Estimating Microvascular Parameters with Pseudo-Random Vascular Fingerprinting: Phantom Validation
Poster No:
1939
Submission Type:
Abstract Submission
Authors:
Mina Namej1, Jonathan Polimeni2, Kamil Uludag3, Molly Bright4, Catie Chang5, J. Jean Chen6
Institutions:
1Univesity of Toronto, Toronto, Ontario, 2Massachusetts General Hospital and Harvard Medical School, Boston, MA, 3University of Toronto, Toronto, Ontario, 4Northwestern University, Chicago, IL, 5Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, 6Rotman Research Institute, Baycrest Health Sciences, Toronto, MT
First Author:
Co-Author(s):
Introduction:
Magnetic Resonance (MR) vascular fingerprinting is a promising technique for non-invasive vascular assessment. Traditional methods use spin echo sequences, but they struggle with complex vascular geometries. This study explores the use of a pseudo-random sequence to improve MR vascular fingerprinting accuracy.
We incorporate a 3D random vessel simulation to better represent real vascular structures. Additionally, we use a phantom with polystyrene microspheres doped with dysprosium. Dysprosium mimics varying blood oxygenation in the brain, while the sizes of the spheres represent different vessel sizes, and the density of microspheres simulates cerebral blood volume (CBV).
We incorporate a 3D random vessel simulation to better represent real vascular structures. Additionally, we use a phantom with polystyrene microspheres doped with dysprosium. Dysprosium mimics varying blood oxygenation in the brain, while the sizes of the spheres represent different vessel sizes, and the density of microspheres simulates cerebral blood volume (CBV).
Methods:
A pseudo-random sequence was optimized to maximize the contrast-to-noise ratio for three targeted estimates: sphere size, oxygenation, and sphere concentration. Standard relaxometry was used in advance to provide T1/T2 values for the model.
Voxels were assumed to be evenly distributed with spheres, and any uneven distribution was ignored. Field inhomogeneities were inferred from computational simulations according to existing literature. A dictionary was created and employed for parameter estimation, with a size of CBV (steps) x SO2 (steps) x Radius (steps) = 200 x 175 x 150 elements.
The sequence was implemented on a Siemens 3T Prisma scanner, and a 64-channel head coil was used for all scans. Fifty different phantoms were constructed, varying in sphere sizes (2 µm - 100 µm), oxygenation levels (0% - 100%), and cerebral blood volume (CBV) ranging from 1% to 20%.
Voxels were assumed to be evenly distributed with spheres, and any uneven distribution was ignored. Field inhomogeneities were inferred from computational simulations according to existing literature. A dictionary was created and employed for parameter estimation, with a size of CBV (steps) x SO2 (steps) x Radius (steps) = 200 x 175 x 150 elements.
The sequence was implemented on a Siemens 3T Prisma scanner, and a 64-channel head coil was used for all scans. Fifty different phantoms were constructed, varying in sphere sizes (2 µm - 100 µm), oxygenation levels (0% - 100%), and cerebral blood volume (CBV) ranging from 1% to 20%.
Results:
Three different pseudo-random configurations were tested to assess their impact on the accuracy of the estimations as can be seen in Figure 1. For all the phantoms that were built, the derived values for the accuracy of all estimations in terms of R² were as follows:
Configuration 1:
SO₂: 0.74 ± 0.12
CBV: 0.81 ± 0.17
Radius: 0.83 ± 0.18
Configuration 2:
SO₂: 0.76 ± 0.11
CBV: 0.82 ± 0.16
Radius: 0.85 ± 0.17
Configuration 3:
SO₂: 0.73 ± 0.13
CBV: 0.80 ± 0.18
Radius: 0.82 ± 0.19
These results demonstrate the robustness of the proposed method across different configurations, with consistent accuracy in estimating SO₂, CBV, and radius.
Configuration 1:
SO₂: 0.74 ± 0.12
CBV: 0.81 ± 0.17
Radius: 0.83 ± 0.18
Configuration 2:
SO₂: 0.76 ± 0.11
CBV: 0.82 ± 0.16
Radius: 0.85 ± 0.17
Configuration 3:
SO₂: 0.73 ± 0.13
CBV: 0.80 ± 0.18
Radius: 0.82 ± 0.19
These results demonstrate the robustness of the proposed method across different configurations, with consistent accuracy in estimating SO₂, CBV, and radius.
·Figure 1. Three different design configurations for a pseudorandom sequence with varying flip angles for our MR vascular fingerprinting
Conclusions:
The proposed method utilizing a pseudo-random sequence for MR vascular fingerprinting demonstrates significantly improved robustness and reproducibility compared to traditional spin-echo-based techniques. The pseudo-random design helps to capture complicated signal dynamics, thereby increasing the accuracy of the results. The consistent accuracy of sphere size, oxygenation, and CBV estimates across different configurations highlights the reliability of this approach. The use of dysprosium as a contrast agent and the incorporation of 3D random vessel simulations contribute to more accurate and realistic assessments.
These advancements suggest that the pseudo-random sequence-based MR vascular fingerprinting method holds great potential for clinical practice. It offers a reliable tool for non-invasive vascular assessment, enabling better diagnosis and treatment planning for various vascular conditions.
These advancements suggest that the pseudo-random sequence-based MR vascular fingerprinting method holds great potential for clinical practice. It offers a reliable tool for non-invasive vascular assessment, enabling better diagnosis and treatment planning for various vascular conditions.
Novel Imaging Acquisition Methods:
Imaging Methods Other 1
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics 2
Keywords:
Data analysis
Design and Analysis
Modeling
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Tippareddy, C., Zhao, W., Sunshine, J. L., Griswold, M. A., Ma, D., & Badve, C. (2021). Magnetic resonance fingerprinting: An overview. European Journal of Nuclear Medicine and Molecular Imaging, 48(6), 4189-4200. https://doi.org/10.1007/s00259-021-05384-2
Delphin, A., Boux, F., Brossard, C., Coudert, T., Warnking, J. M., Lemasson, B., Barbier, E. L., & Christen, T. (2023). Enhancing MR vascular Fingerprinting through realistic microvascular geometries. arXiv preprint arXiv:2305.17092. https://doi.org/10.48550/arXiv.2305.17092
Christen, T., Pannetier, N. A., Ni, W., Qiu, D., Moseley, M., Schuff, N., & Zaharchuk, G. (2014). MR vascular fingerprinting: A new approach to compute cerebral blood volume, mean vessel radius, and oxygenation maps in the human brain. NeuroImage, 89, 262-270. https://doi.org/10.1016/j.neuroimage.2013.11.052
Barrier, A., Coudert, T., Delphin, A., Lemasson, B., & Christen, T. (2024). MARVEL: MR Fingerprinting with Additional micRoVascular Estimates using bidirectional LSTMs. Proceedings of the Medical Image Computing and Computer Assisted Intervention Conference (MICCAI). https://papers.miccai.org/miccai-2024/paper/2226_paper.pdf
Delphin, A., Boux, F., Brossard, C., Coudert, T., Warnking, J. M., Lemasson, B., Barbier, E. L., & Christen, T. (2023). Enhancing MR vascular Fingerprinting through realistic microvascular geometries. arXiv preprint arXiv:2305.17092. https://doi.org/10.48550/arXiv.2305.17092
Delphin, A., Boux, F., Brossard, C., Coudert, T., Warnking, J. M., Lemasson, B., Barbier, E. L., & Christen, T. (2024). Enhancing MR vascular Fingerprinting with realistic microvascular geometries. Imaging Neuroscience, MIT Press. https://doi.org/10.1162/imag_a_00377