External beam radiation therapy (EBRT) delivers targeted radiation from outside the body to destroy cancer cells. Plan-specific quality assurance (PSQA) ensures accurate dose delivery for each treatment. Electronic portal imaging devices (EPIDs) are commonly used for PSQA, as they are widely available in linacs and have proven effective for dosimetric verification.This thesis aims to enhance a 3D EPID-based in vivo dosimetry (EIVD) system for large-scale EBRT dosimetric verification, focusing on automation, accuracy, error detection, and online MRI-guided adaptive workflows.Two extensions to an existing transit EIVD algorithm were developed to enable accurate 3D patient dose reconstruction for both in vivo (transit) and pre-treatment (non-transit) 3D EPID dosimetry, including treatment disease sites with significant tissue inhomogeneities.Sensitivity and specificity analyses revealed variability in performance across error types and magnitudes, indicators, and treatment sites, challenging the conventional use of universal γ-pass rate tolerance limits for error detection. Additionally, deep learning techniques were explored to distinguish between generic and plan-specific deviations during the EIVD review process.Automated EIVD was shown to be a feasible independent end-to-end check for online adaptive strategies on the Unity MR-Linac, highlighting its potential in similar solutions, such as upcoming online CBCT-guided adaptive workflows.Key barriers to clinical adoption include vendor-user gaps, non-water-equivalent EPID dose response, lack of open systems for accessing EPID data and metadata, limitations in EPID acquisition software, automation issues and workflow complexities. Overcoming these challenges is essential for broader clinical implementation of EIVD
