Academic Journal
Postcontrast Medical Image Synthesis in Breast DCE- MRI Using Deep Learning ; Síntesis de imagen médica postcontraste en estudios de DCE-MRI de mama usando aprendizaje profundo
| العنوان: | Postcontrast Medical Image Synthesis in Breast DCE- MRI Using Deep Learning ; Síntesis de imagen médica postcontraste en estudios de DCE-MRI de mama usando aprendizaje profundo |
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| المؤلفون: | Cañaveral, Sara, Mera-Banguero, Carlos, Fonnegra, Rubén D. |
| المصدر: | TecnoLógicas; Vol. 27 No. 60 (2024); e3052 ; TecnoLógicas; Vol. 27 Núm. 60 (2024); e3052 ; 2256-5337 ; 0123-7799 |
| بيانات النشر: | Instituto Tecnológico Metropolitano (ITM) |
| سنة النشر: | 2024 |
| المجموعة: | Portal de Revistas Academicas del ITM (Institución Universitaria adscrita al Municipio de Medellín) |
| مصطلحات موضوعية: | Cáncer de mama, imagen médica, resonancia magnética, generación de imagen postcontraste, aprendizaje profundo, Breast cancer, diagnostic imaging, magnetic resonance imaging, postcontrast image generation, deep learning |
| الوصف: | Breast cancer is one of the leading causes of death in women in the world, so its early detection has become a priority to save lives. For the diagnosis of this type of cancer, there are techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which uses a contrast agent to enhance abnormalities in breast tissue, which improves the detection and characterization of possible tumors. As a limitation, DCE-MRI studies are usually expensive, there is little equipment available to perform them, and in some cases the contrast medium can generate adverse effects due to an allergic reaction. Considering all of the above, the aim of this work was to use deep learning models for the generation of postcontrast synthetic images in DCE-MRI studies. The proposed methodology consisted of the development of a cost function, called CeR-Loss, that takes advantage of the contrast agent uptake behavior. As a result, two new deep learning architectures were trained, which we have named G-RiedGAN and D-RiedGAN, for the generation of postcontrast images in DCE-MRI studies, from precontrast images. Finally, it is concluded that the peak signal-to- noise ratio, structured similarity indexing method, and mean absolute error metrics show that the proposed architectures improve the postcontrast image synthesis process, preserving greater similarity between the synthetic images and the real images, compared to the state- of-the-art base models. ; El cáncer de mama es una de las principales causas de muerte en mujeres en el mundo, por lo que su detección de forma temprana se ha convertido en una prioridad para salvar vidas. Para el diagnóstico de este tipo de cáncer existen técnicas como la imagen de resonancia magnética dinámica con realce de contraste (DCE-MRI, por sus siglas en inglés), la cual usa un agente de contraste para realzar las anomalías en el tejido de la mama, lo que mejora la detección y caracterización de posibles tumores. Como limitación, los estudios de DCE-MRI suelen tener un costo ... |
| نوع الوثيقة: | article in journal/newspaper report |
| وصف الملف: | application/pdf |
| اللغة: | Spanish; Castilian English |
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| الاتاحة: | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3052 |
| Rights: | Derechos de autor 2024 TecnoLógicas ; https://creativecommons.org/licenses/by-nc-sa/4.0 |
| رقم الانضمام: | edsbas.1A44CF93 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |