Pose-Free 3D Quantitative Phase Imaging of Flowing Cellular Populations

2025-09-05

AI Summary

Traditional 3D quantitative phase imaging in flow cytometry relies on single-axis uniform cell rotation and prior knowledge of cell orientation, limiting applicability to near-spherical cells and failing to handle irregular, multi-axial rotational dynamics—thereby compromising statistical robustness. To address this, we propose a pose-agnostic, high-throughput 3D tomographic imaging method that jointly optimizes cellular motion trajectories and internal refractive index distributions, eliminating the restrictive assumption of symmetric rotation. Our approach integrates the Fourier diffraction theorem with implicit neural representations (INRs), enabling stable 3D reconstruction from sparse angular sampling—only 10 projections over a 120° range—under weak-scattering conditions. To our knowledge, this is the first technique enabling label-free, in situ, large-scale 3D morphometric analysis of arbitrarily shaped cells in flow, significantly enhancing both the universality and quantitative accuracy of flow cytometry.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

High-throughput 3D quantitative phase imaging (QPI) in flow cytometry enables label-free, volumetric characterization of individual cells by reconstructing their refractive index (RI) distributions from multiple viewing angles during flow through microfluidic channels. However, current imaging methods assume that cells undergo uniform, single-axis rotation, which require their poses to be known at each frame. This assumption restricts applicability to near-spherical cells and prevents accurate imaging of irregularly shaped cells with complex rotations. As a result, only a subset of the cellular population can be analyzed, limiting the ability of flow-based assays to perform robust statistical analysis. We introduce OmniFHT, a pose-free 3D RI reconstruction framework that leverages the Fourier diffraction theorem and implicit neural representations (INRs) for high-throughput flow cytometry tomographic imaging. By jointly optimizing each cell's unknown rotational trajectory and volumetric structure under weak scattering assumptions, OmniFHT supports arbitrary cell geometries and multi-axis rotations. Its continuous representation also allows accurate reconstruction from sparsely sampled projections and restricted angular coverage, producing high-fidelity results with as few as 10 views or only 120 degrees of angular range. OmniFHT enables, for the first time, in situ, high-throughput tomographic imaging of entire flowing cell populations, providing a scalable and unbiased solution for label-free morphometric analysis in flow cytometry platforms.

Problem

• Enables 3D imaging of flowing cells without pose constraints
• Supports arbitrary cell geometries and complex rotation patterns
• Overcomes limitations of spherical-only cell analysis in flow cytometry

Innovation

• Pose-free 3D reconstruction using Fourier diffraction theorem
• Joint optimization of rotational trajectory and volumetric structure
• Implicit neural representations for sparse view reconstruction