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出典: Scopus
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Kazuhiro Murayamaが活動している研究トピックを掘り下げます。このトピックラベルは、この研究者の研究成果に基づきます。これらがまとまってユニークなフィンガープリントを構成します。

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198 被引用数
7 h-index
41 Article

Compressed sensing and deep learning reconstruction for women's pelvic MRI denoising: Utility for improving image quality and examination time in routine clinical practice
Ueda, T., Ohno, Y., Yamamoto, K., Iwase, A., Fukuba, T., Hanamatsu, S., Obama, Y., Ikeda, H., Ikedo, M., Yui, M., Murayama, K. & Toyama, H., 01-2021, In: European journal of radiology. 134, 109430.
研究成果: Article › 査読
1 被引用数 (Scopus)
Compressed sensing and parallel imaging accelerated T2 FSE sequence for head and neck MR imaging: Comparison of its utility in routine clinical practice
Ikeda, H., Ohno, Y., Murayama, K., Yamamoto, K., Iwase, A., Fukuba, T. & Toyama, H., 02-2021, In: European journal of radiology. 135, 109501.
研究成果: Article › 査読
Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions
Matsukiyo, R., Ohno, Y., Matsuyama, T., Nagata, H., Kimata, H., Ito, Y., Ogawa, Y., Murayama, K., Kato, R. & Toyama, H., 02-2021, In: Japanese journal of radiology. 39, 2, p. 186-197 12 p.
研究成果: Article › 査読
Inspiratory/expiratory xenon-enhanced area-detector CT: Capability for quantitative assessment of lung ventilation changes in surgically treated non-small cell lung cancer patients
Ohno, Y., Fujisawa, Y., Yoshikawa, T., Takenaka, D., Koyama, H., Hattori, H., Murayama, K., Fujii, K., Sugihara, N. & Toyama, H., 03-2021, In: European journal of radiology. 136, 109574.
研究成果: Article › 査読
Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases
Ohno, Y., Aoyagi, K., Takenaka, D., Yoshikawa, T., Ikezaki, A., Fujisawa, Y., Murayama, K., Hattori, H. & Toyama, H., 01-2021, In: European journal of radiology. 134, 109410.
研究成果: Article › 査読

Compressed sensing and deep learning reconstruction for women's pelvic MRI denoising: Utility for improving image quality and examination time in routine clinical practice

Compressed sensing and parallel imaging accelerated T2 FSE sequence for head and neck MR imaging: Comparison of its utility in routine clinical practice

Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions

Inspiratory/expiratory xenon-enhanced area-detector CT: Capability for quantitative assessment of lung ventilation changes in surgically treated non-small cell lung cancer patients

Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases