Geração de tomografia cardíaca com contraste sintético a partir de imagens de tomografia cardíacas sem contraste utilizando modelos de difusão

Detalhes bibliográficos
Ano de defesa: 2024
Autor(a) principal: FERREIRA, Victor Rogerio Sousa lattes
Orientador(a): PAIVA, Anselmo Cardoso de lattes
Banca de defesa: PAIVA, Anselmo Cardoso de lattes, RENNA, Francesco lattes, PEDROSA, João Manuel lattes, SILVA, Aristófanes Correa
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal do Maranhão
Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
País: Brasil
Palavras-chave em Português:
modelos de difusão;
tradução de imagens;
redes adversárias;
U-Net;
Palavras-chave em Inglês:
diffusion models.
image translation.
adversarial networks.
U-Net.
Área do conhecimento CNPq:
Engenharia Elétrica
Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/5700
Resumo: This dissertation proposes the use of a deep learning-based adversarial diffusion model to address the translation of contrast-free computed tomography (CT) images of the heart into synthetic images compatible with images acquired with contrast injection. The work investigates challenges in medical image translation by combining concepts from generative adversarial networks (GANs) and diffusion models. The results were evaluated through several statistics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Frechet Inception Distance (FID), and Root Mean Square Error (RMSE) to demonstrate the model’s performance in generating images with synthetic contrast while preserving quality and visual similarity. The proposed model achieved the following best results: PSNR = 32.85, SSIM = 0.766, and FID = 42.348. CyTran obtained the best RMSE of 0.14, while the model had the worst of 0.2. A comparison of the results obtained with CyTran, Cycle-GAN, and Pix2Pix networks is also presented. Although the obtained results are promising, the analysis of RMSE indicates that there are still challenges to be overcome, highlighting the need for continuous improvements. The intersection of GANs and diffusion models promises future advancements, significantly contributing to clinical practice.

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