Ultra-Low-Dose CTPA Using Sparse Sampling CT Combined with the U-Net for Deep Learning-Based Artifact Reduction: An Exploratory Study.
This retrospective study evaluates U-Net-based artifact reduction for dose-reduced sparse-sampling CT (SpSCT) in terms of image quality and diagnostic performance using a reader study and automated detection. CT pulmonary angiograms from 89 patients were used to generate SpSCT data with 16 to 512 vi...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 3; pp. 2593 - 2606 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2026
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| 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=194225525&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194225525 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2026 vid: 39 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 194225525 189894344 194225525 194225525 10.1007/s10278-025-01639-8 194225525 ppf: 2593 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Ultra-Low-Dose CTPA Using Sparse Sampling CT Combined with the U-Net for Deep Learning-Based Artifact Reduction: An Exploratory Study. aug: au: Sauter, Andreas Philipp Thalhammer, Johannes Meurer, Felix Dorosti, Tina Sasse, Daniel Ritter, Jessica Leonhardt, Yannik Pfeiffer, Franz Schaff, Florian Pfeiffer, Daniela affil: https://ror.org/02kkvpp62 Department of Diagnostic and Interventional Radiology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany sug: subj: Pulmonary Embolism Radiography Artifacts Deep Learning Computed Tomography Angiography Convolutional Neural Networks Drug Tapering Dose-Response Relationship, Radiation Funding Source Human Male Female Adult Retrospective Design Record Review Radiation Dosage Reproducibility of Results Wilcoxon Signed Rank Test Confidence Intervals ROC Curve Data Analysis Software Descriptive Statistics Sensitivity and Specificity Adult: 19-44 years Male Female ab: This retrospective study evaluates U-Net-based artifact reduction for dose-reduced sparse-sampling CT (SpSCT) in terms of image quality and diagnostic performance using a reader study and automated detection. CT pulmonary angiograms from 89 patients were used to generate SpSCT data with 16 to 512 views. Twenty patients were reserved for a reader study and test set, the remaining 69 were used to train (53) and validate (16) a dual-frame U-Net for artifact reduction. U-Net post-processed images were assessed for image quality, diagnostic performance, and automated pulmonary embolism (PE) detection using the top-performing network from the 2020 RSNA PE detection challenge. Statistical comparisons were made using two-sided Wilcoxon signed-rank and DeLong two-sided tests. Post-processing with the dual-frame U-Net significantly improved image quality in the internal test set, with a structural similarity index of 0.634/0.378/0.234/0.152 for FBP and 0.894/0.892/0.866/0.778 for U-Net at 128/64/32/16 views, respectively. The reader study showed significantly enhanced image quality (3.15 vs. 3.53 for 256 views, 0.00 vs. 2.52 for 32 views), increased diagnostic confidence (0.00 vs. 2.38 for 32 views), and fewer artifacts across all subsets (P < 0.05). Diagnostic performance, measured by the Sørensen–Dice coefficient, was significantly better for 64- and 32-view images (0.23 vs. 0.44 and 0.00 vs. 0.09, P < 0.05). Automated PE detection was better at fewer views (64 views: 0.77 vs. 0.80, 16 views: 0.59 vs. 0.80), although the differences were not statistically significant. U-Net-based post-processing of SpSCT data significantly enhances image quality and diagnostic performance, supporting substantial dose reduction in CT pulmonary angiography. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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