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008			261006t20262026my a|||fr|||| 000 0 eng d
020			_aTHE0010816 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKPM .A23 2026 r Thesis
100			_aAbbas Shabudin, _eauthor.
245	1	0	_aInvestigating brain oedema formation in ischaemic stroke using poromechanics and multiscale modelling / _cAbbas Shabudin
264		1	_aKuantan, Pahang : _bUMPSA, _c2026
264		4	_c© 2026
300			_axvii, 136 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-COM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Manufacturing and Mechatronic Engineering Technology
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2026
504			_aIncludes bibliographical references
520	3		_aThis research investigates the estimation of effective poroelastic properties of brain tissue with particular emphasis on the role of capillary networks in cerebrovascular diseases such as ischaemic stroke. Brain oedema following iscahemic stroke leads to tissue swelling and increased intracranial pressure (ICP), which can result in severe neurological damage. Existing models often treat brain tissue as a homogenous material with uniform properties, neglecting the complex microvascular structure that governs fluid transport and mechanical response. Therefore, there is a need to remodel brain oedema formation by incorporating microscale capillary morphology to obtain more physiologically realistic predictions. A multiscale modelling framework based asymptotic expansion homogenization (AEH) is employed to estimate effective parameters governing brain tissue mechanics and fluid transport. These include the effective blood hydraulic conductivity tensor (𝑲), interstitial fluid hydraulic conductivity tensor (𝑮), Biot’s coefficients for blood (𝛼𝑐) and interstitial fluid (𝛼𝑡), and the effective Young’s modulus ( 𝐸̅) and Poisson’s ratio (𝑣̅). This study aims to (i) analyse the influence of capillary morphology using idealized microstructures, (ii) estimate and validate effective properties using data-driven capillary distributions, and (iii) apply the derived parameters to simulate brain tissue swelling at the macroscale under ischaemic conditions. Microscale analysis show that capillary tortuosity significantly reduces 𝑲 and affects 𝛼𝑐, while capillary radius influences all effective parameters. Data-driven microstructures were reconstructed from statistical capillary distributions and adapted to satisfy periodicity requirements for homogenization. Numerical implementation was carried out using finite element analysis within the AEH framework, where geometric modifications introduce changes in the representative volume element that may influence certain effective parameters. Validation against Poiseuille-based formulations demonstrates that the predicted blood conductivity remains within the same order of magnitude, confirming the consistency of the model. Macroscale simulations reveal that variations in capillary tortuosity and radius significantly influence interstitial fluid pressure (𝑃𝑡), blood pressure (𝑃𝑐) and tissue displacement (𝑢), capturing key features of brain oedema such as pressure gradients and swelling. The model also demonstrates that incorporating microscale capillary effects can be achieved with significantly reduced computational complexity compared to fully resolved models. The scientific novelty of this work lies in the integration of data-driven capillary morphology with a complete set of poroelastic effective parameters within a unified multiscale homogenization framework, together with a systematic sensitivity analysis of microstructural features. This contributes to the Body of Knowledge (BoK) in computational biomechanics, multiscale modelling of porous biological media, and poroelastic modelling of brain tissue. Practically, the proposed framework provides a foundation for patient-specific modelling, prediction of oedema progression, and potential clinical decision support in cerebrovascular disease management. Despite its contributions, the model assumes linear elastic tissue behavior, Newtonian blood flow, and periodic microstructures, which may limit quantitative accuracy. Nevertheless, it provides a robust and computationally efficient framework for capturing the dominant effects of microvascular morphology on brain tissue response.
610	2	0	_aFaculty of Manufacturing and Mechatronic Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aThesis _xDissertations
942			_2lcc _cTHESIS
999			_c105676 _d105682