Conventional spectrometer designs necessitate a compromise between their resolution and sensitivity, especially as device and detector dimensions are scaled down. Here, we report on a miniaturizable spectrometer platform where light throughput onto the detector is instead enhanced as the resolution is increased. This planar, CMOS-compatible platform is based around metasurface encoders designed to exhibit photonic bound states in the continuum, where operational range can be altered or extended simply through adjusting geometric parameters. This system can enhance photon collection efficiency by up to two orders of magnitude versus conventional designs; we demonstrate this sensitivity advantage through ultralow-intensity fluorescent and astrophotonic spectroscopy. This work represents a step forward for the practical utility of spectrometers, affording a route to integrated, chip-based devices that maintain high resolution and SNR without requiring prohibitively long integration times.

Stroke is the third leading cause of mortality in many countries. Thermoacoustic imaging has the potential for stroke detection. However, some parameters in thermoacoustic imaging, such as speed of sound, are difficult to be obtained in advance, and a constant velocity value is assumed in the reconstructed algorithm. Thermoacoustic signals propagate through the soft brain tissue and the skull in actual stroke detection. This mismatch between the assumed and actual sound velocity will degrade the imaging quality. In this Letter, we propose a full waveform autofocus inversion method to reconstruct thermoacoustic images for stroke noninvasive and non-ionizing detection. Employing the difference between the simulation forward sensor signals and the measured signals, the approximate speed of sound distribution is updated continuously. The numerical simulation of a real human brain model and the experiment of a real human skull help us to validate the performance of the proposed method in clinical transcranial thermoacoustic detection.

Microwave induced thermoacoustic has attracted broad attention in recent years because of its potential for clinical noninvasive detection. The model matrix in the model-based reconstruction method is often constructed by simulation, assuming that the media is acoustically homogeneous. However, the thermoacoustic signal waves propagate through acoustically heterogeneous media in real imaging scenes and are recorded by sparse sensor array. Thus, there is a perturbation between the actual and pre-designed measurement matrix, called measurement matrix uncertainty. In this Letter, an improved model is proposed to reconstruct the sound pressure distribution from sparse signals with phase distortion caused by variations in the speed of sound between tissues. The measured thermoacoustic signals are employed to construct a series of complex signals, which contain amplitude and phase information. The signal model's sparse constraint of the estimated sound pressure map is combined with the nonzero constraint of the random measurements' phases with respect to the amplitude-only measures. Our simulation and experiment results indicate that the proposed model helps to improve the image quality reconstructed by sparse sampling in acoustically heterogeneous media.

Microwave induced thermoacoustic tomography has shown promise for noninvasive and non-ionizing early tumor detection. Nowadays, thermoacoustic reconstruction methods based on deep learning have achieved good and time-efficient results. However, both deep learning methods based on the initial thermoacoustic image and end-to-end methods lack interpretability due to the black-box property of neural networks. In this Letter, we propose an interpretable end-to-end network structure comprising an unrolling part and a super-resolution part. In the unrolling part, a deep unfolding network interprets the iterations of the model-based algorithm based on compressed sensing as layers of the network. Subsequently, a fast and efficient super-resolution neural network maps the low-resolution image into the super-resolution space. Two breast models with different sizes of tumor targets are used for validation. By comparing with the traditional method and the deep learning method, the proposed method demonstrates superior performance in image quality and imaging time. Moreover, the parameters in the network hold physical significance, offering the potential for the interpretable end-to-end network in thermoacoustic imaging.

Breast cancer is the second leading cause of cancer death in women, and early detection of breast cancer is essential for more effective treatment. Recently, microwave-induced thermoacoustic tomography (MITAT) based on compressive sensing has been proven to have great potential as a new detection tool for early breast cancer within low sampling time. However, the traditional MITAT reconstruction method based on compressive sensing requires many computing resources. To find a balance between low computing resources and high-resolution images for the method based on compressive sensing, especially in the environment of a non-uniform tissue, we developed a MITAT based on deep learning (DL-MITAT) imaging scheme compressive sensing-super-resolution thermoacoustic imaging (CS-SRTAI) method which decomposed the single reconstruction step into the initial reconstruction part and the super-resolution part. The initial reconstruction part extracts the necessary physical information into the low-resolution image space. The super-resolution part maps the low-resolution image space to the high-resolution image space. Specifically, we proposed a neural network in the super-resolution part. Both numerical simulation and the experiment demonstrate the effectiveness of the proposed method. The proposed method achieved 88% structural similarity index measure within computing resources of 21 s and 1.0 GB for the numerical simulation. Moreover, for the real breast tumor and non-uniform tissue experiment, the CS-SRTAI performs well at recovering the location, size, and number of the tumor within computing resources of 65 s and 1.1 GB. It is worth noting that the proposed DL-MITAT imaging strategy reduces computing resources with great imaging quality. It is promising to use in the fields where the computing resources for imaging are restricted.

In recent years, cerebrovascular disease has become one of the leading causes of death among Chinese residents. Early detection of brain disease is, therefore, of great significance in reducing the risks to life and health. Thermoacoustic imaging has emerged as a promising technique for detecting brain disease, which meets the requirements of high penetration depth and real-time imaging in transcranial imaging. However, the acoustic characteristics of the skull can significantly impact the propagation of thermoacoustic signals, leading to attenuation and apparent phase difference, resulting in poor quality of reconstructed image and location deviation of symptom points. In this study, a skull phase compensated method combined full waveform inversion for transcranial thermoacoustic imaging is proposed. The adaptive positioning of skull is realized firstly by improving the W-AIC algorithm in thermoacoustic imaging to solve the phase difference problem and FWI technology is applied for reconstructing the intracranial SoS distribution. Numerical simulation of a human brain model and actual human skull experiments further verify the feasibility of this method in improving the quality of thermoacoustic images, thereby providing a reliable theoretical basis for the clinical application of transcranial thermoacoustic imaging.