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
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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
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