Generation of Brain Dual-Energy CT from Single-Energy CT Using Deep Learning.
Deep learning (DL) has shown great potential in conversions between various imaging modalities. Similarly, DL can be applied to synthesize a high-kV computed tomography (CT) image from its corresponding low-kV CT image. This indicates the feasibility of obtaining dual-energy CT (DECT) images without...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 1; pp. 149 - 162 |
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| Autores principales: | , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2021
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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=148753846&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148753846 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2021 vid: 34 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148753846 148058522 148753846 148753846 10.1007/s10278-020-00414-1 148753846 ppf: 149 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Generation of Brain Dual-Energy CT from Single-Energy CT Using Deep Learning. aug: au: Liu, Chi-Kuang Liu, Chih-Chieh Yang, Cheng-Hsun Huang, Hsuan-Ming affil: Department of Medical Imaging, Changhua Christian Hospital, 135 Nanxiao St, 500, Changhua County, Taiwan sug: subj: Brain Radiation Effects Deep Learning Brain Mapping Methods Radiation Dosage Tomography, X-Ray Computed Methods Human Image Processing, Computer Assisted Methods Neural Networks (Computer) Sensitivity and Specificity Spatial Behavior Descriptive Statistics Noise ab: Deep learning (DL) has shown great potential in conversions between various imaging modalities. Similarly, DL can be applied to synthesize a high-kV computed tomography (CT) image from its corresponding low-kV CT image. This indicates the feasibility of obtaining dual-energy CT (DECT) images without purchasing a DECT scanner. In this study, we investigated whether a low-to-high kV mapping was better than a high-to-low kV mapping. We used a U-Net model to perform conversions between different kV CT images. Moreover, we proposed a double U-Net model to improve the quality of original single-energy CT images. Ninety-eight patients who underwent brain DECT scans were used to train, validate, and test the proposed DL-based model. The results showed that the low-to-high kV conversion was better than the high-to-low kV conversion. In addition, the DL-based DECT images had better signal-to-noise ratios (SNRs) than the true (original) DECT images, but at the expense of a slight loss in spatial resolution. The mean CT number differences between the true and DL-based DECT images were within ± 1 HU. No statistically significant difference in CT number measurements was found between the true and DL-based DECT images (p > 0.05). The DL-based DECT images with improved SNR could produce low-noise virtual monoenergetic images. Our preliminary results indicate that DL has the potential to generate brain DECT images using single-energy brain CT images. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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