RAD-UNet: a Residual, Attention-Based, Dense UNet for CT Sparse Reconstruction.
To suppress the streak artifacts in images reconstructed from sparse-view projections in computed tomography (CT), a residual, attention-based, dense UNet (RAD-UNet) deep network is proposed to achieve accurate sparse reconstruction. The filtered back projection (FBP) algorithm is used to reconstruc...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 6; pp. 1748 - 1759 |
|---|---|
| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160503261&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160503261 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2022 vid: 35 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160503261 158180843 160503261 160503261 10.1007/s10278-022-00685-w 160503261 ppf: 1748 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: RAD-UNet: a Residual, Attention-Based, Dense UNet for CT Sparse Reconstruction. aug: au: Qiao, Zhiwei Du, Congcong affil: School of Computer and Information Technology, Shanxi University, 030006, Taiyuan, Shanxi, China sug: subj: Tomography, X-Ray Computed Image Processing, Computer Assisted Human Artifacts Algorithms Workflow ab: To suppress the streak artifacts in images reconstructed from sparse-view projections in computed tomography (CT), a residual, attention-based, dense UNet (RAD-UNet) deep network is proposed to achieve accurate sparse reconstruction. The filtered back projection (FBP) algorithm is used to reconstruct the CT image with streak artifacts from sparse-view projections. Then, the image is processed by the RAD-UNet to suppress streak artifacts and obtain high-quality CT image. Those images with streak artifacts are used as the input of the RAD-UNet, and the output-label images are the corresponding high-quality images. Through training via the large-scale training data, the RAD-UNet can obtain the capability of suppressing streak artifacts. This network combines residual connection, attention mechanism, dense connection and perceptual loss. This network can improve the nonlinear fitting capability and the performance of suppressing streak artifacts. The experimental results show that the RAD-UNet can improve the reconstruction accuracy compared with three existing representative deep networks. It may not only suppress streak artifacts but also better preserve image details. The proposed networks may be readily applied to other image processing tasks including image denoising, image deblurring, and image super-resolution. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|