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<p>Cone-beam computed tomography (CBCT) acquires three-dimensional internal images, particularly effective for high-mineral density structures like bones. However, especially in sparse view scenarios, its ability to visualize low-density soft tissues is limited, restricting its clinical applications. To address this problem, this study proposes a method called Decoupled Neural Attenuation Fields (DE-NAF). Specifically, DE-NAF utilizes an Adaptive Hybrid Encoder that includes both hash encoding and 3D feature grid encoding methods. This approach decouples the CBCT reconstruction into two components. Hash encoding is used for high-mineral density structures, such as bones, owing to its superior encoding quality and ability to retain features of high-mineral density structures during hash conflict resolution. The 3D feature grid is employed for low-density soft tissues, such as muscles, as it effectively preserves the feature information of low-density soft tissues. The Adaptive Hybrid Encoder extracts these features, which are then decoded by a multilayer perceptron (MLP) decoder to predict X-ray attenuation values for precise reconstruction. In addition, a loss of structural perception was introduced to enhance tissue contrast and detail, further aiding CBCT reconstruction. Extensive experiments demonstrated that DE-NAF effectively addresses the limitations of CBCT in imaging low-density soft tissues, maintaining complete structural integrity and exceeding other methods in reconstruction quality.</p>

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DE-NAF: decoupled neural attenuation fields for sparse-view CBCT reconstruction

  • 期刊论文

  • 发表日期：

    2025年1月31日

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  • Tianning Zhao,
  • Guoping Ding,
  • Zhenyang Liu,
  • Peng Hu,
  • Hangping Wei,
  • Min Tan,
  • Jiajun Ding

摘要

Cone-beam computed tomography (CBCT) acquires three-dimensional internal images, particularly effective for high-mineral density structures like bones. However, especially in sparse view scenarios, its ability to visualize low-density soft tissues is limited, restricting its clinical applications. To address this problem, this study proposes a method called Decoupled Neural Attenuation Fields (DE-NAF). Specifically, DE-NAF utilizes an Adaptive Hybrid Encoder that includes both hash encoding and 3D feature grid encoding methods. This approach decouples the CBCT reconstruction into two components. Hash encoding is used for high-mineral density structures, such as bones, owing to its superior encoding quality and ability to retain features of high-mineral density structures during hash conflict resolution. The 3D feature grid is employed for low-density soft tissues, such as muscles, as it effectively preserves the feature information of low-density soft tissues. The Adaptive Hybrid Encoder extracts these features, which are then decoded by a multilayer perceptron (MLP) decoder to predict X-ray attenuation values for precise reconstruction. In addition, a loss of structural perception was introduced to enhance tissue contrast and detail, further aiding CBCT reconstruction. Extensive experiments demonstrated that DE-NAF effectively addresses the limitations of CBCT in imaging low-density soft tissues, maintaining complete structural integrity and exceeding other methods in reconstruction quality.