Four-dimensional cone-beam computed tomography (4D CBCT) provides
respiration-resolved images and can be used for image-guided radiation therapy.
However, the ability to reveal respiratory motion comes at the cost of image
artifacts. As raw projection data are sorted into multiple respiratory phases,
there is a limited number of cone-beam projections available for image
reconstruction. Consequently, the 4D CBCT images are covered by severe streak
artifacts. Although several deep learning-based methods have been proposed to
address this issue, most algorithms employ ordinary network models, neglecting
the intrinsic structural prior within 4D CBCT images. In this paper, we first
explore the origin and appearance of streak artifacts in 4D CBCT
images.Specifically, we find that streak artifacts exhibit a periodic
rotational motion along with the patient's respiration. This unique motion
pattern inspires us to distinguish the artifacts from the desired anatomical
structures in the spatiotemporal domain. Thereafter, we propose a
spatiotemporal neural network named RSTAR-Net with separable and circular
convolutions for Rotational Streak Artifact Reduction. The specially designed
model effectively encodes dynamic image features, facilitating the recovery of
4D CBCT images. Moreover, RSTAR-Net is also lightweight and computationally
efficient. Extensive experiments substantiate the effectiveness of our proposed
method, and RSTAR-Net shows superior performance to comparison methods.

通过使用 RSTAR-Net 模型，我们能够降低呼吸导致的四维锥束 CT 图像中的旋转条纹伪影，取得了比其他方法更优越的性能。

RSTAR: 4D CBCT 中使用可分离和圆形卷积减少旋转条纹伪影

RSTAR: Rotational Streak Artifact Reduction in 4D CBCT using Separable  and Circular Convolutions

Image-guided radiation therapy can benefit from accurate motion tracking by
ultrasound imaging, in order to minimize treatment margins and radiate moving
anatomical targets, e.g., due to breathing. One way to formulate this tracking
problem is the automatic localization of given tracked anatomical landmarks
throughout a temporal ultrasound sequence. For this, we herein propose a
fully-convolutional Siamese network that learns the similarity between pairs of
image regions containing the same landmark. Accordingly, it learns to localize
and thus track arbitrary image features, not only predefined anatomical
structures. We employ a temporal consistency model as a location prior, which
we combine with the network-predicted location probability map to track a
target iteratively in ultrasound sequences. We applied this method on the
dataset of the Challenge on Liver Ultrasound Tracking (CLUST) with competitive
results, where our work is the first to effectively apply CNNs on this tracking
problem, thanks to our temporal regularization.

本文提出了一种使用全卷积 Siamese 网络进行医学超声图像中任意图像特征（而非仅规定的解剖结构）定位的方法，利用时间连续性模型作为位置先验，结合预测出的位置概率图迭代跟踪超声图像序列中的目标，应用于肝脏超声图像数据集中有竞争力的结果，这是首次成功地将 CNN 应用于这一追踪问题。