Feasibility of Quantitative Tissue Characterization Using Novel Parameters Extracted From Photoacoustic Power Spectrum Considering Multiple Absorbers.

Frequency domain analysis of radio frequency signal is performed to differentiate between different tissue categories in terms of spectral parameters. However, due to complex relationship between the absorber size and spectral parameters, they cannot be used for quantitative tissue characterization. In an earlier study, we showed that using linear relationship between absorber size and two new spectral parameters namely number of lobes and average lobe width, absorber size can be successfully recovered from photoacoustic signal generated by single absorber. As actual biological tissue contains multiple absorbers, in this study we extended the application of these two new spectral parameters for computing absorber size from signals generated by multiple PA absorbers. We revisited our analytical model to establish two new linear relationships between the absorber radius and number of lobes as well as average lobe width considering multiple absorbers with bandlimited acquisition. A simulation study was performed to validate these linear relationships. A retrospective ex vivo study, in which the spectral parameters were computed using multiwavelength photoacoustic signals, was performed with freshly exercised thyroid specimens from 38 actual human patients undergoing thyroidectomy after having a diagnosis of suspected thyroid lesions. From statistical analysis it is shown that both the parameters were significantly different between malignant and non-malignant thyroid and malignant and normal thyroid tissue. Performance of the supervised classification with the computed spectral parameters showed that the extracted parameters could be successfully used to differentiate malignant thyroid tissue from normal thyroid tissue with reasonable degree of accuracy.

Computation of Photoacoustic Absorber Size from Deconvolved Photoacoustic Signal Using Estimated System Impulse Response.

Photoacoustic signal recorded by photoacoustic imaging system can be modeled as convolution of initial photoacoustic response by the photoacoustic absorber with the system impulse response. Our goal was to compute the size of photoacoustic absorber using the initial photoacoustic response, deconvolved from the recorded photoacoustic data. For deconvolution, we proposed to use the impulse response of the photoacoustic system, estimated using discrete wavelet transform based homomorphic filtering. The proposed method was implemented on experimentally acquired photoacoustic data generated by different phantoms and also verified by a simulation study involving photoacoustic targets, identical to the phantoms in experimental study. The photoacoustic system impulse response, which was estimated using the acquired photoacoustic signal corresponding to a lead pencil, was used to extract initial photoacoustic response corresponding to a mustard seed of 0.65 mm radius. The recovered radius values of the mustard seed, corresponding to the experimental and simulation studies were 0.6 mm and 0.7 mm.

Automatic cancer tissue detection using multispectral photoacoustic imaging.

PURPOSE: In the case of multispecimen study to locate cancer regions, such as in thyroidectomy and prostatectomy, a significant labor-intensive processing is required at a high cost. Pathology diagnosis is usually done by a pathologist observing tissue-stained glass slide under a microscope. METHOD: Multispectral photoacoustic (MPA) specimen imaging has proven successful in differentiating photoacoustic (PA) signal characteristics between a histopathology-defined cancer region and normal tissue. This is mainly due to its ability to efficiently map oxyhemoglobin and deoxyhemoglobin contents from MPA images and key features for cancer detection. A fully automated deep learning algorithm is purposed, which learns to detect the presence of malignant tissue in freshly excised ex vivo human thyroid and prostate tissue specimens using the three-dimensional MPA dataset. The proposed automated deep learning model consisted of the convolutional neural network architecture, which extracts spatially colocated features, and a softmax function, which detects thyroid and prostate cancer tissue at once. This is one of the first deep learning models, to the best of our knowledge, to detect the presence of cancer in excised thyroid and prostate tissue of humans at once based on PA imaging. RESULT: The area under the curve (AUC) was used as a metric to evaluate the predictive performance of the classifier. The proposed model detected the cancer tissue with the AUC of 0.96, which is very promising. CONCLUSION: This model is an improvement over the previous work using machine learning and deep learning algorithms. This model may have immediate application in cancer screening of the numerous sliced specimens that result from thyroidectomy and prostatectomy. Since the instrument that was used to capture the ex vivo PA images is now being developed for in vivo use, this model may also prove to be a starting point for in vivo PA image analysis for cancer diagnosis.

Multiview spatial compounding using lens-based photoacoustic imaging system.

Recently, an acoustic lens has been proposed for volumetric focusing as an alternative to conventional reconstruction algorithms in Photoacoustic (PA) Imaging. Acoustic lens can significantly reduce computational complexity and facilitate the implementation of real-time and cost-effective systems. However, due to the fixed focal length of the lens, the Point Spread Function (PSF) of the imaging system varies spatially. Furthermore, the PSF is asymmetric, with the lateral resolution being lower than the axial resolution. For many medical applications, such as in vivo thyroid, breast and small animal imaging, multiple views of the target tissue at varying angles are possible. This can be exploited to reduce the asymmetry and spatial variation of system the PSF with simple spatial compounding. In this article, we present a formulation and experimental evaluation of this technique. PSF improvement in terms of resolution and Signal to Noise Ratio (SNR) with the proposed spatial compounding is evaluated through simulation. Overall image quality improvement is demonstrated with experiments on phantom and ex vivo tissue. When multiple views are not possible, an alternative residual refocusing algorithm is proposed. The performances of these two methods, both separately and in conjunction, are compared and their practical implications are discussed.

Two-sided residual refocusing for an acoustic lens-based photoacoustic imaging system.

In photoacoustic (PA) cameras, an acoustic lens-based system can form a focused image of an object plane. A real-time C-scan PA image can be formed by simply time gating the transducer response. While most of the focusing action is performed by the lens, residual refocusing is needed to image multiple depths with high resolution simultaneously. However, a refocusing algorithm for a PA camera has not been studied so far in the literature. In this work, we reformulate this residual refocusing problem for a PA camera into a two-sided wave propagation from a planar sensor array. One part of the problem deals with forward wave propagation while the other deals with time reversal. We have chosen a fast Fourier transform (FFT) based wave propagation model for the refocusing to maintain the real-time nature of the system. We have conducted point spread function (PSF) measurement experiments at multiple depths and refocused the signal using the proposed method. The full width at half maximum (FWHM), peak value and signal to noise ratio (SNR) of the refocused PSF is analyzed to quantify the effect of refocusing. We believe that using a two-dimensional transducer array combined with the proposed refocusing can lead to real-time volumetric imaging using a PA camera.

Characterization of lens based photoacoustic imaging system.

Some of the challenges in translating photoacoustic (PA) imaging to clinical applications includes limited view of the target tissue, low signal to noise ratio and the high cost of developing real-time systems. Acoustic lens based PA imaging systems, also known as PA cameras are a potential alternative to conventional imaging systems in these scenarios. The 3D focusing action of lens enables real-time C-scan imaging with a 2D transducer array. In this paper, we model the underlying physics in a PA camera in the mathematical framework of an imaging system and derive a closed form expression for the point spread function (PSF). Experimental verification follows including the details on how to design and fabricate the lens inexpensively. The system PSF is evaluated over a 3D volume that can be imaged by this PA camera. Its utility is demonstrated by imaging phantom and an ex vivo human prostate tissue sample.

Frequency Domain Analysis of Multiwavelength Photoacoustic Signals for Differentiating Among Malignant, Benign, and Normal Thyroids in an Ex Vivo Study With Human Thyroids.

OBJECTIVES: This study investigated the capability of spectral parameters, extracted by frequency domain analysis of photoacoustic signals, to differentiate among malignant, benign, and normal thyroid tissue. METHODS: We acquired multiwavelength photoacoustic images of freshly excised thyroid specimens collected from 50 patients who underwent thyroidectomy after having a diagnosis of suspected thyroid lesions. A thyroid cytopathologist marked histologic slides of each tissue specimen. These marked slides were used as ground truth to identify the regions of interest (ROIs) corresponding to malignant, benign, and normal thyroid tissue. Three spectral parameters: namely, slope, midband fit, and intercept, were extracted from photoacoustic signals corresponding to different ROIs. RESULTS: Spectral parameters were extracted from a total of total of 65 ROIs. According to the ground truth, 12 of 65 ROIs belonged to malignant thyroids; 28 of 65 ROIs belonged to benign thyroids; and 25 of 65 ROIs belonged to normal thyroids. Besides slope, the other 2 spectral parameters and grayscale photoacoustic image pixel values were found to be significantly different (P < .05) between malignant and normal thyroids. Between benign and normal thyroids, all 3 spectral parameters and photoacoustic pixel values were significantly different (P < .05). CONCLUSIONS: Preliminary results of our ex vivo human thyroid study show that the spectral parameters extracted from radiofrequency photoacoustic signals as well as the pixel values of 2-dimensional photoacoustic images can be used for differentiating among malignant, benign, and normal thyroid tissue.

Evaluation of Frequency Domain Analysis of a Multiwavelength Photoacoustic Signal for Differentiating Malignant From Benign and Normal Prostates: Ex Vivo Study With Human Prostates.

OBJECTIVES: The purpose of this study was to investigate the feasibility of differentiating malignant prostate from benign prostatic hyperplasia (BPH) and normal prostate tissue by performing frequency domain analysis of photoacoustic images acquired at 2 different wavelengths. METHODS: We performed multiwavelength photoacoustic imaging on freshly excised human prostate specimens taken from a total of 30 patients undergoing prostatectomy for biopsy-confirmed prostate cancer. Histologic slides marked by a genitourinary pathologist were used as ground truth to define regions of interest (ROIs) in the photoacoustic images. Primarily, 3 different prostate tissue categories, namely malignant, BPH, and normal, were considered, while a fourth category named nonmalignant was formed by combining the ROIs corresponding to BPH and normal tissue together. We extracted 3 spectral parameters, namely slope, midband fit, and intercept, from power spectra of the radiofrequency photoacoustic signals corresponding to the 3 primary tissue categories. RESULTS: We analyzed data from 53 ROIs selected from the photoacoustic images of 30 patients. According to the histopathologic analysis, 19 ROIs were malignant, 8 were BPH, and 26 were normal. All the 3 spectral parameters and C-scan grayscale photoacoustic image pixel values were found to be significantly different (P < .01) between malignant and nonmalignant prostate as well as malignant and normal prostate. CONCLUSIONS: Preliminary results of our ex vivo human prostate study suggest that spectral parameters obtained by performing frequency domain analysis of photoacoustic signals can be used to differentiate between malignant and nonmalignant prostate.

Photoacoustic imaging with an acoustic lens detects prostate cancer cells labeled with PSMA-targeting near-infrared dye-conjugates.

There is an urgent need for sensitive and specific tools to accurately image early stage, organ-confined human prostate cancers to facilitate active surveillance and reduce unnecessary treatment. Recently, we developed an acoustic lens that enhances the sensitivity of photoacoustic imaging. Here, we report the use of this device in conjunction with two molecular imaging agents that specifically target the prostate-specific membrane antigen (PSMA) expressed on the tumor cell surface of most prostate cancers. We demonstrate successful imaging of phantoms containing cancer cells labeled with either of two different PSMA-targeting agents, the ribonucleic acid aptamer A10-3.2 and a urea-based peptidomimetic inhibitor, each linked to the near-infrared dye IRDye800CW. By specifically targeting cells with these agents linked to a dye chosen for optimal signal, we are able to discriminate prostate cancer cells that express PSMA.

Preliminary results of ex vivo multispectral photoacoustic imaging in the management of thyroid cancer.

OBJECTIVE: The purpose of this study was to validate whether ex vivo multispectral photoacoustic imaging can be used to differentiate malignant tissue, benign nodules, and normal human thyroid tissue. SUBJECTS AND METHODS: Fifty patients undergoing thyroidectomy because of thyroid lesions participated in this study. Multispectral photoacoustic imaging was performed on surgically excised thyroid tissue, and chromophore images that represented optical absorption of deoxyhemoglobin, oxyhemoglobin, lipid, and water were reconstructed. After the imaging procedure, the pathologist marked malignant tissue, benign nodules, and normal regions on histopathologic slides, and digital images of the marked histopathologic slides were obtained. The histopathologic images were coregistered with chromophore images. Areas corresponding to malignant tissue, benign nodules, and normal tissue were defined on the chromophore images. Pixel values within each area were averaged to determine the mean intensities of deoxyhemoglobin, oxyhemoglobin, lipid, and water. RESULTS: There was a statistically significant difference between malignant and benign nodules with respect to mean intensity of deoxyhemoglobin (p = 0.014). There was a difference between malignant and normal tissue in mean intensity of deoxyhemoglobin (p = 0.003), lipid (p = 0.001), and water (p < 0.0001). A difference between benign nodules and normal tissue was found in mean intensity of oxyhemoglobin (p < 0.0001), lipid (p < 0.0001), and water (p < 0.0001). The sensitivity, specificity, and positive and negative predictive values of the system tested in differentiating malignant from nonmalignant thyroid tissue were 69.2%, 96.9%, 81.8%, and 93.9%. CONCLUSION: The preliminary results of this ex vivo human thyroid study suggest that multispectral photoacoustic imaging can be used to differentiate malignant and benign nodules and normal human thyroid tissue.

Multispectral Photoacoustic Imaging of Prostate Cancer: Preliminary Ex-vivo Results.

OBJECTIVE: The objective of this study is to validate if ex-vivo multispectral photoacoustic (PA) imaging can differentiate between malignant prostate tissue, benign prostatic hyperplasia (BPH), and normal human prostate tissue. MATERIALS AND METHODS: Institutional Review Board's approval was obtained for this study. A total of 30 patients undergoing prostatectomy for biopsy-confirmed prostate cancer were included in this study with informed consent. Multispectral PA imaging was performed on surgically excised prostate tissue and chromophore images that represent optical absorption of deoxyhemoglobin (dHb), oxyhemoglobin (HbO2), lipid, and water were reconstructed. After the imaging procedure is completed, malignant prostate, BPH and normal prostate regions were marked by the genitourinary pathologist on histopathology slides and digital images of marked histopathology slides were obtained. The histopathology images were co-registered with chromophore images. Region of interest (ROI) corresponding to malignant prostate, BPH and normal prostate were defined on the chromophore images. Pixel values within each ROI were then averaged to determine mean intensities of dHb, HbO2, lipid, and water. RESULTS: Our preliminary results show that there is statistically significant difference in mean intensity of dHb (P < 0.0001) and lipid (P = 0.0251) between malignant prostate and normal prostate tissue. There was difference in mean intensity of dHb (P < 0.0001) between malignant prostate and BPH. Sensitivity, specificity, positive predictive value, and negative predictive value of our imaging system were found to be 81.3%, 96.2%, 92.9% and 89.3% respectively. CONCLUSION: Our preliminary results of ex-vivo human prostate study suggest that multispectral PA imaging can differentiate between malignant prostate, BPH and normal prostate tissue.

Photoacoustic imaging: opening new frontiers in medical imaging.

In today's world, technology is advancing at an exponential rate and medical imaging is no exception. During the last hundred years, the field of medical imaging has seen a tremendous technological growth with the invention of imaging modalities including but not limited to X-ray, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography, and single-photon emission computed tomography. These tools have led to better diagnosis and improved patient care. However, each of these modalities has its advantages as well as disadvantages and none of them can reveal all the information a physician would like to have. In the last decade, a new diagnostic technology called photoacoustic imaging has evolved which is moving rapidly from the research phase to the clinical trial phase. This article outlines the basics of photoacoustic imaging and describes our hands-on experience in developing a comprehensive photoacoustic imaging system to detect tissue abnormalities.

Acoustic lens characterization for ultrasound and photoacoustic C-scan imaging modalities.

From a fundamental perspective, image reconstruction tasks in both ultrasound pulse echo and photoacoustic imaging are identical. We propose a C-scan imaging scheme that is applicable to both modalities where the image reconstruction is achieved through focusing action of an acoustic lens. The theory to characterize the imaging system is presented. Experimental methodology to determine the system point-spread-function is outlined and demonstrated with preliminary results.

Independent component analysis applied to ultrasound speckle texture analysis and tissue characterization.

Analysis of ultrasound speckle texture will provide us information about the underlying properties of tissue, could find applications in early lesion detection and tissue characterization. Traditional first and second order statistics based approaches ignore the higher order statistics information in the texture. On the other hand, conventional multichannel filtering or multiresolution analysis approaches rely on the predefined analytical bases which are not fully adaptive to the data being analyzed. In this paper Independent Component Analysis (ICA), which is based on higher order statistics, is proposed to deal with the ultrasound speckle texture analysis problem. ICA image bases obtained from the training images are applied as a filter bank to the testing images. Then the independent features containing higher order statistics information can be extracted from the marginal distributions of the filtered images. ICA is used here as a dimensionality reduction tool to overcome the difficulty of estimating high dimensional joint density of texture. Support Vector Machine (SVM) is then used as a classifier to classify the tissues. By using the digitally simulated tissues and corresponding B-scan images, we can further correlate the change of tissue microstructure or change of imaging conditions with the change of the ICA feature vectors. Our numerical simulation has shown ICA to be a promising technique for ultrasound speckle texture analysis and tissue characterization compared with some traditional methods such as PCA and Gabor transform.