PURPOSE The purpose of this study was to quantify the improvement in tumor tracking, with and without fiducial markers, afforded by employing a multi-layer (MLI) electronic portal imaging device (EPID) over the current state-of-the-art, single-layer, digital megavolt imager (DMI) architecture. METHODS An ideal observer signal-to-noise ratio (d') approach was used to quantify the ability of an MLI EPID and a current, state-of-the-art DMI EPID to track lung tumors from the treatment beam's-eye-view. Using each detector modulation transfer function (MTF) and noise power spectrum (NPS) as inputs, a detection task was employed with object functions describing simple three-dimensional Cartesian shapes (spheres and cylinders). Marker-less tumor tracking algorithms often use texture discrimination to differentiate benign and malignant tissue. The performance of such algorithms is simulated by employing a discrimination task for the ideal observer, which measures the ability of a system to differentiate two image quantities. These were defined as the measured textures for benign and malignant lung tissue. RESULTS The NNPS of the MLI ∼25% of that of the DMI at the expense of decreased MTF at intermediate frequencies (0.25≤<f≤<0.5 cycles/mm). Lung tissue textures were found to follow a power-law trend in the frequency domain. It was found that for both fiducial marker tracking and lung tissue texture differentiation, the signal power of each task is retained at low frequencies. Thus, little difference may be observed in signal power when DMI and MLI are compared. In all cases, improvements in tracking were greater than a factor of 2. CONCLUSION MLI performance in tumor tracking is greatly improved by the additional imager layers. This implies that further improvements in tracking may be gained through increasing the thickness of each MLI layer. For tracking, the MLI performance is limited by noise response. Losses in MTF result in negligible differences in d'. The project was partially supported by a grant from Varian Medical Systems, Inc. and grant No. R01CA188446-01 from the National Cancer Institute.