Deep Learning Accelerated Brain Diffusion-Weighted MRI with Super Resolution Processing

Sebastian Altmann; Nils F Grauhan; Mario Alberto Abello Mercado; Sebastian Steinmetz; Andrea Kronfeld; Roman Paul; Thomas Benkert; Timo Uphaus; Sergiu Groppa; Yaroslav Winter; Marc A Brockmann; Ahmed E Othman

doi:10.1016/j.acra.2024.02.049

Deep Learning Accelerated Brain Diffusion-Weighted MRI with Super Resolution Processing

Acad Radiol. 2024 Mar 22:S1076-6332(24)00139-9. doi: 10.1016/j.acra.2024.02.049. Online ahead of print.

Affiliations

¹ Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany. Electronic address: Sebastian.altmann@unimedizin-mainz.de.
² Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany.
³ Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Mainz, Johannes Gutenberg University, Rhabanusstr. 3/Tower A, 55118 Mainz, Germany.
⁴ Siemens Healthineers AG, Erlangen, Germany.
⁵ Department of Neurology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany.
⁶ Department of Neurology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany; Department of Neurology, Philipps-University Marburg, Baldingerstr, 35043 Marburg, Germany.

PMID: 38521612
DOI: 10.1016/j.acra.2024.02.049

Abstract

Objectives: To investigate the clinical feasibility and image quality of accelerated brain diffusion-weighted imaging (DWI) with deep learning image reconstruction and super resolution.

Methods: 85 consecutive patients with clinically indicated MRI at a 3 T scanner were prospectively included. Conventional diffusion-weighted data (c-DWI) with four averages were obtained. Reconstructions of one and two averages, as well as deep learning diffusion-weighted imaging (DL-DWI), were accomplished. Three experienced readers evaluated the acquired data using a 5-point Likert scale regarding overall image quality, overall contrast, diagnostic confidence, occurrence of artefacts and evaluation of the central region, basal ganglia, brainstem, and cerebellum. To assess interrater agreement, Fleiss' kappa (ϰ) was determined. Signal intensity (SI) levels for basal ganglia and the central region were estimated via automated segmentation, and SI values of detected pathologies were measured.

Results: Intracranial pathologies were identified in 35 patients. DL-DWI was significantly superior for all defined parameters, independently from applied averages (p-value <0.001). Optimum image quality was achieved with DL-DWI by utilizing a single average (p-value <0.001), demonstrating very good (80.9%) to excellent image quality (14.5%) in nearly all cases, compared to 12.5% with very good and 0% with excellent image quality for c-MRI (p-value <0.001). Comparable results could be shown for diagnostic confidence. Inter-rater Fleiss' Kappa demonstrated moderate to substantial agreement for virtually all defined parameters, with good accordance, particularly for the assessment of pathologies (p = 0.74). Regarding SI values, no significant difference was found.

Conclusion: Ultra-fast diffusion-weighted imaging with super resolution is feasible, resulting in highly accelerated brain imaging while increasing diagnostic image quality.

Keywords: Accelerated brain imaging; Deep learning acceleration; Diffusion-weighted brain MRI; Image quality; Super resolution.