Summary
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.
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101068572 |
| Start date: | 01-12-2022 |
| End date: | 28-02-2026 |
| Total budget - Public funding: | - 189 687,00 Euro |
Cordis data
Original description
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.
Status
SIGNEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
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