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- Dokumenttyp:
- Journal Article
- Autor(en):
- Thalhammer, Johannes; Schultheiß, Manuel; Dorosti, Tina; Lasser, Tobias; Pfeiffer, Franz; Pfeiffer, Daniela; Schaff, Florian
- Titel:
- Improving Automated Hemorrhage Detection in Sparse-view CT via U-Net-based Artifact Reduction.
- Abstract:
- "Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To explore the potential benefits of deep learning-based artifact reduction in sparse-view cranial CT scans and its impact on automated hemorrhage detection. Materials and Methods In this retrospective study, a U-Net was trained for artifact reduction on simulated sparseview cranial CT scans from 3000 patients obtained from a public dataset and reconstructed with varying sparse-view levels. Additionally, the EfficientNetB2 was trained on full-view CT data from 17,545 patients for automated hemorrhage detection. Detection performance was evaluated using the area under the receiver operator characteristic curve (AUC), with differences assessed using the DeLong test, along with confusion matrices. A total variation (TV) postprocessing approach, commonly applied to sparse-view, served as the basis for comparison. A Bonferronicorrected significance level of 0.001/6 = 0.00017 was used to accommodate for multiple hypotheses testing. Results Images with U-Net postprocessing were better than unprocessed and TV-processed images with respect to image quality and automated hemorrhage detection. With U-Net postprocessing, the number of views could be reduced from 4096 (AUC: 0.97; 95% CI: 0.97-0.98) to 512 (0.97; 0.97-0.98; P < .00017) and to 256 views (0.97; 0.96-0.97; P < .00017) with minimal decrease in hemorrhage detection performance. This was accompanied by mean structural similarity index measure increases of 0.0210 (95% CI: 0.0210-0.0211) and 0.0560 (95% CI: 0.0559-0.0560) relative to unprocessed images. Conclusion U-Net based artifact reduction substantially enhances automated hemorrhage detection in sparse-view cranial CTs. ©RSNA, 2024.
- Zeitschriftentitel:
- Radiol Artif Intell
- Jahr:
- 2024
- Volltext / DOI:
- doi:10.1148/ryai.230275
- PubMed:
- http://view.ncbi.nlm.nih.gov/pubmed/38717293
- TUM Einrichtung:
- Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski)
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