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Prediction Of Laser Ablation In Brain: Sensitivity, Calibration, And Validation, Samuel J. Fahrenholtz
Prediction Of Laser Ablation In Brain: Sensitivity, Calibration, And Validation, Samuel J. Fahrenholtz
Dissertations & Theses (Open Access)
The surgical planning of MR-guided laser induced thermal therapy (MRgLITT) stands to benefit from predictive computational modeling. The dearth of physical model parameter data leads to modeling uncertainty. This work implements a well-accepted framework with three key steps for model-building: model-parameter sensitivity analysis, model calibration, and model validation.
The sensitivity study is via generalized polynomial chaos (gPC) paired with a transient finite element (FEM) model. Uniform probability distribution functions (PDFs) capture the plausible range of values suggested by the literature for five model parameters. The five PDFs are input separately into the FEM model to gain a probabilistic sensitivity response …
Investigation Of Quatitative Image Features From Pretreatment Ct And Fdg-Pet Scans In Stage Iii Nsclc Patients Undergoing Defintive Radiation Therapy, David Fried
Dissertations & Theses (Open Access)
The purpose of this work was to determine if quantitative image features (QIFs) extracted from computed tomography (CT) and flourodeoxyglucose (FDG) positron emission tomography (PET) could provide prognostic information to improve outcome models. Our goal for this work was to determine if it may one day be feasible to incorporate QIFs into personalized cancer care. QIFs were used to quantitatively characterize patient disease as seen on imaging. A leave-one-out cross-validation procedure was used to assess the prognostic ability of QIFs extracted from CT and PET in addition to conventional prognostic factors (CPFs). QIFs were found to improve model fit for …