Usefulness of dictionary learning-based processing for improving image quality of sub-millisievert low-dose chest CT: initial experience.
Purpose: To develop a dictionary learning (DL)-based processing technique for improving the image quality of sub-millisievert chest computed tomography (CT).Materials and Methods: Standard-dose and sub-millisievert chest CT were acquired in 12 patients. Dictionaries including standard- and low-dose...
| Publicado en: | Japanese Journal of Radiology Vol. 38; no. 3; pp. 215 - 222 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2020
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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=142128915&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142128915 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18671071 AUCM jtl: Japanese Journal of Radiology issn: 18671071 maglogo: N pubinfo: dt: Mar2020 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142128915 142128915 NLM31863329 10.1007/s11604-019-00912-5 NLM31863329 142128915 ppf: 215 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Usefulness of dictionary learning-based processing for improving image quality of sub-millisievert low-dose chest CT: initial experience. aug: au: Kanii, Yoshinori Ichikawa, Yasutaka Nakayama, Ryohei Nagata, Motonori Ishida, Masaki Kitagawa, Kakuya Murashima, Shuichi Sakuma, Hajime affil: Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507, Tsu, Mie, Japan sug: subj: Radiographic Image Interpretation, Computer-Assisted Methods Lung Neoplasms Tomography, X-Ray Computed Methods Lung Prospective Studies Male Radiography, Thoracic Methods Reproducibility of Results Aged Radiation Dosage Algorithms Pilot Studies Female Aged, 80 and Over Ferrans and Powers Quality of Life Index Aged: 65+ years Aged, 80 & over Male Female ab: Purpose: To develop a dictionary learning (DL)-based processing technique for improving the image quality of sub-millisievert chest computed tomography (CT).Materials and Methods: Standard-dose and sub-millisievert chest CT were acquired in 12 patients. Dictionaries including standard- and low-dose image patches were generated from the CT datasets. For each patient, DL-based processing was performed for low-dose CT using the dictionaries generated from the remaining 11 patients. This procedure was repeated for all 12 patients. Image quality of normal thoracic structures on the processed sub-millisievert CT images was assessed with a 5-point scale (5 = excellent, 1 = very poor). Lung lesion conspicuity was also assessed on a 5-point scale.Results: Image noise on sub-millisievert CT was significantly decreased with DL-based image processing (48.5 ± 13.7 HU vs 20.4 ± 7.9 HU, p = 0.0005). Image quality of lung structures was significantly improved with DL-based method (middle level of lung, 2.25 ± 0.75 vs 2.92 ± 0.79, p = 0.0078). Lung lesion conspicuity was also significantly improved with DL-based technique (solid nodules, 3.4 ± 0.6 vs 2.7 ± 0.6, p = 0.0273).Conclusion: Image quality and lesion conspicuity on sub-millisievert chest CT images may be improved by DL-based post-processing. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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