Improving Image Resolution of Whole-Heart Coronary MRA Using Convolutional Neural Network.
Whole-heart coronary magnetic resonance angiography (WHCMRA) permits the noninvasive assessment of coronary artery disease without radiation exposure. However, the image resolution of WHCMRA is limited. Recently, convolutional neural networks (CNNs) have obtained increased interest as a method for i...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 2; pp. 497 - 504 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2020
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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=142764010&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142764010 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2020 vid: 33 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142764010 142764010 142764010 10.1007/s10278-019-00264-6 142764010 ppf: 497 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improving Image Resolution of Whole-Heart Coronary MRA Using Convolutional Neural Network. aug: au: Kobayashi, Hiroki Nakayama, Ryohei Hizukuri, Akiyoshi Ishida, Masaki Kitagawa, Kakuya Sakuma, Hajime affil: Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, 525-8577, Kusatsu, Shiga, Japan sug: subj: Coronary Angiography Methods Magnetic Resonance Angiography Methods Neural Networks (Computer) Human Coronary Arteriosclerosis Radiography Validity ab: Whole-heart coronary magnetic resonance angiography (WHCMRA) permits the noninvasive assessment of coronary artery disease without radiation exposure. However, the image resolution of WHCMRA is limited. Recently, convolutional neural networks (CNNs) have obtained increased interest as a method for improving the resolution of medical images. The purpose of this study is to improve the resolution of WHCMRA images using a CNN. Free-breathing WHCMRA images with 512 × 512 pixels (pixel size = 0.65 mm) were acquired in 80 patients with known or suspected coronary artery disease using a 1.5 T magnetic resonance (MR) system with 32 channel coils. A CNN model was optimized by evaluating CNNs with different structures. The proposed CNN model was trained based on the relationship of signal patterns between low-resolution patches (small regions) and the corresponding high-resolution patches using a training dataset collected from 40 patients. Images with 512 × 512 pixels were restored from 256 × 256 down-sampled WHCMRA images (pixel size = 1.3 mm) with three different approaches: the proposed CNN, bicubic interpolation (BCI), and the previously reported super-resolution CNN (SRCNN). High-resolution WHCMRA images obtained using the proposed CNN model were significantly better than those of BCI and SRCNN in terms of root mean squared error, peak signal to noise ratio, and structure similarity index measure with respect to the original WHCMRA images. The proposed CNN approach can provide high-resolution WHCMRA images with better accuracy than BCI and SRCNN. The high-resolution WHCMRA obtained using the proposed CNN model will be useful for identifying coronary artery disease. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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