Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions
Abstract To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-54502-1 |
| _version_ | 1797274735878340608 |
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| author | Wojciech Kazimierczak Natalia Kazimierczak Justyna Wilamowska Olaf Wojtowicz Ewa Nowak Zbigniew Serafin |
| author_facet | Wojciech Kazimierczak Natalia Kazimierczak Justyna Wilamowska Olaf Wojtowicz Ewa Nowak Zbigniew Serafin |
| author_sort | Wojciech Kazimierczak |
| collection | DOAJ |
| description | Abstract To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity – 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality. |
| first_indexed | 2024-03-07T15:02:31Z |
| format | Article |
| id | doaj.art-9dccf82fd69f43488364572047d71405 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:02:31Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-9dccf82fd69f43488364572047d714052024-03-05T19:03:37ZengNature PortfolioScientific Reports2045-23222024-02-0114111110.1038/s41598-024-54502-1Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructionsWojciech Kazimierczak0Natalia Kazimierczak1Justyna Wilamowska2Olaf Wojtowicz3Ewa Nowak4Zbigniew Serafin5Collegium Medicum, Nicolaus Copernicus University in TorunKazimierczak Private Medical PracticeCollegium Medicum, Nicolaus Copernicus University in TorunCollegium Medicum, Nicolaus Copernicus University in TorunUniversity Hospital No 1 in BydgoszczCollegium Medicum, Nicolaus Copernicus University in TorunAbstract To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity – 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality.https://doi.org/10.1038/s41598-024-54502-1Abdominal aortic aneurysmsEndoleakEndovascular aneurysm repairDual-energy computed tomography angiographyVirtual monoenergetic imagesImage reconstruction, deep learning model |
| spellingShingle | Wojciech Kazimierczak Natalia Kazimierczak Justyna Wilamowska Olaf Wojtowicz Ewa Nowak Zbigniew Serafin Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions Scientific Reports Abdominal aortic aneurysms Endoleak Endovascular aneurysm repair Dual-energy computed tomography angiography Virtual monoenergetic images Image reconstruction, deep learning model |
| title | Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions |
| title_full | Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions |
| title_fullStr | Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions |
| title_full_unstemmed | Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions |
| title_short | Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions |
| title_sort | enhanced visualization in endoleak detection through iterative and ai noise optimized spectral reconstructions |
| topic | Abdominal aortic aneurysms Endoleak Endovascular aneurysm repair Dual-energy computed tomography angiography Virtual monoenergetic images Image reconstruction, deep learning model |
| url | https://doi.org/10.1038/s41598-024-54502-1 |
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