Yu Minwoo, Ahn Junhyun, Baek Jongduk
Department of Artificial Intelligence, Yonsei University, Seoul, South Korea.
School of Integrated Technology, Yonsei University, Seoul, South Korea.
Med Phys. 2025 Jul;52(7):e17849. doi: 10.1002/mp.17849. Epub 2025 May 2.
Computed tomography (CT) imaging has been developed to acquire a higher resolution image for detecting early-stage lesions. However, the lack of spatial resolution of CT images is still a limitation to fully utilize the capabilities of display devices for radiologists.
This limitation can be addressed by improving the quality of the reconstructed image using super-resolution (SR) techniques without changing data acquisition protocols. In particular, local implicit representation-based techniques proposed in the field of low-level computer vision have shown promising performance, but their integration into CT image reconstruction is limited by considerable memory and runtime requirements due to excessive input data size.
To address these limitations, we propose a continuous image representation-based CT image reconstruction (CRET) structure. Our CRET ensures fast and memory-efficient reconstruction for the specific region of interest (ROI) image by adapting our proposed sinogram squeezing and decoding via a set of sinusoidal basis functions. Furthermore, post-restoration step can be employed to mitigate residual artifacts and blurring effects, leading to improve image quality.
Our proposed method shows superior image quality than other local implicit representation methods and can be further improved with additional post-processing. In addition proposed structure achieves superior performance in terms of anthropomorphic observer model evaluation compared to conventional techniques. This results highlights that CRET can be used to improve diagnostic capabilities by setting the reconstruction resolution higher than the ground truth images in training.
Our proposed CRET method offers a promising solution for improving CT image resolution while addressing excessive memory and runtime consumption. The source code of our proposed CRET is available at https://github.com/minwoo-yu/CRET.
计算机断层扫描（CT）成像技术已经发展到能够获取更高分辨率的图像以检测早期病变。然而，CT图像缺乏空间分辨率仍然是一个限制因素，阻碍了放射科医生充分利用显示设备的功能。
可以通过使用超分辨率（SR）技术来提高重建图像的质量，而不改变数据采集协议，从而解决这一限制。特别是，低级计算机视觉领域中提出的基于局部隐式表示的技术已经显示出了有前景的性能，但是由于输入数据量过大，将它们集成到CT图像重建中受到相当大的内存和运行时要求的限制。
为了解决这些限制，我们提出了一种基于连续图像表示的CT图像重建（CRET）结构。我们的CRET通过一组正弦基函数调整我们提出的正弦图压缩和解码，确保对特定感兴趣区域（ROI）图像进行快速且内存高效的重建。此外，可以采用后恢复步骤来减轻残留伪影和模糊效应，从而提高图像质量。
我们提出的方法显示出比其他局部隐式表示方法更好的图像质量，并且可以通过额外的后处理进一步改善。此外，与传统技术相比，所提出的结构在拟人化观察者模型评估方面表现出卓越的性能。这一结果突出表明，CRET可用于通过在训练中将重建分辨率设置得高于真实图像来提高诊断能力。
我们提出的CRET方法为提高CT图像分辨率同时解决过多的内存和运行时消耗提供了一个有前景的解决方案。我们提出的CRET的源代码可在https://github.com/minwoo-yu/CRET获取。
