Several factors can influence SLICE imaging and processing speed; however, the key principle is to minimize the total amount of data acquired, as this simultaneously enhances both imaging and processing speeds.
Objective selection is one of the most effective ways to control acquisition and data size. Compare the specifications for the 10x and 5x detection objectives in the tables below. Acquiring a sample with the 5x detection objective instead of the 10x detection objective can significantly reduce acquisition time and data volume generated (4x faster acquisition, 4x less data generation, resulting in faster data transfer and processing speeds).
10x Objective
Suitable for high-resolution imaging of fine structures throughout the tissue sample.
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Lateral Resolution: ~0.75 µm
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Axial Resolution: ~8 µm
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Field of View: ~1x1.5 mm
5x Objective
Preferred for high-throughput imaging when ultra-high resolution is not required.
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Lateral Resolution: ~1.5 µm
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Axial Resolution: ~9 µm
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Field of View: ~2x3 mm
The Z-step size is also a key factor affecting both acquisition and processing. Increasing the Z distance between planes reduces the number of Z planes per volume, therefore reducing acquisition time and decreasing data size. Using an appropriate Z-step size of 5–10 µm helps avoid unnecessary oversampling while still preserving most cross-sectional structural information within the tissue.
Exposure time determines the achievable camera frame rate and is a direct way to improve acquisition speed. Reducing exposure time leads to faster frame rates and shorter acquisition, especially when imaging large volumes. However, shorter exposures decrease the number of detected photons, which can reduce signal-to-noise ratio and image quality, particularly when imaging dim fluorophores. The SLICE camera supports up to 16 frames per second with an exposure time of ~17 ms. Please note that reducing exposure time lowers photon counts and can degrade signal-to-noise ratio, so it must be balanced against image quality. Importantly, exposure time does not change the total amount of data acquired and has minimal impact on data size and processing speed.
Applying camera binning is an effective way to reduce image data size and improve image processing efficiency. By combining adjacent pixels into larger effective pixels, binning reduces the total pixel count, leading to smaller file sizes and faster data transfer and analysis. It is particularly useful when ultra-high resolution is not required. Additionally, binning can improve signal-to-noise ratio, which may allow shorter exposure times in some cases.