Continuous representation-based reconstruction for computed tomography

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2025-05-02 DOI:10.1002/mp.17849

Minwoo Yu, Junhyun Ahn, Jongduk Baek

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引用次数: 0

Abstract

Background

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.

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.

Abstract Image

查看原文本刊更多论文

基于连续表示的计算机断层扫描重建。

背景：计算机断层扫描（CT）成像已经发展到获得更高分辨率的图像来检测早期病变。然而，CT图像空间分辨率的不足仍然是限制放射科医生充分利用显示设备的能力。目的：这一限制可以通过使用超分辨率（SR）技术在不改变数据采集协议的情况下提高重建图像的质量来解决。特别是，在低级计算机视觉领域提出的基于局部隐式表示的技术已显示出良好的性能，但由于输入数据量过大，其集成到CT图像重建中受到相当大的内存和运行时间需求的限制。方法：为了解决这些局限性，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来适应我们提出的正弦图压缩和解码，确保了对特定感兴趣区域（ROI）图像的快速和内存高效重建。此外，后恢复步骤可以用来减轻残余的伪像和模糊的影响，从而提高图像质量。结果：该方法的图像质量优于其他局部隐式表示方法，并且可以通过额外的后处理进一步提高图像质量。此外，与传统技术相比，该结构在拟人观测器模型评估方面具有优越的性能。这一结果表明，通过在训练中将重建分辨率设置为高于地面真实图像的分辨率，可以使用CRET来提高诊断能力。结论：我们提出的CRET方法在提高CT图像分辨率的同时解决了过多的内存和运行时间消耗问题。我们建议的CRET的源代码可在https://github.com/minwoo-yu/CRET上获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.