Calibrating reconstruction radius in a multi single-element ultrasound-transducer-based photoacoustic computed tomography system

Deep learning on photoacoustic tomography to remove image distortion due to inaccurate measurement of the scanning radius

Photoacoustic tomography (PAT) is a non-invasive, non-ionizing hybrid imaging modality that holds great potential for various biomedical applications and the incorporation with deep learning (DL) methods has experienced notable advancements in recent times. In a typical 2D PAT setup, a single-element ultrasound detector (USD) is used to collect the PA signals by making a 360° full scan of the imaging region. The traditional backprojection (BP) algorithm has been widely used to reconstruct the PAT images from the acquired signals. Accurate determination of the scanning radius (SR) is required for proper image reconstruction. Even a slight deviation from its nominal value can lead to image distortion compromising the quality of the reconstruction. To address this challenge, two approaches have been developed and examined herein. The first framework includes a modified version of dense U-Net (DUNet) architecture. The second procedure involves a DL-based convolutional neural network (CNN) for image classification followed by a DUNet. The first protocol was trained with heterogeneous simulated images generated from three different phantoms to learn the relationship between the reconstructed and the corresponding ground truth (GT) images. In the case of the second scheme, the first stage was trained with the same heterogeneous dataset to classify the image type and the second stage was trained individually with the appropriate images. The performance of these architectures has been tested on both simulated and experimental images. The first method can sustain SR deviation up to approximately 6% for simulated images and 5% for experimental images and can accurately reproduce the GTs. The proposed DL-approach extends the limits further (approximately 7% and 8% for simulated and experimental images, respectively). Our results suggest that classification-based DL method does not need a precise assessment of SR for accurate PAT image formation.

Image reconstruction algorithm for laser-induced ultrasonic imaging: The single sensor scanning synthetic aperture focusing technique

This paper aims to implement a laser-induced ultrasound imaging reconstruction method based on the delay-and-sum beamforming through the synthetic aperture focusing technique (SAFT) for a circular scanning, performed with a tomograph that had one acoustic sensor and a system that rotates the sample around a fixed axis. The proposed method, called the Single-sensor Scanning Synthetic Aperture Focusing Technique, considers the size of the sensor and the detection procedure inside the SAFT's algebra. This image reconstruction method was evaluated numerically, using the Green function for the laser-induced ultrasound wave equation to generate a forward problem, and experimentally, using a solid object of polylactic acid, and a Sprague-Dawley rat heart located in a tissue-mimicking phantom. The resulting images were compared to those obtained from the time reversal and the conventional delay-and-sum reconstruction algorithms. The presented method removes the sidelobe artifacts and the comet tail sign, which produces a more distinguishable target on the image. In addition, the proposed method has a faster performance and lower computational load. The implementation of this method in photoacoustic microscopy techniques for image reconstruction is discussed.

Image reconstruction for endoscopic photoacoustic tomography including effects of detector responses

In photoacoustic tomography (PAT), conventional image reconstruction methods are generally based on the assumption of an ideal point-like ultrasonic detector. This assumption is appropriate when the receiving surface of the detector is sufficiently small and/or the distance between the imaged object and the detector is large enough. However, it does not hold in endoscopic applications of PAT. In this study, we propose a model-based image reconstruction method for endoscopic photoacoustic tomography (EPAT), considering the effect of detector responses on image quality. We construct a forward model to physically describe the imaging process of EPAT, including the generation of the initial pressure due to optical absorption and thermoelastic expansion, the propagation of photoacoustic waves in tissues, and the acoustic measurement. The model outputs the theoretical sampling voltage signal, which is the response of the ultrasonic detector to the acoustic pressure reaching its receiving surface. The images representing the distribution map of the optical absorption energy density on cross-sections of the imaged luminal structures are reconstructed from the sampling voltage signals output by the detector through iterative inversion of the forward model. Compared with the conventional approaches based on back-projection and other imaging models, our method improved the quality and spatial resolution of the resulting images.

Deep-learning-based multi-transducer photoacoustic tomography imaging without radius calibration

Pulsed laser diodes are used in photoacoustic tomography (PAT) as excitation sources because of their low cost, compact size, and high pulse repetition rate. In combination with multiple single-element ultrasound transducers (SUTs) the imaging speed of PAT can be improved. However, during PAT image reconstruction, the exact radius of each SUT is required for accurate reconstruction. Here we developed a novel deep learning approach to alleviate the need for radius calibration. We used a convolutional neural network (fully dense U-Net) aided with a convolutional long short-term memory block to reconstruct the PAT images. Our analysis on the test set demonstrates that the proposed network eliminates the need for radius calibration and improves the peak signal-to-noise ratio by ∼73% without compromising the image quality. In vivo imaging was used to verify the performance of the network.

Another decade of photoacoustic imaging

Photoacoustic imaging - a hybrid biomedical imaging modality finding its way to clinical practices. Although the photoacoustic phenomenon was known more than a century back, only in the last two decades it has been widely researched and used for biomedical imaging applications. In this review we focus on the development and progress of the technology in the last decade (2010-2020). From becoming more and more user friendly, cheaper in cost, portable in size, photoacoustic imaging promises a wide range of applications, if translated to clinic. The growth of photoacoustic community is steady, and with several new directions researchers are exploring, it is inevitable that photoacoustic imaging will one day establish itself as a regular imaging system in the clinical practices.

Photoacoustic imaging in the second near-infrared window: a review

Photoacoustic (PA) imaging is an emerging medical imaging modality that combines optical excitation and ultrasound detection. Because ultrasound scatters much less than light in biological tissues, PA generates high-resolution images at centimeters depth. In recent years, wavelengths in the second near-infrared (NIR-II) window (1000 to 1700 nm) have been increasingly explored due to its potential for preclinical and clinical applications. In contrast to the conventional PA imaging in the visible (400 to 700 nm) and the first NIR-I (700 to 1000 nm) window, PA imaging in the NIR-II window offers numerous advantages, including high spatial resolution, deeper penetration depth, reduced optical absorption, and tissue scattering. Moreover, the second window allows a fivefold higher light excitation energy density compared to the visible window for enhancing the imaging depth significantly. We highlight the importance of the second window for PA imaging and discuss the various NIR-II PA imaging systems and contrast agents with strong absorption in the NIR-II spectral region. Numerous applications of NIR-II PA imaging, including whole-body animal imaging and human imaging, are also discussed.

High-speed, low-cost, pulsed-laser-diode-based second-generation desktop photoacoustic tomography system

Bulky, expensive Nd:YAG lasers are used in conventional photoacoustic tomography (PAT) systems, making them difficult to translate into clinics. Moreover, real-time imaging is not feasible when a single-element ultrasound transducer is used with these low-pulse-repetition-rate lasers (10-100 Hz). Low-cost pulsed laser diodes (PLDs) can be used instead for photoacoustic imaging due to their high-pulse-repetition rates and compact size. Together with acoustic-reflector-based multiple single-element ultrasound transducers, a portable desktop PAT system was developed. This second-generation PLD-based PAT achieved 0.5 s cross-sectional imaging time with high spatial resolution of ∼165  μm and an imaging depth of 3 cm. The performance of this system was characterized using phantom and in vivo studies. Dynamic in vivo imaging was also demonstrated by monitoring the fast uptake and clearance of indocyanine green in small animal (rat) brain vasculature.