Ultrafast Brain MRI with Deep Learning Reconstruction for Suspected Acute Ischemic Stroke.

Journal: Radiology
Published Date: Feb 1, 2024

Abstract

Background Deep learning (DL)-accelerated MRI can substantially reduce examination times. However, studies prospectively evaluating the diagnostic performance of DL-accelerated MRI reconstructions in acute suspected stroke are lacking. Purpose To investigate the interchangeability of DL-accelerated MRI with conventional MRI in patients with suspected acute ischemic stroke at 1.5 T. Materials and Methods In this prospective study, 211 participants with suspected acute stroke underwent clinically indicated MRI at 1.5 T between June 2022 and March 2023. For each participant, conventional MRI (including T1-weighted, T2-weighted, T2*-weighted, T2 fluid-attenuated inversion-recovery, and diffusion-weighted imaging; 14 minutes 18 seconds) and DL-accelerated MRI (same sequences; 3 minutes 4 seconds) were performed. The primary end point was the interchangeability between conventional and DL-accelerated MRI for acute ischemic infarction detection. Secondary end points were interchangeability regarding the affected vascular territory and clinically relevant secondary findings (eg, microbleeds, neoplasm). Three readers evaluated the overall occurrence of acute ischemic stroke, affected vascular territory, clinically relevant secondary findings, overall image quality, and diagnostic confidence. For acute ischemic lesions, size and signal intensities were assessed. The margin for interchangeability was chosen as 5%. For interrater agreement analysis and interrater reliability analysis, multirater Fleiss κ and the intraclass correlation coefficient, respectively, was determined. Results The study sample consisted of 211 participants (mean age, 65 years ± 16 [SD]); 123 male and 88 female). Acute ischemic stroke was confirmed in 79 participants. Interchangeability was demonstrated for all primary and secondary end points. No individual equivalence indexes (IEIs) exceeded the interchangeability margin of 5% (IEI, -0.002 [90% CI: -0.007, 0.004]). Almost perfect interrater agreement was observed ( > .91). DL-accelerated MRI provided higher overall image quality ( < .001) and diagnostic confidence ( < .001). The signal properties of acute ischemic infarctions were similar in both techniques and demonstrated good to excellent interrater reliability (intraclass correlation coefficient, ≥0.8). Conclusion Despite being four times faster, DL-accelerated brain MRI was interchangeable with conventional MRI for acute ischemic lesion detection. © RSNA, 2024 See also the editorial by Haller in this issue.

Authors

  • Sebastian Altmann
    Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany (S.A., M.A.M., L.B., A.K., M.A.B., A.E.O.). Electronic address: Sebastian.altmann@unimedizin-mainz.de.
  • Nils F Grauhan
    Department of Radiology, Charité - Universitätsmedizin Berlin, Hindenburgdamm 30, 12203, Berlin, Germany. nils-friedrich.grauhan@charite.de.
  • Lavinia Brockstedt
    Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany (S.A., M.A.M., L.B., A.K., M.A.B., A.E.O.).
  • Mariya Kondova
    From the Department of Neuroradiology (S.A., N.F.G., L.B., M.K., M.A.B., A.E.O.), Institute of Medical Biostatistics, Epidemiology and Informatics (I.S., R.P.), and Department of Neurology (T.U., S.G.), University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr 1, 55131 Mainz, Germany; Siemens Medical Solutions USA, Boston, Mass (B.C.); and Siemens Healthcare, Erlangen, Germany (T.F., Z.H.).
  • Irene Schmidtmann
    From the Department of Neuroradiology (S.A., N.F.G., L.B., M.K., M.A.B., A.E.O.), Institute of Medical Biostatistics, Epidemiology and Informatics (I.S., R.P.), and Department of Neurology (T.U., S.G.), University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr 1, 55131 Mainz, Germany; Siemens Medical Solutions USA, Boston, Mass (B.C.); and Siemens Healthcare, Erlangen, Germany (T.F., Z.H.).
  • Roman Paul
    From the Department of Neuroradiology (S.A., N.F.G., L.B., M.K., M.A.B., A.E.O.), Institute of Medical Biostatistics, Epidemiology and Informatics (I.S., R.P.), and Department of Neurology (T.U., S.G.), University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr 1, 55131 Mainz, Germany; Siemens Medical Solutions USA, Boston, Mass (B.C.); and Siemens Healthcare, Erlangen, Germany (T.F., Z.H.).
  • Bryan Clifford
    Siemens Medical Solutions USA, Boston, Massachusetts, USA.
  • Thorsten Feiweier
    Siemens Healthcare GmbH, Erlangen, Germany.
  • Zahra Hosseini
    Siemens Medical Solutions USA, Atlanta, Georgia, USA.
  • Timo Uphaus
    Department of Neurology, University Medical Center Mainz, Johannes Gutenberg University, Mainz, Germany (T.U., S.G.).
  • Sergiu Groppa
    Department of Neurology, University Medical Center Mainz, Johannes Gutenberg University, Mainz, Germany (T.U., S.G.).
  • Marc A Brockmann
    Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany (S.A., M.A.M., L.B., A.K., M.A.B., A.E.O.).
  • Ahmed E Othman
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany; Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany. Electronic address: ahmed.e.othman@googlemail.com.

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