bioRxivBioengineeringPreprint
Compressive axial-integrated planar scanning (CAPS) microscopy for high-speed volumetric imaging of cardiac dynamics
Xinyuan ZhangiD, Jichen Chai, Yi GongiD, Milad AlmasianiD, Jonathan Aaron Brewer, Alireza SaberigarakaniiD, Jianqing Jia, Alyssa Hines, Kelli J. CarrolliD, Yifei LouiD, Yichen DingiD
Abstract
Investigating cardiac dynamics, including contractile function and intracardiac flow, requires volumetric imaging capable of resolving whole-organ events at micrometer resolution and millisecond timescales. However, the limited readout bandwidth of detectors imposes fundamental trade-offs among spatial sampling, field of view, and achievable volume rates. Here we introduce compressive axial-integrated planar scanning (CAPS) microscopy, a computational imaging framework that combines rapid light-sheet scanning, detection-side axial multiplexing with model-based reconstruction to enhance detector bandwidth utilization for high-speed volumetric imaging. Using widely accessible optical sensors and components, CAPS achieves cellular-scale resolving power across heart chambers at 200 volumes per second with an effective detector pixel rate of 5.82 GHz, representing a [~]15-fold increase in spatiotemporal throughput relative to uncompressed volumetric acquisition. Coordinated high-speed encoding and computational reconstruction further mitigate rolling-shutter distortions in CMOS sensors while preserving frame rate and intrinsic optical sectioning. We demonstrate that CAPS enables beat-resolved imaging of single-cell cardiomyocyte kinematics, chamber-scale contractile dynamics, and intracardiac hemodynamics in zebrafish larvae under both healthy and pharmacologically perturbed conditions. Collectively, these advances establish CAPS as a powerful framework for quantitative, in vivo characterization of coordinated and disrupted cardiac dynamics at cellular resolution, supporting high-speed volumetric interrogation of organ-level function and disease progression.
- organism: zebrafish
- assay: imaging
- research focus: cell type annotation
- research focus: spatial domain detection
- approach: deep learning
- approach: statistical model
- Citations
- 0
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