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TwitterMovienet: a novel deep learning approach for 4D MRI reconstruction that exploits space-time-coil correlations and motion preservation instead of k-space data consistency to accelerate the acquisition of golden-angle radial data and enable sub-second reconstruction times in dynamic MRI.
Main Python code located at: https://github.com/victor-murray/Movienet.
Please, use this citation: Murray V, Siddiq S, Crane C, El Homsi M, Kim T, Wu C, Otazo R. Movienet: Deep space-time-coil reconstruction network without k-space data consistency for fast motion-resolved 4D MRI. Magn Reson Med. 2024; 91: 600–614. doi: 10.1002/mrm.29892
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VITA-MLLM/MovieNet-Summary dataset hosted on Hugging Face and contributed by the HF Datasets community
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Dataset Card for RLAIF-V-Bias-Dataset
🚀 Project Page: https://zhangzef.github.io/NaPO-Project-Page/ The RLAIF-V-Bias-Dataset is constructed based on the RLAIF-V-Dataset to mitigate the issue of modality bias in MLLMs using the LLaVA-v1.5-7b model.
RLAIF-V-Dataset provides high-quality feedback with a total number of 83,132 preference pairs, where the instructions are collected from a diverse range of datasets including MSCOCO, ShareGPT-4V, MovieNet, Google Landmark v2, VQA v2… See the full description on the dataset page: https://huggingface.co/datasets/Starrrrrry/RLAIF-V-Bias-Dataset.
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forum-movie.net is ranked #9711 in JP with 264.41K Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!
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TwitterHD-Movienet enables accelerated 3D radial k-space acquisition for motion-resolved 4D MRI of the lungs. It achieves an isotropic resolution of 1.1 × 1.1 × 1.1 mm³, with scan times of 2 minutes for anatomical imaging at both expiration and inspiration, encompassing 4 respiratory phases.
This file is for a 3D HD-Movienet model state dictionary for 4 motion states.
Main Python code located at: https://github.com/victor-murray/HD-Movienet.
Please, use this citation: Murray V, Wu C, Otazo R. High-definition motion-resolved MRI using 3D radial kooshball acquisition and deep learning spatial-temporal 4D reconstruction. Phys Med Biol. 2025 Jun 17;70(12). doi: 10.1088/1361-6560/ade195. PMID: 40472864.
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TwitterMovienet: a novel deep learning approach for 4D MRI reconstruction that exploits space-time-coil correlations and motion preservation instead of k-space data consistency to accelerate the acquisition of golden-angle radial data and enable sub-second reconstruction times in dynamic MRI.
Main Python code located at: https://github.com/victor-murray/Movienet.
Please, use this citation: Murray V, Siddiq S, Crane C, El Homsi M, Kim T, Wu C, Otazo R. Movienet: Deep space-time-coil reconstruction network without k-space data consistency for fast motion-resolved 4D MRI. Magn Reson Med. 2024; 91: 600–614. doi: 10.1002/mrm.29892