Publication

Integrating time-resolved NIR and SWIR imaging for high-resolution mesoscopic fluorescence lifetime imaging using deep learning (Conference Presentation)

Claudio Bruschini
2025

Abstract

We developed a deep learning (DL) model to enhance mesoscopic fluorescence lifetime imaging (FLI). By fusing low-resolution time-resolved images from a time-gated SPAD detector with high-resolution intensity images from a shortwave infrared (SWIR) InGaAs detector, we produced upsampled high-resolution FLI images. The DL model, featuring LSTM and 3D convolutional layers, was trained on simulated temporal point spread functions and validated with tissue-mimicking phantoms. Results showed highly accurate upsampled images, with future testing planned for tumor xenograft imaging to study tumor heterogeneity.

Official source

https://infoscience.epfl.ch/entities/publication/a9949845-7ebc-4886-89a3-740260f35537

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Claudio Bruschini
2025

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/a9949845-7ebc-4886-89a3-740260f35537

About this result

Related concepts (17)

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

Hyperspectral imaging

Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes. There are three general types of spectral imagers. There are push broom scanners and the related whisk broom scanners (spatial scanning), which read images over time, band sequential scanners (spectral scanning), which acquire images of an area at different wavelengths, and snapshot hyperspectral imagers, which uses a staring array to generate an image in an instant.