PurposeThe purpose of this study is to develop a freehand scan three-dimensional (3D) shear wave elasticity imaging (SWEI) method for characterizing the anisotropy of elastic properties in biological tissues. The motivation behind this work lies in addressing the limitations of traditional two-dimensional (2D) SWEI, which only measures shear wave speeds in a single direction, as well as fulfilling the clinical demand for improved medical imaging.ApproachOur imaging system utilizes a high-definition optical camera to continuously track the ultrasonic transducer, collecting spatial position-angle data of the transducer and corresponding two-dimensional SWEI data. By reconstructing three-dimensional SWEI images using these data, we achieved freehand SWEI.ResultsWe validated the accuracy of 2D SWEI on a standard elastic phantom, and then performed 3D SWEI on the pork tenderloin and the triceps brachii of two volunteers. We obtained shear wave speed of 1.82 to 3.12 m / s in the pork tenderloin, shear wave speed of 1.16 to 2.36 m / s in the triceps brachii of non-exercising volunteers, and shear wave speed of 0.55 to 1.63 m / s in the triceps brachii of exercising volunteers, and the maximum shear wave speed directions were generally aligned with the orientation of muscle fibers.ConclusionsWe proposed a method that can overcome the limitations of 2D-SWEI regarding imaging angle while also extending the imaging angle of 3D-SWEI, which could have significant implications for improving the accuracy and safety of medical diagnoses.
In the field of public safety surveillance, suspects may disguise themselves by painting their faces, challenging the face recognition system. The well-developed ordinary, two-dimensional image face recognition methods rely mainly on the texture features of the face images, which leaves the traditional face recognition system relatively vulnerable to the camouflage attacks. We set our sights on three-dimensional (3D) alternatives. A 3D facial recognition method is proposed which extracts 3D features of each individual that are not related to face texture. Considering actual requirements, we applied binocular stereo matching to obtain 3D face point clouds. Hereafter, we included a spatial representation for classification and recognition of 3D face, based on which we then adopted a multicascade classifier. By reconstructing, extracting features, and identifying multiple 3D faces, comparison experiment results demonstrate that our proposed method discerned 3D faces correctly, defeated the face camouflage attacks effectively, and showed promising application prospects for public safety surveillance.
To eliminate the limited angle effect of photoacoustic imaging based on ultrasound linear array, spatially distributed ultrasound sensor array is applied. The accurate sensor array position determines the quality of the imaging results. We proposed two methods based on photoacoustic and ultrasound signals to enhance the imaging quality using a full-ring array. Photoacoustic signals are used to regress the position of each element sensor. In phantom study and mouse brain study, imaging results can yield details clearly with average error rate of less than 50 μm. The proposed methods can contribute to precise biomedical imaging in future application scenarios.
Distinguishing between acute and chronic intestinal obstruction is essential for the treatment of Crohn's disease (CD). We have demonstrated the capability of spectroscopy photoacoustic (PA) imaging in quantifying hemoglobin and collagen changes. We also developed strain-PA imaging as a novel method for quantifying the intestinal stiffness, which is a mechanical marker of CD. In this study, we combined the spectroscopy and stiffness measurements using a catheter probe and examined the proposed approach in a rabbit model of CD in vivo. The quantitative accuracy of the imaging was validated by histology and micro-elastometry.
Photoacoustic (PA) imaging has shown its capability of characterizing intestinal inflammation and fibrosis endoscopically. With the purpose of clinical translation, we developed an endoscopic probe integrating an intracardiac ultrasound array and an 800 µm side-firing fiber optic inside a medical balloon catheter. The catheter probe, when collapsed, fits to the instrument channel of a colonoscope and can inflate for acoustic coupling when positioned at the disease location inside intestine. The performance of the probe in assessing the disease conditions including inflammation, fibrosis and muscle hypertrophy is under investigation in rabbits in vivo. The imaging results are validated by histopathology.
KEYWORDS: Photoacoustic spectroscopy, Imaging spectroscopy, Spectroscopy, Photoacoustic imaging, Human subjects, Inflammation, Brain-machine interfaces, Animal model studies, In vivo imaging, Collagen
Identifying fibrosis against inflammation in the intestinal strictures is critical to the management of CD.
Our pioneering study has shown that spectroscopic photoacoustic (PA) imaging is capable of
differentiating inflammatory and fibrotic intestinal strictures in animals in vivo. We also validated the
feasibility of acquiring PA signals from intestinal strictures transcutaneously. In this study, we further
investigated the capability of transcutaneous PA imaging in characterizing intestinal inflammation and
fibrosis in human subjects. The findings in PA imaging were validated by US Doppler images and
histopathology.
Hyperosteogeny and Osteoporosis are two common bone diseases that have a high incidence in the middle-aged and elderly groups. Mild symptoms may only affect the daily life of patients, while severe ones are life-threatening. At present, detection methods based on X-ray film and ultrasound are generally applied. However, the former exist errors introduced by manual reading and a certain radiation hazard, the diagnostic results of the latter are not that satisfying as well. Photoacoustic effect combines the advantages of optics for sensitive light absorption contrast and acoustics for lower acoustic scattering in soft tissue. As a non-ionizing and non-invasive technique, its application in biomedicine is also emerging. In this paper, a classification model built on Convolutional Neural Network (CNN) was proposed to achieve automated diagnosis of hyperosteogeny, osteoporosis and normal bone. Time-domain photoacoustic signals generated by different bone types are set as the inputs of the CNN while the output results indicate the corresponding categories of the samples. The analysis results of ex vivo data demonstrated that the established model could accurately accomplish the research of classification. Thus, the proposed method has certain auxiliary value for improving the efficiency, accuracy and objectivity of clinical diagnosis of the three bone types.
KEYWORDS: Reconstruction algorithms, Transducers, Signal to noise ratio, Photoacoustic imaging, Computer simulations, Signal detection, Photoacoustic spectroscopy, Interference (communication), Real time imaging, Image restoration
Delay and Sum (DAS) is one of the most common beamforming algorithms for photoacoustic imaging reconstruction that can function well in real-time imaging for its simplicity and quickness. However, high sidelobes and intense artifacts usually appear in the reconstructed image using DAS algorithm. To solve this problem, a novel beamforming algorithm called Multiple Delay and Sum with Enveloping (multi-DASE) is introduced in this paper, which can suppress sidelobes and artifacts efficiently. Compared to DAS, multi-DASE beamforming algorithm calculates not only the initial beamformed signal but also the N-shaped photoacoustic signal for each pixel. Firstly, Delay and Sum is performed multiply based on time series to recover the N-shaped photoacoustic signal for each pixel in the reconstructed image. And then, the recovered signal is enveloped to transform the N-shaped wave into a pulse wave to remove the negative part of the signal. Finally, signal suppression is performed on the enveloped signal which can lead to the suppression of sidelobes and artifacts in the reconstructed image. The multi-DASE beamforming algorithm was tested on the simulated data acquired with MATLAB k-Wave Toolbox. Experiment was also conducted to evaluate the efficiency of the multiDASE algorithm for clinical application. Both in computer simulation and experiment, our multi-DASE beamforming algorithm showed great performance in removing artifacts and improving image quality. In our multi-DASE beamforming algorithm, only fundamental operations and Discrete Fourier Transform (DFT) are performed, which means it can be a promising method for real-time clinical application.
Breast ultrasound has been used in the USA primarily as an adjunct breast cancer diagnosis to projection x-ray mammography or DBT. Ultrasound is employed in screening in most of the world and its use for such dense breast is increasing in the USA. In either application, finding corresponding masses in images from both x-ray and ultrasound is time-consuming and prone to correlation errors, leading to delays in cancer diagnosis. Previously, we have shown that, when automated breast ultrasound is performed through a special mammographic paddle in the same or slightly reduced compression as the x-ray exam, such correlation errors were reduced [1-2]. Even for hand-controlled scanning, it should be useful to track the physical position and orientation of the ultrasound transducer in the coordinates of the x-ray to help in reducing both exam time and correlation errors in mass identification. A tracking system for hand scanning through a mesh mammographic paddle is achieved via the coordination of a full-HD camera and a 6-axis sensor, locates the path of the real time ultrasound image plane through the x-ray image or image stack. The tracking system requires minimal setup, with the camera mounted to a fixed location relative to the paddle and the 6-axis sensor attached to the transducer body. The tracking system can achieve an overall frame rate of 5 Hz and mean position error within 6.62mm. In a parallel display, a mass identified in the x-ray image volume will be used to generate trajectories for an ultrasound transducer to reach the same mass. Feasible, improved position tracking should allow creation of spliced 3D volumes and precise, multimodality image fusion.
The change of tissue elasticity has been recognized as a biomarker of many disease conditions. Elastography has been investigated by observing the strain and stress correlation or shear wave propagation in tissue. The strain measurement can be achieved via speckle tracking in ultrasound (US) and optical modalities whereas the stress in deep tissue cannot be directly measured. Assuming that the collapsing of the vasculature could reflect the stress exerted on a tissue volume, the photoacoustic (PA) signals of the hemoglobin content within the vasculature could be an alternative measurement of the stress. This study investigates the strain-PA correlation with phantoms and a rat model in vivo. Parallel PA-US imaging was achieved by combining a compact linear US array and fiber optics delivering 720nm illumination. The phantom study simulated the vasculature with a piece of sponge soaked in ink, and the surrounding tissue with porcine gel with varied elasticity. The strain was generated by pushing the PA-US probe against the phantom surface. A correlation of 0.9 was found between the strain within the sponge and the PA signal changes. The rat model possesses inflammatory and fibrotic intestinal strictures comparable to those in the Crohn’s disease patients. The strain within the strictures was achieved by pushing the PA-US probe against the rats’ abdominal walls. Approximately twice more significant PA signal changes were observed in the fibrotic strictures than those in inflammatory ones under the same strain (p<0.001). All the results support that strain-PA imaging is capable of estimating the tissue elasticity.
KEYWORDS: Deconvolution, Signal processing, Signal detection, Image quality, Transducers, Photoacoustic imaging, Tissue optics, Ultrasonography, Reconstruction algorithms, Signal to noise ratio
Photoacoustic (PA) signal of an ideal optical absorb particle is a single N-shape wave. PA signals of a complicated biological tissue can be considered as the combination of individual N-shape waves. However, the N-shape wave basis not only complicates the subsequent work, but also results in aliasing between adjacent micro-structures, which deteriorates the quality of the final PA images. In this paper, we propose a method to improve PA image quality through signal processing method directly working on raw signals, which including deconvolution and empirical mode decomposition (EMD). During the deconvolution procedure, the raw PA signals are de-convolved with a system dependent point spread function (PSF) which is measured in advance. Then, EMD is adopted to adaptively re-shape the PA signals with two constraints, positive polarity and spectrum consistence. With our proposed method, the built PA images can yield more detail structural information. Micro-structures are clearly separated and revealed. To validate the effectiveness of this method, we present numerical simulations and phantom studies consist of a densely distributed point sources model and a blood vessel model. In the future, our study might hold the potential for clinical PA imaging as it can help to distinguish micro-structures from the optimized images and even measure the size of objects from deconvolved signals.
In B-mode images from dual-sided ultrasound, it has been shown that by delineating structures suspected of being
relatively homogeneous, one can enhance limited angle tomography to produce speed of sound images in the same view
as X-ray Digital Breast Tomography (DBT). This could allow better breast cancer detection and discrimination, as well
as improved registration of the ultrasound and X-ray images, because of the similarity of SOS and X-ray contrast in the
breast. However, this speed of sound reconstruction method relies strongly on B-mode or other reflection mode
segmentation. If that information is limited or incorrect, artifacts will appear in the reconstructed images. Therefore, the
iterative speed of sound reconstruction algorithm has been modified in a manner of simultaneously utilizing the image
segmentations and removing most artifacts. The first step of incorporating a priori information is solved by any nonlinearnonconvex
optimization method while artifact removal is accomplished by employing the fast split Bregman method to
perform total-variation (TV) regularization for image denoising. The proposed method was demonstrated in simplified
simulations of our dual-sided ultrasound scanner. To speed these computations two opposed 40-element ultrasound linear
arrays with 0.5 MHz center frequency were simulated for imaging objects in a uniform background. The proposed speed
of sound reconstruction method worked well with both bent-ray and full-wave inversion methods. This is also the first
demonstration of successful full-wave medical ultrasound tomography in the limited angle geometry. Presented results
lend credibility to a possible translation of this method to clinical breast imaging.
Photoacoustic spectrum analysis (PASA) offers potential advantages in identifying optically absorbing microstructures in biological tissues. Working at high ultrasound frequency, PASA is capable of identifying the morphological features of cells based on their intrinsic optical absorption. Adipocyte size is correlated with metabolic disease risk in the form of diabetes mellitus, thus it can be adopted as a pathology predictor to evaluate the condition of obese patient, and can be helpful for assessing the patient response to bariatric surgery. In order to acquire adipocyte size, usually adipose tissue biopsy is performed and histopathology analysis is conducted. The whole procedure is not well tolerated by patients, and is also labor and cost intensive. An unmet need is to quantify and predict adipocyte size in a mild and more efficient way. This work aims at studying the feasibility to analyze the adipocyte size of human fat tissue using the method of PASA. PA measurements were performed at the optical wavelength of 1210 nm where lipid has strong optical absorption, enabling the study of adipocyte without need of staining. Both simulation and ex vivo experiments have been completed. Good correlation between the quantified photoacoustic spectral parameter slope and the average adipocyte size obtained by the gold-standard histology has been established. This initial study suggests the potential opportunity of applying PASA to future clinical management of obesity.
Osteoporosis is a progressive bone disease which is characterized by a decrease in the bone mass and deterioration in bone micro-architecture. In theory, photoacoustic (PA) imaging analysis has potential to obtain the characteristics of the bone effectively. Previous study demonstrated that photoacoustic spectral analysis (PASA) method with the qualified parameter slope could provide an objective assessment of bone microstructure and deterioration. In this study, we tried to compare PASA method with the traditional quantitative ultrasound (QUS) method in osteoporosis assessment. Numerical simulations of both PA and ultrasound (US) signal are performed on computerized tomographic (CT) images of trabecular bone with different bone mineral densities (BMDs). Ex vivo experiments were conducted on porcine femur bone model of different BMDs. We compared the quantified parameter slope and the broadband ultrasound attenuation (BUA) coefficient from the PASA and QUS among different bone models, respectively. Both the simulation and ex vivo experiment results show that bone with low BMD has a higher slope value and lower BUA value. Our result demonstrated that the PASA method has the same efficacy with QUS in bone assessment, considering PA is a non-ionizing, non-invasive technique, PASA method holds potential for clinical diagnosis in osteoporosis and other bone diseases.
Biomedical photoacoustic (PA) signal is characterized with extremely low signal to noise ratio which will yield significant artifacts in photoacoustic tomography (PAT) images. Since PA signals acquired by ultrasound transducers are non-linear and non-stationary, traditional data analysis methods such as Fourier and wavelet method cannot give useful information for further research. In this paper, we introduce an adaptive method to improve the quality of PA imaging based on empirical mode decomposition (EMD) and reconstruction. Data acquired by ultrasound transducers are adaptively decomposed into several intrinsic mode functions (IMFs) after a sifting pre-process. Since noise is randomly distributed in different IMFs, depressing IMFs with more noise while enhancing IMFs with less noise can effectively enhance the quality of reconstructed PAT images. However, searching optimal parameters by means of brute force searching algorithms will cost too much time, which prevent this method from practical use. To find parameters within reasonable time, heuristic algorithms, which are designed for finding good solutions more efficiently when traditional methods are too slow, are adopted in our method. Two of the heuristic algorithms, Simulated Annealing Algorithm, a probabilistic method to approximate the global optimal solution, and Artificial Bee Colony Algorithm, an optimization method inspired by the foraging behavior of bee swarm, are selected to search optimal parameters of IMFs in this paper. The effectiveness of our proposed method is proved both on simulated data and PA signals from real biomedical tissue, which might bear the potential for future clinical PA imaging de-noising.
KEYWORDS: Image segmentation, Ultrasonography, Tissues, 3D image processing, Breast cancer, Breast, Diagnostics, Image analysis, Magnetic resonance imaging, Speckle, Signal to noise ratio, Visualization
Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.
Photoacoustic spectrum analysis (PASA) has been found to have the ability to characterize microstructures in phantoms. The ability of PASA technique which is subjected to the ultrasound detector has been reported in identify tissue with hundreds of microns in size. This paper demonstrated the feasibility of micro-ring, an ultrasonic detector with ultra-broad bandwidth, in characterizing microspheres’ sizes ranging from few microns to 100 microns using PASA technique and the results were compared with a commercial hydrophone. In order to further verify the capability of micro-ring, spectrum of single micro-spheres with sizes of tens of microns were measured and compared to simulation result. Our work proves that micro-ring based PASA technique has the ability of differentiating the particles with different size in phantoms.
Sound velocity measurement is of great importance to the application of biomedical especially in the research of acoustic detection and acoustic tomography. Using correct sound velocities in each medium other than one unified sound propagation speed, we can effectively enhance sound based imaging resolution. Photoacoustic tomography (PAT), is defined as cross-sectional or three-dimensional (3D) imaging of a material based on the photoacoustic effect and it is a developing, non-invasive imaging method in biomedical research. This contribution proposes a method to concurrently calculate multiple acoustic speeds in different mediums. Firstly, we get the size of infra-structure of the target by B-mode ultrasonic imaging method. Then we build the photoacoustic (PA) image of the same target with different acoustic speed in different medium. By repeatedly evaluate the quality of reconstruct PA image, we dynamically calibrate the acoustic speeds in different medium to build a finest PA image. Thus, we take these speeds of sound as the correct acoustic propagation velocities in according mediums. Experiments show that our non-invasive method can yield correct speed of sound with less than 0.3% error which might benefit future research in biomedical science.
The ducal imaging with photoacoustic imaging (PAI) that is an emerging technology and clinical ultrasound
imaging that is an established modality is developed for the imaging of early inflammatory arthritis. PAI is sensitive
to blood volume, not limited by flow like ultrasound, holding great promise for the earliest detection of increase in
blood volume and angiogenesis - a key early finding inflammation PAI has the capability of assessing inflammation
in superficial human soft tissues, offering potential benefits in diagnosis, treatment and monitoring of inflammatory
arthritis. PAI combined with ultrasonography (US), is a real time dual-modality system developed and tested to
identify active synovitis in metacarpophalangeal (MCP) joints of 10 arthritis patients and 10 normal volunteers.
Photoacoustic images of the joints were acquired at 580-nm laser wavelength, which provided the desired balance
between the optical contrast of hemoglobin over bone cortex and the imaging depth. Confirmed by US Doppler
imaging, the results from ten patients and ten normal volunteers demonstrated satisfactory sensitivity of PAI in
assessing enhanced blood flow due to active synovitis. This preliminary study suggests that photoacoustic imaging,
by identifying early increase in blood volume, related to increased vascularity, a hallmark of joint inflammation,
could be a valuable supplement to musculoskeletal US.
Photoacoustic (PA) technique involving both ultrasound and light has been explored for potential application in the assessment of bone health. The optical and ultrasound penetration in bone have been studied. The feasibility of conducting 3D PA imaging of bone, and performing quantitative evaluation of bone microstructures by using photoacoustic spectrum analysis (PASA) has also been investigated. The findings from the experiments demonstrate that PA measurement could offer information of bone mineral density and bone microstructure, both relevant to bone health.
With the capability of assessing high resolution optical information in soft tissues at imaging depth up to several centimeters, innovative biomedical photoacoustic imaging (PAI) offers benefits to diagnosis and treatment monitoring of inflammatory arthritis, particularly in combination with more established ultrasonography (US). In this work, a PAI and US dual-modality system facilitating both imaging functions in a real-time fashion was developed and initially tested for its clinical performance on patients with active inflammatory arthritis. Photoacoustic (PA) images of metacarpophalangeal (MCP) joints were acquired at 580-nm wavelength that provides a desired balance between optical absorption of blood and attenuation in background tissue. The results from six patients and six normal volunteers used as a control demonstrated the satisfactory sensitivity of PAI in assessing the physiological changes in the joints, specifically enhanced blood flow as a result of active synovitis. This preliminary study suggests that PAI, by revealing vascular features suggestive of joint inflammation, could be a valuable supplement to musculoskeletal US for rheumatology clinic.
The feasibility of an innovative biomedical diagnostic technique, thermal photoacoustic (TPA) measurement, for nonionizing and non-invasive assessment of bone health is investigated. Unlike conventional photoacoustic PA methods which are mostly focused on the measurement of absolute signal intensity, TPA targets the change in PA signal intensity as a function of the sample temperature, i.e. the temperature dependent Grueneisen parameter which is closely relevant to the chemical and molecular properties in the sample. Based on the differentiation measurement, the results from TPA technique is less susceptible to the variations associated with sample and system, and could be quantified with improved accurately. Due to the fact that the PA signal intensity from organic components such as blood changes faster than that from non-organic mineral under the same modulation of temperature, TPA measurement is able to objectively evaluate bone mineral density (BMD) and its loss as a result of osteoporosis. In an experiment on well established rat models of bone loss and preservation, PA measurements of rat tibia bones were conducted over a temperature range from 370 C to 440 C. The slope of PA signal intensity verses temperature was quantified for each specimen. The comparison among three groups of specimens with different BMD shows that bones with lower BMD have higher slopes, demonstrating the potential of the proposed TPA technique in future clinical management of osteoporosis.
Osteoporosis is a progressive bone disease that is characterized by a decrease in bone mass and deterioration in microarchitecture. This study investigates the feasibility of characterizing bone microstructure by analyzing the frequency spectrum of the photoacoustic signals from the bone. Modeling and numerical simulation of photoacoustic signals and their frequency-domain analysis were performed on trabecular bones with different mineral densities. The resulting quasilinear photoacoustic spectra were fit by linear regression, from which spectral parameter slope can be quantified. The modeling demonstrates that, at an optical wavelength of 685 nm, bone specimens with lower mineral densities have higher slope. Preliminary experiment on osteoporosis rat tibia bones with different mineral contents has also been conducted. The finding from the experiment has a good agreement with the modeling, both demonstrating that the frequency-domain analysis of photoacoustic signals can provide objective assessment of bone microstructure and deterioration. Considering that photoacoustic measurement is non-ionizing, non-invasive, and has sufficient penetration in both calcified and noncalcified tissues, this new technology holds unique potential for clinical translation.
Laser-induced thermotherapy (LITT), i.e. tissue destruction induced by a local increase of temperature by means of laser light energy transmission, has been frequently used for minimally invasive treatments of various diseases such as benign thyroid nodules and liver cancer. The emerging photoacoustic (PA) imaging, when integrated with ultrasound (US), could contribute to LITT procedure. PA can enable a good visualization of percutaneous apparatus deep inside tissue and, therefore, can offer accurate guidance of the optical fibers to the target tissue. Our initial experiment demonstrated that, by picking the strong photoacoustic signals generated at the tips of optical fibers as a needle, the trajectory and position of the fibers could be visualized clearly using a commercial available US unit. When working the conventional US Bscan mode, the fibers disappeared when the angle between the fibers and the probe surface was larger than 60 degree; while working on the new PA mode, the fibers could be visualized without any problem even when the angle between the fibers and the probe surface was larger than 75 degree. Moreover, with PA imaging function integrated, the optical fibers positioned into the target tissue, besides delivering optical energy for thermotherapy, can also be used to generate PA signals for on-line evaluation of LITT. Powered by our recently developed PA physio-chemical analysis, PA measurements from the tissue can provide a direct and accurate feedback of the tissue responses to laser ablation, including the changes in not only chemical compositions but also histological microstructures. The initial experiment on the rat liver model has demonstrated the excellent sensitivity of PA imaging to the changes in tissue temperature rise and tissue status (from native to coagulated) when the tissue is treated in vivo with LITT.
Photoacoustic tomography (PAT) is an effective optical biomedical imaging method which is characterized with noninonizing and noninvasive, presenting good soft tissue contrast with excellent spatial resolution. To build a multi-dimensional breast PAT image, more ultrasound sensors are needed, which brings difficulties to data acquisition. The time complexity for multi-dimensional breast PAT image reconstruction also rises tremendously. Compressive sensing (CS) theory breaks the restriction of Nyquist sampling theorem and is capable to rebuild signals with fewer measurements. In this contribution, we propose an effective optimization method for multi-dimensional breast PAT, which combines the theory of CS and an unevenly, adaptively distributing data acquisition algorithm. With this method, the quality of our reconstructed breast PAT images are better than those using existing multi-dimensional breast PAT system. To build breast PAT images with the same quality, the required number of ultrasound transducers is decreased by using our proposed method. We have verified our method on simulation data and achieved expected results in both two dimensional and three dimensional PAT image reconstruction. In the future, our method can be applied to various aspects of biomedical PAT imaging such as early stage tumor detection and in vivo imaging monitoring.
Photoacoustic tomography (PAT) offers structural and functional imaging of living biological tissue with highly sensitive optical absorption contrast and excellent spatial resolution comparable to medical ultrasound (US) imaging. We report the development of a fully integrated PAT and US dual-modality imaging system, which performs signal scanning, image reconstruction, and display for both photoacoustic (PA) and US imaging all in a truly real-time manner. The back-projection (BP) algorithm for PA image reconstruction is optimized to reduce the computational cost and facilitate parallel computation on a state of the art graphics processing unit (GPU) card. For the first time, PAT and US imaging of the same object can be conducted simultaneously and continuously, at a real-time frame rate, presently limited by the laser repetition rate of 10 Hz. Noninvasive PAT and US imaging of human peripheral joints in vivo were achieved, demonstrating the satisfactory image quality realized with this system. Another experiment, simultaneous PAT and US imaging of contrast agent flowing through an artificial vessel, was conducted to verify the performance of this system for imaging fast biological events. The GPU-based image reconstruction software code for this dual-modality system is open source and available for download from http://sourceforge.net/projects/patrealtime.
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