Data of section 4.2.3 of Integration of a GPU-accelerated 3D fuzzy filtering method with low-dose CT reconstruction

This data corresponds to section 4.2.3 of Integration of a GPU-accelerated 3D fuzzy filtering method with low-dose CT reconstruction, and it consists of images reconstructed from parallel3D projections generated from CT images, and results of processing with different denoising methods. This work includes reconstructed and filtered images derived from the Low Dose CT Image and Projection Data (LDCT-and-Projection-Data) dataset. Modifications include preprocessing, denoising, reconstruction, and visualization. The original dataset is available from The Cancer Imaging Archive (TCIA) under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Users of this work should cite the following reference: McCollough, C., Chen, B., Holmes III, D., Duan, X., Yu, Z., Yu, L., Leng, S., Fletcher, J. (2020). Low Dose CT Image and Projection Data (LDCT-and-Projection-data) (Version 7) [dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/9npb-2637 File description The MAT files contain the following volumes, the projections reconstructed were obtained from the forward-projection of L212 volume to a Parallel3D geometry: LOW_NORM: Image volume reconstructed from the Parallel3D with back-projection from a quarter of the full range of projections. REFHU_NORM: Image volume reconstructed from the Parallel3D with back-projection from the full range of projections. REDCNN_NORM: Low-dose images denoised with the RED-CNN method. CNCL_NORM: Low-dose images denoised with the CNCL method. FGI_NORM: Low-dose images denoised with the F3D-FGI method, with different parameters. The volume presented in the paper is FGI_NORM_3. FGISIRT_NORM: Image volume reconstructed from the Parallel3D with the iterative SIRT3D method regularized with F3D-FGI from a quarter of the full range of projections. The iterative method was run for 500 iterations, applying F3D-FGI every 100 iterations. This data is presented in the HU range of [-360,3000]本数据集对应《融合GPU加速三维模糊滤波方法与低剂量CT重建》一文的4.2.3节，其包含由CT图像生成的平行三维投影所重建得到的图像，以及经多种降噪方法处理后的结果。 本研究所用图像均源自低剂量CT图像与投影数据集（Low Dose CT Image and Projection Data, LDCT-and-Projection-Data），并经过预处理、降噪、重建及可视化等处理流程。 原始数据集可从癌症影像档案库（The Cancer Imaging Archive, TCIA）获取，采用知识共享署名4.0国际许可协议（Creative Commons Attribution 4.0 International License, CC BY 4.0）进行授权。使用本研究成果的用户需引用以下参考文献： McCollough, C., Chen, B., Holmes III, D., Duan, X., Yu, Z., Yu, L., Leng, S., Fletcher, J. (2020). 低剂量CT图像与投影数据集（LDCT-and-Projection-data）（版本7）[数据集]. 癌症影像档案库. https://doi.org/10.7937/9npb-2637 文件说明 本数据集的MAT文件包含以下体数据：所使用的投影由L212体数据经正向投影至平行三维（Parallel3D）几何空间后得到： LOW_NORM：由完整投影范围四分之一的投影经反向投影，通过平行三维重建得到的图像体数据。 REFHU_NORM：由完整投影范围的全部投影经反向投影，通过平行三维重建得到的图像体数据。 REDCNN_NORM：经RED-CNN方法降噪后的低剂量图像体数据。 CNCL_NORM：经CNCL方法降噪后的低剂量图像体数据。 FGI_NORM：经不同参数的F3D-FGI方法降噪后的低剂量图像体数据。本文中呈现的体数据为FGI_NORM_3。 FGISIRT_NORM：由完整投影范围四分之一的投影出发，采用经F3D-FGI正则化的迭代SIRT3D方法进行平行三维重建得到的图像体数据。该迭代方法共运行500次，每100次迭代应用一次F3D-FGI正则化。 本数据集的数据亨氏单位（Hounsfield Unit, HU）范围为[-360, 3000]。