Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
561-04-011 ISMRM Abstract

The impact of deep learning reconstruction on T1-weighted structural image quality (MRIQC) and brain morphometry (FreeSurfer)

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Analysis Methods - Software Tools
561-04-011 · Deep Learning Meets k-Space: New Frontiers in Reconstruction · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row B
Keywords: Deep learning reconstruction Freesurfer Image quality Structural T1 MRI MRIQC
Accepted
Synne B Johnsen1, Robin Antony Birkeland Bugge2, Dag Alnæs3,4, Stener Nerland5,6, Ellen R Olsrud7, Henning S Rise3,8, Lars T Westlye3,4, Wibeke Nordhøy 2
1Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
2Department of Physics and Computational Analysis, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
3Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
4Department of Psychology, University of Oslo, Oslo, Norway
5Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
6Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
7Department of Radiography, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
8Institute of Clinical Medicine, University of Oslo, Oslo, Norway
Presenting Author: Wibeke Nordhøy

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References

1. Lebel RM. Performance characterization of a novel deep learning-based MR image reconstruction pipeline. 2020. http://arxiv.org/abs/2008.06559
2. Behl N. Deep Resolve: Unrivaled Speed in MRI. MAGNETOM Flash. 2024;89:4.
3. Peeters H, et. al. Philips SmartSpeed No compromise. Philips Healthcare. 2022.
4. Esteban O, et al. MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites; PLOS ONE. 2017;12(9): e0184661. doi:10.1371/journal.pone.0184661 [doi]
5. Hagen MP, et. al. Quality assessment and control of unprocessed anatomical, functional, and diffusion MRI of the human brain using MRIQC. bioRxiv [Preprint]. 2024. doi:10.1101/2024.10.21.619532 [doi]
6. Dale AM, et al. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999 Feb;9(2):179-94. doi:10.1006/nimg.1998.0395 [doi]
7. Casey BJ, et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev Cogn Neurosci. 2018; 32: 43-54. doi:10.1016/j.dcn.2018.03.001 [doi]
8. Bernstein MA, et al. Effect of Windowing and Zero-Filled Reconstruction of MRI Data on Spatial Resolution and Acquisition Strategy. J Magn Reson Imaging. 2001;14(3): 270-80. doi:10.1002/jmri.1183 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/561-04-011