{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/398822"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/398822","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Biomechanics of Cranial Decompression Procedures in Traumatic Brain Injury","abstract":"Cranial decompression procedures such as decompressive craniectomy remove a portion of the skull to relieve elevated intracranial pressure in severe TBI cases. As these procedures can be seen as a mechanical, pressure vessel type problem, engineering methods such as finite element (FE) modelling may be used to inform neurosurgeons of the effects of this type of surgery so they can optimise on both a patient-specific and more general basis. This thesis develops finite element models of decompressive craniectomy using patient CT data. Load cases include uniform (diffuse) and focal swelling scenarios. The same load cases are evaluated for alternative hinge and floating craniotomy procedures to facilitate comparison. Main findings of this finite element analysis comparison are that decompressive craniectomy allows for higher maximum brain surface displacement (more herniation) and a corresponding higher average maximum principal strain at the anterior region of interest compared to hinge then to floating craniotomy. The hinge and floating craniotomy procedures resulted in a different deformation and strain pattern, with additional, small strain concentrations where brain tissue contacts with an edge. This thesis also seeks to evaluate whether the conditions that cause strain concentration can be linked to tissue damage using neuroimaging. A cohort of 20 patients with longitudinal data were processed to get additional herniation area on a scan by scan basis. A linear mixed effect model was fitted to this data and multiple comparisons corrected through using estimated marginal means. Significant difference in herniation area between patients with scan at 0-48 hour timepoint and scan at 8-42 days (‘subacute’) were observed, p < 0.05 as well as between the 8-42 day (‘subacute’) timepoint category and 42 day + (‘chronic’) timepoint category, p < 0.01. Additional analysis of herniation area contour was performed through fitting an ellipse. Strong correlation of ellipse vs. contour area (0.96) enabled development of h parameter for clinical herniation monitoring. Finally, diffusion MRI was used to evaluate differences in white matter integrity (fractional anisotropy, FA) at the regions of strain concentration observed in strain modelling and mirror regions on the contralateral to craniectomy (control) hemisphere. A linear mixed effects model was performed for both anterior and posterior regions, with no significant differences observed in FA over time but statistically significant differences observed between regions. In the anterior region, a statistically significant correlation was found between herniation area and fractional anisotropy, additional to existing differences between control and craniectomy hemisphere (p < 0.014). In summary, this work demonstrates three key findings: (1) finite element modelling effectively predicts strain patterns in decompressive procedures with decompressive craniectomy showing higher deformation and strain to alternative procedures; (2) Herniation area changes significantly over time, peaking before 6 weeks post surgery, with the novel h parameter providing a mathematically rigorous tool for clinical monitoring; (3) diffusion MRI confirms quantifiable tissue changes at predicted high strain regions in cases with herniation, validating FE model predictions. Together, these findings provide both predictive modelling tools and clinical monitoring methods, from which cranial decompression procedures can be optimised.","abstract_html":"Cranial decompression procedures such as decompressive craniectomy remove a portion of the skull to relieve elevated intracranial pressure in severe TBI cases. As these procedures can be seen as a mechanical, pressure vessel type problem, engineering methods such as finite element (FE) modelling may be used to inform neurosurgeons of the effects of this type of surgery so they can optimise on both a patient-specific and more general basis. This thesis develops finite element models of decompressive craniectomy using patient CT data. Load cases include uniform (diffuse) and focal swelling scenarios. The same load cases are evaluated for alternative hinge and floating craniotomy procedures to facilitate comparison. Main findings of this finite element analysis comparison are that decompressive craniectomy allows for higher maximum brain surface displacement (more herniation) and a corresponding higher average maximum principal strain at the anterior region of interest compared to hinge then to floating craniotomy. The hinge and floating craniotomy procedures resulted in a different deformation and strain pattern, with additional, small strain concentrations where brain tissue contacts with an edge. This thesis also seeks to evaluate whether the conditions that cause strain concentration can be linked to tissue damage using neuroimaging. A cohort of 20 patients with longitudinal data were processed to get additional herniation area on a scan by scan basis. A linear mixed effect model was fitted to this data and multiple comparisons corrected through using estimated marginal means. Significant difference in herniation area between patients with scan at 0-48 hour timepoint and scan at 8-42 days (‘subacute’) were observed, p &lt; 0.05 as well as between the 8-42 day (‘subacute’) timepoint category and 42 day + (‘chronic’) timepoint category, p &lt; 0.01. Additional analysis of herniation area contour was performed through fitting an ellipse. Strong correlation of ellipse vs. contour area (0.96) enabled development of h parameter for clinical herniation monitoring. Finally, diffusion MRI was used to evaluate differences in white matter integrity (fractional anisotropy, FA) at the regions of strain concentration observed in strain modelling and mirror regions on the contralateral to craniectomy (control) hemisphere. A linear mixed effects model was performed for both anterior and posterior regions, with no significant differences observed in FA over time but statistically significant differences observed between regions. In the anterior region, a statistically significant correlation was found between herniation area and fractional anisotropy, additional to existing differences between control and craniectomy hemisphere (p &lt; 0.014). In summary, this work demonstrates three key findings: (1) finite element modelling effectively predicts strain patterns in decompressive procedures with decompressive craniectomy showing higher deformation and strain to alternative procedures; (2) Herniation area changes significantly over time, peaking before 6 weeks post surgery, with the novel h parameter providing a mathematically rigorous tool for clinical monitoring; (3) diffusion MRI confirms quantifiable tissue changes at predicted high strain regions in cases with herniation, validating FE model predictions. Together, these findings provide both predictive modelling tools and clinical monitoring methods, from which cranial decompression procedures can be optimised.","abstract_has_math":false,"creators":["Brass, Charlotte"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Sutcliffe, Michael"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-08","date_published":"2025-08-08","updated_at":"2026-07-22T22:24:18Z","subjects":["Traumatic Brain Injury","Finite Element Modelling","Neuroimaging","Diffusion MRI","MRI","Biomechanics","Brain","Decompressive Craniectomy","Hinge Craniotomy","Floating Craniotomy","Expansile Craniotomy","Structural MRI"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/a6606b38-fc3f-4f2e-a88e-71f758951bcc/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000197294460"],"render_values":[{"text":"0000-0001-9729-4460","href":"https://orcid.org/0000-0001-9729-4460","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.127575","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Sutcliffe, Michael"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["WD Armstrong Trust Pembroke College Department of Engineering"]},{"key":"dc:creator","label":"Author","values":["Brass, Charlotte"]},{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000197294460"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-08-08"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/398822"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Traumatic Brain Injury","Finite Element Modelling","Neuroimaging","Diffusion MRI","MRI","Biomechanics","Brain","Decompressive Craniectomy","Hinge Craniotomy","Floating Craniotomy","Expansile Craniotomy","Structural MRI"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/a6606b38-fc3f-4f2e-a88e-71f758951bcc/download","http://purl.org/NET/rdflicense/allrightsreserved"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.127575"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/72f03ab7-58f3-4b31-a0b6-8d0ce7cb608d/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Cranial decompression procedures such as decompressive craniectomy remove a portion of the skull to relieve elevated intracranial pressure in severe TBI cases. 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The hinge and floating craniotomy procedures resulted in a different deformation and strain pattern, with additional, small strain concentrations where brain tissue contacts with an edge. This thesis also seeks to evaluate whether the conditions that cause strain concentration can be linked to tissue damage using neuroimaging. A cohort of 20 patients with longitudinal data were processed to get additional herniation area on a scan by scan basis. A linear mixed effect model was fitted to this data and multiple comparisons corrected through using estimated marginal means. Significant difference in herniation area between patients with scan at 0-48 hour timepoint and scan at 8-42 days (‘subacute’) were observed, p < 0.05 as well as between the 8-42 day (‘subacute’) timepoint category and 42 day + (‘chronic’) timepoint category, p < 0.01. Additional analysis of herniation area contour was performed through fitting an ellipse. Strong correlation of ellipse vs. contour area (0.96) enabled development of h parameter for clinical herniation monitoring. Finally, diffusion MRI was used to evaluate differences in white matter integrity (fractional anisotropy, FA) at the regions of strain concentration observed in strain modelling and mirror regions on the contralateral to craniectomy (control) hemisphere. A linear mixed effects model was performed for both anterior and posterior regions, with no significant differences observed in FA over time but statistically significant differences observed between regions. In the anterior region, a statistically significant correlation was found between herniation area and fractional anisotropy, additional to existing differences between control and craniectomy hemisphere (p < 0.014). In summary, this work demonstrates three key findings: (1) finite element modelling effectively predicts strain patterns in decompressive procedures with decompressive craniectomy showing higher deformation and strain to alternative procedures; (2) Herniation area changes significantly over time, peaking before 6 weeks post surgery, with the novel h parameter providing a mathematically rigorous tool for clinical monitoring; (3) diffusion MRI confirms quantifiable tissue changes at predicted high strain regions in cases with herniation, validating FE model predictions. Together, these findings provide both predictive modelling tools and clinical monitoring methods, from which cranial decompression procedures can be optimised."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["caae057766168247e958edff07d7140c","87eda9de84448d1f82354d60eee3eb5f"]},{"key":"dc:title","label":"Title","values":["Biomechanics of Cranial Decompression Procedures in Traumatic Brain Injury"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sutcliffe, Michael"],"dc:contributor.sponsor":["WD Armstrong Trust Pembroke College Department of Engineering"],"dc:creator":["Brass, Charlotte"],"dc:creator.authoridentifier":["0000000197294460"],"dc:date.issued":["2025-08-08"],"dc:description.abstract":["Cranial decompression procedures such as decompressive craniectomy remove a portion of the skull to relieve elevated intracranial pressure in severe TBI cases. 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The hinge and floating craniotomy procedures resulted in a different deformation and strain pattern, with additional, small strain concentrations where brain tissue contacts with an edge. This thesis also seeks to evaluate whether the conditions that cause strain concentration can be linked to tissue damage using neuroimaging. A cohort of 20 patients with longitudinal data were processed to get additional herniation area on a scan by scan basis. A linear mixed effect model was fitted to this data and multiple comparisons corrected through using estimated marginal means. Significant difference in herniation area between patients with scan at 0-48 hour timepoint and scan at 8-42 days (‘subacute’) were observed, p < 0.05 as well as between the 8-42 day (‘subacute’) timepoint category and 42 day + (‘chronic’) timepoint category, p < 0.01. Additional analysis of herniation area contour was performed through fitting an ellipse. Strong correlation of ellipse vs. contour area (0.96) enabled development of h parameter for clinical herniation monitoring. Finally, diffusion MRI was used to evaluate differences in white matter integrity (fractional anisotropy, FA) at the regions of strain concentration observed in strain modelling and mirror regions on the contralateral to craniectomy (control) hemisphere. A linear mixed effects model was performed for both anterior and posterior regions, with no significant differences observed in FA over time but statistically significant differences observed between regions. In the anterior region, a statistically significant correlation was found between herniation area and fractional anisotropy, additional to existing differences between control and craniectomy hemisphere (p < 0.014). In summary, this work demonstrates three key findings: (1) finite element modelling effectively predicts strain patterns in decompressive procedures with decompressive craniectomy showing higher deformation and strain to alternative procedures; (2) Herniation area changes significantly over time, peaking before 6 weeks post surgery, with the novel h parameter providing a mathematically rigorous tool for clinical monitoring; (3) diffusion MRI confirms quantifiable tissue changes at predicted high strain regions in cases with herniation, validating FE model predictions. 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