Impact of Deep Learning-based Optimization Algorithm on Image Quality of Low-dose Coronary CT Angiography with Noise Reduction: A Prospective Study.
Rationale and Objectives: To evaluate deep learning (DL)-based optimization algorithm for low-dose coronary CT angiography (CCTA) image noise reduction and image quality (IQ) improvement.Materials and Methods: A postprocessing platform for the CCTA image was built using a DL-based algorithm. Seventy...
| Publicado en: | Academic Radiology Vol. 27; no. 9; pp. 1241 - 1249 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Sep2020
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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=145298412&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145298412 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10766332 T4X jtl: Academic Radiology issn: 10766332 maglogo: N pubinfo: dt: Sep2020 vid: 27 iid: 9 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 145298412 145298412 NLM31864809 145298412 10.1016/j.acra.2019.11.010 NLM31864809 145298412 ppf: 1241 ppct: 8 formats: tig: atl: Impact of Deep Learning-based Optimization Algorithm on Image Quality of Low-dose Coronary CT Angiography with Noise Reduction: A Prospective Study. aug: au: Liu, Peijun Wang, Man Wang, Yining Yu, Min Wang, Yun Liu, Zhuoheng Li, Yumei Jin, Zhengyu affil: Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China sug: subj: Radiographic Image Interpretation, Computer-Assisted Algorithms Prospective Studies Contrast Media Human Sensitivity and Specificity Radiation Dosage Coronary Angiography Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Rationale and Objectives: To evaluate deep learning (DL)-based optimization algorithm for low-dose coronary CT angiography (CCTA) image noise reduction and image quality (IQ) improvement.Materials and Methods: A postprocessing platform for the CCTA image was built using a DL-based algorithm. Seventy subjects referred for CCTA were randomly divided into two groups (study group A with 80 kVp and control group B with 100 kVp). Group C was obtained by DL optimization of group A. Subjective IQ was blindly graded by two experienced radiologists on a four-point scale (4-excellent,1-poor). The image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated to evaluate IQ objectively. The difference between the time consumed of iterative reconstruction and DL algorithm was also recorded.Results: The subjective IQ score of group C using the DL algorithm was significantly better than that of group A (p = 0.005). The noise of group C was significantly decreased, while SNR and CNR were significantly increased compared to group A (p < 0.001). The subjective IQ scores were lower in group A compared to group B (p = 0.037), whereas subjective IQ scores in group C were not significantly different (p = 0.874). For objective IQ, the noise of group A was significantly higher, while SNR and CNR were significantly lower than that of group B (p < 0.05). There was no significant difference in noise and SNR between group C and group B (p > 0.05), but CNR in group C was significantly higher than that in group B (p < 0.05). The DL algorithm processes the image twice as fast as the iterative reconstruction speed.Conclusion: The DL-based optimization algorithm could effectively improve the IQ of low-dose CCTA by noise reduction. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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