A compressive sensing approach for enhancing breast cancer detection using a hybrid DBT / NRI configuration

用于准确估计乳腺密度和患者特异性辐射剂量的数字乳腺断层合成图像深度学习重建

Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation

针对乳腺X线摄影无法准确估计乳腺密度和患者特异性辐射剂量的问题，提出基于深度学习的数字乳腺断层合成重建算法DBToR，通过展开近端-对偶优化并引入乳腺厚度先验知识，实现了高精度且无偏的密度与剂量估计。

Detecting Breast Cancer using a Compressive Sensing Unmixing Algorithm

Traditional breast cancer imaging methods using microwave Nearfield Radar Imaging (NRI) seek to recover the complex permittivity of the tissues at each voxel in the imaging region. This approach is suboptimal, in that it does not directly consider the permittivity values that healthy and cancerous breast tissues typically have. In this paper, we describe a novel unmixing algorithm for detecting breast cancer. In this approach, the breast tissue is separated into three components, low water content (LWC), high water content (HWC), and cancerous tissues, and the goal of the optimization procedur…

通过双通道保真加速有限角度DBT的低频收敛

Accelerating Low-Frequency Convergence for Limited-Angle DBT via Two-Channel Fidelity in PDHG

本文提出双通道保真策略，利用平方根汉宁滤波器分解sinogram残差，通过独立的ℓ₂球约束和PDHG算法的对偶更新，提升有限角度DBT中低频成分的收敛速度，实验显示在不同分辨率下RMSE提升19%-61%。

高效对偶算法用于成像应用中的矩阵堆叠，应用于乳腺断层摄影图像重建

Efficient primal-dual algorithm for imaging applications with matrix stacking, applied to DBT image reconstruction

本文提出了一种简化步长参数选择的高效对偶算法，用于解决包含多个线性变换的优化问题，应用于乳腺断层摄影图像重建，提升了重建体积的定量精度和深度分辨率。

提高宽角DBT的深度分辨率、面内对比度并减少非均匀性伪影

Improving depth-resolution, in-plane contrast, and reducing non-uniformity artifacts for wide-angle DBT

本文提出一种基于稀疏正则化迭代重建的两阶段算法，通过估计背景图像吸收低频伪影，以改善宽角数字乳腺断层合成（DBT）的深度分辨率、面内对比度并减少非均匀性伪影。