Deep learning for accelerating Radon inversion in single-cells tomographic phase imaging flow cytometry

Abstract

Tomographic phase microscopy (TPM) in flow cytometry is an emerging imaging technology completely stain-free and able to provide unique 3D biophysical data. However, the required throughput for TPM would correspond to a very huge amount of volumetric data to be processed. To date, the most accurate Radon inversion algorithms for tomographic reconstructions are based on iterative methods aided by regularizations, thus providing high performance but at the cost of a demanding computation time. To balance the trade-off between reconstruction performance and reconstruction speed, learning-based approaches have been successfully introduced, thus demonstrating high accuracy in solving the Radon inversion problem. However, the complexity of the proposed models remains a bottleneck due to the high computational resources still required for the training phase. Here we show that, employing a multi-scale fully convolutional Context Aggregation Network (CAN) model, a significant speeding-up of the Radon inversion computation can be achieved. Compared to other conventional encoder-decoder networks such as U-Net, the proposed method has proven to be accurate, faster and particularly suitable for on-board processing. Moreover, we show the generalization capacity of CAN by training the network with simulated tomographic data and testing the learned model on experimental tomographic data.