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Neural Radiance Fields in Medical Imaging: A Survey

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arxiv 2402.17797 v4 pith:MDVZSO32 submitted 2024-02-26 eess.IV cs.CV

Xin Wang , Yineng Chen , Shu Hu , Heng Fan , Hongtu Zhu , Xin Li This is my paper · ORCID
classification eess.IVcs.CV
keywords imagingmedicalapplicationschallengesdiscussfieldsneuralradiance
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Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data. However, they face unique challenges when applied to medical applications. This paper presents a comprehensive examination of applications of NeRFs in medical imaging, highlighting four imminent challenges, including fundamental imaging principles, inner structure requirement, object boundary definition, and color density significance. We discuss current methods on different organs and discuss related limitations. We also review several datasets and evaluation metrics and propose several promising directions for future research.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models

    cs.CL 2024-12 reject novelty 4.0 of 10

    A multi-agent Llama-3.1-70B system generates supportive clinical cases before voting and reports 77.2% accuracy on a 300-question MedQA sample, about 7% relative above zero-shot baselines.

  2. Editing Implicit and Explicit Representations of Radiance Fields: A Survey

    cs.CV 2024-12 conditional novelty 4.0 of 10

    A review that classifies radiance field editing into explicit, latent space, text-guided, compositional, and other categories, with application and dataset tables.

  3. Robustness and Stability Analysis of Differentiable Shift-Variant FBP for Cone-Beam CT under Challenging Acquisition Settings

    eess.IV 2026-07 conditional novelty 3.5 of 10

    Differentiable SV-FBP is robust to discontinuous and multi-isocenter CBCT trajectories, competitive at moderate sparse views, and limited under severe undersampling.

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