Image guided radiation therapy solutions based on megavoltage computed tomography (MVCT) involve the extension of electronic portal imaging devices (EPIDs) from their traditional role of weekly localization imaging and planar dose mapping to volumetric imaging for 3D setup and dose verification. To sustain the potential advantages of MVCT, EPIDs are required to provide improved levels of portal image quality. Therefore, it is vital that the performance of EPIDs in clinical use is maintained at an optimal level through regular and rigorous quality assurance (QA). Traditionally, portal imaging QA has been carried out by imaging calibrated line‐pair and contrast resolution phantoms and obtaining arbitrarily defined QA indices that are usually dependent on imaging conditions and merely indicate relative trends in imaging performance. They are not adequately sensitive to all aspects of image quality unlike fundamental imaging metrics such as the modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE) that are widely used to characterize detector performance in radiographic imaging and would be ideal for QA purposes. However, due to the difficulty of performing conventional MTF measurements, they have not been used for routine clinical QA. The authors present a simple and quick QA methodology based on obtaining the MTF, NPS, and DQE of a megavoltage imager by imaging standard open fields and a bar‐pattern QA phantom containing 2 mm thick tungsten line‐pair bar resolution targets. Our bar‐pattern based MTF measurement features a novel zero‐frequency normalization scheme that eliminates normalization errors typically associated with traditional bar‐pattern measurements at megavoltage x‐ray energies. The bar‐pattern QA phantom and open‐field images are used in conjunction with an automated image analysis algorithm that quickly computes the MTF, NPS, and DQE of an EPID system. Our approach combines the fundamental advantages of linear systems metrics such as robustness, sensitivity across the full spatial frequency range of interest, and normalization to imaging conditions (magnification, system gain settings, and exposure), with the simplicity, ease, and speed of traditional phantom imaging. The algorithm was analyzed for accuracy and sensitivity by comparing with a commercial portal imaging QA method (PIPSPRO™, Standard Imaging, Middleton, WI) on both first‐generation lens‐coupled and modern flat‐panel based clinical EPID systems. The bar‐pattern based QA measurements were found to be far more sensitive to even small levels of degradation in spatial resolution and noise. The bar‐pattern based QA methodology offers a comprehensive image quality assessment tool suitable for both commissioning and routine EPID QA.