Zobrazeno 1 - 10
of 916
pro vyhledávání: '"VISVIKIS, DIMITRIS"'
Autor:
Pinton, Noel Jeffrey, Bousse, Alexandre, Wang, Zhihan, Cheze-Le-Rest, Catherine, Maxim, Voichita, Comtat, Claude, Sureau, Florent, Visvikis, Dimitris
We propose in this work a framework for synergistic positron emission tomography (PET)/computed tomography (CT) reconstruction using a joint generative model as a penalty. We use a synergistic penalty function that promotes PET/CT pairs that are like
Externí odkaz:
http://arxiv.org/abs/2411.07339
Autor:
Sadikine, Amine, Badic, Bogdan, Tasu, Jean-Pierre, Noblet, Vincent, Visvikis, Dimitris, Conze, Pierre-Henri
Publikováno v:
2022 IEEE ICIP, Bordeaux, France, 2022, pp. 586-590
The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy or surgical planning. Despite a go
Externí odkaz:
http://arxiv.org/abs/2409.13001
Autor:
Sadikine, Amine, Badic, Bogdan, Ferrante, Enzo, Noblet, Vincent, Ballet, Pascal, Visvikis, Dimitris, Conze, Pierre-Henri
Publikováno v:
2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 2024, pp. 1-5
The precise delineation of blood vessels in medical images is critical for many clinical applications, including pathology detection and surgical planning. However, fully-automated vascular segmentation is challenging because of the variability in sh
Externí odkaz:
http://arxiv.org/abs/2409.12334
Autor:
Sadikine, Amine, Badic, Bogdan, Tasu, Jean-Pierre, Noblet, Vincent, Ballet, Pascal, Visvikis, Dimitris, Conze, Pierre-Henri
Publikováno v:
2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 2023, pp. 1-5
Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this respect, an automated liver blood vessel extraction is widely summoned. D
Externí odkaz:
http://arxiv.org/abs/2409.12333
This paper presents a novel image reconstruction pipeline for three-gamma (3-{\gamma}) positron emission tomography (PET) aimed at improving spatial resolution and reducing noise in nuclear medicine. The proposed Direct3{\gamma} pipeline addresses th
Externí odkaz:
http://arxiv.org/abs/2407.18337
Dosimetry is an essential tool to provide the best and safest radio-therapies to a patient. In this field, Monte-Carlo simulations are considered to be the golden standard for predicting accurately the deposited dose in the body. Such methods are ver
Externí odkaz:
http://arxiv.org/abs/2405.02477
This paper presents a novel approach for learned synergistic reconstruction of medical images using multi-branch generative models. Leveraging variational autoencoders (VAEs), our model learns from pairs of images simultaneously, enabling effective d
Externí odkaz:
http://arxiv.org/abs/2404.08748
Autor:
Cao, Yi-Heng, Bourbonne, Vincent, Lucia, François, Schick, Ulrike, Bert, Julien, Jaouen, Vincent, Visvikis, Dimitris
Objective: Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning tar
Externí odkaz:
http://arxiv.org/abs/2404.00163
Autor:
Vazia, Corentin, Bousse, Alexandre, Froment, Jacques, Vedel, Béatrice, Vermet, Franck, Wang, Zhihan, Dassow, Thore, Tasu, Jean-Pierre, Visvikis, Dimitris
This paper proposes a novel approach to spectral computed tomography (CT) material decomposition that uses the recent advances in generative diffusion models (DMs) for inverse problems. Spectral CT and more particularly photon-counting CT (PCCT) can
Externí odkaz:
http://arxiv.org/abs/2403.10183
Autor:
Vazia, Corentin, Bousse, Alexandre, Vedel, Béatrice, Vermet, Franck, Wang, Zhihan, Dassow, Thore, Tasu, Jean-Pierre, Visvikis, Dimitris, Froment, Jacques
Using recent advances in generative artificial intelligence (AI) brought by diffusion models, this paper introduces a new synergistic method for spectral computed tomography (CT) reconstruction. Diffusion models define a neural network to approximate
Externí odkaz:
http://arxiv.org/abs/2403.06308