Vascular branching point counts using photoacoustic imaging in the superficial layer of the breast: A potential biomarker for breast cancer

Ultrafast Dynamic Contrast-Enhanced MRI of the Breast: From Theory to Practice

The development of ultrafast dynamic contrast-enhanced (UF-DCE) MRI has occurred in tandem with fast MRI scan techniques, particularly view-sharing and compressed sensing. Understanding the strengths of each technique and optimizing the relevant parameters are essential to their implementation. UF-DCE MRI has now shifted from research protocols to becoming a part of clinical scan protocols for breast cancer. UF-DCE MRI is expected to compensate for the low specificity of abbreviated MRI by adding kinetic information from the upslope of the time-intensity curve. Because kinetic information from UF-DCE MRI is obtained from the shape and timing of the initial upslope, various new kinetic parameters have been proposed. These parameters may be associated with receptor status or prognostic markers for breast cancer. In addition to the diagnosis of malignant lesions, more emphasis has been placed on predicting and evaluating treatment response because hyper-vascularity is linked to the aggressiveness of breast cancers. In clinical practice, it is important to note that breast lesion images obtained from UF-DCE MRI are slightly different from those obtained by conventional DCE MRI in terms of morphology. A major benefit of using UF-DCE MRI is avoidance of the marked or moderate background parenchymal enhancement (BPE) that can obscure the target enhancing lesions. BPE is less prominent in the earlier phases of UF-DCE MRI, which offers better lesion-to-noise contrast. The excellent contrast of early-enhancing vessels provides a key to understanding the detailed pathological structure of tumor-associated vessels. UF-DCE MRI is normally accompanied by a large volume of image data for which automated/artificial intelligence-based processing is expected to be useful. In this review, both the theoretical and practical aspects of UF-DCE MRI are summarized. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Artifact reduction in photoacoustic images by generating virtual dense array sensor from hemispheric sparse array sensor using deep learning

PURPOSE

Vascular distribution is important information for diagnosing diseases and supporting surgery. Photoacoustic imaging is a technology that can image blood vessels noninvasively and with high resolution. In photoacoustic imaging, a hemispherical array sensor is especially suitable for measuring blood vessels running in various directions. However, as a hemispherical array sensor, a sparse array sensor is often used due to technical and cost issues, which causes artifacts in photoacoustic images. Therefore, in this study, we reduce these artifacts using deep learning technology to generate signals of virtual dense array sensors.

METHODS

Generating 2D virtual array sensor signals using a 3D convolutional neural network (CNN) requires huge computational costs and is impractical. Therefore, we installed virtual sensors between the real sensors along the spiral pattern in three different directions and used a 2D CNN to generate signals of the virtual sensors in each direction. Then we reconstructed a photoacoustic image using the signals from both the real sensors and the virtual sensors.

RESULTS

We evaluated the proposed method using simulation data and human palm measurement data. We found that these artifacts were significantly reduced in the images reconstructed using the proposed method, while the artifacts were strong in the images obtained only from the real sensor signals.

CONCLUSION

Using the proposed method, we were able to significantly reduce artifacts, and as a result, it became possible to recognize deep blood vessels. In addition, the processing time of the proposed method was sufficiently applicable to clinical measurement.

Current status of optoacoustic breast imaging and future trends in clinical application: is it ready for prime time?

Optoacoustic imaging (OAI) is an emerging field with increasing applications in patients and exploratory clinical trials for breast cancer. Optoacoustic imaging (or photoacoustic imaging) employs non-ionizing, laser light to create thermoelastic expansion in tissues and detect the resulting ultrasonic emission. By combining high optical contrast capabilities with the high spatial resolution and anatomic detail of grayscale ultrasound, OAI offers unique opportunities for visualizing biological function of tissues in vivo. Over the past decade, human breast applications of OAI, including benign/malignant mass differentiation, distinguishing cancer molecular subtype, and predicting metastatic potential, have significantly increased. We discuss the current state of optoacoustic breast imaging, as well as future opportunities and clinical application trends. CLINICAL RELEVANCE STATEMENT: Optoacoustic imaging is a novel breast imaging technique that enables the assessment of breast cancer lesions and tumor biology without the risk of ionizing radiation exposure, intravenous contrast, or radionuclide injection. KEY POINTS: • Optoacoustic imaging (OAI) is a safe, non-invasive imaging technique with thriving research and high potential clinical impact. • OAI has been considered a complementary tool to current standard breast imaging techniques. • OAI combines parametric maps of molecules that absorb light and scatter acoustic waves (like hemoglobin, melanin, lipids, and water) with anatomical images, facilitating scalable and real-time molecular evaluation of tissues.

Deep learning on photoacoustic tomography to remove image distortion due to inaccurate measurement of the scanning radius

Photoacoustic tomography (PAT) is a non-invasive, non-ionizing hybrid imaging modality that holds great potential for various biomedical applications and the incorporation with deep learning (DL) methods has experienced notable advancements in recent times. In a typical 2D PAT setup, a single-element ultrasound detector (USD) is used to collect the PA signals by making a 360° full scan of the imaging region. The traditional backprojection (BP) algorithm has been widely used to reconstruct the PAT images from the acquired signals. Accurate determination of the scanning radius (SR) is required for proper image reconstruction. Even a slight deviation from its nominal value can lead to image distortion compromising the quality of the reconstruction. To address this challenge, two approaches have been developed and examined herein. The first framework includes a modified version of dense U-Net (DUNet) architecture. The second procedure involves a DL-based convolutional neural network (CNN) for image classification followed by a DUNet. The first protocol was trained with heterogeneous simulated images generated from three different phantoms to learn the relationship between the reconstructed and the corresponding ground truth (GT) images. In the case of the second scheme, the first stage was trained with the same heterogeneous dataset to classify the image type and the second stage was trained individually with the appropriate images. The performance of these architectures has been tested on both simulated and experimental images. The first method can sustain SR deviation up to approximately 6% for simulated images and 5% for experimental images and can accurately reproduce the GTs. The proposed DL-approach extends the limits further (approximately 7% and 8% for simulated and experimental images, respectively). Our results suggest that classification-based DL method does not need a precise assessment of SR for accurate PAT image formation.

Ultrasound high-definition microvasculature imaging with novel quantitative biomarkers improves breast cancer detection accuracy

OBJECTIVES

To overcome the limitations of power Doppler in imaging angiogenesis, we sought to develop and investigate new quantitative biomarkers of a contrast-free ultrasound microvasculature imaging technique for differentiation of benign from malignant pathologies of breast lesion.

METHODS

In this prospective study, a new high-definition microvasculature imaging (HDMI) was tested on 521 patients with 527 ultrasound-identified suspicious breast masses indicated for biopsy. Four new morphological features of tumor microvessels, microvessel fractal dimension (mvFD), Murray's deviation (MD), bifurcation angle (BA), and spatial vascularity pattern (SVP) as well as initial biomarkers were extracted and analyzed, and the results correlated with pathology. Multivariable logistic regression analysis was used to study the performance of different prediction models, initial biomarkers, new biomarkers, and combined new and initial biomarkers in differentiating benign from malignant lesions.

RESULTS

The new HDMI biomarkers, mvFD, BA, MD, and SVP, were statistically significantly different in malignant and benign lesions, regardless of tumor size. Sensitivity and specificity of the new biomarkers in lesions > 20 mm were 95.6% and 100%, respectively. Combining the new and initial biomarkers together showed an AUC, sensitivity, and specificity of 97% (95% CI: 95-98%), 93.8%, and 89.2%, respectively, for all lesions regardless of mass size. The classification was further improved by adding the Breast Imaging Reporting and Data System (BI-RADS) score to the prediction model, showing an AUC, sensitivity, and specificity of 97% (95% CI: 95-98%), 93.8%, and 89.2%, respectively.

CONCLUSION

The addition of new quantitative HDMI biomarkers significantly improved the accuracy in breast lesion characterization when used as a complementary imaging tool to the conventional ultrasound.

KEY POINTS

• Novel quantitative biomarkers extracted from tumor microvessel images increase the sensitivity and specificity in discriminating malignant from benign breast masses. • New HDMI biomarkers Murray's deviation, bifurcation angles, microvessel fractal dimension, and spatial vascularity pattern outperformed the initial biomarkers. • The addition of BI-RADS scores based on US descriptors to the multivariable analysis using all biomarkers remarkably increased the sensitivity, specificity, and AUC in all size groups.

Volumetric imaging and morphometric analysis of breast tumor angiogenesis using a new contrast-free ultrasound technique: a feasibility study

BACKGROUND

There is a strong correlation between the morphological features of new tumor vessels and malignancy. However, angiogenic heterogeneity necessitates 3D microvascular data of tumor microvessels for more reliable quantification. To provide more accurate information regarding vessel morphological features and improve breast lesion characterization, we introduced a quantitative 3D high-definition microvasculature imaging (q3D-HDMI) as a new easily applicable and robust tool to morphologically characterize microvasculature networks in breast tumors using a contrast-free ultrasound-based imaging approach.

METHODS

In this prospective study, from January 2020 through December 2021, a newly developed q3D-HDMI technique was evaluated on participants with ultrasound-identified suspicious breast lesions recommended for core needle biopsy. The morphological features of breast tumor microvessels were extracted from the q3D-HDMI. Leave-one-out cross-validation (LOOCV) was applied to test the combined diagnostic performance of multiple morphological parameters of breast tumor microvessels. Receiver operating characteristic (ROC) curves were used to evaluate the prediction performance of the generated pooled model.

RESULTS

Ninety-three participants (mean age 52 ± 17 years, 91 women) with 93 breast lesions were studied. The area under the ROC curve (AUC) generated with q3D-HDMI was 95.8% (95% CI 0.901-1.000), yielding a sensitivity of 91.7% and a specificity of 98.2%, that was significantly higher than the AUC generated with the q2D-HDMI (p = 0.02). When compared to q2D-HDMI, the tumor microvessel morphological parameters obtained from q3D-HDMI provides distinctive information that increases accuracy in differentiating breast tumors.

CONCLUSIONS

The proposed quantitative volumetric imaging technique augments conventional breast ultrasound evaluation by increasing specificity in differentiating malignant from benign breast masses.

Hybrid high-definition microvessel imaging/shear wave elastography improves breast lesion characterization

BACKGROUND

Low specificity in current breast imaging modalities leads to increased unnecessary follow-ups and biopsies. The purpose of this study is to evaluate the efficacy of combining the quantitative parameters of high-definition microvasculature imaging (HDMI) and 2D shear wave elastography (SWE) with clinical factors (lesion depth and age) for improving breast lesion differentiation.

METHODS

In this prospective study, from June 2016 through April 2021, patients with breast lesions identified on diagnostic ultrasound and recommended for core needle biopsy were recruited. HDMI and SWE were conducted prior to biopsies. Two new HDMI parameters, Murray's deviation and bifurcation angle, and a new SWE parameter, mass characteristic frequency, were included for quantitative analysis. Lesion malignancy prediction models based on HDMI only, SWE only, the combination of HDMI and SWE, and the combination of HDMI, SWE and clinical factors were trained via elastic net logistic regression with 70% (360/514) randomly selected data and validated with the remaining 30% (154/514) data. Prediction performances in the validation test set were compared across models with respect to area under the ROC curve as well as sensitivity and specificity based on optimized threshold selection.

RESULTS

A total of 508 participants (mean age, 54 years ± 15), including 507 female participants and 1 male participant, with 514 suspicious breast lesions (range, 4-72 mm, median size, 13 mm) were included. Of the lesions, 204 were malignant. The SWE-HDMI prediction model, combining quantitative parameters from SWE and HDMI, with AUC of 0.973 (95% CI 0.95-0.99), was significantly higher than the result predicted with the SWE model or HDMI model alone. With an optimal cutoff of 0.25 for the malignancy probability, the sensitivity and specificity were 95.5% and 89.7%, respectively. The specificity was further improved with the addition of clinical factors. The corresponding model defined as the SWE-HDMI-C prediction model had an AUC of 0.981 (95% CI 0.96-1.00).

CONCLUSIONS

The SWE-HDMI-C detection model, a combination of SWE estimates, HDMI quantitative biomarkers and clinical factors, greatly improved the accuracy in breast lesion characterization.

Photoacoustic Signal Simulation Using Discrete Particle Approach and its Application in Tomography

A theoretical framework for photoacoustic (PA) signal simulation using a discrete particle approach is discussed, and the tomographic image reconstruction using such signals is reported. Various numerical phantoms in two dimensions were constructed by inserting monodisperse/polydisperse solid circles/disks of uniform strength occupying regular or random locations within the imaging region. In particular, a blood vessel network phantom was simulated by positioning solid circles mimicking red blood cells randomly within the vessel using a Monte Carlo method. The PA signal from a single disk was obtained by numerically evaluating the analytical formula, and then, such signals from many disks were summed up linearly to generate the resultant signals at detector locations. Classical backprojection and time-reversal algorithms were employed to form reconstructed images. Two model-based approaches, namely impulse response-based (IRB) and interpolation-based (IPB) methods, were also deployed for image reconstruction. Some standard parameters were calculated to assess the performance of these reconstruction algorithms. The simulation results demonstrate that the Monte Carlo method can be applied in practice for the fast simulation of tissue realization keeping microscopic details intact, and accordingly, PA signals can be calculated for photoacoustic tomography (PAT) imaging. Furthermore, the IRB technique produces images with superior quality and outperforms other algorithms.

Visualization of Lymphatic Vessels Using Photoacoustic Imaging

Lymphedema occurs when interstitial fluid and fibroadipose tissues accumulate abnormally because of decreased drainage of lymphatic fluid as a result of injury, infection, or congenital abnormalities of the lymphatic system drainage pathway. An accurate anatomical map of the lymphatic vasculature is needed not only for understanding the pathophysiology of lymphedema but also for surgical planning. However, because of their limited spatial resolution, no imaging modalities are currently able to noninvasively provide a clear visualization of the lymphatic vessels. Photoacoustic imaging is an emerging medical imaging technique that provides unique scalability of optical resolution and acoustic depth of penetration. Moreover, light-absorbing biomolecules, including oxy- and deoxyhemoglobin, lipids, water, and melanin, can be imaged. Using exogenous contrast agents that are taken up by lymphatic vessels, e.g., indocyanine green, photoacoustic lymphangiography, which has a higher spatial resolution than previous imaging modalities, is possible. Using a new prototype of a photoacoustic imaging system with a wide field of view developed by a Japanese research group, high-resolution three-dimensional structural information of the vasculatures was successfully obtained over a large area in both healthy and lymphedematous extremities. Anatomical information on the lymphatic vessels and adjacent veins provided by photoacoustic lymphangiography is helpful for the management of lymphedema. In particular, such knowledge will facilitate the planning of microsurgical lymphaticovenular anastomoses to bypass the excess fluid component by joining with the circulatory system peripherally. Although challenges remain to establish its implementation in clinical practice, photoacoustic lymphangiography may contribute to improved treatments for lymphedema patients in the near future.

Multiple Delay and Sum With Enveloping Beamforming Algorithm for Photoacoustic Imaging

Delay and Sum (DAS) is one of the most common beamforming algorithms for photoacoustic imaging (PAI) reconstruction. Based on calculating beamformed signal with simple delaying and summing, DAS can function in a quick response and is quite suitable for real-time PAI. However, high sidelobes and intense artifacts may appear when using DAS due to summing with unnecessary data. In this paper, a beamforming algorithm called Multiple Delay and Sum with Enveloping (multi-DASE) is introduced to solve this problem. Compared to DAS, the multi-DASE algorithm calculates not only the initial value of the beamformed signal but also the complete N-shaped photoacoustic signal for each pixel. Through computer simulation, a phantom experiment and experiment on human finger joint, the multi-DASE algorithm is compared with other beamforming methods in removing artifacts by evaluating the quality of the reconstructed images. Furthermore, by rearranging the calculation sequences, the multi-DASE algorithm can be computing in parallel using GPU acceleration to meet the needs of real-time clinical application.

Vascular bifurcation mapping with photoacoustic microscopy

The early detection of microvascular changes in cancer diagnosis is needed in the clinic. A change in the vascular bifurcation density is a biomarker for the sprouting activity. Here, Optical-Resolution PhotoAcoustic Microscopy is used for quantitative vascular bifurcation mapping in 2D after the creation of Virtual Tubes out of Bifurcations. In stacks of OR-PAM images of the hemoglobin distribution, bifurcations become tubes and are selected by the 3D tubeness filter. These fast analyses will be compared to a classical approach and are easier to implement for functional analysis of the vascular bifurcation density in healthy and diseased tissues.

Photoacoustic Imaging in Tissue Engineering and Regenerative Medicine

Several imaging modalities are available for investigation of the morphological, functional, and molecular features of engineered tissues in small animal models. While research in tissue engineering and regenerative medicine (TERM) would benefit from a comprehensive longitudinal analysis of new strategies, researchers have not always applied the most advanced methods. Photoacoustic imaging (PAI) is a rapidly emerging modality that has received significant attention due to its ability to exploit the strong endogenous contrast of optical methods with the high spatial resolution of ultrasound methods. Exogenous contrast agents can also be used in PAI for targeted imaging. Applications of PAI relevant to TERM include stem cell tracking, longitudinal monitoring of scaffolds in vivo, and evaluation of vascularization. In addition, the emerging capabilities of PAI applied to the detection and monitoring of cancer and other inflammatory diseases could be exploited by tissue engineers. This article provides an overview of the operating principles of PAI and its broad potential for application in TERM. Impact statement Photoacoustic imaging, a new hybrid imaging technique, has demonstrated high potential in the clinical diagnostic applications. The optical and acoustic aspect of the photoacoustic imaging system works in harmony to provide better resolution at greater tissue depth. Label-free imaging of vasculature with this imaging can be used to track and monitor disease, as well as the therapeutic progression of treatment. Photoacoustic imaging has been utilized in tissue engineering to some extent; however, the full benefit of this technique is yet to be explored. The increasing availability of commercial photoacoustic systems will make application as an imaging tool for tissue engineering application more feasible. This review first provides a brief description of photoacoustic imaging and summarizes its current and potential application in tissue engineering.

Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives

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.

Photoacoustic imaging of breast cancer: a mini review of system design and image features

Breast cancer is one of the leading causes for cancer related deaths in women, and early detection is extremely important to improve survival rates. Currently, x-ray mammogram is the only modality for mass screening of asymptomatic women. However, it has decreased sensitivity in radiographically dense breasts, which is also associated with a higher risk for breast cancer. Photoacoustic (PA) imaging is an emerging modality that enables deep tissue imaging of optical contrast at ultrasonically defined spatial resolution, which is much higher than that can be achieved in purely optical imaging modalities. Because of high optical absorption from hemoglobin molecules, PA imaging can map out hemo distribution and dynamics in breast tissue and identify malignant lesions based on tumor associated angiogenesis and hypoxia. We review various PA breast imaging systems proposed over the past few years and summarize the PA features of breast cancer identified in these systems.

Photoacoustic imaging as a tool to probe the tumour microenvironment

The tumour microenvironment (TME) is a complex cellular ecosystem subjected to chemical and physical signals that play a role in shaping tumour heterogeneity, invasion and metastasis. Studying the roles of the TME in cancer progression would strongly benefit from non-invasive visualisation of the tumour as a whole organ in vivo, both preclinically in mouse models of the disease, as well as in patient tumours. Although imaging techniques exist that can probe different facets of the TME, they face several limitations, including limited spatial resolution, extended scan times and poor specificity from confounding signals. Photoacoustic imaging (PAI) is an emerging modality, currently in clinical trials, that has the potential to overcome these limitations. Here, we review the biological properties of the TME and potential of existing imaging methods that have been developed to analyse these properties non-invasively. We then introduce PAI and explore the preclinical and clinical evidence that support its use in probing multiple features of the TME simultaneously, including blood vessel architecture, blood oxygenation, acidity, extracellular matrix deposition, lipid concentration and immune cell infiltration. Finally, we highlight the future prospects and outstanding challenges in the application of PAI as a tool in cancer research and as part of a clinical oncologist's arsenal.

Summary: This Review details the potential of photoacoustic imaging to visualise features of the tumour microenvironment such as blood vessels, hypoxia, fibrosis and immune infiltrate to provide unprecedented insight into tumour biology.

Impact of sensor apodization on the tangential resolution in photoacoustic tomography

Photoacoustic tomographic (PAT) image reconstruction with apodized sensors is discussed. A Gaussian function was used to model axisymmetric apodization of sensors, and its full width at half-maximum (FWHM) was varied to investigate the role of apodization on the PAT image reconstruction. The well-known conventional delay-and-sum (CDAS) algorithm and recently developed modified delay-and-sum (MDAS) algorithm were implemented to generate reconstructed images. The performances of these algorithms were examined by comparing simulated images formed by these methods and that of ideal point detectors. Simulations in two dimensions were conducted using the k-Wave toolbox for three different phantoms. The results produced by the CDAS method are very close to that of ideal point detectors when the FWHM of the Gaussian function is small. The MDAS algorithm for flat sensors provides excellent results (comparable to that of point detectors) when the FWHM of the Gaussian profile is large. This study elucidates how sensor apodization affects PAT image reconstruction.