Estimation of Brain Biomechanics using MRI

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Abstract

The MRI scanner on which all our prior work on brain deformation using tagged MRI, the Siemens Biograph mMR 3T, was retired in early February of this year (2022). In FY2022, we have done considerable work moving the acquisition protocol for tagged MRI to a different MRI scanner, a Siemens Aera 1.5T. Pulse sequences were recompiled and parameters re-optimized, in part to compensate for differences in field strength. An advantage of moving the new scanner is that this scanner is currently equipped with the necessary hardware and software to perform MRE acquisitions, simplifying the logistics of scanning both tagging and MRE. We have successfully performed multiple phantom scans, as well as a test subject with the new scanner and are moving forward with recruitment soon. Prior to the scanner change, we had acquired 12 tagged data sets and 3 MRE data sets from healthy volunteers. We have prepared 10 new tagged data sets for upload to NITRC with 10 more pending after processing and quality review, for a total of 30 cumulative updates by the end of year 3. These include four datasets from two subjects in the 50+ age group (with both motions), and one subject in the 18-21 age group. In FY22, we have constructed a novel brain-skull phantom for evaluating our MRI acquisition techniques, under our supplemental NIH Bench-to-Bedside project on the characterization of skull brain mechanics in traumatic brain injury patients. The brain-skull phantom uses a cylindrical shape composed of polyacrylamide gel, and additionally has small, evenly spaced slots around the perimeter that can be filled with a fat simulant to simulate skull marrow. The polyacrylamide gel brain simulant can be prepared with a fixed boundary condition that moves with the surrounding container wall, or with a free boundary that it is only fixed at the bottom of the cylinder and has a fluid layer between the gel and the container wall. This provides multiple conditions under which our MRI acquisition characterizing skull-brain mechanics can be evaluated. In FY22, our collaborator, Curtis Johnson implemented the needed modifications to potentially allow for obtain MRE data from tissues of the brain (in which the signal comes from water protons) and from the skull (in which the signal comes from protons in marrow fat). Previous MRE methods required suppression of one or the other of these two tissues, interfering with the reliability by which one could measure the relative motion of brain and skull in an elastography experiment. In FY22, Under our collaboration with Drs. Michaelann Tartis/Ricardo Mejia-Alvarez/Adam Willis (New Mexico Tech/Mich St/SAMMC) on the biomechanical evaluation of biofidelic human head phantom, we have submitted a journal article to Mechanical Behavior of Biomedical Materials. In FY22 our proposal to investigate the skull brain interface in patients with a history of head trauma has moved forward to IRB review, after having passed through scientific review at the departmental and institute level. We hope to be able to begin patient recruitment in FY23. Achievements to date included the following: Acquired 12 tagged MRI data sets and 3 MRE data sets. Prepared 10 tagged MRI data sets for upload to NITRC, with 10 more pending after completion of processing and quality assurance. Migrated and re-optimized the tagged MRI acquisition protocol from a Siemens Biograph mMR 3T scanner to a Siemens Aera 1.5T scanner Submitted a journal paper on biomechanical characterization of a gyrencephalic phantom using tagged MRI and MRE. Developed a novel MRI phantom for evaluation of MRI techniques on characterization of skull-brain mechanics. Developed a novel MRI method for simultaneous acquisition of MRE data from skull and brain The following journal papers were submitted for publication this period. 1. A.K. Knutsen, S. Vidhate, G. McIlvain, J. Luster, E.J. Galindo, C.L. Johnson, D.L. Pham, J.A. Butman, R. Mejia-Alvarez, M. Tartis, A.M. Willis, Characterization of Material Properties and Deformation in the ANGUS Phantom during Mild Head Impacts using MRI, submitted to Journal of the Mechanical Behavior of Biomedical Materials, 2022. The following archival journal papers were accepted for publication this period. 2. A. Alshareef, A.K. Knutsen, C.L. Johnson, A. Carass, K. Upadhyay, P.V. Bayly, D.L. Pham, J.L. Prince, and K.T. Ramesh. Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain. Brain Multiphysics, 2:100038, 2021. 3. P.V. Bayly, A. Alshareef, A.K. Knutsen, K. Upadhyay, R.J. Okamoto A. Carass, J.A. Butman, D.L. Pham, J.L. Prince, K.T. Ramesh, and C.L. Johnson. MR imaging of human brain mechanics in vivo: new measurements to facilitate the development of computational models of brain injury. Annals of Biomedical Engineering, 2021. The following conference papers were accepted in this period. 4. A. Alshareef, C.L. Johnson, A. Carass, A.K. Knutsen, P.L. Delgorio, G. McIlvain, A.M. Diano, K.T. Ramesh, P.V. Bayly, D.L. Pham, and J.L. Prince. Relationship between cerebral vasculature and brain stiffness measured using MR elastography. In SPIE Medical Imaging, San Diego, CA, Feb 20-24, 2022. The following conference abstracts were accepted in this period. 5. S. Vidhate, A. Knutsen, G. McIlvain, J. Luster, C. Johnson, D. Pham, J. Butman, R. Mejia-Alvarez, M. Tartis, A. Willis. MRI Characterization of Material Properties and Deformation in the ANGUS Phantom during Mild Accelerative Loading. Military Health System Research Symposium, 2022. 6. A.M. Diano, G. McIlvain, A.K. Knutsen, S. Vidhate, D.L. Pham, C.L. Johnson. Quantifying relative displacement and phase at the skull-brain interface using MR elastography, International Society for Magnetic Resonance in Medicine, 2022. 7. A. Alshareef, P.V. Bayly, A.K. Knutsen, K. Upadhyay, R.J. Okamoto, A. Carass, J.A. Butman, D.L. Pham, J.L. Prince, K.T. Ramesh and C.L. Johnson. A publicly available dataset of human brain biomechanics using MR imaging for computational model validation, National Neurotrauma Society, 2022. 8. A. Alshareef, A.K. Knutsen, A. Carass, K. Upadhyay, R.J. Okamoto, C.L. Johnson, P.V. Bayly, D.L. Pham, K.T. Ramesh, and J.L. Prince. A publicly available dataset of human brain biomechanics using MR imaging for computational model validation, National Neurotrauma Society, 2022.