Fig. 2: Imaging pipeline and workflow.

a The architecture of piTEAM pipeline. It is composed of distributed autoTEMs for parallel imaging, an image record database, data servers, a sample database (TAO) and Multi-System Monitor (MSM). On each individual autoTEM, imaging is operated through pyTEM (acquisition software), pyTEM server, pyTEM GUI and TEM Graph. b pyTEM GUI. The left EM image is a preview while the right is an example of parallel imaging on five systems for 1 mm² montage. The pyTEM GUI provides the user with an intuitive, web-based interface to perform manual imaging surveys as well as long serial montage runs containing hundreds or thousands of ROIs. From the web UI, any running autoTEM system can be observed and controlled. c TEM Graph key components. Images are acquired and loaded into GPU memory. A series of filter graphs apply corrections to the image (flatfield, down sampling for GUI preview). Separate graphs check image quality and statistics while the image is written to disk in parallel. d Closed-loop imaging workflow. After pyTEM receives ROIs and acquisition parameters, image acquisition is triggered, and image data are then analyzed on-the-fly on the acquisition computer. Rejected montages (those failing to meet QC thresholds) are flagged as a montage database instance to be re-imaged. If a montage passes inspection, it is sent to a data center for post-processing, alignment, and storage.