Výsledky vyhledávání - "Ferreira Pedro F"

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Groupwise Deformable Registration of Diffusion Tensor Cardiovascular Magnetic Resonance: Disentangling Diffusion Contrast, Respiratory and Cardiac Motions

Autor: Wang, Fanwen, Luo, Yihao, Wen, Ke, Huang, Jiahao, Ferreira, Pedro F., Luo, Yaqing, Wu, Yinzhe, Munoz, Camila, Pennell, Dudley J., Scott, Andrew D., Nielles-Vallespin, Sonia, Yang, Guang

Diffusion tensor based cardiovascular magnetic resonance (DT-CMR) offers a non-invasive method to visualize the myocardial microstructure. With the assumption that the heart is stationary, frames are acquired with multiple repetitions for different d

Externí odkaz: http://arxiv.org/abs/2406.13788

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Low-rank based motion correction followed by automatic frame selection in DT-CMR

Autor: Wang, Fanwen, Ferreira, Pedro F., Munoz, Camila, Wen, Ke, Luo, Yaqing, Huang, Jiahao, Wu, Yinzhe, Pennell, Dudley J., Scott, Andrew D., Nielles-Vallespin, Sonia, Yang, Guang

Publikováno v: ISMRM 2024 Digital poster 2141

Motivation: Post-processing of in-vivo diffusion tensor CMR (DT-CMR) is challenging due to the low SNR and variation in contrast between frames which makes image registration difficult, and the need to manually reject frames corrupted by motion. Goal

Externí odkaz: http://arxiv.org/abs/2406.13708

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Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation

Autor: Yeung, Michael, Watts, Todd, Tan, Sean YW, Ferreira, Pedro F., Scott, Andrew D., Nielles-Vallespin, Sonia, Yang, Guang

Stain variation is a unique challenge associated with automated analysis of digital pathology. Numerous methods have been developed to improve the robustness of machine learning methods to stain variation, but comparative studies have demonstrated li

Externí odkaz: http://arxiv.org/abs/2311.06552

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Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation

Autor: Wang, Lichao, Huang, Jiahao, Xing, Xiaodan, Wu, Yinzhe, Rajakulasingam, Ramyah, Scott, Andrew D., Ferreira, Pedro F, De Silva, Ranil, Nielles-Vallespin, Sonia, Yang, Guang

This study proposes a pipeline that incorporates a novel style transfer model and a simultaneous super-resolution and segmentation model. The proposed pipeline aims to enhance diffusion tensor imaging (DTI) images by translating them into the late ga

Externí odkaz: http://arxiv.org/abs/2309.15485

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Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration

Autor: Wang, Fanwen, Ferreira, Pedro F., Wu, Yinzhe, Munoz, Camila, Wen, Ke, Luo, Yaqing, Huang, Jiahao, Pennell, Dudley J., Scott, Andrew D., Nielles-Vallespin, Sonia, Yang, Guang

Diffusion tensor cardiac magnetic resonance (DT-CMR) is a method capable of providing non-invasive measurements of myocardial microstructure. Image registration is essential to correct image shifts due to intra and inter breath-hold motion and imperf

Externí odkaz: http://arxiv.org/abs/2309.06598

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Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study

Autor: Huang, Jiahao, Ferreira, Pedro F., Wang, Lichao, Wu, Yinzhe, Aviles-Rivero, Angelica I., Schonlieb, Carola-Bibiane, Scott, Andrew D., Khalique, Zohya, Dwornik, Maria, Rajakulasingam, Ramyah, De Silva, Ranil, Pennell, Dudley J., Nielles-Vallespin, Sonia, Yang, Guang

In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the micro-structure of myocardial tissue in the living heart, providing insights into cardiac function and enabling the developme

Externí odkaz: http://arxiv.org/abs/2304.00996

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