scholarly journals Towards a Whole Body [18F] FDG Positron Emission Tomography Attenuation Correction Map Synthesizing using Deep Neural Networks

2021 ◽  
Vol 21 (1) ◽  
pp. e4
Author(s):  
Ramiro Germán Rodríguez Colmeiro ◽  
Claudio Verrastro ◽  
Daniel Minsky ◽  
Thomas Grosges
Keyword(s):  
Neural Networks ◽  
Positron Emission Tomography ◽  
Attenuation Correction ◽  
Deep Neural Networks ◽  
Absolute Error ◽  
Emission Tomography ◽  
Whole Body ◽  
Multiple Sources ◽  
Attenuation Map ◽  
Positron Emission

The correction of attenuation effects in Positron Emission Tomography (PET) imaging is fundamental to obtain a correct radiotracer distribution. However direct measurement of this attenuation map is not error-free and normally results in additional ionization radiation dose to the patient. Here, we explore the task of whole body attenuation map generation using 3D deep neural networks. We analyze the advantages thar an adversarial network training cand provide to such models. The networks are trained to learn the mapping from non attenuation corrected [18 ^F]-fluorodeoxyglucose PET images to a synthetic Computerized Tomography (sCT) and also to label the input voxel tissue. Then the sCT image is further refined using an adversarial training scheme to recover higher frequency details and lost structures using context information. This work is trained and tested on public available datasets, containing several PET images from different scanners with different radiotracer administration and reconstruction modalities. The network is trained with 108 samples and validated on 10 samples. The sCT generation was tested on 133 samples from 8 distinct datasets. The resulting mean absolute error of the networks is 90±20  and 103±18HU and a peak signal to noise ratio of 19.3±1.7 dB and 18.6±1.5, for the base model and the adversarial model respectively. The attenuation correction is tested by means of attenuation sinograms, obtaining a line of response attenuation mean error lower than 1% with a standard deviation lower than 8%. The proposeddeep learning topologies are capable of generating whole body attenuation maps from uncorrected PET image data. Moreover, the accuracy of both methods holds in the presence of data from multiple sources and modalities and are trained on publicly available datasets. Finally, while the adversarial layer enhances visual appearance of the produced samples, the 3D U-Net achieves higher metric performance

scholarly journals Whole Body Positron Emission Tomography Attenuation Correction Map Synthesizing using 3D Deep Generative Adversarial Networks

2020 ◽  
Author(s):  
Ramiro Rodriguez Colmeiro ◽  
Claudio Verrastro ◽  
Daniel Minsky ◽  
Thomas Grosges
Keyword(s):  
Positron Emission Tomography ◽  
Structural Information ◽  
Absolute Error ◽  
Emission Tomography ◽  
Whole Body ◽  
Generative Adversarial Networks ◽  
Multiple Sources ◽  
Generative Adversarial Network ◽  
Attenuation Map ◽  
Positron Emission

Abstract Background: The correction of attenuation effects in Positron Emission Tomography (PET) imaging is fundamental to obtain a correct radiotracer distribution. However direct measurement of this attenuation map is not error-free and normally results in additional ionization radiation dose to the patient. Here, we propose to obtain the whole body attenuation map using a 3D U-Net generative adversarial network. The network is trained to learn the mapping from non attenuation corrected 18-F-fluorodeoxyglucose PET images to a synthetic Computerized Tomography (sCT) and also to label the input voxel tissue. The sCT image is further refined using an adversarial training scheme to recover higher frequency details and lost structures using context information. This work is trained and tested on public available datasets, containing several PET images from different scanners with different radiotracer administration and reconstruction modalities. The network is trained with 108 samples and validated on 10 samples.Results: The sCT generation was tested on 133 samples from 8 distinct datasets. The resulting mean absolute error of the network is 103 ± 18 HU and a peak signal to noise ratio of 18.6 ± 1.5 dB. The generated images show good correlation with the unknown structural information.Conclusions: The proposed deep learning topology is capable of generating whole body attenuation maps from uncorrected PET image data. Moreover, the method accuracy holds in the presence of data form multiple sources and modalities and is trained on publicly available datasets.

A hybrid method of attenuation correction for positron emission tomography brain studies

1994 ◽  
Vol 21 (12) ◽  
pp. 1279-1284 ◽  
Author(s):  
Valentino Bettinardi ◽  
Maria Carla Gilardi ◽  
Serena Cargnel ◽  
Giovanna Rizzo ◽  
Mika Teräs ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Attenuation Correction ◽  
Hybrid Method ◽  
Emission Tomography ◽  
Positron Emission

POSITRON EMISSION TOMOGRAPHY WITH 18F -FDG IN THE DIAGNOSIS OF PROSTHETIC HEART VALVE ENDOCARDITIS

2021 ◽  
Vol 5 (1) ◽  
pp. 1151-1160
Author(s):  
A.S. Lukashevich ◽  
Keyword(s):  
Computed Tomography ◽  
Positron Emission Tomography ◽  
Fdg Pet ◽  
Prosthetic Heart Valve ◽  
Emission Tomography ◽  
Whole Body ◽  
Positron Emission ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

Purpose. The purpose of the article is to evaluate the diagnostic significance of positron emission tomography / computed tomography with 18F -fluorodeoxyglucose (18F -FDG PET/CT) for the diagnosis of prosthetic endocarditis. Methods of research. The study included 82 patients with suspected prosthetic endocarditis in accordance with the criteria proposed by Duke University [1-5]. The patients received hospital treatment at the State Institution RSPC "Cardiology" from January 2016 to March 2021. The study was of a prospective, non-randomized, single-center cohort design. The duration of the monitor period was 12 months from the moment of patients’ inclusion in the study. Whole-body positron emission tomography / computed tomography (PET/CT) examinations were performed in 82 patients. 27 patients were selected for surgical treatment. Conservative treatment group included 16 patients. 27 patients were selected into the observation group, they were suspected to have prosthetic heart valve infection in the primary referral and underwent PET/CT scanning, according to which the diagnosis of prosthetic endocarditis was excluded. The event under the study did not develop in this group during the year of observation. Results and conclusion. The history of infective endocarditis was not statistically significant and did not increase the risk of developing prosthetic endocarditis in the sample presented. The Duke criteria are less reliable in establishing the diagnosis of prosthetic endocarditis. The median number of days from the date of the first prosthesis implantation to the onset of prosthetic endocarditis was about 4 years. This study revealed that the development of the infectious process in the area of the prosthesis was noted in a more distant postoperative period compared to literature data. Histological confirmation of infection was noted in 100% (27 patients) of cases in reoperated patients. The presence of a more formidable complication such as valve ring abscess located mainly in the projection of the aortic valve ring was quite common in both groups. Presepsin and Interleukin-6 have a statistically significant (U = 394,50 p = 0,01 and U = 94,50 p = 0.004) value in the prognosis of prosthetic endocarditis. Considering the data obtained from ROC analysis, it can be said that the cut-off point at which it is possible to diagnose prosthetic endocarditis based on PETCT is 2.85. The presented methods for the interpretation of whole-body FDG-PET/CT images of patients with suspected infectious complications after cardiac surgery, as well as with the presence of prosthetic endocarditis, show high sensitivity and specificity.

Evaluation of individual skeletal muscle activity by glucose uptake during pedaling exercise at different workloads using positron emission tomography

2009 ◽  
Vol 107 (2) ◽  
pp. 599-604 ◽  
Author(s):  
Yuichi Gondoh ◽  
Manabu Tashiro ◽  
Masatoshi Itoh ◽  
Mohammad M. Masud ◽  
Hiroomi Sensui ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Positron Emission Tomography ◽  
Glucose Uptake ◽  
Muscle Activity ◽  
Rectus Femoris ◽  
Emission Tomography ◽  
Whole Body ◽  
Control Subjects ◽  
Positron Emission ◽  
Iliacus Muscle

Skeletal muscle glucose uptake closely reflects muscle activity at exercise intensity levels <55% of maximal oxygen consumption (V̇o2max). Our purpose was to evaluate individual skeletal muscle activity from glucose uptake in humans during pedaling exercise at different workloads by using [18F]fluorodeoxyglucose (FDG) and positron emission tomography (PET). Twenty healthy male subjects were divided into two groups (7 exercise subjects and 13 control subjects). Exercise subjects were studied during 35 min of pedaling exercise at 40 and 55% V̇o2max exercise intensities. FDG was injected 10 min after the start of exercise or after 20 min of rest. PET scanning of the whole body was conducted after completion of the exercise or rest period. In exercise subjects, mean FDG uptake [standardized uptake ratio (SUR)] of the iliacus muscle and muscles of the anterior part of the thigh was significantly greater than uptake in muscles of control subjects. At 55% V̇o2max exercise, SURs of the iliacus muscle and thigh muscles, except for the rectus femoris, increased significantly compared with SURs at 40% V̇o2max exercise. Our results are the first to clarify that the iliacus muscle, as well as the muscles of the anterior thigh, is the prime muscle used during pedaling exercise. In addition, the iliacus muscle and all muscles in the thigh, except for the rectus femoris, contribute when the workload of the pedaling exercise increases from 40 to 55% V̇o2max.

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