Kabika Twaha, Hongsen Cai, Hongling Zhu, Jingxian Dong, Siyuan Zhang, Ding Mingyue, Xianbo Deng, Wenguang Hou, Yan Wang
College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, P.R. China.
Cardiovascular Medicine Department, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, P.R. China.
Med Phys. 2025 Jul;52(7):e17832. doi: 10.1002/mp.17832. Epub 2025 Apr 14.
Neural radiance field (NeRF) models have garnered significant attention for their impressive ability to synthesize high-quality novel scene views from posed 2D images. Recently, the MedNeRF algorithm was developed to render complete computed tomography (CT) projections from a single or a few x-ray images further. Despite this advancement, MedNeRF struggles with accurate pose reconstruction, crucial for radiologists during image analysis, leading to blurry geometry in the generated outputs.
Motivated by these challenges, our research aims to address MedNeRF's limitations in pose accuracy and image clarity. Specifically, we seek to improve the pose accuracy of reconstructed images and enhance the generated output's anatomical detail and quality.
We propose a novel pose-aware discriminator that estimates pose differences between generated and real patches, ensuring accurate poses and deeper anatomical structures in generated images. We enhance volumetric rendering from single-view x-rays by introducing a customized distortion adaptive loss function and present the HTDataset, a new dataset pair that better mimics machine-generated x-rays, offering clearer anatomical depictions with reduced noise.
Our method successfully renders images with correct poses and high fidelity, outperforming existing state-of-the-art methods. The results demonstrate superior performance in both qualitative and quantitative metrics.
The proposed approach addresses the pose reconstruction challenge in MedNeRF, enhances the anatomical detail, and reduces noise in generated images. The use of HTDataset and the innovative discriminator structure lead to significant improvements in the accuracy and quality of the rendered images, setting a new benchmark in the field.
神经辐射场（NeRF）模型因其能够从有姿态的二维图像中合成高质量的新颖场景视图的出色能力而备受关注。最近，MedNeRF算法被开发出来，以进一步从单张或几张X射线图像渲染完整的计算机断层扫描（CT）投影。尽管有这一进展，但MedNeRF在精确的姿态重建方面存在困难，而姿态重建在图像分析过程中对放射科医生至关重要，这导致生成的输出中几何形状模糊。
受这些挑战的推动，我们的研究旨在解决MedNeRF在姿态准确性和图像清晰度方面的局限性。具体而言，我们寻求提高重建图像的姿态准确性，并增强生成输出的解剖细节和质量。
我们提出了一种新颖的姿态感知判别器，该判别器估计生成的补丁与真实补丁之间的姿态差异，以确保生成图像中的姿态准确以及解剖结构更清晰。我们通过引入定制的失真自适应损失函数来增强单视图X射线的体渲染，并展示了HTDataset，这是一个新的数据集对，能更好地模拟机器生成的X射线，提供噪声减少且解剖结构更清晰的描绘。
我们的方法成功地以正确的姿态和高保真度渲染图像，优于现有的最先进方法。结果在定性和定量指标上均显示出卓越的性能。
所提出的方法解决了MedNeRF中的姿态重建挑战，增强了解剖细节，并减少了生成图像中的噪声。HTDataset的使用和创新的判别器结构导致渲染图像的准确性和质量有显著提高，为该领域设定了新的基准。
