Segmentation and Quantitative Analysis of Photoacoustic Imaging: A Review

Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review

Nuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various diseases; however, challenges persist regarding image quality and accessibility during NMI-based treatment. This paper reviews the use of deep learning methods for generating synthetic nuclear medicine images, aimed at improving the interpretability and utility of nuclear medicine protocols. We discuss advanced image generation algorithms designed to recover details from low-dose scans, uncover information hidden by specific radiopharmaceutical properties, and enhance the sensing of physiological processes. By analyzing 30 of the newest publications in this field, we explain how deep learning models produce synthetic nuclear medicine images that closely resemble their real counterparts, significantly enhancing diagnostic accuracy when images are acquired at lower doses than the clinical policies' standard. The implementation of deep learning models facilitates the combination of NMI with various imaging modalities, thereby broadening the clinical applications of nuclear medicine. In summary, our review underscores the significant potential of deep learning in NMI, indicating that synthetic image generation may be essential for addressing the existing limitations of NMI and improving patient outcomes.

Potential applications for photoacoustic imaging using functional nanoparticles: A comprehensive overview

This paper presents a comprehensive overview of the potential applications for Photo-Acoustic (PA) imaging employing functional nanoparticles. The exploration begins with an introduction to nanotechnology and nanomaterials, highlighting the advancements in these fields and their crucial role in shaping the future. A detailed discussion of the various types of nanomaterials and their functional properties sets the stage for a thorough examination of the fundamentals of the PA effect. This includes a thorough chronological review of advancements, experimental methodologies, and the intricacies of the source and detection of PA signals. The utilization of amplitude and frequency modulation, design of PA cells, pressure sensor-based signal detection, and quantification methods are explored in-depth, along with additional mechanisms induced by PA signals. The paper then delves into the versatile applications of photoacoustic imaging facilitated by functional nanomaterials. It investigates the influence of nanomaterial shape, size variation, and the role of composition, alloys, and hybrid materials in harnessing the potential of PA imaging. The paper culminates with an insightful discussion on the future scope of this field, focusing specifically on the potential applications of photoacoustic (PA) effect in the domain of biomedical imaging and nanomedicine. Finally, by providing the comprehensive overview, the current work provides a valuable resource underscoring the transformative potential of PA imaging technique in biomedical research and clinical practice.

Extractor-attention-predictor network for quantitative photoacoustic tomography

Quantitative photoacoustic tomography (qPAT) holds great potential in estimating chromophore concentrations, whereas the involved optical inverse problem, aiming to recover absorption coefficient distributions from photoacoustic images, remains challenging. To address this problem, we propose an extractor-attention-predictor network architecture (EAPNet), which employs a contracting-expanding structure to capture contextual information alongside a multilayer perceptron to enhance nonlinear modeling capability. A spatial attention module is introduced to facilitate the utilization of important information. We also use a balanced loss function to prevent network parameter updates from being biased towards specific regions. Our method obtains satisfactory quantitative metrics in simulated and real-world validations. Moreover, it demonstrates superior robustness to target properties and yields reliable results for targets with small size, deep location, or relatively low absorption intensity, indicating its broader applicability. The EAPNet, compared to the conventional UNet, exhibits improved efficiency, which significantly enhances performance while maintaining similar network size and computational complexity.

High-frequency photoacoustic and ultrasound imaging for skin evaluation: Pilot study for the assessment of a chemical burn

Skin architecture and its underlying vascular structure could be used to assess the health status of skin. A non-invasive, high resolution and deep imaging modality able to visualize skin subcutaneous layers and vasculature structures could be useful for determining and characterizing skin disease and trauma. In this study, a multispectral high-frequency, linear array-based photoacoustic/ultrasound (PAUS) probe is developed and implemented for the imaging of rat skin in vivo. The study seeks to demonstrate the probe capabilities for visualizing the skin and its underlying structures, and for monitoring changes in skin structure and composition during a 5-day course of a chemical burn. We analayze composition of lipids, water, oxy-hemoglobin, and deoxy-hemoglobin (for determination of oxygen saturation) in the skin tissue. The study successfully demonstrated the high-frequency PAUS imaging probe was able to provide 3D images of the rat skin architecture, underlying vasculature structures, and oxygen saturation, water, lipids and total hemoglobin.

Vascular regional analysis unveils differential responses to anti-angiogenic therapy in pancreatic xenografts through macroscopic photoacoustic imaging

Pancreatic cancer (PC) is a highly lethal malignancy and the third leading cause of cancer deaths in the U.S. Despite major innovations in imaging technologies, there are limited surrogate radiographic indicators to aid in therapy planning and monitoring. Amongst the various imaging techniques Ultrasound-guided photoacoustic imaging (US-PAI) is a promising modality based on endogenous blood (hemoglobin) and blood oxygen saturation (StO 2 ) contrast to monitor response to anti-angiogenic therapies. Adaptation of US-PAI to the clinical realm requires macroscopic configurations for adequate depth visualization, illuminating the need for surrogate radiographic markers, including the tumoral microvessel density (MVD). In this work, subcutaneous xenografts with PC cell lines AsPC-1 and MIA-PaCa-2 were used to investigate the effects of receptor tyrosine kinase inhibitor (sunitinib) treatment on MVD and StO 2 . Through histological correlation, we have shown that regions of high and low vascular density (HVD and LVD) can be identified through frequency domain filtering of macroscopic PA images which could not be garnered from purely global analysis. We utilized vascular regional analysis (VRA) of treatment-induced StO 2 and total hemoglobin (HbT) changes. VRA as a tool to monitor treatment response allowed us to identify potential timepoints of vascular remodeling, highlighting its ability to provide insights into the TME not only for sunitinib treatment but also other anti-angiogenic therapies.

In-vivo assessment of a rat rectal tumor using optical-resolution photoacoustic endoscopy

Optical-resolution photoacoustic endoscopy (OR-PAE) has been proven to realize imaging on the vascular network in the gastrointestinal (GI) tract with high sensitivity and spatial resolution, providing morphological information. Various photoacoustic endoscopic catheters were developed to improve the resolution and adaptivity of in-vivo imaging. However, this technology has not yet been validated on in-vivo GI tumors, which generally feature angiogenesis. The tumor causes thickened mucosa and neoplasia, requiring large depth-of-field (DOF) in imaging, which contradicts to high-resolution imaging. In this work, a novel catheter was developed with a high resolution of ∼27 µm, providing a matched DOF of ∼400 µm to cover the vessels up to the submucosa layer. Optical-resolution photoacoustic endoscopic imaging was first performed on in-vivo rat rectal tumors. In addition, to further characterize the vessel morphology, tumor-suspected regions and normal regions were selected for quantification and analysis of vessel dimension distribution and tortuosity. All the results suggest that the OR-PAE has great application potential in tumor diagnosis, evaluation, and monitoring of therapeutic efficacy.

Evaluation of Cholesterol Thickness of Blood Vessels Using Photoacoustic Technology

One of the primary indicators of plaque vulnerability is the lipid composition of atherosclerotic plaques. Therefore, the medical industry requires a method to evaluate necrotic nuclei in atherosclerosis imaging with sensitivity. In this regard, photoacoustic imaging is a plaque detection method that provides chemical information on lipids and cholesterol thickness in the arterial walls of the patient. This aspect aims to increase the low-frequency axial resolution by developing a new photoacoustic-based system. A photoacoustic system has been developed to detect the cholesterol thickness of the blood vessels to observe the progression of plaque in the heart's blood vessels. The application of the coherent photoacoustic discontinuous correlation tomography technique, which is based on a novel signal processing, significantly increased the cholesterol oleate's sensitivity to plaque necrosis. By enhancing the quality of thickness detection, the system for measuring the thickness of cholesterol in blood vessels has been reduced to approximately 23 microns. The results show that the phase spectrum peaked at 100 Hz at 58.66 degrees, and at 400 Hz, the phase spectrum was 46.37 degrees. The minimum amplitude is 1.95 at 100 Hz and 17.67 at 400 Hz. In conclusion, it can be stated that photoacoustic imaging as a method based on new technologies is of great importance in medical research, which is based on the use of nonionizing radiation to perform diagnostic processes and measure different types of body tissues.

A Signal-Domain Object Segmentation Method for Ultrasound and Photoacoustic Computed Tomography

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 (TOF) 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.