Adaptive Compressive Sampling for Mid-infrared Spectroscopic Imaging
Mahsa Lotfollahi, Nguyen Tran, Sebastian Berisha, Chalapathi Gajjela,, Zhu Han, David Mayerich, and Rohith Reddy

TL;DR
This paper presents an adaptive compressive sampling method for mid-infrared spectroscopic imaging that significantly reduces data acquisition time while maintaining high spatial resolution, enabling faster and more practical clinical imaging.
Contribution
It introduces an adaptive sampling technique leveraging spectral and spatial sparsity, combined with tensor completion, to accelerate hyperspectral imaging without sacrificing resolution.
Findings
Reduces hyperspectral data acquisition time by an order of magnitude.
Achieves spatial resolution comparable to photothermal MIRSI.
Demonstrates practical speed advantages over traditional FTIR imaging.
Abstract
Minfrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qualitative, often making histopathologic examination subjective and difficult to quantify. MIRSI addresses these challenges through quantitative and repeatable imaging that leverages native molecular contrast. Fourier transform infrared (FTIR) imaging, the best-known MIRSI technology, has two challenges that have hindered its widespread adoption: data collection speed and spatial resolution. Recent technological breakthroughs, such as photothermal MIRSI, provide an order of magnitude improvement in spatial resolution. However, this comes at the cost of acquisition speed, which is impractical for clinical tissue samples. This paper introduces an…
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Taxonomy
TopicsSpectroscopy Techniques in Biomedical and Chemical Research · Sparse and Compressive Sensing Techniques · Photoacoustic and Ultrasonic Imaging
