基于Swin-Transformer迭代展开的有限角CT图像重建用于PTCT成像  

Limited-Angle CT Image Reconstruction Based on Swin-Transformer Iterative Unfolding for PTCT Imaging

作　　者：袁伟[1,2] 席雅睿 谭川东 刘川江 朱国荣 刘丰林[1,2] Yuan Wei;Xi Yarui;Tan Chuandong;Liu Chuanjiang;Zhu Guorong;Liu Fenglin(ICT Research Center,Key Laboratory of Optoelectronic Technology&Systems,Ministry of Education,Chongqing University,Chongqing 400044,China;Industrial CT Non-Destructive Testing Engineering Research Center,Ministry of Education,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学ICT研究中心光电技术及系统教育部重点实验室,重庆400044 [2]重庆大学工业CT无损检测教育部工程研究中心,重庆400044

出　　处：《光学学报》2024年第8期296-307,共12页Acta Optica Sinica

基　　金：国家自然科学基金(62171067);重庆市自然科学基金(CSTB2022NSCQ-MSX1311)。

摘　　要：针对相对平行直线扫描CT(PTCT)图像重建存在的有限角伪影问题,提出一种学习局部和非局部正则项的深度迭代展开方法。该方法将具有固定迭代次数的梯度下降算法迭代展开到神经网络,利用具有坐标注意力(CA)机制的卷积模块和Swin-Transformer模块作为迭代模块交替级联部署,构成端到端的深度重建网络。卷积模块学习局部正则化,其中CA用于减少图像过平滑;Swin-Transformer模块学习非局部正则化,提高网络对图像细节的恢复能力;在相邻模块间,使用迭代连接(IC)增强模型提取深层特征的能力,提高每次迭代的效率。通过消融实验验证了网络各部分的有效性,并在两种类型的数据集上进行实验,结果证明了本文方法的效果。实验结果表明,本文方法在抑制PTCT重建图像有限角伪影的同时,能较好地保留重建图像细节,提高重建图像质量。Objective Computed tomography(CT)is an imaging technique that employs X-ray transmission and multi-angle projection to reconstruct the internal structure of an object.Meanwhile,it is commonly adopted in medical diagnosis and industrial non-destructive testing due to its non-invasive and intuitive characteristics.Parallel translational computed tomography(PTCT)acquires projection data by moving a flat panel detector(FPD)and a radiation source in parallel linear motion relative to the detection object.This method has promising applications in industrial inspection.Due to the limitations of the inspection environment and the structure of the inspection system,there are scenarios where it is difficult to realize multi-segment PTCT scanning and imaging,and only single-segment PTCT scanning and imaging can be performed.Since the single-segment PTCT can only obtain the equivalent projection data at a limited angle,its reconstruction problem belongs to limited-angle CT reconstruction.Images reconstructed by traditional algorithms will suffer from serious artifacts.Deep learning-based limited-angle CT image reconstruction has yielded remarkable results,among which model-based data-driven methods have caught much attention.However,such deep networks with CNNs as the main structure tend to focus on the local neighborhood information of the image and ignore the non-local features.Additionally,research on iterative algorithms shows that non-local features can improve detail preservation,which is important for limited-angle CT reconstruction.Methods To address the limited-angle artifact in PTCT image reconstruction,we propose a deep iterative unfolding method(STICA-Net,Fig.3)that learns local and non-local regular terms.The method unfolds a gradient descent algorithm with a fixed number of iterations to a neural network and utilizes convolutional modules with the coordinate attention(CA)mechanism and Swin-Transformer modules deployed as iterative modules in alternating cascades to form an end-to-end deep reconstruction network

关 键 词：X射线光学 计算机断层成像 相对平行直线扫描 图像重建 有限角 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]