Deep-E Enhanced Photoacoustic Tomography Using Three-Dimensional Reconstruction for High-Quality Vascular Imaging.

Due to their low cost, hand-held convenience, wide selection of bandwidths, and ultrasound imaging capability, linear ultrasonic transducer arrays have been widely studied for photoacoustic computed tomography (PACT). As linear-array PACT suffers from a limited view, full-view imaging requires either the transducer or the object to be rotated. So far, both the central frequencies and bandwidth of linear transducer arrays applied in full-view PACT are low, limiting the spatial resolutions of the reconstructed images. Here, we present a multiview high-frequency PACT imaging system implemented with a commercial 40-MHz central frequency linear transducer array. By rotating the object through multiple angles with respect to the linear transducer array, we acquired full-view photoacoustic pressure measurements. Further, to quantify the unipolar initial pressures and overcome the limitations of the single-view Hilbert transformation, we developed a multiview Hilbert transformation method. The in-plane spatial resolution of this full-view linear-array PACT was quantified to be isotropically 60 μm within a 10×10 mm² field of view. The system was demonstrated by imaging both a leaf skeleton and a zebrafish in vivo.

Photoacoustic computed tomography (PACT) is an advanced biomedical imaging modality that integrates optical and ultrasound imaging techniques. A central goal in PACT is to estimate the initial pressure rise from raw photoacoustic data (sinogram), a task complicated by the need for accurate inverse modeling, sensitivity to noise, and hardware limitations. Conventional reconstruction methods typically rely on multi-step processing and require careful parameter tuning to achieve high-quality results. While deep learning has proven effective in addressing challenges like noise suppression and computational efficiency, its use in PACT has largely been limited to image-domain or sensor data (sinogram)-domain enhancement, rather than direct image reconstruction from sensor data (sinograms) - a direction more extensively explored in computed tomography (CT) and magnetic resonance imaging (MRI). In this study, we propose a novel, data-driven methodology that formulates PACT image reconstruction as a supervised learning problem. Our approach employs a hybrid fully connected network followed by a Swin-UNETR to learn a direct mapping from raw sinograms (sensor data) to reconstruct images, effectively eliminating the need for conventional, handcrafted reconstruction steps. The proposed method achieved high image quality, with peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of 33.878 dB and 0.975, respectively.

Image segmentation is important in improving the diagnostic capability of ultrasound computed tomography (USCT) and photoacoustic computed tomography (PACT), as it can be included in the image reconstruction process to improve image quality and quantification abilities. Segmenting the imaged object out of the background using image domain methods is easily complicated by low contrast, noise, and artifacts in the reconstructed image. Here, we introduce a new signal domain object segmentation method for USCT and PACT which does not require image reconstruction beforehand and is automatic, robust, computationally efficient, accurate, and straightforward. We first establish the relationship between the time-of-flight of the received first arrival waves and the object's boundary which is described by ellipse equations. Then, we show that the ellipses are tangent to the boundary. By looking for tangent points on the common tangent of neighboring ellipses, the boundary can be approximated with high fidelity. Imaging experiments of human fingers and mice cross-sections showed that our method provided equivalent or better segmentations than the optimal ones by active contours. In summary, our method greatly reduces the overall complexity of object segmentation and shows great potential in eliminating user dependency without sacrificing segmentation accuracy. The method can be further seamlessly incorporated into algorithms for other processing purposes in USCT and PACT, such as high-quality image reconstruction.

Deep Learning for scaling large-aperture photoacoustic computed tomography : From single fingers to the human hand.

Photoacoustic imaging combines advantages of both ultrasound imaging and optical imaging to provide high ultrasonic resolution and high optical contrast in noninvasive medical images. A photoacoustic computer tomography （PCT） system with a focused digital phased array transducer is proposed. In particular each element of the transducer array has a thin cylindrical ultrasonic lens to select 2D image plane and suppress the out-of-plane signals to realize photoacoustic computed tomography. A fast full-view photoacoustic imaging system for visualizing tissue structures using a linear transducer array with combined scan, whies; detects photoacoustic signals by combined electronic and mechanical scanning. An improved limited-field filtered back projection algorithm with directivity factors was applied to reconstruct the optical absorption distribution and further improve the lateral resolution. In the experiment, the spatial resolution of this imaging system is determined as 0.2mm, and the slice resolution of Z axis is 1.5mm. Mechanical step scanning was performed, leading to a measurement time of 6.4 seconds per position, and a total scan duration of 150 seconds to obtain the two-dimensional photoacoustic image. High spatial resolution of 2D photoacoustic vascular image and quasi-3D photoacoustic CT for breast cancer imaging are achicned. The experimental results demonstrate that this method and imaging system can realize the high resolution photoacoustic tomography wtich can be applied to noninvasive detection of early breast cancer.

Mechanical high-intensity focused ultrasound (HIFU) has been used for cancer treatment and drug delivery. Existing monitoring methods for mechanical HIFU therapies such as MRI and ultrasound imaging often suffer from high cost, poor spatial-temporal resolution, and/or low sensitivity to tissue's hemodynamic changes. Evaluating vascular injury during mechanical HIFU treatment, therefore, remains challenging. Photoacoustic computed tomography (PACT) is a promising tool to meet this need. Intrinsically sensitive to optical absorption, PACT provides high-resolution imaging of blood vessels using hemoglobin as the endogenous contrast. In this study, we have developed an integrated HIFU-PACT system for detecting vascular rupture in mechanical HIFU treatment. We have demonstrated singular value decomposition for enhancing hemorrhage detection. We have validated the HIFU-PACT performance on phantoms and in vivo animal tumor models. We expect that PACT-HIFU will find practical applications in oncology research using small animal models.

Typically, multi-single-element photoacoustic computed tomography (PACT) systems utilize numerous ultrasound transducers arranged in cylindrical or hemispherical configurations for detection, combined with a single diffuse light source or multiple sparse light sources to illuminate the imaging target. While these systems produce high-quality 3D PA images, they require complex, multi-channel data acquisition (DAQ) systems to acquire data from all transducers. These DAQ systems are often bulky and expensive, significantly limiting the clinical translation of PACT systems for patient care. In this study, we evaluated the feasibility of using a compact and cost-effective Texas Instruments analog front-end DAQ module for multi-single-element PACT systems. By imaging a simple 3D phantom, we demonstrated the capability of this affordable DAQ board, with reconstructed images showing promise for practical and economical solutions in PACT systems. This advancement paves the way for broader applications of PACT in both research and clinical settings.

Photoacoustic computed tomography (PACT) is a non-invasive imaging technique offering high contrast, high resolution, and deep penetration in biological tissues. We report a PACT system equipped with a high frequency linear transducer array for mapping the microvascular network of a whole mouse brain with the skull intact and studying its hemodynamic activities. The linear array was scanned in the coronal plane to collect data from different angles, and full-view images were synthesized from the limited-view images in which vessels were only partially revealed. We investigated spontaneous neural activities in the deep brain by monitoring the concentration of hemoglobin in the blood vessels and observed strong interhemispherical correlations between several chosen functional regions, both in the cortical layer and in the deep regions. We also studied neural activities during an epileptic seizure and observed the epileptic wave spreading around the injection site and the wave propagating in the opposite hemisphere.

The influences of finite aperture size in photoacoustic computed tomography

Photoacoustic computed tomography (PACT) combines the advantages of optical and ultrasound imaging, offering significant potential for both preclinical and clinical applications. The development and refinement of PACT imaging systems and reconstruction methods should ideally be guided by performance measures that reflect the clinical utility of the images produced. However, widely employed conventional image quality (IQ) measures, including mean squared error (MSE) and structural similarity index (SSIM), do not always correlate with the utility of images with consideration of a specified diagnostic task, such as lesion detection. To address this, task-based IQ measures can be employed, which directly quantify the utility of an image for a specified clinical purpose. However, currently, such task-based IQ measures are not widely utilized in the photoacoustics research community. In this study, a task-based IQ measure is introduced and employed to quantify the performance of PACT reconstruction methods with respect to a clinically significant task, namely binary signal detection. Through systematic virtual imaging studies that involve realistic numerical breast phantoms and both physicsand learning-based image reconstruction methods, the advantages of task-based over conventional IQ measures are demonstrated. Examples are provided in which conventional IQ measures suggest comparable performance across reconstruction methods, while the task-based IQ measure demonstrates substantial differences, particularly when clinically significant lesions are missed. These results highlight the limitations of conventional IQ metrics in characterizing the diagnostic performance of PACT methods and underscore the necessity of integrating task-based IQ assessment into the development and evaluation of PACT reconstruction methods for clinical translation.

Masked cross-domain self-supervised deep learning framework for photoacoustic computed tomography reconstruction

Due to its unique capability of visualizing optical absorption in deep tissues, photoacoustic tomography is increasingly used in biomedical imaging. Among various types of transducer arrays, the linear array is perhaps the most widely used in photoacoustic tomography because it is commercially available and readily allows ultrasound imaging. However, the three-dimensional imaging capability of a linear array is limited due to its poor elevational resolution. While various scanning schemes have been proposed to address this problem, they all suffer from long scanning time to the best of our knowledge. To address this issue, we introduce slit-enabled three-dimensional photoacoustic tomography. The metal slit, placed at the array focus, causes the incoming photoacoustic waves to diffract along the elevation direction and, hence, significantly improves the elevation detection aperture and resolution. We tested the new system in both phantoms and animals. The slit improves the elevation resolution by 10 times without compromising scanning time.

Electrostatic out-of-plane microactuators have been widely used in applications of variable capacitors, optical attenuators, optical switches and scanning displays due to their small size, low cost, simple and diverse structure, low power consumption and high compatibility with semiconductor process. The large out-of-plane displacement of the microactuator with high reliability is preferred in order to increase the tuning range, tunability and the display size. However, the “pull-in” instability associated with conventional attractive-force electrostatic microactuators significantly limits the out-of-plane displacement and lowers the operation stability. A repulsive-force microactuator has been previously developed which can achieve large out-of-plane rotation and does not suffer from the “pull-in” instability. However, a larger rotation angle of the repulsive-force actuator is highly desired in order to improve its performance in the applications such as increasing the tunability and the scanning angle. In this thesis two novel repulsive-force actuators, i.e., two-row interdigitating-finger and two-width-finger (TWF) actuators are developed which output much larger out-of-plane rotation than the previous repulsive-force actuator without suffering from the “pull-in” instability. The mathematical models are established for both actuators using a hybrid approach. The actuators require only two thin layers and are suitable for surface micromachining process. The measured results show that the two microactuators can achieve rotation angles of 11.5° and 7.5° at 150 V respectively. The improvements are 100% and 35% in comparison to the previous repulsive-force actuator with the same size, stiffness and driving voltage. A 2D scanning micromirror has been developed and fabricated based on the two-row-finger (TRF) actuator. Experimental results show the micromirror has larger rotation angle and faster response speed than those of the micromirror driven by the previous repulsive-force microactuator. The vector scanning display based on the micromirror is demonstrated. An advanced display approach is developed to generate displays with less distortion and higher refreshing rate compared to the previous generic display approach. The automotive Head-up Display (HUD) based on the micromirror and advanced display approach has been constructed for both real and virtual image configurations, which has advantages of small size, low cost, large viewing angle and good visibility over those HUDs in the market.

Simultaneous multi-slice (SMS) bSSFP imaging enables stress myocardial perfusion imaging with high spatial resolution and increased spatial coverage. Standard parallel imaging techniques (e.g., TGRAPPA) can be used for image reconstruction but result in high noise level. Alternatively, iterative reconstruction techniques based on temporal regularization (ITER) improve image quality but are associated with reduced temporal signal fidelity and long computation time limiting their online use. The aim is to develop an image reconstruction technique for SMS-bSSFP myocardial perfusion imaging combining parallel imaging and image-based denoising using a novel noise map estimation network (NoiseMapNet), which preserves both sharpness and temporal signal profiles and that has low computational cost. The proposed reconstruction of SMS images consists of a standard temporal parallel imaging reconstruction (TGRAPPA) with motion correction (MOCO) followed by image denoising using NoiseMapNet. NoiseMapNet is a deep learning network based on a 2D Unet architecture and aims to predict a noise map from an input noisy image, which is then subtracted from the noisy image to generate the denoised image. This approach was evaluated in 17 patients who underwent stress perfusion imaging using a SMS-bSSFP sequence. Images were reconstructed with (a) TGRAPPA with MOCO (thereafter referred to as TGRAPPA), (b) iterative reconstruction with integrated motion compensation (ITER), and (c) proposed NoiseMapNet-based reconstruction. Normalized mean squared error (NMSE) with respect to TGRAPPA, myocardial sharpness, image quality, perceived SNR (pSNR), and number of diagnostic segments were evaluated. NMSE of NoiseMapNet was lower than using ITER for both myocardium (0.045 ± 0.021 vs. 0.172 ± 0.041, p < 0.001) and left ventricular blood pool (0.025 ± 0.014 vs. 0.069 ± 0.020, p < 0.001). There were no significant differences between all methods for myocardial sharpness (p = 0.77) and number of diagnostic segments (p = 0.36). ITER led to higher image quality than NoiseMapNet/TGRAPPA (2.7 ± 0.4 vs. 1.8 ± 0.4/1.3 ± 0.6, p < 0.001) and higher pSNR than NoiseMapNet/TGRAPPA (3.0 ± 0.0 vs. 2.0 ± 0.0/1.3 ± 0.6, p < 0.001). Importantly, NoiseMapNet yielded higher pSNR (p < 0.001) and image quality (p < 0.008) than TGRAPPA. Computation time of NoiseMapNet was only 20s for one entire dataset. NoiseMapNet-based reconstruction enables fast SMS image reconstruction for stress myocardial perfusion imaging while preserving sharpness and temporal signal profiles.

Epigenetic modifications are closely related to diseases and physiological health, mainly including DNA methylation, RNA methylation, histone acetylation, and noncoding RNA. Recently, a large amount of research has been conducted on the detection of epigenetic modifications. Electrochemical biosensors, with their low cost, high sensitivity, high compatibility, and simple operation, have been widely used in the detection of epigenetic biomarkers. This review discusses the detection of epigenetic biomarkers using different electrochemical sensing methods. Here we discuss various aspects, including free labels, signal labeling, signal amplification, nano-based electrodes, and the combined use of other methods. By summarizing the existing electrochemical detection methods for epigenetic modifications, this review also proposes future development trends and challenges for electrochemical biosensors in this field.