The spatial resolution of optical imaging systems is fundamentally restricted by the diffraction limit. However, in widefield live-cell microscopy, the achievable resolution is further constrained by specimen motion, which indicates the existence of a fundamental spatio-temporal resolution trade-off between signal accumulation during full frame integration and the resulting motion blur. To improve the fidelity with which moving objects can be imaged, a quantitative understanding of this spatio-temporal trade-off is necessary. Here, we present a systematic analysis of motion-induced resolution dynamics measured with the spectral signal-to-noise ratio (SSNR). We developed a simulation framework that models the image formation of objects undergoing arbitrary motion to evaluate the degradation of the spatial resolution under translational, diffusive, and rotational dynamics. Our results demonstrate that for translating objects, the spatial resolution is anisotropically reduced as a function of the orientation of the object relative to the motion vector, leading to the SSNR degrading by up to 50% and the resolution by up to 40% for a 90° change in the motion direction. This motion-blur-induced spectral degradation was further observed on simulated points under Brownian motion and dynamic Drosophila data. Moreover, we show that for rotational motion, conventional radially averaged metrics such as the Fourier ring correlation cannot quantify the effects of angular blur, which the SSNR quantifies accurately. These findings underscore the necessity of an object-oriented imaging approach, in which acquisition parameters such as exposure time are tuned to specific biological spatio-temporal characteristics to optimize the trade-off between motion blur and spatial fidelity.

This study introduces an innovative approach combining deep-learning techniques with classical physics-based electrocardiographic imaging (ECGI) methods. Our objective is to enhance the accuracy and robustness of ECGI reconstructions. We reshape the optimization expression by splitting variables and formulating building blocks based on update expressions. Specifically, we propose a sequential application of analytical solutions and denoiser neural network blocks (PULSE). The denoiser learns the proximal operator associated with the prior distribution of cardiac potentials directly from data, avoiding hand-crafted assumptions about the distribution. The proposed method is compared with zero-order Tikhonov regularization, Bayesian MAP estimation, and an end-to-end learning technique. We achieved more than 10% improvement in all metrics over Bayesian-MAP, end-to-end learning, and Tikhonov solutions. The performance remained consistent throughout cardiac beats, resulting in a 60% reduction in the interquartile ranges of the reconstruction metrics. Geometric variations did not compromise accuracy, with a median localization error consistently below 1 cm. Our framework, adaptable to classical methods, augments the clinical pipeline. Improving the accuracy and robustness of pacing site localization holds significant promise for premature ventricular contraction (PVC) research.