Non-invasive single photon imaging through strongly scattering media

Maruca, Stephanie; Rehain, Patrick; Sua, Yong Meng; Zhu, Shenyu; Huang, Yu‐Ping

doi:10.1364/oe.417299

Cited by 18 publications

(15 citation statements)

References 38 publications

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“…One possible solution for freely moving rodents might be pupillometry through eyelids by combining head-mounted, infrared, back-illumination pupillometry, 33 a miniaturized ocular videography system, 53,54 and scatter-resistant single photon imaging. 55 Another limitation of the present method arises from its application to albino mice: we were unable to detect their pupils using a USB camera under infrared-red illumination.…”

Section: Discussionmentioning

confidence: 98%

Pupil Dynamics-derived Sleep Stage Classification of a Head-fixed Mouse Using a Recurrent Neural Network

2023

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The standard method for sleep state classification is thresholding the amplitudes of electroencephalography (EEG) and electromyography (EMG) data, followed by manual correction by an expert. Although popular, this method has some shortcomings: (1) the time-consuming manual correction by human experts is sometimes a bottleneck hindering sleep studies, (2) EEG electrodes on the skull interfere with wide-field imaging of the cortical activity of a head-fixed mouse under a microscope, (3) invasive surgery to fix the electrodes on the thin mouse skull risks brain tissue injury, and (4) metal electrodes for EEG and EMG recording are difficult to apply to some experimental apparatus such as that for functional magnetic resonance imaging. To overcome these shortcomings, we propose a pupil dynamicsbased vigilance state classification method for a head-fixed mouse using a long short-term memory (LSTM) model, a variant of a recurrent neural network, for multi-class labeling of NREM, REM, and WAKE states. For supervisory hypnography, EEG and EMG recording were performed on head-fixed mice. This setup was combined with left eye pupillometry using a USB camera and a markerless tracking toolbox, DeepLabCut. Our open-source LSTM model with feature inputs of pupil diameter, pupil location, pupil velocity, and eyelid opening for 10 s at a 10 Hz sampling rate achieved vigilance state estimation with a higher classification performance (macro F1 score, 0.77; accuracy, 86%) than a feedforward neural network. Findings from a diverse range of pupillary dynamics implied possible subdivision of the vigilance states defined by EEG and EMG. Pupil dynamics-based hypnography can expand the scope of alternatives for sleep stage scoring of head-fixed mice.

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Section: Discussionmentioning

confidence: 98%

Pupil Dynamics-derived Sleep Stage Classification of a Head-fixed Mouse Using a Recurrent Neural Network

2023

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“…One possible solution for freely moving rodents might be a pupillometry through eyelids by combining a headmount infrared back-illumination pupillometry (iBip), 33 a miniaturized ocular-videography system 53 and scatter-resistant single photon imaging. 54 Another limitation of the present method arises from its application to albino mice: we were unable to detect their pupils using a USB camera under infrared-red illumination.…”

Section: Discussionmentioning

confidence: 98%

Pupil dynamics-derived sleep stage classification of a head-fixed mouse using a recurrent neural network

Kobayashi

Tanaka

Takata

2022

Preprint

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The standard method for sleep state classification is thresholding amplitudes of electroencephalography (EEG) and electromyography (EMG), followed by an expert's manual correction. Although popular, the method entails some shortcomings: 1) the time-consuming manual correction by human experts is sometimes a bottleneck hindering sleep studies; 2) EEG electrodes on the skull interfere with wide-field imaging of the cortical activity of a head-fixed mouse under a microscope; 3) invasive surgery to fix the electrodes on the thin skull of a mouse risks brain tissue injury; and 4) metal electrodes for EEG and EMG are difficult to apply to some experiment apparatus such as that for functional magnetic resonance imaging. To overcome these shortcomings, we propose a pupil dynamics-based vigilance state classification for a head-fixed mouse using a long short-term memory (LSTM) model, a variant of recurrent neural networks, for multi-class labeling of NREM, REM, and WAKE states. For supervisory hypnography, EEG and EMG recording were performed for a head-fixed mouse, combined with left eye pupillometry using a USB camera and a markerless tracking toolbox, DeepLabCut. Our open-source LSTM model with feature inputs of pupil diameter, location, velocity, and eyelid opening for 10 s at a 10 Hz sampling rate achieved vigilance state estimation with a higher classification performance (macro F1 score, 0.77; accuracy, 86%) than a feed forward neural network. Findings from diverse pupillary dynamics implied subdivision of a vigilance state defined by EEG and EMG. Pupil dynamics-based hypnography can expand the scope of alternatives for sleep stage scoring of head fixed mice.

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“…The second is QPMS, which maximally rejects the background noises from ambient light or multiscattering [12], so that the ultraweak, single-photon level signals can be detected reliably despite much stronger noises. This is the key for the technique to work in ambient environment and achieve high penetration depth [13].…”

Section: Introductionmentioning

confidence: 99%

Photon Counting Elastography

Huang,

Zheng,

Sua

et al. 2023

Preprint

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We demonstrate a non-contact elastography with low-intensity illumination, mode-selective single photon detection, and acoustic excitation. It probes target samples with milliwatt, picosecond laser pulses and measures the backscattered photons via quantum parametric mode sorting and upconversion detection. As the acoustic frequency is swept, the samples' vibrational responses are mapped onto the photon counting histograms from which their mechanical properties--including elasticity--can be derived. By lateral and longitudinal laser scanning at a fixed frequency, an elastogram can be reliably reconstructed upon photon level signals. This technique has the potential for remote sensing and imaging of tissues and materials with differential mechanical properties in photon starved or restricted environment.

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Non-invasive single photon imaging through strongly scattering media

Cited by 18 publications

References 38 publications

Pupil Dynamics-derived Sleep Stage Classification of a Head-fixed Mouse Using a Recurrent Neural Network

Pupil Dynamics-derived Sleep Stage Classification of a Head-fixed Mouse Using a Recurrent Neural Network

Pupil dynamics-derived sleep stage classification of a head-fixed mouse using a recurrent neural network

Photon Counting Elastography

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