Ring artifacts in computed tomography images, arising from the undesirable
responses of detector units, significantly degrade image quality and diagnostic
reliability. To address this challenge, we propose a dual-domain regularization
model to effectively remove ring artifacts, while maintaining the integrity of
the original CT image. The proposed model corrects the vertical stripe
artifacts on the sinogram by innovatively updating the response inconsistency
compensation coefficients of detector units, which is achieved by employing the
group sparse constraint and the projection-view direction sparse constraint on
the stripe artifacts. Simultaneously, we apply the sparse constraint on the
reconstructed image to further rectified ring artifacts in the image domain.
The key advantage of the proposed method lies in considering the relationship
between the response inconsistency compensation coefficients of the detector
units and the projection views, which enables a more accurate correction of the
response of the detector units. An alternating minimization method is designed
to solve the model. Comparative experiments on real photon counting detector
data demonstrate that the proposed method not only surpasses existing methods
in removing ring artifacts but also excels in preserving structural details and
image fidelity.

提出了一种双域正则化模型来有效地消除计算机断层扫描图像中的环形伪影，并保持原始 CT 图像的完整性。该方法通过创新地更新探测器单元的响应不一致性补偿系数，以解决正弦图中的垂直条纹伪影，通过对条纹伪影应用组稀疏约束和投影视图方向稀疏约束。同时，对重建图像施加稀疏约束，进一步纠正图像域中的环形伪影。实验证明，该方法不仅在消除环形伪影方面超越了现有方法，而且在保持结构细节和图像保真度方面也表现出色。

X 光 CT 环形伪影去除的双域正则化方法

A Dual-domain Regularization Method for Ring Artifact Removal of X-ray  CT

Rectal cancer segmentation of CT image plays a crucial role in timely
clinical diagnosis, radiotherapy treatment, and follow-up. Although current
segmentation methods have shown promise in delineating cancerous tissues, they
still encounter challenges in achieving high segmentation precision. These
obstacles arise from the intricate anatomical structures of the rectum and the
difficulties in performing differential diagnosis of rectal cancer.
Additionally, a major obstacle is the lack of a large-scale, finely annotated
CT image dataset for rectal cancer segmentation. To address these issues, this
work introduces a novel large scale rectal cancer CT image dataset CARE with
pixel-level annotations for both normal and cancerous rectum, which serves as a
valuable resource for algorithm research and clinical application development.
Moreover, we propose a novel medical cancer lesion segmentation benchmark model
named U-SAM. The model is specifically designed to tackle the challenges posed
by the intricate anatomical structures of abdominal organs by incorporating
prompt information. U-SAM contains three key components: promptable information
(e.g., points) to aid in target area localization, a convolution module for
capturing low-level lesion details, and skip-connections to preserve and
recover spatial information during the encoding-decoding process. To evaluate
the effectiveness of U-SAM, we systematically compare its performance with
several popular segmentation methods on the CARE dataset. The generalization of
the model is further verified on the WORD dataset. Extensive experiments
demonstrate that the proposed U-SAM outperforms state-of-the-art methods on
these two datasets. These experiments can serve as the baseline for future
research and clinical application development.

本研究介绍了一个新颖的大规模直肠癌 CT 图像数据集 CARE，其具有对正常和癌症直肠进行像素级注释的宝贵资源，用于算法研究和临床应用开发；此外，我们提出了一种名为 U-SAM 的新型医疗癌症病变分割基准模型，它通过具备即时信息处理功能以解决腹部器官结构复杂性所带来的挑战，包含提示信息、捕捉病变细节的卷积模块以及保留和恢复空间信息的跳过连接；通过与几种流行的分割方法在 CARE 数据集上的性能比较以及在 WORD 数据集上的泛化验证，大量实验证明了所提出的 U-SAM 在这两个数据集上优于现有方法，为未来的研究和临床应用开发提供了基线。

CARE：大规模 CT 图像数据集和临床适用的直肠癌分割评估模型

CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark  Model for Rectal Cancer Segmentation

In this paper, we introduce a new CT image denoising method based on the
generative adversarial network (GAN) with Wasserstein distance and perceptual
similarity. The Wasserstein distance is a key concept of the optimal transform
theory, and promises to improve the performance of the GAN. The perceptual loss
compares the perceptual features of a denoised output against those of the
ground truth in an established feature space, while the GAN helps migrate the
data noise distribution from strong to weak. Therefore, our proposed method
transfers our knowledge of visual perception to the image denoising task, is
capable of not only reducing the image noise level but also keeping the
critical information at the same time. Promising results have been obtained in
our experiments with clinical CT images.

本文介绍了一种基于生成式对抗网络（GAN）的新型 CT 图像去噪方法，该方法基于 Wasserstein 距离和特征相似性，能够从图像数据噪声分布强到弱的迁移，并在临床 CT 影像中取得了良好效果。