Reconstruction of Image Sequences From Ungated and Scanning-Aberrated Laser Scanning Microscopy Images of the Beating Heart

Abstract

Fluorescence laser-scanning microscopy is a wellestablished imaging technique in biology, available in many imaging facilities to investigate structures within live animal embryos such as zebrafish. Laser scanning microscopes (LSM) are limited when used to study dynamic heart morphology or function. Despite their ability to resolve static cardiac structures, the fast motion of the beating heart introduces severe artifacts in the scanned images and gating the acquisitions to the heartbeat is difficult to implement on traditional microscopes. Furthermore, although alternative high-speed imaging instruments exist, they are not widely available (due to cost or hardware complications), putting dynamic cardio-vascular imaging off-limits for many researchers. Here, we propose a method that allows imaging the beating heart on conventional LSMs. Our approach takes a set of images containing scanning aberrations, each triggered at an arbitrary time in the cardiac cycle, and assembles an image sequence that covers a single cardiac heartbeat. The steps are: (i) frame sorting by solving a traveling salesman problem; (ii) heartbeat duration estimation; and (iii) scan-delay compensation via space-time resampling. We characterize the performance of our method on synthetic data under several light intensities and scanning speeds. We further illustrate our method's applicability on experimental images acquired in live zebrafish larvae, and show that the reconstruction quality approaches that of fast, state-of-the-art microscopes. Our technique opens the possibility of using LSMs to carry out studies of cardiac dynamics, without the need for prospective gating or fast microscopes.

PAAQ: Paired Alternating AcQuisitions for Virtual High Frame Rate Multichannel Cardiac Fluorescence Microscopy

“…However, our technique has lower hardware requirements, making it more cost-effective and easier to integrate into existing imaging platforms. Our method is currently limited to 2D imaging, although it might be generalized similarly to work that extended sorting-based frame rate enhancement techniques to reconstruct volumes (26) .…”

“…Previous works used simplified simulation models of the heart for performance characterization, consisting of a circle (or cylinder) textured with a sinusoidal pattern, with a radius varying over time to represent beating (22,(25)(26)(27) . These models lack properties such as irregular texture, asymmetric contraction or small variations in the periodicity that we find in experimental cardiac images.…”

“…When solving this step for the first iteration, we initialize θ𝑛 with the uniform sampling approximation θ𝑛 defined in Eq. ( 7) which gives a mean error of less than 10% of the period if the sorted sequence contains at least 10 images (26) .…”

“…Their results are usually not as precise as those of prospective gating, but as they do not require additional triggering hardware they can be easier to implement with existing imaging setups. For example, it is possible to sort images acquired at random times in a period based only on image distance to reconstruct a singe-channel video with a virtually increased frame rate (25,26,46) . In addition to their limited accuracy, these methods can struggle with multichannel data.…”

“…When imaging with slower cameras, one possibility would be to first generate virtual high frame rate videos of each channel separately using the sorting methods above (25,26) . These virtual high-speed sequences could then be synchronized using a mutual information-based algorithm designed for raw high-speed movies (22) to obtain the final a multichannel video.…”