Conjugate gradient and deep learning reconstructions: reduced time without affecting image quality and nodule detection.

Journal: European radiology
Published Date: Dec 24, 2025

Abstract

OBJECTIVES: The purpose of this study was to determine the utility of conjugate gradient reconstruction (CG Recon) and deep learning reconstruction (DLR) for reducing scan time while maintaining the image quality and nodule detection capability on lung MRI with ultrashort TE (UTE-MRI) as compared with grid reconstruction (Grid Recon). MATERIALS AND METHODS: In the in vitro and in vivo studies, the NEMA phantom and 35 patients with pulmonary nodules were scanned by UTE-MRI with original (TEoriginal), 1/2 (UTE1/2), and 1/4 (UTE1/4) spoke numbers obtained by both methods and reconstructed with and without DLR. In this study, the standard protocol was UTEoriginal obtained by Grid Recon without DLR. Then, signal-to-noise ratios (SNR) of the phantom, lung and lesion were assessed. In the in vivo study, overall image quality and nodule detection capability were visually assessed on each UTE-MRI. Quantitative and qualitative indices were then compared between the standard protocol and others. Finally, a receiver operating characteristic (ROC) analysis was performed to compare the standard and other protocols. RESULTS: In in vitro and in vivo studies, all SNRs were significantly different between the standard protocol and each UTE-MRI with CG Recon and DLR (p < 0.05). Overall image quality of the standard protocol differed significantly from that of all UTE1/4s (p < 0.05). The area under the curve of each UTEOriginal obtained by CG Recon was significantly larger than that of the standard protocol (p < 0.05). CONCLUSION: CG Recon and DLR can reduce scan time while maintaining image quality and nodule detection capabilities on lung UTE-MRI. KEY POINTS: Question To determine the utility of conjugate gradient reconstruction (CG Recon) to reduce scan time without nodule detection capability on MRI with ultrashort TE (UTE-MRI). Findings Nodule detection capability was not significantly decreased by CG Recon with or without deep learning reconstruction when reducing scan time from the standard UTE-MRI protocol. Clinical relevance Conjugate gradient reconstruction (CG Recon) and deep learning reconstruction (DLR) have the potential to reduce scan time while maintaining image quality and nodule detection capability in lung MR imaging with ultrashort TE.

Authors

  • Yoshiharu Ohno
    From the Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan (Y.O.); Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Japan (Y.O.); Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., S.S., T.Y.); Canon Medical Systems, Otawara, Japan (K.A.); Corporate Research and Development Center, Toshiba, Kawasaki, Japan (A.Y.); Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U., Y.K.); Department of Radiology, Kohnan Hospital, Kobe, Japan (Y.K.); and Department of Radiology, Hyogo Cancer Center, Akashi, Japan (D.T.).
  • Yoshiyuki Ozawa
    Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
  • Takahiro Ueda
    Department of Radiology, Fujita Health University, School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
  • Masahiko Nomura
    Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan.
  • Natsuka Yazawa
    Canon Medical Systems Corporation, Otawara, Japan.
  • Maiko Shinohara
    Canon Medical Systems Corporation.
  • Kaori Yamamoto
    Canon Medical Systems Corporation, 1385, Shimoishigami, Otawara, Tochigi, 324-0036, Japan. Electronic address: [email protected].
  • Yuichiro Sano
    MRI Systems Division, Canon Medical Systems Corporation, Otawara, Tochigi, Japan.
  • Masato Ikedo
    Canon Medical Systems Corporation, 1385, Shimoishigami, Otawara, Tochigi, 324-0036, Japan. Electronic address: [email protected].
  • Masanori Ozaki
    Canon Medical Systems Corporation, Otawara, Japan.
  • Masao Yui
    Canon Medical Systems Corporation, 1385, Shimoishigami, Otawara, Tochigi, 324-0036, Japan. Electronic address: [email protected].
  • Shohei Harada
    Department of Radiology, Fujita Health University Hospital, Toyoake, Japan.
  • Saki Takeda
    Department of Radiology, Fujita Health University Hospital, Toyoake, Japan.
  • Akiyoshi Iwase
    Department of Radiology, Fujita Health University Hospital, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
  • Takeshi Yoshikawa
    From the Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan (Y.O.); Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Japan (Y.O.); Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., S.S., T.Y.); Canon Medical Systems, Otawara, Japan (K.A.); Corporate Research and Development Center, Toshiba, Kawasaki, Japan (A.Y.); Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U., Y.K.); Department of Radiology, Kohnan Hospital, Kobe, Japan (Y.K.); and Department of Radiology, Hyogo Cancer Center, Akashi, Japan (D.T.).
  • Daisuke Takenaka
    From the Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan (Y.O.); Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Japan (Y.O.); Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.O., S.S., T.Y.); Canon Medical Systems, Otawara, Japan (K.A.); Corporate Research and Development Center, Toshiba, Kawasaki, Japan (A.Y.); Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U., Y.K.); Department of Radiology, Kohnan Hospital, Kobe, Japan (Y.K.); and Department of Radiology, Hyogo Cancer Center, Akashi, Japan (D.T.).

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals