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 photo - acoustic 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.

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In this review article, a detailed chronological account of the research related to photoacoustic imaging for the management of breast cancer is presented. Performing a detailed analysis of the breast cancer detection related photoacoustic imaging studies undertaken by different research groups, this review attempts to present the clinical evidence in support of using photoacoustic imaging for breast cancer detection. Based on the experimental evidence obtained from the clinical studies conducted so far, the performance of photoacoustic imaging is compared with that of conventional breast imaging modalities. While we find that there is enough experimental evidence to support the use of photoacoustic imaging for breast cancer detection, additional clinical studies are required to be performed to evaluate the diagnostic potential of photoacoustic imaging for identifying different types of breast cancer. To establish the utility of photoacoustic imaging for breast cancer screening, clinical studies with high-risk asymptomatic patients need to be done.

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Objective This study investigated the capability of spectral parameters, extracted using frequency domain analysis of photoacoustic (PA) signals, to differentiate between malignant, benign and normal thyroid tissue. Methods We acquired multiwavelength PA images of the freshly excised thyroid specimens, collected from 50 patients who underwent thyroidectomy after being diagnosed with suspected thyroid lesion. A thyroid cytopathologist marked histology slides of each tissue specimen. These marked histology slides were used as ground truth to identify the region of interests (ROI) corresponding to malignant, benign and normal thyroid tissue. Three spectral parameters, namely slope, midband fit and intercept were extracted from PA signals corresponding to different ROIs. Results Spectral parameters were extracted from a total of total of 65 ROIs. According to the ground truth, 12 out of 65 ROIs belonged to malignant thyroid, 28 out of 65 ROIs belonged to benign thyroid and 25 out of 65 ROIs belonged to normal thyroid. Besides slope, the other two spectral parameters and grayscale PA image pixel values were found to be significantly different (p < 0.05) between malignant and normal thyroid. Between benign and normal thyroid, all three spectral parameters and PA pixel values were significantly different (p < 0.05). Conclusion Preliminary results of our ex vivo human thyroid study show that the spectral parameters extracted from radio frequency PA signals as well as the pixel value of 2D PA images can be used for differentiating between malignant, benign and normal thyroid tissue.

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.

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This study investigates the feasibility of using frequency analysis of multispectral PA (Photoacoustics) signals generated by excised human thyroid tissue to differentiate between malignant and normal thyroid regions. Multispectral PA imaging was performed on freshly excised thyroid tissue from 6 patients undergoing thyroidectomy or thyroid lobectomy. The regions of interests in the PA images corresponding to malignant and normal tissue have been selected with the help of histopathology slides. The calibrated power spectrum of each PA signal from each region of interest was fit to a linear model for extracting the values of slope, midband fit and intercept parameters. The results show that mean values of intercept and midband fit parameters are significantly different between malignant and normal regions for all five wavelengths and mean values of slope are significantly different for two wavelengths.