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.

Enhanced 3D visualization of human fallopian tube morphology using a miniature optical coherence tomography catheter.

We demonstrate the use of our miniature optical coherence tomography catheter to acquire three-dimensional human fallopian tube images. Images of the fallopian tube's tissue morphology, vasculature, and tissue heterogeneity distribution are enhanced by adaptive thresholding, masking, and intensity inverting, making it easier to differentiate malignant tissue from normal tissue. The results show that normal fallopian tubes tend to have rich vasculature accompanied by a patterned tissue scattering background, features that do not appear in malignant cases. This finding suggests that miniature OCT catheters may have great potential for fast optical biopsy of the fallopian tube.

Cylindrical lens configuration for optimizing light delivery in a curvilinear endocavity photoacoustic imaging system.

Curvilinear endocavity ultrasound images capture a wide field of view with a miniature probe. In adapting photoacoustic imaging (PAI) to work with such ultrasound systems, light delivery is challenged by the trade-off between image quality and laser safety concerns. Here, we present two novel, to the best of our knowledge, designs based on cylindrical lenses that are optimized for transvaginal PAI B-scan imaging. Our simulation and experimental results demonstrate that, compared to conventional light delivery methods for PAI imaging, the proposed designs are safer for higher pulse energies and provide deeper imaging and a wider lateral field of view. The proposed designs could also improve the performance of endoscopic co-registered ultrasound/photoacoustic imaging in other clinical applications.

Deep learning based on co-registered ultrasound and photoacoustic imaging improves the assessment of rectal cancer treatment response.

Identifying complete response (CR) after rectal cancer preoperative treatment is critical to deciding subsequent management. Imaging techniques, including endorectal ultrasound and MRI, have been investigated but have low negative predictive values. By imaging post-treatment vascular normalization using photoacoustic microscopy, we hypothesize that co-registered ultrasound and photoacoustic imaging will better identify complete responders. In this study, we used in vivo data from 21 patients to develop a robust deep learning model (US-PAM DenseNet) based on co-registered dual-modality ultrasound (US) and photoacoustic microscopy (PAM) images and individualized normal reference images. We tested the model's accuracy in differentiating malignant from non-cancer tissue. Compared to models based on US alone (classification accuracy 82.9 ± 1.3%, AUC 0.917(95%CI: 0.897-0.937)), the addition of PAM and normal reference images improved the model performance significantly (accuracy 92.4 ± 0.6%, AUC 0.968(95%CI: 0.960-0.976)) without increasing model complexity. Additionally, while US models could not reliably differentiate images of cancer from those of normalized tissue with complete treatment response, US-PAM DenseNet made accurate predictions from these images. For use in the clinical settings, US-PAM DenseNet was extended to classify entire US-PAM B-scans through sequential ROI classification. Finally, to help focus surgical evaluation in real time, we computed attention heat maps from the model predictions to highlight suspicious cancer regions. We conclude that US-PAM DenseNet could improve the clinical care of rectal cancer patients by identifying complete responders with higher accuracy than current imaging techniques.

Depth-resolved attenuation mapping of the human ovary and fallopian tube using optical coherence tomography.

Due to the lack of reliable early-diagnostic tools, most ovarian cancers are diagnosed at late stages. Although optical coherence tomography (OCT) has shown promise for identifying diseased ovaries and fallopian tubes at an earlier stage, previous studies either did not provide quantitative scattering mapping or simply used Beer's law to fit the scattering coefficients of each A-line. In this paper, we calculated the pixel-wise attenuation coefficients of ovaries and fallopian tubes in OCT images. Data from 73 freshly excised human ovaries and fallopian tubes from 36 patients have shown that statistical features are statistically different between cancerous ovaries, infundibula, and fimbriae and normal ones.

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.

Fiber endface photoacoustic generator for quantitative photoacoustic tomography.

We demonstrate a novel fiber endface photoacoustic (PA) generator using infrared (IR) 144 laser dye dispersed within an ultraviolet adhesive. The generator provides a wide acoustic bandwidth in the transducer frequency range of 2-7 MHz, high thermal conversion efficiency (${\gt}90\%$), good PA signal controllability (well-controlled IR 144 concentration), and high feasibility (simple procedures). Through a series of experimental validations, we show that this fiber-based endface PA generator can be a useful tool for a broad range of biomedical applications such as calibrating the local absorption coefficient of biological tissue for quantitative PA tomography.

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.