Frequency analysis of multispectral photoacoustic images for differentiating malignant region from normal region in excised human prostate

Sinha, Saugata; Rao, Navalgund; Valluru, K.; Chinni, Bhargava; Dogra, Vikram S.; Helguera, María

doi:10.1117/12.2043802

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…The objective of the study was to verify the feasibility of differentiating among malignant prostate tissue, benign prostatic hyperplasia (BPH), and normal prostate tissue using frequency domain analysis of the 3D photoacoustic data. Although the experimental protocol and the experimental setup for acquiring photoacoustic data generated by the excised human prostate specimens were described in some of our earlier reports, [30][31][32] here those things are described in a concise way for the sake of completeness and continuity. Figure 1 shows the ex vivo photoacoustic imaging system developed in our laboratory.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Frequency Domain Analysis of a Multiwavelength Photoacoustic Signal for Differentiating Malignant From Benign and Normal Prostates

Sinha

Rao

Chinni

et al. 2016

J of Ultrasound Medicine

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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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Section: Methodsmentioning

confidence: 99%

Evaluation of Frequency Domain Analysis of a Multiwavelength Photoacoustic Signal for Differentiating Malignant From Benign and Normal Prostates

Sinha

Rao

Chinni

et al. 2016

J of Ultrasound Medicine

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“…Three‐dimensional (3D) photoacoustic signals generated by freshly excised thyroid specimens were acquired by an ex vivo photoacoustic imaging system at multiple wavelengths. Although detailed descriptions of the experimental protocol and photoacoustic image acquisition system are available in some of our earlier reports, a concise description of those aspects are provided here for continuity.…”

Section: Methodsmentioning

confidence: 99%

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

Sinha

Dogra

Chinni

et al. 2017

J of Ultrasound Medicine

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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.

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“…In multispectral photoacoustic (MPA) imaging, the large difference in light absorption coefficient between blood and other tissue constituents enables detection of tissue angiogenesis associated with rapid tumor growth in early stages [61]. Furthermore, there is strong evidence of the dependence of the frequency content of the PA signal with the size of the tumor [47,91] (see chapter 3, section 3.2). During tissue pathology, the structural change of a tumor is expected, and hence, we hypothesized that frequency content would help to detect cancer [47,91].…”

Section: Introduction and Overviewmentioning

confidence: 99%

Automatic cancer tissue detection using multispectral photoacoustic imaging

et al. 2019

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Convolutional neural networks (CNNs) have become increasingly popular in recent years because of their ability to tackle complex learning problems such as object detection, and object localization. They are being used for a variety of tasks, such as tissue abnormalities detection and localization, with an accuracy that comes close to the level of human predictive performance in medical imaging. The success is First and foremost, I would like to express my sincere gratitude to my advisor Professor Dr. Navalgund Rao for his guidance, patience, and intellectual support throughout my Ph.D. research. I feel very fortunate to get a chance to work with him who offered invaluable research advice and always provided me opportunities to participate in collaborative research projects and conferences that allowed me to grow academically and intellectually. I am also very grateful to my Committee, Dr. Vikram Dogra, Dr. Guoyu Lu, and Dr. Pengcheng Shi for their time for providing helpful suggestions and feedbacks during my Ph.D. thesis preparation, without whom this accomplishment would not even have been possible. I am very thankful to the Center for Imaging Science for all the support throughout the Ph.D. study. I would like to thank Center for Imaging Science, and the director of the center for imaging science Dr. David Messinger for providing me with a travel grant which allowed me to attend and present my works at Medical Imaging, SPIE, Defense + Commercial Sensing, SPIE, and International Conference on Semantic Computing, IEEE conferences. I would also like to extend my thanks to Elizabeth Lockwood for her countless assistance in my study arrangement during my Ph.D. study.

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Frequency analysis of multispectral photoacoustic images for differentiating malignant region from normal region in excised human prostate

Cited by 5 publications

References 9 publications

Evaluation of Frequency Domain Analysis of a Multiwavelength Photoacoustic Signal for Differentiating Malignant From Benign and Normal Prostates

Evaluation of Frequency Domain Analysis of a Multiwavelength Photoacoustic Signal for Differentiating Malignant From Benign and Normal Prostates

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

Automatic cancer tissue detection using multispectral photoacoustic imaging

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