Ultrasound-enhanced Unet model for quantitative photoacoustic tomography of ovarian lesions

Extractor-attention-predictor network for quantitative photoacoustic tomography

Quantitative photoacoustic tomography (qPAT) holds great potential in estimating chromophore concentrations, whereas the involved optical inverse problem, aiming to recover absorption coefficient distributions from photoacoustic images, remains challenging. To address this problem, we propose an extractor-attention-predictor network architecture (EAPNet), which employs a contracting-expanding structure to capture contextual information alongside a multilayer perceptron to enhance nonlinear modeling capability. A spatial attention module is introduced to facilitate the utilization of important information. We also use a balanced loss function to prevent network parameter updates from being biased towards specific regions. Our method obtains satisfactory quantitative metrics in simulated and real-world validations. Moreover, it demonstrates superior robustness to target properties and yields reliable results for targets with small size, deep location, or relatively low absorption intensity, indicating its broader applicability. The EAPNet, compared to the conventional UNet, exhibits improved efficiency, which significantly enhances performance while maintaining similar network size and computational complexity.

Deep Learning Based Automatic Left Ventricle Segmentation from the Transgastric Short-Axis View on Transesophageal Echocardiography: A Feasibility Study

Segmenting the left ventricle from the transgastric short-axis views (TSVs) on transesophageal echocardiography (TEE) is the cornerstone for cardiovascular assessment during perioperative management. Even for seasoned professionals, the procedure remains time-consuming and experience-dependent. The current study aims to evaluate the feasibility of deep learning for automatic segmentation by assessing the validity of different U-Net algorithms. A large dataset containing 1388 TSV acquisitions was retrospectively collected from 451 patients (32% women, average age 53.42 years) who underwent perioperative TEE between July 2015 and October 2023. With image preprocessing and data augmentation, 3336 images were included in the training set, 138 images in the validation set, and 138 images in the test set. Four deep neural networks (U-Net, Attention U-Net, UNet++, and UNeXt) were employed for left ventricle segmentation and compared in terms of the Jaccard similarity coefficient (JSC) and Dice similarity coefficient (DSC) on the test set, as well as the number of network parameters, training time, and inference time. The Attention U-Net and U-Net++ models performed better in terms of JSC (the highest average JSC: 86.02%) and DSC (the highest average DSC: 92.00%), the UNeXt model had the smallest network parameters (1.47 million), and the U-Net model had the least training time (6428.65 s) and inference time for a single image (101.75 ms). The Attention U-Net model outperformed the other three models in challenging cases, including the impaired boundary of left ventricle and the artifact of the papillary muscle. This pioneering exploration demonstrated the feasibility of deep learning for the segmentation of the left ventricle from TSV on TEE, which will facilitate an accelerated and objective alternative of cardiovascular assessment for perioperative management.

Photoacoustic imaging plus X: a review

Significance

Photoacoustic (PA) imaging (PAI) represents an emerging modality within the realm of biomedical imaging technology. It seamlessly blends the wealth of optical contrast with the remarkable depth of penetration offered by ultrasound. These distinctive features of PAI hold tremendous potential for various applications, including early cancer detection, functional imaging, hybrid imaging, monitoring ablation therapy, and providing guidance during surgical procedures. The synergy between PAI and other cutting-edge technologies not only enhances its capabilities but also propels it toward broader clinical applicability.

Aim

The integration of PAI with advanced technology for PA signal detection, signal processing, image reconstruction, hybrid imaging, and clinical applications has significantly bolstered the capabilities of PAI. This review endeavor contributes to a deeper comprehension of how the synergy between PAI and other advanced technologies can lead to improved applications.

Approach

An examination of the evolving research frontiers in PAI, integrated with other advanced technologies, reveals six key categories named "PAI plus X." These categories encompass a range of topics, including but not limited to PAI plus treatment, PAI plus circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities.

Results

After conducting a comprehensive review of the existing literature and research on PAI integrated with other technologies, various proposals have emerged to advance the development of PAI plus X. These proposals aim to enhance system hardware, improve imaging quality, and address clinical challenges effectively.

Conclusions

The progression of innovative and sophisticated approaches within each category of PAI plus X is positioned to drive significant advancements in both the development of PAI technology and its clinical applications. Furthermore, PAI not only has the potential to integrate with the above-mentioned technologies but also to broaden its applications even further.

Mini review of photoacoustic clinical imaging: a noninvasive tool for disease diagnosis and treatment evaluation

Significance

Photoacoustic (PA) imaging is an imaging modality that integrates anatomical, functional, metabolic, and histologic insights. It has been a hot topic of medical research and draws extensive attention.

Aim

This review aims to explore the applications of PA clinical imaging in human diseases, highlighting recent advancements.

Approach

A systemic survey of the literature concerning the clinical utility of PA imaging was conducted, with a particular focus on its application in tumors, autoimmune diseases, inflammatory conditions, and endocrine disorders.

Results

PA imaging is emerging as a valuable tool for human disease investigation. Information provided by PA imaging can be used for diagnosis, grading, and prognosis in multiple types of tumors including breast tumors, ovarian neoplasms, thyroid nodules, and cutaneous malignancies. PA imaging facilitates the monitoring of disease activity in autoimmune and inflammatory diseases such as rheumatoid arthritis, systemic sclerosis, arteritis, and inflammatory bowel disease by capturing dynamic functional alterations. Furthermore, its unique capability of visualizing vascular structure and oxygenation levels aids in assessing diabetes mellitus comorbidities and thyroid function.

Conclusions

Despite extant challenges, PA imaging offers a promising noninvasive tool for precision disease diagnosis, long-term evaluation, and prognosis anticipation, making it a potentially significant imaging modality for clinical practice.

Characterization of adnexal lesions using photoacoustic imaging to improve sonographic O-RADS risk assessment

OBJECTIVE

To assess the impact of photoacoustic imaging (PAI) on the assessment of ovarian/adnexal lesion(s) of different risk categories using the sonographic ovarian-adnexal imaging-reporting-data system (O-RADS) in women undergoing planned oophorectomy.

METHOD

This prospective study enrolled women with ovarian/adnexal lesion(s) suggestive of malignancy referred for oophorectomy. Participants underwent clinical ultrasound (US) examination followed by coregistered US and PAI prior to oophorectomy. Each ovarian/adnexal lesion was graded by two radiologists using the US O-RADS scale. PAI was used to compute relative total hemoglobin concentration (rHbT) and blood oxygenation saturation (%sO2 ) colormaps in the region of interest. Lesions were categorized by histopathology into malignant ovarian/adnexal lesion, malignant Fallopian tube only and several benign categories, in order to assess the impact of incorporating PAI in the assessment of risk of malignancy with O-RADS. Malignant and benign histologic groups were compared with respect to rHbT and %sO2 and logistic regression models were developed based on tumor marker CA125 alone, US-based O-RADS alone, PAI-based rHbT with %sO2 , and the combination of CA125, O-RADS, rHbT and %sO2. Areas under the receiver-operating-characteristics curve (AUC) were used to compare the diagnostic performance of the models.

RESULTS

There were 93 lesions identified on imaging among 68 women (mean age, 52 (range, 21-79) years). Surgical pathology revealed 14 patients with malignant ovarian/adnexal lesion, two with malignant Fallopian tube only and 52 with benign findings. rHbT was significantly higher in malignant compared with benign lesions. %sO2 was lower in malignant lesions, but the difference was not statistically significant for all benign categories. Feature analysis revealed that rHbT, CA125, O-RADS and %sO2 were the most important predictors of malignancy. Logistic regression models revealed an AUC of 0.789 (95% CI, 0.626-0.953) for CA125 alone, AUC of 0.857 (95% CI, 0.733-0.981) for O-RADS only, AUC of 0.883 (95% CI, 0.760-1) for CA125 and O-RADS and an AUC of 0.900 (95% CI, 0.815-0.985) for rHbT and %sO2 in the prediction of malignancy. A model utilizing all four predictors (CA125, O-RADS, rHbT and %sO2 ) achieved superior performance, with an AUC of 0.970 (95% CI, 0.932-1), sensitivity of 100% and specificity of 82%.

CONCLUSIONS

Incorporating the additional information provided by PAI-derived rHbT and %sO2 improves significantly the performance of US-based O-RADS in the diagnosis of adnexal lesions. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

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.

A Coregistered Ultrasound and Photoacoustic Imaging Protocol for the Transvaginal Imaging of Ovarian Lesions

Ovarian cancer remains the deadliest of all the gynecological malignancies due to the lack of reliable screening tools for early detection and diagnosis. Photoacoustic imaging or tomography (PAT) is an emerging imaging modality that can provide the total hemoglobin concentration (relative scale, rHbT) and blood oxygen saturation (%sO2) of ovarian/adnexal lesions, which are important parameters for cancer diagnosis. Combined with coregistered ultrasound (US), PAT has demonstrated great potential for detecting ovarian cancers and for accurately diagnosing ovarian lesions for effective risk assessment and the reduction of unnecessary surgeries of benign lesions. However, PAT imaging protocols in clinical applications, to our knowledge, largely vary among different studies. Here, we report a transvaginal ovarian cancer imaging protocol that can be beneficial to other clinical studies, especially those using commercial ultrasound arrays for the detection of photoacoustic signals and standard delay-and-sum beamforming algorithms for imaging.