MR-Eye: A High-Resolution Motion-Resolved MRI Protocol for Anatomical Imaging of the Human Eye
Poster No:
1864
Submission Type:
Abstract Submission
Authors:
Yiwei Jia1,2,3, Bastien Milani1,3, Eleonora Fornari4,5, Oscar Esteban4, Jean-Baptiste Ledoux4,5, Helene Vitali6,7, Jessica Bastiaansen8,9, Benedetta Franceschiello1,3
Institutions:
1Institute of Systems Engineering, School of Engineering, HES-SO Valais-Wallis, Sion, Switzerland, 2Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland, 3The Sense Innovation and Research Centre, Lausanne and Sion, Switzerland, 4Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 5CIBM Center for Biomedical Imaging, Lausanne, Switzerland, 6Unit for visually impaired people, Italian Institute of Technology, Genoa, Italy, 7Department of Computer Science, Bioengineering, Robotics and Systems, University of Genoa, Genoa, Italy, 8Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern, Switzerland, 9Translation Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
First Author:
Yiwei Jia
Institute of Systems Engineering, School of Engineering, HES-SO Valais-Wallis|Graduate School for Cellular and Biomedical Sciences, University of Bern|The Sense Innovation and Research Centre
Sion, Switzerland|Bern, Switzerland|Lausanne and Sion, Switzerland
Institute of Systems Engineering, School of Engineering, HES-SO Valais-Wallis|Graduate School for Cellular and Biomedical Sciences, University of Bern|The Sense Innovation and Research Centre
Sion, Switzerland|Bern, Switzerland|Lausanne and Sion, Switzerland
Co-Author(s):
Bastien Milani
Institute of Systems Engineering, School of Engineering, HES-SO Valais-Wallis|The Sense Innovation and Research Centre
Sion, Switzerland|Lausanne and Sion, Switzerland
Institute of Systems Engineering, School of Engineering, HES-SO Valais-Wallis|The Sense Innovation and Research Centre
Sion, Switzerland|Lausanne and Sion, Switzerland
Eleonora Fornari
Department of Radiology, Lausanne University Hospital and University of Lausanne|CIBM Center for Biomedical Imaging
Lausanne, Switzerland|Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital and University of Lausanne|CIBM Center for Biomedical Imaging
Lausanne, Switzerland|Lausanne, Switzerland
Oscar Esteban
Department of Radiology, Lausanne University Hospital and University of Lausanne
Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital and University of Lausanne
Lausanne, Switzerland
Jean-Baptiste Ledoux
Department of Radiology, Lausanne University Hospital and University of Lausanne|CIBM Center for Biomedical Imaging
Lausanne, Switzerland|Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital and University of Lausanne|CIBM Center for Biomedical Imaging
Lausanne, Switzerland|Lausanne, Switzerland
Helene Vitali
Unit for visually impaired people, Italian Institute of Technology|Department of Computer Science, Bioengineering, Robotics and Systems, University of Genoa
Genoa, Italy|Genoa, Italy
Unit for visually impaired people, Italian Institute of Technology|Department of Computer Science, Bioengineering, Robotics and Systems, University of Genoa
Genoa, Italy|Genoa, Italy
Jessica Bastiaansen
Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital|Translation Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine
Bern, Switzerland|Bern, Switzerland
Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital|Translation Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine
Bern, Switzerland|Bern, Switzerland
Benedetta Franceschiello
Institute of Systems Engineering, School of Engineering, HES-SO Valais-Wallis|The Sense Innovation and Research Centre
Sion, Switzerland|Lausanne and Sion, Switzerland
Institute of Systems Engineering, School of Engineering, HES-SO Valais-Wallis|The Sense Innovation and Research Centre
Sion, Switzerland|Lausanne and Sion, Switzerland
Introduction:
MRI remains the only noninvasive, imaging technique capable of investigating pathologies behind the ocular globe, including tumors and inflammation(Niendorf,2021), making it a pivotal tool in vision neuroscience. However, eye movements introduce motion artifacts particularly due to the long acquisition times of clinical protocols for the eyes(T1w-VIBE and T2w-TSE) and rapid eye movements(Fanea,2012). We present a clinical 3T MRI protocol for eyes that achieves high spatial resolution and resilience to eye motion. We integrated a motion artifact correction strategy into a Compressed Sensing (CS) image reconstruction that leverages eye-tracking signals. This motion-robust reconstruction is demonstrated on a continuous gradient recalled echo (GRE) sequence with water-excitation(LIBRE(Bastiaansen,2018,2019)). Enhanced structural detail and reduced fat regions compared to the standard clinical protocols are highlighted by CNR, SNR and Entropy Focus Criterion(EFC).
Methods:
Dataset and Material: Three healthy humans were scanned on a 3T clinical scanner (MAGNETOM PrismaFit, Siemens Healthineers), using a 64-channel head-neck coil with an attached mirror. We back-projected visual stimuli on the mirror(Franceschiello,2020) to guide subjects' fixation.
In Phase-1, a central dot changed color at 10Hz.
In Phase-2, a gray dot appeared sequentially at the top, bottom, left and right of the screen. Gaze positions of the right eye were recorded by an EyeLink 1000Plus (SR Research) eye-tracking (ET) system at 1kHz, and synchronized with the MRI scanner via Syncbox (NordicNeuroLab)(Fig1).
Data acquisition:
Phase-1: T1-weighted(T1w) imaging, we used LIBRE sequence and a 3D radial phyllotaxis sampling trajectory(Piccini,2011;Sengupta,2017;Feng,2018) to record the data. After a 15.03s delay, the clinical T1w-VIBE sequence was applied as the baseline for clinical comparison. For T2w, the LIBRE sequence was applied followed by the clinical T2w-TSE sequence as the baseline for comparison(Table-1).
Phase-2: Three subjects underwent the same T1w-LIBRE sequence as Phase-1, except with TA=10:55min.
Reconstruction: We processed eye tracking data to remove MR readouts hindered by blinks and saccades. Gaze points were used to estimate eye rotation angles and corresponding mm-displacements, filtered to ensure x/y displacements stayed within 0.15mm(~one-third voxel size). We categorized gaze positions into a temporal ET mask, aligned with MRI data timestamps(8-30 gaze points per MRI line). We preserved MRI readouts if >75% of the gaze points in the window met the displacement criterion. This produced a motion-resolved binning mask that ensured only stable, motion-free raw data were included in the final CS reconstruction.
Phase-1: Binned T1w and T2w LIBRE images were compared to standard clinical protocols.
Phase-2: To show the effectiveness of motion correction from ET data, four binned T1w-LIBRE images(representing 4 different motion states) were reconstructed and compared to non-binned images, where readouts were reunited without motion tracking selection.
In Phase-1, a central dot changed color at 10Hz.
In Phase-2, a gray dot appeared sequentially at the top, bottom, left and right of the screen. Gaze positions of the right eye were recorded by an EyeLink 1000Plus (SR Research) eye-tracking (ET) system at 1kHz, and synchronized with the MRI scanner via Syncbox (NordicNeuroLab)(Fig1).
Data acquisition:
Phase-1: T1-weighted(T1w) imaging, we used LIBRE sequence and a 3D radial phyllotaxis sampling trajectory(Piccini,2011;Sengupta,2017;Feng,2018) to record the data. After a 15.03s delay, the clinical T1w-VIBE sequence was applied as the baseline for clinical comparison. For T2w, the LIBRE sequence was applied followed by the clinical T2w-TSE sequence as the baseline for comparison(Table-1).
Phase-2: Three subjects underwent the same T1w-LIBRE sequence as Phase-1, except with TA=10:55min.
Reconstruction: We processed eye tracking data to remove MR readouts hindered by blinks and saccades. Gaze points were used to estimate eye rotation angles and corresponding mm-displacements, filtered to ensure x/y displacements stayed within 0.15mm(~one-third voxel size). We categorized gaze positions into a temporal ET mask, aligned with MRI data timestamps(8-30 gaze points per MRI line). We preserved MRI readouts if >75% of the gaze points in the window met the displacement criterion. This produced a motion-resolved binning mask that ensured only stable, motion-free raw data were included in the final CS reconstruction.
Phase-1: Binned T1w and T2w LIBRE images were compared to standard clinical protocols.
Phase-2: To show the effectiveness of motion correction from ET data, four binned T1w-LIBRE images(representing 4 different motion states) were reconstructed and compared to non-binned images, where readouts were reunited without motion tracking selection.
·Fig-1
Results:
When comparing LIBRE images to standard MRI alternatives (Fig2-ab) from Phase-1: LIBRE images showed higher contrast around optical. T1w-LIBRE images exhibited fewer motion artifacts, particularly in the lens and vitreous regions. T1w-LIBRE outperformed standard protocols on most metrics, including lower EFC, higher SNR, and higher CNR. For T2w, LIBRE again showed higher EFC and SNR but lower CNR against TSE, indicating a contrast trade-off due to reduced intensity in the globes.
LIBRE images reconstructed from Phase-2 with motion-resolved binning masks showed better quality than non-binning images in terms of SNR, CNR, and EFC (Fig2-cd).
LIBRE images reconstructed from Phase-2 with motion-resolved binning masks showed better quality than non-binning images in terms of SNR, CNR, and EFC (Fig2-cd).
·Fig-2
Conclusions:
This new protocol with motion-resolved reconstruction represents a significant advance in mitigating motion artifacts, improving clarity, and enhancing MRI's potential for ophthalmic and neuroscience-oriented imaging.
Modeling and Analysis Methods:
Motion Correction and Preprocessing 2
Novel Imaging Acquisition Methods:
Anatomical MRI 1
Keywords:
Data analysis
Design and Analysis
MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Bastiaansen, J. A. M. (2019). Noncontrast free-breathing respiratory self-navigated coronary artery cardiovascular magnetic resonance angiography at 3 T using lipid insensitive binomial off-resonant excitation (LIBRE). Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 21(1), 38. https://doi.org/10.1186/s12968-019-0543-6
Di Sopra, L. (2019). An automated approach to fully self-gated free-running cardiac and respiratory motion-resolved 5D whole-heart MRI. Magnetic resonance in medicine, 82(6), 2118–2132. https://doi.org/10.1002/mrm.27898
Fanea, L.(2012). Review: magnetic resonance imaging techniques in ophthalmology. Molecular vision, 18, 2538–2560.
Feng, L. (2018). 5D whole-heart sparse MRI. Magnetic resonance in medicine, 79(2), 826–838. https://doi.org/10.1002/mrm.26745
Franceschiello, B.(2020). 3-Dimensional magnetic resonance imaging of the freely moving human eye. Progress in neurobiology, 194, 101885. https://doi.org/10.1016/j.pneurobio.2020.101885
Niendorf, T.(2021). Ophthalmic Magnetic Resonance Imaging: Where Are We (Heading To)?. Current eye research, 46(9), 1251–1270. https://doi.org/10.1080/02713683.2021.1874021
Plewes, D. B., & Kucharczyk, W. (2012). Physics of MRI: a primer. Journal of magnetic resonance imaging : JMRI, 35(5), 1038–1054. https://doi.org/10.1002/jmri.23642
Piccini, D. (2011). Spiral phyllotaxis: the natural way to construct a 3D radial trajectory in MRI. Magnetic resonance in medicine, 66(4), 1049–1056. https://doi.org/10.1002/mrm.22898
Sengupta, S. (2017). Dynamic Imaging of the Eye, Optic Nerve, and Extraocular Muscles With Golden Angle Radial MRI. Investigative ophthalmology & visual science, 58(10), 4390–4398. https://doi.org/10.1167/iovs.17-21861