Literature DB >> 26929936

Towards tracer dose reduction in PET studies: Simulation of dose reduction by retrospective randomized undersampling of list-mode data.

Sergios Gatidis1, Christian Würslin, Ferdinand Seith, Jürgen F Schäfer, Christian la Fougère, Konstantin Nikolaou, Nina F Schwenzer, Holger Schmidt.   

Abstract

OBJECTIVE: Optimization of tracer dose regimes in positron emission tomography (PET) imaging is a trade-off between diagnostic image quality and radiation exposure. The challenge lies in defining minimal tracer doses that still result in sufficient diagnostic image quality. In order to find such minimal doses, it would be useful to simulate tracer dose reduction as this would enable to study the effects of tracer dose reduction on image quality in single patients without repeated injections of different amounts of tracer. The aim of our study was to introduce and validate a method for simulation of low-dose PET images enabling direct comparison of different tracer doses in single patients and under constant influencing factors.
METHODS: (18)F-fluoride PET data were acquired on a combined PET/magnetic resonance imaging (MRI) scanner. PET data were stored together with the temporal information of the occurrence of single events (list-mode format). A predefined proportion of PET events were then randomly deleted resulting in undersampled PET data. These data sets were subsequently reconstructed resulting in simulated low-dose PET images (retrospective undersampling of list-mode data). This approach was validated in phantom experiments by visual inspection and by comparison of PET quality metrics contrast recovery coefficient (CRC), background-variability (BV) and signal-to-noise ratio (SNR) of measured and simulated PET images for different activity concentrations. In addition, reduced-dose PET images of a clinical (18)F-FDG PET dataset were simulated using the proposed approach.
RESULTS: (18)F-PET image quality degraded with decreasing activity concentrations with comparable visual image characteristics in measured and in corresponding simulated PET images. This result was confirmed by quantification of image quality metrics. CRC, SNR and BV showed concordant behavior with decreasing activity concentrations for measured and for corresponding simulated PET images. Simulation of dose-reduced datasets based on clinical (18)F-FDG PET data demonstrated the clinical applicability of the proposed data.
CONCLUSION: Simulation of PET tracer dose reduction is possible with retrospective undersampling of list-mode data. Resulting simulated low-dose images have equivalent characteristics with PET images actually measured at lower doses and can be used to derive optimal tracer dose regimes.

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Year:  2016        PMID: 26929936     DOI: 10.1967/s002449910333

Source DB:  PubMed          Journal:  Hell J Nucl Med        ISSN: 1790-5427            Impact factor:   1.102


  14 in total

1.  Improved discrimination between benign and malignant LDCT screening-detected lung nodules with dynamic over static 18F-FDG PET as a function of injected dose.

Authors:  Qing Ye; Jing Wu; Yihuan Lu; Mika Naganawa; Jean-Dominique Gallezot; Tianyu Ma; Yaqiang Liu; Lynn Tanoue; Frank Detterbeck; Justin Blasberg; Ming-Kai Chen; Michael Casey; Richard E Carson; Chi Liu
Journal:  Phys Med Biol       Date:  2018-09-06       Impact factor: 3.609

2.  Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.

Authors:  Kevin T Chen; Matti Schürer; Jiahong Ouyang; Mary Ellen I Koran; Guido Davidzon; Elizabeth Mormino; Solveig Tiepolt; Karl-Titus Hoffmann; Osama Sabri; Greg Zaharchuk; Henryk Barthel
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-06-13       Impact factor: 9.236

Review 3.  Clinical use of cardiac PET/MRI: current state-of-the-art and potential future applications.

Authors:  Patrick Krumm; Stefanie Mangold; Sergios Gatidis; Konstantin Nikolaou; Felix Nensa; Fabian Bamberg; Christian la Fougère
Journal:  Jpn J Radiol       Date:  2018-03-10       Impact factor: 2.374

Review 4.  [Simultaneous whole-body PET-MRI in pediatric oncology : More than just reducing radiation?].

Authors:  S Gatidis; B Gückel; C la Fougère; J Schmitt; J F Schäfer
Journal:  Radiologe       Date:  2016-07       Impact factor: 0.635

Review 5.  Neuroimaging in the Era of Artificial Intelligence: Current Applications.

Authors:  Robert Monsour; Mudit Dutta; Ahmed-Zayn Mohamed; Andrew Borkowski; Narayan A Viswanadhan
Journal:  Fed Pract       Date:  2022-04-12

6.  Activity Dose Reduction in 64Cu-DOTATATE PET in Patients with Neuroendocrine Neoplasms: Impact on Image Quality and Lesion Detection Ability.

Authors:  Mathias Loft; Esben A Carlsen; Camilla B Johnbeck; Christoffer V Jensen; Flemming L Andersen; Seppo W Langer; Peter Oturai; Ulrich Knigge; Andreas Kjaer
Journal:  Mol Imaging Biol       Date:  2022-02-15       Impact factor: 3.484

7.  Defining optimal tracer activities in pediatric oncologic whole-body 18F-FDG-PET/MRI.

Authors:  Sergios Gatidis; Holger Schmidt; Christian la Fougère; Konstantin Nikolaou; Nina F Schwenzer; Jürgen F Schäfer
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-08-26       Impact factor: 9.236

8.  Projection Space Implementation of Deep Learning-Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in Image Space.

Authors:  Amirhossein Sanaat; Hossein Arabi; Ismini Mainta; Valentina Garibotto; Habib Zaidi
Journal:  J Nucl Med       Date:  2020-01-10       Impact factor: 11.082

9.  Ultra-Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.

Authors:  Kevin T Chen; Enhao Gong; Fabiola Bezerra de Carvalho Macruz; Junshen Xu; Athanasia Boumis; Mehdi Khalighi; Kathleen L Poston; Sharon J Sha; Michael D Greicius; Elizabeth Mormino; John M Pauly; Shyam Srinivas; Greg Zaharchuk
Journal:  Radiology       Date:  2018-12-11       Impact factor: 29.146

10.  Initial Experience With Low-Dose 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging With Deep Learning Enhancement.

Authors:  Christian J Park; Weijie Chen; Ali Pirasteh; David H Kim; Scott B Perlman; Jessica B Robbins; Alan B McMillan
Journal:  J Comput Assist Tomogr       Date:  2021 Jul-Aug 01       Impact factor: 1.826

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