Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks – Initial results

Schwyzer, Moritz; Ferraro, Daniela A.; Muehlematter, Urs J.; Curioni-Fontecedro, Alessandra; Huellner, Martin W.; Schulthess, Gustav K. von; Kaufmann, Philipp A.; Burger, Irene A.; Messerli, Michael

doi:10.1016/j.lungcan.2018.11.001

Cited by 98 publications

(56 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In related work, a preliminary task‐based observer study has been undertaken, in which a reduced number of MR methods are evaluated for the diagnosis of Alzheimer's disease or temporal lobe epilepsy from reduced count PET images . Automating the task‐based observer process is still an area of active research within medical imaging, even for the relatively simple task of tumor detection …”

Section: Limitationsmentioning

confidence: 99%

Intercomparison of MR‐informed PET image reconstruction methods

et al. 2019

View full text Add to dashboard Cite

Purpose Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise and reduced partial volume effects. However, the influence of MR structural information also increases the possibility of suppression or bias of structures present only in the PET data (PET‐unique regions). To address this, further developments for MR‐informed methods have been proposed, for example, through inclusion of the current reconstructed PET image, alongside the MR image, in the iterative reconstruction process. In this present work, a number of kernel and maximum a posteriori (MAP) methodologies are compared, with the aim of identifying methods that enable a favorable trade‐off between the suppression of noise and the retention of unique features present in the PET data. Methods The reconstruction methods investigated were: the MR‐informed conventional and spatially compact kernel methods, referred to as KEM and KEM largest value sparsification (LVS) respectively; the MR‐informed Bowsher and Gaussian MR‐guided MAP methods; and the PET‐MR‐informed hybrid kernel and anato‐functional MAP methods. The trade‐off between improving the reconstruction of the whole brain region and the PET‐unique regions was investigated for all methods in comparison with postsmoothed maximum likelihood expectation maximization (MLEM), evaluated in terms of structural similarity index (SSIM), normalized root mean square error (NRMSE), bias, and standard deviation. Both simulated BrainWeb (10 noise realizations) and real [18F] fluorodeoxyglucose (FDG) three‐dimensional datasets were used. The real [18F]FDG dataset was augmented with simulated tumors to allow comparison of the reconstruction methodologies for the case of known regions of PET‐MR discrepancy and evaluated at full counts (100%) and at a reduced (10%) count level. Results For the high‐count simulated and real data studies, the anato‐functional MAP method performed better than the other methods under investigation (MR‐informed, PET‐MR‐informed and postsmoothed MLEM), in terms of achieving the best trade‐off for the reconstruction of the whole brain and PET‐unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. The inclusion of PET information in the anato‐functional MAP method enables the reconstruction of PET‐unique regions to attain similarly low levels of bias as unsmoothed MLEM, while moderately improving the whole brain image quality for low levels of regularization. However, for low count simulated datasets the anato‐functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularization term. For the low counts simulated dataset, KEM LVS and to a lesser extent, HKEM performed better than the other methods under investigation in terms of achieving the best trade‐off for the reconstruction of the whole brain and PET‐unique regions, assessed in terms of t...

show abstract

Section: Limitationsmentioning

confidence: 99%

Intercomparison of MR‐informed PET image reconstruction methods

et al. 2019

View full text Add to dashboard Cite

show abstract

“…74 Automating the task-based observer process is still an area of active research within medical imaging, even for the relatively simple task of tumor detection. 75,76 .…”

Section: Limitationsmentioning

confidence: 99%

Intercomparison of MR-Informed Methods for PET Image Reconstruction

Bland

Mehranian

Belzunce

et al. 2018

2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC)

View full text Add to dashboard Cite

Purpose: Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise and reduced partial volume effects. However, the influence of MR structural information also increases the possibility of suppression or bias of structures present only in the PET data (PET-unique regions). To address this, further developments for MR-informed methods have been proposed, for example, through inclusion of the current reconstructed PET image, alongside the MR image, in the iterative reconstruction process. In this present work, a number of kernel and maximum a posteriori (MAP) methodologies are compared, with the aim of identifying methods that enable a favorable trade-off between the suppression of noise and the retention of unique features present in the PET data. Methods: The reconstruction methods investigated were: the MR-informed conventional and spatially compact kernel methods, referred to as KEM and KEM largest value sparsification (LVS) respectively; the MR-informed Bowsher and Gaussian MR-guided MAP methods; and the PET-MR-informed hybrid kernel and anato-functional MAP methods. The trade-off between improving the reconstruction of the whole brain region and the PET-unique regions was investigated for all methods in comparison with postsmoothed maximum likelihood expectation maximization (MLEM), evaluated in terms of structural similarity index (SSIM), normalized root mean square error (NRMSE), bias, and standard deviation. Both simulated BrainWeb (10 noise realizations) and real [ 18 F] fluorodeoxyglucose (FDG) threedimensional datasets were used. The real [ 18 F]FDG dataset was augmented with simulated tumors to allow comparison of the reconstruction methodologies for the case of known regions of PET-MR discrepancy and evaluated at full counts (100%) and at a reduced (10%) count level. Results: For the high-count simulated and real data studies, the anato-functional MAP method performed better than the other methods under investigation (MR-informed, PET-MR-informed and postsmoothed MLEM), in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. The inclusion of PET information in the anato-functional MAP method enables the reconstruction of PET-unique regions to attain similarly low levels of bias as unsmoothed MLEM, while moderately improving the whole brain image quality for low levels of regularization. However, for low count simulated datasets the anato-functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularization term. For the low counts simulated dataset, KEM LVS and to a lesser extent, HKEM performed better than the other methods under investigation in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in te...

show abstract

“…Besides cutting-edge PET investigations currently applied on selected patients and on specific indications, this contradiction highlights the further need of development of light PET protocols as previously developed in radiology for today the very useful low-dose thoracic CT [8], allowing broader exploration availability with shorter procedures for acquisition duration and perhaps also for the uptake period, but probably by preserving the whole-body exploration to better characterize the extension of the disease and its prognosis. New technological achievements based on ultra-low dose whole-body PET instrumentation [9] combined with deep neural networks for reconstruction including generative adversarial networks [10,11] constitute a great opportunity for such developments which need to be encouraged, with also ultimately possible larger applications in other contexts for example for cancer screening [12]. This article is part of the Topical Collection on Infection and inflammation…”

mentioning

confidence: 99%