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Dual Tracer PET

Financiado

Cerrado

Machine learning methods for the reconstruction of dual tracer PET images

Positron Emission Tomography (PET) is a medical imaging technique that displays in vivo brain-wide metabolic processes. Prior to PET image acquisition, a tracer, labelled with a positron emitting isotope, is injected to the patien... ver más

28/02/2025

FZJ

190K€

Presupuesto del proyecto: 190K€

Líder del proyecto

FORSCHUNGSZENTRUM JULICH GMBH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-10-10

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021 ExpectedOutcome:

I+D

Cerrada hace 3 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

FZJ

189.69K€ | Lider

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Duración del proyecto: 28 meses Fecha Inicio: 2022-10-10
Fecha Fin: 2025-02-28

Líder del proyecto

FORSCHUNGSZENTRUM JULICH GMBH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 190K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Positron Emission Tomography (PET) is a medical imaging technique that displays in vivo brain-wide metabolic processes. Prior to PET image acquisition, a tracer, labelled with a positron emitting isotope, is injected to the patient. This so-called radiotracer distributes over the body and accumulates in e.g. inflammatory tissue. To date, the acquisiton of a PET image is limited to one tracer, thereby only displaying one metabolic process in the brain. However, the acquisition of two separate PET images acquired with different tracer simultaneously would give novel and unique insights in the communication between e.g. neurotransmitter systems. These insights could be used for the development of new drugs for e.g Alzheimer patients and would increase the understanding of the healthy and diseased brain. The acquisition of 'dual tracer' PET images can be achieved by combining two radiotracers with different properties: The first 'standard' radiotracer emits two photons that are recorded by the PET system, while the second 'non-standard' tracer emits two photons and an additional gamma ray. By identifying the additional gamma ray, the photon detections of the two tracers can be separated and two PET images can be reconstructed. 'Dual tracer' PET image reconstruction yields challenges due to undesirable effect of photon detection. In this project, a machine learning algorithm will be trained to detect and correct for these effects. Moreover, the image acquired with the 'non-standard' tracer yields low image quality. A Convolutional Neural Network will be trained to denoise this PET image. In the proposed project, a reconstruction algorithm for dual tracer PET based on machine learning techniques and resulting in two separate PET images with high image quality will be developed and implemented. The proposed project will demonstrate if dual tracer PET imaging has future potential and will help to understand the highly interactive neuronal communication in the brain.

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