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26 de may. de 2024 · The proposed all-in-one DL framework enables a single model to enhance MR image quality in a multi-dimensional manner and to be compatible across a broad spectrum of scenarios, including various vendors, field strengths, pulse sequences, contrast weightings, scan parameters, and anatomical regions.
23 de may. de 2024 · Accelerated MRI reconstructions via variational network and feature domain learning. Article Open access 14 May 2024. Introduction. Real-time magnetic resonance (MR) imaging allows...
23 de may. de 2024 · Real-time undersampled dynamic MR images were reconstructed using DL networks trained with cardiac data and natural videos, and compressed sensing (CS).
14 de may. de 2024 · We introduce three architecture modifications to enhance the performance of the end-to-end (E2E) variational network (VarNet) for undersampled MRI reconstructions.
23 de may. de 2024 · Magnetic resonance imaging (MRI) is a commonly used tool in clinical medicine, but it suffers from the disadvantage of slow imaging speed. To address this, we propose a novel MRI reconstruction algorithm based on image decomposition to realize accurate image reconstruction with undersampled k-space data.
23 de may. de 2024 · The developed pipeline enabled learning dynamic MR reconstruction from natural videos preserving DL reconstruction advantages such as high quality fast and ultra-fast reconstructions while overcoming some limitations (data scarcity or sharing). To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K).
28 de may. de 2024 · In this work, we propose a novel method to simultaneously accelerate imaging and correct motion. This is achieved by integrating a motion module into the deep learning-based MRI reconstruction process, enabling real-time detection and correction of motion.
