Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging.
Comput Methods Programs Biomed
; 256: 108375, 2024 Nov.
Article
em En
| MEDLINE
| ID: mdl-39180914
ABSTRACT
INTRODUCTION:
We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation.METHODS:
Our method utilizes a combination of three-dimensional depth-wise separable convolutional layers and a physically informed deep neural network to incorporatea priori knowledge about the AIF's functional form and shape, enabling precise predictions of the concentrations of [11C]PBR28 in whole blood and the free tracer in metabolite-corrected plasma.RESULTS:
We found a robust linear correlation between our model's predicted AIF curves and those obtained through traditional, invasive measurements. We achieved an average cross-validated Pearson correlation of 0.86 for whole blood and 0.89 for parent plasma curves. Moreover, our method's ability to estimate the volumes of distribution across several key brain regions - without significant differences between the use of predicted versus actual AIFs in a two-tissue compartmental model - successfully captures the intrinsic variability related to sex, the binding affinity of the translocator protein (18 kDa), and age.CONCLUSIONS:
These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Encéfalo
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Redes Neurais de Computação
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Tomografia por Emissão de Pósitrons
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article