Abstract
The introduction of fast CMOS detectors is moving the field of transmission electron microscopy into the computer science field of big data. Automated data pipelines control the instrument and initial processing steps which imposes more onerous data transfer and archiving requirements. Here we conduct a technical demonstration whereby storage and read/write times are improved 10× at a dose rate of 1 e-/pix/frame for data from a Gatan K2 direct-detection device by combination of integer decimation and lossless compression. The example project is hosted at github.com/em-MRCZ and released under the BSD license.
Highlights
The introduction of complementary metal-oxide semiconductor (CMOS)-based direct electron detectors for transmission electron microscopy greatly improved the duty cycle to nearly 100% compared to traditional slow-scan CCD detectors
The introduction of CMOS-based direct electron detectors for transmission electron microscopy greatly improved the duty cycle to nearly 100% compared to traditional slow-scan CCD detectors
The high duty-cycle allows for nearly continuous read-out, such that dose fractionation has become ubiquitous as a means to record many-frame micrograph stacks in-place of traditional 2D images
Summary
The introduction of CMOS-based direct electron detectors for transmission electron microscopy greatly improved the duty cycle to nearly 100% compared to traditional slow-scan CCD detectors. The addition of a time-dimension, plus the large pixel counts of CMOS detectors, greatly increases both archival and data transfer requirements and associated costs to a laboratory. If instead the micrographs are stored as 8-bit integers, with the gain reference (and potentially other operations) stored in meta-data, a 4× reduction in storage and transfer requirements is realized. In this case, the gain reference and other bias corrections must be performed at the computing center, rather than using the software provided by the direct electron detector vendor. Due to the repetition of intensity values, integer-format data can be compressed much more efficiently than gain-normalized floating-point data. We propose using common serialization tools to embed metadata in the MRC2014 extended header, and compare JSON (ECMA-404, 2013) and Message Pack (msgpack.org, accessed 03/2017)
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