Name
Title | ||
|---|---|---|
Prediction Of Fdg Uptake In Lung Tumors From Ct Images Using Generative Adversarial Networks |
Abstract | ||
|---|---|---|
In modern medicine, combined PET-CT is a commonly-used tool in clinical diagnostics, which is especially important in oncology for staging or treatment planning. Variations in FDG uptake visible in a PET image, which indicate variances in metabolic activity, are not visually recognizable within a CT scan from the same region, making both imaging modalities necessary for diagnosis and exposing the patient to a high amount of radiation. In this study, we investigate the possibility of using generative adversarial networks (GANs) to synthesize a PET image from a CT scan to predict metabolic activity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/EUSIPCO.2019.8902935 | 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) |
Keywords | Field | DocType |
PET/CT, lung cancer, FDG uptake, machine learning, GANs | Lung cancer,PET-CT,Lung,Imaging modalities,Radiation treatment planning,Computed tomography,Radiology,Stage (cooking),Medicine | Conference |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Annika Liebgott | 1 | 3 | 2.44 |
| Darius Hindere | 2 | 0 | 0.34 |
| Karim Armanious | 3 | 7 | 4.17 |
| Alexander Bartler | 4 | 0 | 1.69 |
| Konstantin Nikolaou | 5 | 23 | 4.36 |
| Sergios Gatidis | 6 | 31 | 8.17 |
| Bin Yang | 7 | 201 | 49.22 |