Original Investigation|Articles in Press

Ultrafast Brain MRI Protocol at 1.5 T Using Deep Learning and Multi-shot EPI

      Rationale and Objectives

      To evaluate clinical feasibility and image quality of a comprehensive ultrafast brain MRI protocol with multi-shot echo planar imaging and deep learning-enhanced reconstruction at 1.5 T.

      Materials and Methods

      Thirty consecutive patients who underwent clinically indicated MRI at a 1.5 T scanner were prospectively included. A conventional MRI (c-MRI) protocol, including T1-, T2-, T2*-, T2-FLAIR, and diffusion-weighted images (DWI)-weighted sequences were acquired. In addition, ultrafast brain imaging with deep learning-enhanced reconstruction and multi-shot EPI (DLe-MRI) was performed. Subjective image quality was evaluated by three readers using a 4-point Likert scale. To assess interrater agreement, Fleiss’ kappa (ϰ) was determined. For objective image analysis, relative signal intensity levels for grey matter, white matter, and cerebrospinal fluid were calculated.


      Time of acquisition (TA) of c-MRI protocols added up to 13:55 minutes, whereas the TA of DLe-MRI-based protocol added up to 3:04 minutes, resulting in a time reduction of 78%. All DLe-MRI acquisitions yielded diagnostic image quality with good absolute values for subjective image quality. C-MRI demonstrated slight advantages for DWI in overall subjective image quality (c-MRI: 3.93 [+/− 0.25] vs DLe-MRI: 3.87 [+/− 0.37], P = .04) and diagnostic confidence (c-MRI: 3.93 [+/− 0.25] vs DLe-MRI: 3.83 [+/− 3.83], P = .01). For most evaluated quality scores, moderate interobserver agreement was found. Objective image evaluation revealed comparable results for both techniques.


      DLe-MRI is feasible and allows for highly accelerated comprehensive brain MRI within 3 minutes at 1.5 T with good image quality. This technique may potentially strengthen the role of MRI in neurological emergencies.


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        • Earnest F.
        • Baker Jr., H.L.
        • Kispert D.B.
        • et al.
        Magnetic resonance imaging vs. computed tomography: advantages and disadvantages.
        Clin Neurosurg. 1985; 32: 540-573
        • Parry D.A.
        • Booth T.
        • Roland P.S.
        Advantages of magnetic resonance imaging over computed tomography in preoperative evaluation of pediatric cochlear implant candidates.
        Otol Neurotol. 2005; 26: 976-982
        • Chalela J.A.
        • Kidwell C.S.
        • Nentwich L.M.
        • et al.
        Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison.
        Lancet. 2007; 369: 293-298
        • Verma R.
        • Lalla R.
        Why MRI of brain is superior to CT in multiple neurocysticercosis?.
        BMJ Case Rep. 2012; 2012: 1-3
        • Buttram S.D.
        • Garcia-Filion P.
        • Miller J.
        • et al.
        Computed tomography vs magnetic resonance imaging for identifying acute lesions in pediatric traumatic brain injury.
        Hosp Pediatr. 2015; 5: 79-84
        • Kim I.
        • Torrey S.B.
        • Milla S.S.
        • et al.
        Benefits of brain magnetic resonance imaging over computed tomography in children requiring emergency evaluation of ventriculoperitoneal shunt malfunction: reducing lifetime attributable risk of cancer.
        Pediatr Emerg Care. 2015; 31: 239-242
        • Bhatkar S.
        • Mahesh K.V.
        • Sachdeva J.
        • et al.
        Magnetic resonance imaging (MRI) versus computed tomographic scan (CT scan) of brain in evaluation of suspected cavernous sinus syndrome.
        Neuroradiol J. 2020; 33: 501-507
        • Hauptmann M.
        • Byrnes G.
        • Cardis E.
        • et al.
        Brain cancer after radiation exposure from CT examinations of children and young adults: results from the EPI-CT cohort study.
        Lancet Oncol. 2023; 24: 45-53
        • Nekolla E.A.
        • Schegerer A.A.
        • Griebel J.
        • et al.
        [Frequency and doses of diagnostic and interventional X‑ray applications: trends between 2007 and 2014].
        Radiologe. 2017; 57: 555-562
      1. Bundesamt für Strahlenschutz. Röntgendiagnostik: Häufigkeit und Strahlenexposition für die deutsche Bevölkerung, Im Internet, Accessed date 4th of April 2023

      2. Team PA. Diagnostic Imaging Dataset Annual Statistical Release 2019/202020; 1.0. Accessed date 6th of February 2023 Available at:–20-PDF-1.4MB.pdf.

        • Griswold M.A.
        • Jakob P.M.
        • Heidemann R.M.
        • et al.
        Generalized autocalibrating partially parallel acquisitions (GRAPPA).
        Magn Reson Med. 2002; 47: 1202-1210
        • Breuer F.A.
        • Blaimer M.
        • Mueller M.F.
        • et al.
        Controlled aliasing in volumetric parallel imaging (2D CAIPIRINHA).
        Magn Reson Med. 2006; 55: 549-556
        • Lustig M.
        • Donoho D.
        • Pauly J.M.
        Sparse MRI: the application of compressed sensing for rapid MR imaging.
        Magn Reson Med. 2007; 58: 1182-1195
        • Zahneisen B.
        • Ernst T.
        • Poser B.A.
        SENSE and simultaneous multislice imaging.
        Magn Reson Med. 2015; 74: 1356-1362
        • Pruessmann K.P.
        • Weiger M.
        • Scheidegger M.B.
        • et al.
        SENSE: sensitivity encoding for fast MRI.
        Magn Reson Med. 1999; 42: 952-962
        • Skare S.
        • Sprenger T.
        • Norbeck O.
        • et al.
        A 1-minute full brain MR exam using a multicontrast EPI sequence.
        Magn Reson Med. 2018; 79: 3045-3054
        • Ryu K.H.
        • Choi D.S.
        • Baek H.J.
        • et al.
        Clinical feasibility of 1-min ultrafast brain MRI compared with routine brain MRI using synthetic MRI: a single center pilot study.
        J Neurol. 2019; 266: 431-439
        • Tamada T.
        • Kido A.
        • Ueda Y.
        • et al.
        Comparison of single-shot EPI and multi-shot EPI in prostate DWI at 3.0 T.
        Sci Rep. 2022; 12: 16070
        • Naganawa S.
        • Yamazaki M.
        • Kawai H.
        • et al.
        Anatomical details of the brainstem and cranial nerves visualized by high resolution readout-segmented multi-shot echo-planar diffusion-weighted images using unidirectional MPG at 3T.
        Magn Reson Med Sci. 2011; 10: 269-275
        • Wang F.
        • Dong Z.
        • Reese T.G.
        • et al.
        Echo planar time-resolved imaging (EPTI).
        Magn Reson Med. 2019; 81: 3599-3615
        • Clifford B.
        • Conklin J.
        • Huang S.Y.
        • et al.
        An artificial intelligence-accelerated 2-minute multi-shot echo planar imaging protocol for comprehensive high-quality clinical brain imaging.
        Magn Reson Med. 2022; 87: 2453-2463
        • Bilgic B.
        • Chatnuntawech I.
        • Manhard M.K.
        • et al.
        Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction.
        Magn Reson Med. 2019; 82: 1343-1358
        • van der Velde N.
        • Hassing H.C.
        • Bakker B.J.
        • et al.
        Improvement of late gadolinium enhancement image quality using a deep learning-based reconstruction algorithm and its influence on myocardial scar quantification.
        Eur Radiol. 2021; 31: 3846-3855
        • Xie D.
        • Li Y.
        • Yang H.
        • et al.
        Denoising arterial spin labeling perfusion MRI with deep machine learning.
        Magn Reson Imaging. 2020; 68: 95-105
        • Herrmann J.
        • Keller G.
        • Gassenmaier S.
        • et al.
        Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction: a prospective intraindividual comparison with a standard MRI protocol.
        Eur Radiol. 2022; 32: 6215-6229
        • Gassenmaier S.
        • Afat S.
        • Nickel M.D.
        • et al.
        Accelerated T2-weighted TSE imaging of the prostate using deep learning image reconstruction: a prospective comparison with standard T2-weighted TSE imaging.
        Cancers. 2021; 13: 3593
        • Herrmann J.
        • Koerzdoerfer G.
        • Nickel D.
        • et al.
        Feasibility and implementation of a deep learning MR reconstruction for TSE sequences in musculoskeletal imaging.
        Diagnostics. 2021; 11: 1484
        • Koktzoglou I.
        • Huang R.
        • Ankenbrandt W.J.
        • et al.
        Super-resolution head and neck MRA using deep machine learning.
        Magn Reson Med. 2021; 86: 335-345
        • Kustner T.
        • Munoz C.
        • Psenicny A.
        • et al.
        Deep-learning based super-resolution for 3D isotropic coronary MR angiography in less than a minute.
        Magn Reson Med. 2021; 86: 2837-2852
        • Gassenmaier S.
        • Afat S.
        • Nickel D.
        • et al.
        Deep learning-accelerated T2-weighted imaging of the prostate: reduction of acquisition time and improvement of image quality.
        Eur J Radiol. 2021; 137109600
        • Demir S.
        • Clifford B.
        • Lo W.C.
        • et al.
        Optimization of magnetization transfer contrast for EPI FLAIR brain imaging.
        Magn Reson Med. 2022; 87: 2380-2387
        • Hammernik K.
        • Schlemper J.
        • Qin C.
        • et al.
        Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination.
        Magn Reson Med. 2021; 86: 1859-1872
      3. Hosseini Z., Feiweier T., Conklin J., et al. A data-driven method for automatic regularization selection in a hybrid DL-SENSE reconstruction. ISMRM annual meeting & exhibition, London, 2022.

        • Clifford B.
        • Conklin J.
        • Huang S.
        • et al.
        Ultrafast brain imaging with deep-learning multi-shot EPI: technical implementation.
        MAGNETOM Flash. 2021; 79: 14-20
        • Dietrich O.
        • Raya J.G.
        • Reeder S.B.
        • et al.
        Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters.
        J Magn Reson Imaging. 2007; 26: 375-385
        • Ashburner J.
        • Friston K.J.
        Unified segmentation.
        Neuroimage. 2005; 26: 839-851
        • Tang W.
        • Hu J.
        • Zhang H.
        • et al.
        Kappa coefficient: a popular measure of rater agreement.
        Shanghai Arch Psychiatry. 2015; 27: 62-67
        • Knoll F.
        • Hammernik K.
        • Kobler E.
        • et al.
        Assessment of the generalization of learned image reconstruction and the potential for transfer learning.
        Magn Reson Med. 2019; 81: 116-128
        • Kidoh M.
        • Shinoda K.
        • Kitajima M.
        • et al.
        Deep learning based noise reduction for brain MR imaging: tests on phantoms and healthy volunteers.
        Magn Reson Med Sci. 2020; 19: 195-206
        • Liu F.
        • Kijowski R.
        • Feng L.
        • et al.
        High-performance rapid MR parameter mapping using model-based deep adversarial learning.
        Magn Reson Imaging. 2020; 74: 152-160
        • Xue J.
        • Wang B.
        • Ming Y.
        • et al.
        Deep learning-based detection and segmentation-assisted management of brain metastases.
        Neuro Oncology. 2020; 22: 505-514
        • Srinivas C.
        • Nandini Prasad K.S.
        • Zakariah M.
        • et al.
        Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images.
        J Healthc Eng. 2022; 20223264367
        • Bash S.
        • Wang L.
        • Airriess C.
        • et al.
        Deep learning enables 60% accelerated volumetric brain MRI while preserving quantitative performance: a prospective, multicenter, multireader trial.
        AJNR Am J Neuroradiol. 2021; 42: 2130-2137
        • Lang M.
        • Cartmell S.
        • Tabari A.
        • et al.
        Evaluation of the aggregated time savings in adopting fast brain MRI techniques for outpatient brain MRI.
        Acad Radiol. 2023; 30: 341-348
        • Tabari A.
        • Clifford B.
        • Filho A.
        • et al.
        Ultrafast brain imaging with deep learning multi-shot EPI: preliminary clinical evaluation.
        MAGNETOM Flash. 2021; 79: 66-70