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.

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.

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.