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Scanning Ion Conductance Microscopy (SICM) provides high-resolution, nanoscale imaging of living cells, but it is generally limited by a slow scan rate, making it challenging to capture dynamic processes in real time. To tackle this challenge, an integrated data acquisition and computational framework is proposed that improves the temporal resolution of SICM by selectively skipping certain scan lines. A partial convolutional neural network (Partial-CNN) model is developed and trained on SICM images and their corresponding masks to reconstruct the complete images from the undersampled data, ensuring the retention of structural integrity. This approach significantly reduces the image acquisition time (i.e., by 30-63%) without compromising quality, as validated through multiple quantitative metrics. Compared to conventional deep learning methods, the Partial-CNN demonstrates higher accuracy in reconstructing fine details and maintaining consistent height maps across skipped regions. It is shown that this method provides an increased temporal resolution and retains image fidelity, making it suitable for real-time dynamic SICM imaging and improving the smart scanning microscopy applications in time-resolved biological imaging.
Ayar et al. (Mon,) studied this question.