDG-PET quantification protocols. The aim of this work is the ground truth-based evaluation of different intensity normalization methods on brain FDG-PET quantification output. METHODS: Realistic FDG-PET images were generated using Monte Carlo simulation from activity and attenuation maps directly derived from 25 healthy subjects (adding theoretical relative hypometabolisms on 6 regions of interest and for 5 hypometabolism levels). Single-subject statistical parametric mapping (SPM) was applied to compare each simulated FDG-PET image with a healthy database after intensity normalization based on reference regions methods such as the brain stem (RRBS), cerebellum (RRC) and the temporal lobe contralateral to the lesion (RRTL), and data-driven methods, such as proportional scaling (PS), histogram-based method (HN) and iterative versions of both methods (iPS and iHN). The performance of these methods was evaluated in terms of the recovery of the introduced theoretical hypometabolic pattern and the appearance of unspecific hypometabolic and hypermetabolic findings. RESULTS: Detected hypometabolic patterns had significantly lower volumes than the introduced hypometabolisms for all intensity normalization methods particularly for slighter reductions in metabolism . Among the intensity normalization methods, RRC and HN provided the largest recovered hypometabolic volumes, while the RRBS showed the smallest recovery. In general, data-driven methods overcame reference regions and among them, the iterative methods overcame the non-iterative ones. Unspecific hypermetabolic volumes were similar for all methods, with the exception of PS, where it became a major limitation (up to 250 cm3) for extended and intense hypometabolism. On the other hand, unspecific hypometabolism was similar far all methods, and usually solved with appropriate clustering. CONCLUSIONS: Our findings showed that the inappropriate use of intensity normalization methods can provide remarkable bias in the detected hypometabolism and it represents a serious concern in terms of false positives. Based on our findings, we recommend the use of histogram-based intensity normalization methods. Reference region methods performance was equivalent to data-driven methods only when the selected reference region is large and stable.
BACKGROUND: The lack of standardization of intensity normalization methods and its unknown effect on the quantification output is recognized as a major drawback for the harmonization of brain FDG-PET quantification protocols. The aim of this work is the ground truth-based evaluation of different intensity normalization methods on brain FDG-PET quantification output. METHODS: Realistic FDG-PET images were generated using Monte Carlo simulation from activity and attenuation maps directly derived from 25 healthy subjects (adding theoretical relative hypometabolisms on 6 regions of interest and for 5 hypometabolism levels). Single-subject statistical parametric mapping (SPM) was applied to compare each simulated FDG-PET image with a healthy database after intensity normalization based on reference regions methods such as the brain stem (RRBS), cerebellum (RRC) and the temporal lobe contralateral to the lesion (RRTL), and data-driven methods, such as proportional scaling (PS), histogram-based method (HN) and iterative versions of both methods (iPS and iHN). The performance of these methods was evaluated in terms of the recovery of the introduced theoretical hypometabolic pattern and the appearance of unspecific hypometabolic and hypermetabolic findings. RESULTS: Detected hypometabolic patterns had significantly lower volumes than the introduced hypometabolisms for all intensity normalization methods particularly for slighter reductions in metabolism . Among the intensity normalization methods, RRC and HN provided the largest recovered hypometabolic volumes, while the RRBS showed the smallest recovery. In general, data-driven methods overcame reference regions and among them, the iterative methods overcame the non-iterative ones. Unspecific hypermetabolic volumes were similar for all methods, with the exception of PS, where it became a major limitation (up to 250 cm3) for extended and intense hypometabolism. On the other hand, unspecific hypometabolism was similar far all methods, and usually solved with appropriate clustering. CONCLUSIONS: Our findings showed that the inappropriate use of intensity normalization methods can provide remarkable bias in the detected hypometabolism and it represents a serious concern in terms of false positives. Based on our findings, we recommend the use of histogram-based intensity normalization methods. Reference region methods performance was equivalent to data-driven methods only when the selected reference region is large and stable.
Authors: Yang Nan; Javier Del Ser; Simon Walsh; Carola Schönlieb; Michael Roberts; Ian Selby; Kit Howard; John Owen; Jon Neville; Julien Guiot; Benoit Ernst; Ana Pastor; Angel Alberich-Bayarri; Marion I Menzel; Sean Walsh; Wim Vos; Nina Flerin; Jean-Paul Charbonnier; Eva van Rikxoort; Avishek Chatterjee; Henry Woodruff; Philippe Lambin; Leonor Cerdá-Alberich; Luis Martí-Bonmatí; Francisco Herrera; Guang Yang Journal: Inf Fusion Date: 2022-06 Impact factor: 17.564
Authors: Eric Guedj; Andrea Varrone; Ronald Boellaard; Nathalie L Albert; Henryk Barthel; Bart van Berckel; Matthias Brendel; Diego Cecchin; Ozgul Ekmekcioglu; Valentina Garibotto; Adriaan A Lammertsma; Ian Law; Iván Peñuelas; Franck Semah; Tatjana Traub-Weidinger; Elsmarieke van de Giessen; Donatienne Van Weehaeghe; Silvia Morbelli Journal: Eur J Nucl Med Mol Imaging Date: 2021-12-09 Impact factor: 10.057
Authors: Daniel Gillett; Russell Senanayake; James MacFarlane; Merel van der Meulen; Olympia Koulouri; Andrew S Powlson; Rosy Crawford; Bethany Gillett; Nick Bird; Sarah Heard; Angelos Kolias; Richard Mannion; Luigi Aloj; Iosif A Mendichovszky; Heok Cheow; Waiel A Bashari; Mark Gurnell Journal: EJNMMI Res Date: 2022-05-07 Impact factor: 3.434