Computational Modelling and Treatment Optimization of Acute Endovascular and Respiratory Conditions
This thesis aims to use computational tools and a deterministic clinical design process to optimize treatment for acute endovascular and respiratory conditions. Specifically, focus is placed on optimizing treatment for two acute pathologies: (1) the Coronavirus disease 2019 (COVID-19), and (2) Abdom...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/138959 https://orcid.org/0000-0003-0863-9063 |
| _version_ | 1826204525925498880 |
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| author | Dillon, Tom |
| author2 | Roche, Ellen T. |
| author_facet | Roche, Ellen T. Dillon, Tom |
| author_sort | Dillon, Tom |
| collection | MIT |
| description | This thesis aims to use computational tools and a deterministic clinical design process to optimize treatment for acute endovascular and respiratory conditions. Specifically, focus is placed on optimizing treatment for two acute pathologies: (1) the Coronavirus disease 2019 (COVID-19), and (2) Abdominal Aortic Aneurysms (AAA).
In light of the recent COVID-19 pandemic, a low-cost, rapidly deployable emergency ventilator design using a novel fluidic oscillator was developed. The design addresses potential ventilator shortages resulting from the ongoing and future pandemics by converting a continuous positive airway pressure (CPAP) machine into a mechanical ventilator using a part that is (i) inexpensive, (ii) easily manufactured without the need for specialized equipment, (iii) simple to assemble and maintain, (iv) does not require any electronics, and (v) has no moving components that could be prone to failure. A Computational Fluid Dynamics (CFD) model was used to assess flow characteristics of the system, and a prototype was developed and tested with a commercial benchtop respiratory simulator. The simulations showed clinically relevant periodic oscillations and outlet pressures between 8-23 cm H2O. Both the prototype and simulations responded promptly to disrupted oscillations, an analogue for patient‐initiated breaths.
AAA is a swelling in the lower portion of the aorta, the largest blood vessel in the body. The incidence of this potentially fatal condition is 5-10 cases per 100,000 in the U.S.. The preferred treatment for AAA is minimally invasive endovascular repair (EVAR), whereby a compliant tubular material reinforced with a metallic stent (an endograft) is implanted inside the aneurysm. For aneurysms that extend across major abdominal vessels (juxtarenal aneurysms), a fenestrated (or sub-branched) endograft is required. The lead time to obtain a patient-specific fenestrated graft from a commercial manufacturer is in the order of a few weeks, which is often unsuitable for patients that present with an emergent medical condition. Physicians instead choose mostly to manually modify off-the-shelf non-fenestrated endografts, though this process is often tedious and subject to calculation inaccuracies. In this thesis, a computer program for automated fitting of fenestrations on non-fenestrated endografts is proposed - "FenFit". FenFit provides the physician with an efficient, intuitive user interface for modifying endovascular grafts, developed using MATLAB GUI designer. A novel search algorithm using 3D to 2D projection mapping was developed to determine the optimal placement of fenestrations on the endograft at reduced computational cost, and a bijective conformal mapping algorithm was developed for texture mapping of the fenestrations to the 3D aortic graft space. A pilot clinical study was conducted in conjunction with our collaborators at Beth Israel Deaconess Medical Center (BIDMC), Boston, to evaluate the efficiency of FenFit against physician manual planning. Results to date have shown that FenFit can reduce workflow planning time from 22.5 minutes to 32 seconds (n = 25, p < 0.001). In 20% of cases, FenFit found a valid graft alignment where the physician could not via trial and error.
Guided by computational tools, these combined bodies of work propose expedited, patient-specific treatment for urgent medical conditions. It is hoped that these accelerated treatment regimes may ultimately translate to improved clinical outcomes and reduced fatality rates. |
| first_indexed | 2024-09-23T12:56:55Z |
| format | Thesis |
| id | mit-1721.1/138959 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:56:55Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1389592022-01-15T03:30:11Z Computational Modelling and Treatment Optimization of Acute Endovascular and Respiratory Conditions Dillon, Tom Roche, Ellen T. Massachusetts Institute of Technology. Department of Mechanical Engineering This thesis aims to use computational tools and a deterministic clinical design process to optimize treatment for acute endovascular and respiratory conditions. Specifically, focus is placed on optimizing treatment for two acute pathologies: (1) the Coronavirus disease 2019 (COVID-19), and (2) Abdominal Aortic Aneurysms (AAA). In light of the recent COVID-19 pandemic, a low-cost, rapidly deployable emergency ventilator design using a novel fluidic oscillator was developed. The design addresses potential ventilator shortages resulting from the ongoing and future pandemics by converting a continuous positive airway pressure (CPAP) machine into a mechanical ventilator using a part that is (i) inexpensive, (ii) easily manufactured without the need for specialized equipment, (iii) simple to assemble and maintain, (iv) does not require any electronics, and (v) has no moving components that could be prone to failure. A Computational Fluid Dynamics (CFD) model was used to assess flow characteristics of the system, and a prototype was developed and tested with a commercial benchtop respiratory simulator. The simulations showed clinically relevant periodic oscillations and outlet pressures between 8-23 cm H2O. Both the prototype and simulations responded promptly to disrupted oscillations, an analogue for patient‐initiated breaths. AAA is a swelling in the lower portion of the aorta, the largest blood vessel in the body. The incidence of this potentially fatal condition is 5-10 cases per 100,000 in the U.S.. The preferred treatment for AAA is minimally invasive endovascular repair (EVAR), whereby a compliant tubular material reinforced with a metallic stent (an endograft) is implanted inside the aneurysm. For aneurysms that extend across major abdominal vessels (juxtarenal aneurysms), a fenestrated (or sub-branched) endograft is required. The lead time to obtain a patient-specific fenestrated graft from a commercial manufacturer is in the order of a few weeks, which is often unsuitable for patients that present with an emergent medical condition. Physicians instead choose mostly to manually modify off-the-shelf non-fenestrated endografts, though this process is often tedious and subject to calculation inaccuracies. In this thesis, a computer program for automated fitting of fenestrations on non-fenestrated endografts is proposed - "FenFit". FenFit provides the physician with an efficient, intuitive user interface for modifying endovascular grafts, developed using MATLAB GUI designer. A novel search algorithm using 3D to 2D projection mapping was developed to determine the optimal placement of fenestrations on the endograft at reduced computational cost, and a bijective conformal mapping algorithm was developed for texture mapping of the fenestrations to the 3D aortic graft space. A pilot clinical study was conducted in conjunction with our collaborators at Beth Israel Deaconess Medical Center (BIDMC), Boston, to evaluate the efficiency of FenFit against physician manual planning. Results to date have shown that FenFit can reduce workflow planning time from 22.5 minutes to 32 seconds (n = 25, p < 0.001). In 20% of cases, FenFit found a valid graft alignment where the physician could not via trial and error. Guided by computational tools, these combined bodies of work propose expedited, patient-specific treatment for urgent medical conditions. It is hoped that these accelerated treatment regimes may ultimately translate to improved clinical outcomes and reduced fatality rates. S.M. 2022-01-14T14:41:03Z 2022-01-14T14:41:03Z 2021-06 2021-06-30T15:16:17.916Z Thesis https://hdl.handle.net/1721.1/138959 https://orcid.org/0000-0003-0863-9063 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Dillon, Tom Computational Modelling and Treatment Optimization of Acute Endovascular and Respiratory Conditions |
| title | Computational Modelling and Treatment Optimization of Acute Endovascular and Respiratory Conditions |
| title_full | Computational Modelling and Treatment Optimization of Acute Endovascular and Respiratory Conditions |
| title_fullStr | Computational Modelling and Treatment Optimization of Acute Endovascular and Respiratory Conditions |
| title_full_unstemmed | Computational Modelling and Treatment Optimization of Acute Endovascular and Respiratory Conditions |
| title_short | Computational Modelling and Treatment Optimization of Acute Endovascular and Respiratory Conditions |
| title_sort | computational modelling and treatment optimization of acute endovascular and respiratory conditions |
| url | https://hdl.handle.net/1721.1/138959 https://orcid.org/0000-0003-0863-9063 |
| work_keys_str_mv | AT dillontom computationalmodellingandtreatmentoptimizationofacuteendovascularandrespiratoryconditions |