Acoustic resolution photoacoustic Doppler flowmetry for assessment of patient rectal cancer blood perfusion.

Significance: Photoacoustic Doppler flowmetry offers quantitative blood perfusion information in addition to photoacoustic vascular contrast for rectal cancer assessment. Aim: We aim to develop and validate a correlational Doppler flowmetry utilizing an acoustic resolution photoacoustic microscopy (AR-PAM) system for blood perfusion analysis. Approach: To extract blood perfusion information, we implemented AR-PAM Doppler flowmetry consisting of signal filtering and conditioning, A-line correlation, and angle compensation. We developed flow phantoms and contrast agent to systemically investigate the flowmetry's efficacy in a series of phantom studies. The developed correlational Doppler flowmetry was applied to images collected during in vivo AR-PAM for post-treatment rectal cancer evaluation. Results: The linearity and accuracy of the Doppler flow measurement system were validated in phantom studies. Imaging rectal cancer patients treated with chemoradiation demonstrated the feasibility of using correlational Doppler flowmetry to assess treatment response and distinguish residual cancer from cancer-free tumor bed tissue and normal rectal tissue. Conclusions: A new correlational Doppler flowmetry was developed and validated through systematic phantom evaluations. The results of its application to in vivo patients suggest it could be a useful addition to photoacoustic endoscopy for post-treatment rectal cancer assessment.

Quantification of ovarian lesion and fallopian tube vasculature using optical-resolution photoacoustic microscopy.

The heterogeneity in the pathological and clinical manifestations of ovarian cancer is a major hurdle impeding early and accurate diagnosis. A host of imaging modalities, including Doppler ultrasound, MRI, and CT, have been investigated to improve the assessment of ovarian lesions. We hypothesized that pathologic conditions might affect the ovarian vasculature and that these changes might be detectable by optical-resolution photoacoustic microscopy (OR-PAM). In our previous work, we developed a benchtop OR-PAM and demonstrated it on a limited set of ovarian and fallopian tube specimens. In this study, we collected data from over 50 patients, supporting a more robust statistical analysis. We then developed an efficient custom analysis pipeline for characterizing the vascular features of the samples, including the mean vessel diameter, vascular density, global vascular directionality, local vascular definition, and local vascular tortuosity/branchedness. Phantom studies using carbon fibers showed that our algorithm was accurate within an acceptable error range. Between normal ovaries and normal fallopian tubes, we observed significant differences in five of six extracted vascular features. Further, we showed that distinct subsets of vascular features could distinguish normal ovaries from cystic, fibrous, and malignant ovarian lesions. In addition, a statistically significant difference was found in the mean vascular tortuosity/branchedness values of normal and abnormal tubes. The findings support the proposition that OR-PAM can help distinguish the severity of tubal and ovarian pathologies.

Rectal Cancer Treatment Management: Deep-Learning Neural Network Based on Photoacoustic Microscopy Image Outperforms Histogram-Feature-Based Classification.

We have developed a novel photoacoustic microscopy/ultrasound (PAM/US) endoscope to image post-treatment rectal cancer for surgical management of residual tumor after radiation and chemotherapy. Paired with a deep-learning convolutional neural network (CNN), the PAM images accurately differentiated pathological complete responders (pCR) from incomplete responders. However, the role of CNNs compared with traditional histogram-feature based classifiers needs further exploration. In this work, we compare the performance of the CNN models to generalized linear models (GLM) across 24 ex vivo specimens and 10 in vivo patient examinations. First order statistical features were extracted from histograms of PAM and US images to train, validate and test GLM models, while PAM and US images were directly used to train, validate, and test CNN models. The PAM-CNN model performed superiorly with an AUC of 0.96 (95% CI: 0.95-0.98) compared to the best PAM-GLM model using kurtosis with an AUC of 0.82 (95% CI: 0.82-0.83). We also found that both CNN and GLMs derived from photoacoustic data outperformed those utilizing ultrasound alone. We conclude that deep-learning neural networks paired with photoacoustic images is the optimal analysis framework for determining presence of residual cancer in the treated human rectum.

Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning.

Background Conventional radiologic modalities perform poorly in the radiated rectum and are often unable to differentiate residual cancer from treatment scarring. Purpose To report the development and initial patient study of an imaging system comprising an endorectal coregistered photoacoustic (PA) microscopy (PAM) and US system paired with a convolution neural network (CNN) to assess the rectal cancer treatment response. Materials and Methods In this prospective study (ClinicalTrials.gov identifier NCT04339374), participants completed radiation and chemotherapy from September 2019 to September 2020 and images were obtained with the PAM/US system prior to surgery. Another group's colorectal specimens were studied ex vivo. The PAM/US system consisted of an endorectal imaging probe, a 1064-nm laser, and one US ring transducer. The PAM CNN and US CNN models were trained and validated to distinguish normal from malignant colorectal tissue using ex vivo and in vivo patient data. The PAM CNN and US CNN were then tested using additional in vivo patient data that had not been seen by the CNNs during training and validation. Results Twenty-two patients' ex vivo specimens and five patients' in vivo images (a total of 2693 US regions of interest [ROIs] and 2208 PA ROIs) were used for CNN training and validation. Data from five additional patients were used for testing. A total of 32 participants (mean age, 60 years; range, 35-89 years) were evaluated. Unique PAM imaging markers of the complete tumor response were found, specifically including recovery of normal submucosal vascular architecture within the treated tumor bed. The PAM CNN model captured this recovery process and correctly differentiated these changes from the residual tumor. The imaging system remained highly capable of differentiating tumor from normal tissue, achieving an area under the receiver operating characteristic curve of 0.98 (95% CI: 0.98, 0.99) for data from five participants. By comparison, the US CNN had an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.70, 0.73). Conclusion An endorectal coregistered photoacoustic microscopy/US system paired with a convolutional neural network model showed high diagnostic performance in assessing the rectal cancer treatment response and demonstrated potential for optimizing posttreatment management. © RSNA, 2021 Supplemental material is available for this article. See also the editorial by Klibanov in this issue.

Optical Resolution Photoacoustic Microscopy of Ovary and Fallopian Tube.

Ovarian cancer is the leading cause of death among gynecological cancers, but is poorly amenable to preoperative diagnosis. In this study, we investigate the feasibility of "optical biopsy," using high-optical-resolution photoacoustic microscopy (OR-PAM) to quantify the microvasculature of ovarian and fallopian tube tissue. The technique is demonstrated using excised human ovary and fallopian tube specimens imaged immediately after surgery. Quantitative parameters are derived using Amira software. The parameters include three-dimensional vascular segment count, total volume and length, which are associated with tumor angiogenesis. Qualitative results of OR-PAM demonstrate that malignant ovarian tissue has larger and more tortuous blood vessels as well as smaller vessels of different sizes, while benign and normal ovarian tissue has smaller vessels of uniform size. Quantitative analysis shows that malignant ovaries have greater tumor vessel volume, length and number of segments, as compared with benign and normal ovaries. The vascular pattern of benign fallopian tube is different than that of benign ovarian tissue. Our initial results demonstrate the potential of OR-PAM as an imaging tool for fast assessment of ovarian tissue and fallopian tube and could avoid unnecessary surgery if the risk of the examined ovary is extremely low.

Feasibility of co-registered ultrasound and acoustic-resolution photoacoustic imaging of human colorectal cancer.

Colorectal cancer is the second leading cause of cancer death in the United States. Significant limitations in screening and surveillance modalities continue to hamper early detection of primary cancers or recurrences after therapy. In this study, we describe a new registered ultrasound (US) and acoustic-resolution photoacoustic microscopy (AR-PAM) system and report its initial testing in ex vivo human colorectal tissue. A total of 8 colorectal specimens were imaged, which included 2 polyps, 4 malignant colon cancers, and 2 treated colorectal cancers. In each specimen, normal tissue was also imaged for internal control. Initial data have demonstrated the feasibility of identifying colorectal cancer imaging features and the invasion depth using co-registered US and an AR-PAM system. In normal tissue, we found that our system consistently demonstrates the multi-layer structure of normal colonic tissue while differentiating layers with elevated vascularity; these findings highly correlated with histologic findings of each specimen. For malignant colorectal samples, the tissue structure is highly disorganized as seen in US, and photoacoustic imaging revealed distorted vascular distribution inside the tumor. Notably, AR-PAM of tumor beds after complete tumor destruction by radiation and chemotherapy yielded a pattern identical to benign tissue. Quantitative analysis of photoacoustic spectral slope has demonstrated more high-frequency components in malignant tissue as compared to the normal colon tissue, which may be caused by significantly increased microvessel networks. In summary, we demonstrate the successful differentiation of benign and malignant colorectal tissue with our co-registered ultrasound and photoacoustic system.

Fano resonance in all-dielectric binary nanodisk array realizing optical filter with efficient linewidth tuning.

In this research paper, we study the Fano resonance originating from the interaction of in-phased lattice collective resonance and anti-phased lattice collective resonance supported by a binary silicon nanodisk array. Experimental results agree well with the calculations using finite-difference-time-domain method and show a strong dependence of such Fano lineshapes on the radius difference of the particles in the array. Further calculations demonstrate that such binary silicon nanodisk array can be used as an optical filter and offers an efficient way to tune the linewidth simply by changing the radius of the particles, linewidth from 12 nm to 0.7 nm and corresponding Q factor from 72 to 1290 as the radius R(2) increasing from 60 nm to 115 nm. Such scheme possessing the merits of being easily fabricated, simulated, and tuned is very promising for practical applications.