Deep Learning Reconstruction in Abdominopelvic Contrast-Enhanced CT for The Evaluation of Hemorrhages.
Purpose To investigate the effects of deep learning reconstruction on depicting arteries and providing suitable images for the evaluation of hemorrhages with abdominopelvic contrast-enhanced computed tomography (CT) compared with hybrid iterative reconstruction. Methods This retrospective study incl...
| Publicado en: | Radiologic Technology Vol. 96; no. 2; pp. 99 - 107 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
American Society of Radiologic Technologists
Nov/Dec2024
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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=180445617&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180445617 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00338397 39J jtl: Radiologic Technology issn: 00338397 maglogo: N pubinfo: dt: Nov/Dec2024 vid: 96 iid: 2 pid: 7052 pub: American Society of Radiologic Technologists place: Alburquerque, New Mexico artinfo: ui: 180445617 180445617 180445617 180445617 ppf: 99 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Deep Learning Reconstruction in Abdominopelvic Contrast-Enhanced CT for The Evaluation of Hemorrhages. aug: au: Akira Katayama Koichiro Yasaka Hiroshi Hirakawa Yuta Ohtake Osamu Abe affil: University of Tokyo sug: subj: Hemorrhage Radiography Tomography, X-Ray Computed Image Processing, Computer Assisted Evaluation Contrast Media Diagnostic Use Deep Learning Utilization Abdomen Pelvis Human Female Male Adult Middle Age Aged Retrospective Design Picture Archiving and Communication Systems Algorithms Radiologists Paired T-Tests Wilcoxon Signed Rank Test Interrater Reliability kappa Statistic Data Analysis Software Descriptive Statistics Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Purpose To investigate the effects of deep learning reconstruction on depicting arteries and providing suitable images for the evaluation of hemorrhages with abdominopelvic contrast-enhanced computed tomography (CT) compared with hybrid iterative reconstruction. Methods This retrospective study included 16 patients (mean age: 54.2 ± 22.1 years; 8 men and 8 women) with acute hemorrhage who underwent contrast-enhanced CT. Unenhanced axial, arterial phase axial, arterial phase coronal, and delayed phase axial images were reconstructed with deep learning reconstruction, hybrid iterative reconstruction, and filtered back projection, which was used as a control in qualitative analyses. Circular and line regions of interest were placed on the aorta and superior mesenteric artery (SMA), respectively, in quantitative analyses. Using a blind process, 2 radiologists independently evaluated image noise, depiction of arteries, and suitability for the evaluation of hemorrhage in qualitative image analyses. Results Image noise in deep learning reconstruction was significantly reduced compared with hybrid iterative reconstruction in the quantitative (P < .001) and qualitative analyses (Reader 1, P ≤ .001 for all series; Reader 2, P = .002, .001, and < .001). The slope at the half maximum in deep learning reconstruction (123.8 ± 63.2 HU/mm) significantly improved compared with hybrid iterative reconstruction (105.3 ± 51.0 HU/mm) in the CT attenuation profile of the SMA (P < .001). Qualitative analyses revealed a significantly improved depiction of arteries (Reader 1, P < .001 for all series; Reader 2, P = .037, .008, and < .001) and suitability for evaluating acute hemorrhage in the arterial phase image (Reader 1, P < .001 for both series; Reader 2, P = .041 and .004) with deep learning reconstruction compared with hybrid iterative reconstruction. Discussion Deep learning reconstruction provided images with a significantly better depiction of arteries and more suitable quality arterial phase images for the evaluation of abdominopelvic hemorrhage compared with hybrid iterative reconstruction. Conclusion Deep learning reconstruction is better for reconstructing abdominopelvic contrast-enhanced CT images when evaluating hemorrhages; however, a prospective study including a large number of patients is needed to strengthen the findings of this study. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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