Breast Cancer Assessment With Pulse-Echo Speed of Sound Ultrasound From Intrinsic Tissue Reflections: Proof-of-Concept

Breast Glandular and Ductal Volume Changes during the Menstrual Cycle: A Study in 48 Breasts Using Ultralow-Frequency Transmitted Ultrasound Tomography/Volography

The aim of this study was to show for the first time that low-frequency 3D-transmitted ultrasound tomography (3D UT, volography) can differentiate breast tissue types using tissue properties, accurately measure glandular and ductal volumes in vivo, and measure variation over time. Data were collected for 400 QT breast scans on 24 women (ages 18-71), including four (4) postmenopausal subjects, 6-10 times over 2+ months of observation. The date of onset of menopause was noted, and the cases were further subdivided into three (3) classes: pre-, post-, and peri-menopausal. The ducts and glands were segmented using breast speed of sound, attenuation, and reflectivity images and followed over several menstrual cycles. The coefficient of variation (CoV) for glandular tissue in premenopausal women was significantly larger than for postmenopausal women, whereas this is not true for the ductal CoV. The glandular standard deviation (SD) is significantly larger in premenopausal women vs. postmenopausal women, whereas this is not true for ductal tissue. We conclude that ducts do not appreciably change over the menstrual cycle in either pre- or post-menopausal subjects, whereas glands change significantly over the cycle in pre-menopausal women, and 3D UT can differentiate ducts from glands in vivo.

Windowed Radon Transform for Robust Speed-of-Sound Imaging With Pulse-Echo Ultrasound

In recent years, methods estimating the spatial distribution of tissue speed of sound with pulse-echo ultrasound are gaining considerable traction. They can address limitations of B-mode imaging, for instance in diagnosing fatty liver diseases. Current state-of-the-art methods relate the tissue speed of sound to local echo shifts computed between images that are beamformed using restricted transmit and receive apertures. However, the aperture limitation affects the robustness of phase-shift estimations and, consequently, the accuracy of reconstructed speed-of-sound maps. Here, we propose a method based on the Radon transform of image patches able to estimate local phase shifts from full-aperture images. We validate our technique on simulated, phantom and in-vivo data acquired on a liver and compare it with a state-of-the-art method. We show that the proposed method enhances the stability to changes of beamforming speed of sound and to a reduction of the number of insonifications. In particular, the deployment of pulse-echo speed-of-sound estimation methods onto portable ultrasound devices can be eased by the reduction of the number of insonifications allowed by the proposed method.

Artificial Intelligence-Enhanced Quantitative Ultrasound for Breast Cancer: Pilot Study on Quantitative Parameters and Biopsy Outcomes

Traditional B-mode ultrasound has difficulties distinguishing benign from malignant breast lesions. It appears that Quantitative Ultrasound (QUS) may offer advantages. We examined the QUS imaging system's potential, utilizing parameters like Attenuation Coefficient (AC), Speed of Sound (SoS), Effective Scatterer Diameter (ESD), and Effective Scatterer Concentration (ESC) to enhance diagnostic accuracy. B-mode images and radiofrequency signals were gathered from breast lesions. These parameters were processed and analyzed by a QUS system trained on a simulated acoustic dataset and equipped with an encoder-decoder structure. Fifty-seven patients were enrolled over six months. Biopsies served as the diagnostic ground truth. AC, SoS, and ESD showed significant differences between benign and malignant lesions (p < 0.05), but ESC did not. A logistic regression model was developed, demonstrating an area under the receiver operating characteristic curve of 0.90 (95% CI: 0.78, 0.96) for distinguishing between benign and malignant lesions. In conclusion, the QUS system shows promise in enhancing diagnostic accuracy by leveraging AC, SoS, and ESD. Further studies are needed to validate these findings and optimize the system for clinical use.

Robust Imaging of Speed of Sound Using Virtual Source Transmission

Speed of sound (SoS) is a novel imaging biomarker for assessing the biomechanical characteristics of soft tissues. SoS imaging in the pulse-echo mode using conventional ultrasound (US) systems with hand-held transducers has the potential to enable new clinical uses. Recent work demonstrated that diverging waves (DWs) from a single element (SE) transmit to outperform plane-wave sequences. However, SE transmits have severely limited power and hence produce a low signal-to-noise ratio (SNR) in echo data. We herein propose Walsh-Hadamard (WH) coded and virtual-source (VS) transmit sequences for the improved SNR in SoS imaging. We additionally present an iterative method of estimating beamforming (BF) SoS in the medium, which otherwise confounds SoS reconstructions due to beamforming inaccuracies in the images used for reconstruction. Through numerical simulations, phantom experiments, and in vivo imaging data, we show that WH is not robust against motion, which is often unavoidable in clinical imaging scenarios. Our proposed VS sequence is shown to provide the highest SoS reconstruction performance, especially robust to motion artifacts. In phantom experiments, despite having a comparable SoS root-mean-square error (RMSE) of 17.5-18.0 m/s at rest, with a minor axial probe motion of ≈ 0.67 mm/s the RMSE for SE, WH, and VS already deteriorate to 20.2, 105.4, and 19.0 m/s, respectively, showing that WH produces unacceptable results, not robust to motion. In the clinical data, the high SNR and motion resilience of VS sequences are seen to yield superior contrast compared to SE and WH sequences.

Investigating multi-material hydrogel three-dimensional printing for in vitro representation of the neo-vasculature of solid tumours: a comprehensive mechanical analysis and assessment of nitric oxide release from human umbilical vein endothelial cells

Many solid tumours (e.g. sarcoma, carcinoma and lymphoma) form a disorganized neo-vasculature that initiates uncontrolled vessel formation to support tumour growth. The complexity of these environments poses a significant challenge for tumour medicine research. While animal models are commonly used to address some of these challenges, they are time-consuming and raise ethical concerns. In vitro microphysiological systems have been explored as an alternative, but their production typically requires multi-step lithographic processes that limit their production. In this work, a novel approach to rapidly develop multi-material tissue-mimicking, cell-compatible platforms able to represent the complexity of a solid tumour's neo-vasculature is investigated via stereolithography three-dimensional printing. To do so, a series of acrylate resins that yield covalently photo-cross-linked hydrogels with healthy and diseased mechano-acoustic tissue-mimicking properties are designed and characterized. The potential viability of these materials to displace animal testing in preclinical research is assessed by studying the morphology, actin expression, focal adhesions and nitric oxide release of human umbilical vein endothelial cells. These materials are exploited to produce a simplified multi-material three-dimensional printed model of the neo-vasculature of a solid tumour, demonstrating the potential of our approach to replicate the complexity of solid tumours in vitro without the need for animal testing.

Aberration correction in diagnostic ultrasound: A review of the prior field and current directions

Medical ultrasound images are reconstructed with simplifying assumptions on wave propagation, with one of the most prominent assumptions being that the imaging medium is composed of a constant sound speed. When the assumption of a constant sound speed are violated, which is true in most in vivoor clinical imaging scenarios, distortion of the transmitted and received ultrasound wavefronts appear and degrade the image quality. This distortion is known as aberration, and the techniques used to correct for the distortion are known as aberration correction techniques. Several models have been proposed to understand and correct for aberration. In this review paper, aberration and aberration correction are explored from the early models and correction techniques, including the near-field phase screen model and its associated correction techniques such as nearest-neighbor cross-correlation, to more recent models and correction techniques that incorporate spatially varying aberration and diffractive effects, such as models and techniques that rely on the estimation of the sound speed distribution in the imaging medium. In addition to historical models, future directions of ultrasound aberration correction are proposed.

Excluding Echo Shift Noise in Real-Time Pulse-Echo Speed-of-Sound Imaging

Computed ultrasound tomography in echo mode (CUTE) allows real-time imaging of the tissue speed of sound (SoS) using handheld ultrasound. The SoS is retrieved by inverting a forward model that relates the spatial distribution of the tissue SoS to echo shift maps detected between varying transmit and receive angles. Despite promising results, in vivo SoS maps often show artifacts due to elevated noise in echo shift maps. To minimize artifacts, we propose a technique where an individual SoS map is reconstructed for each echo shift map separately, as opposed to a single SoS map from all echo shift maps simultaneously. The final SoS map is then obtained as a weighted average over all SoS maps. Due to the partial redundancy between different angle combinations, artifacts that appear only in a subset of the individual maps can be excluded via the averaging weights. We investigate this real-time capable technique in simulations using two numerical phantoms, one with a circular inclusion and one with two layers. Our results demonstrate that the SoS maps reconstructed using the proposed technique are equivalent to the ones using simultaneous reconstruction when considering uncorrupted data but show significantly reduced artifact level for data that are corrupted by noise.

Analytical estimation of beamforming speed-of-sound using transmission geometry

Most ultrasound imaging techniques necessitate the fundamental step of converting temporal signals received from transducer elements into a spatial echogenecity map. This beamforming (BF) step requires the knowledge of speed-of-sound (SoS) value in the imaged medium. An incorrect assumption of BF SoS leads to aberration artifacts, not only deteriorating the quality and resolution of conventional brightness mode (B-mode) images, hence limiting their clinical usability, but also impairing other ultrasound modalities such as elastography and spatial SoS reconstructions, which rely on faithfully beamformed images as their input. In this work, we propose an analytical method for estimating BF SoS. We show that pixel-wise relative shifts between frames beamformed with an assumed SoS is a function of geometric disparities of the transmission paths and the error in such SoS assumption. Using this relation, we devise an analytical model, the closed form solution of which yields the difference between the assumed and the true SoS in the medium. Based on this, we correct the BF SoS, which can also be applied iteratively. Both in simulations and experiments, lateral B-mode resolution is shown to be improved by ≈25% compared to that with an initial SoS assumption error of 3.3% (50m/s), while localization artifacts from beamforming are also corrected. After 5 iterations, our method achieves BF SoS errors of under 0.6m/s in simulations. Residual time-delay errors in beamforming 32 numerical phantoms are shown to reduce down to 0.07μs, with average improvements of up to 21 folds compared to initial inaccurate assumptions. We additionally show the utility of the proposed method in imaging local SoS maps, where using our correction method reduces reconstruction root-mean-square errors substantially, down to their lower-bound with actual BF SoS.

Optimization of reconstruction time of ultrasound computed tomography with a piecewise homogeneous region-based refract-ray model

In this article, a novel ultrasound computed tomography (USCT) reconstruction algorithm for breast imaging is proposed. This algorithm is based on an ultrasound propagation model, the refract-ray model (RRM). In this model, the field of imaging is assumed as piecewise homogenous and is divided into several regions. The ultrasound propagation paths are considered polylines that only refract at the borders of the regions. The edge information is provided by B-mode imaging. Both simulations and experiments are implemented to validate the proposed algorithm. Compared with the traditional bent-ray model (BRM), the time of reconstructions using RRM decreases by over 90 %. In simulations, the imaging qualities for RRM and BRM are comparable, in terms of the root mean square error, the Tenengrad value, and the deformation of digital phantom. In the experiments, a cylindrical agar phantom is imaged using a customized imaging system. When imaging using RRM, the estimate of the phantom radius is about 0.1 mm in error, while it is about 0.3 mm in error using BRM. Moreover, the Tenengrad value of the result using RRM is much higher than that using BRM (9.76 compared to 0.79). The results show that the proposed algorithm can better delineate the phantom within a water bath. In future work, further experimental work is required to validate the method for improving imaging quality under breast-mimicking imaging conditions.

Ultrasound Image Features under Deep Learning in Breast Conservation Surgery for Breast Cancer

This study was to analyze the effect of the combined application of deep learning technology and ultrasound imaging on the effect of breast-conserving surgery for breast cancer. A deep label distribution learning (LDL) model was designed, and the semiautomatic segmentation algorithm based on the region growing and active contour technology (RA) and the segmentation model based on optimized nearest neighbors (ON) were introduced for comparison. The designed algorithm was applied to the breast-conserving surgery of breast cancer patients. According to the difference in intraoperative guidance methods, 102 female patients with early breast cancer were divided into three groups: 34 cases in W1 group (ultrasound guidance based on deep learning segmentation model), 34 cases in W2 group (ultrasound guidance), and 34 cases in W3 group (palpation guidance). The results revealed that the tumor area segmented by the LDL algorithm constructed in this study was closer to the real tumor area; the segmentation accuracy (AC), Jaccard, and true-positive (TP) values of the LDL algorithm were obviously greater than those of the RA and ON algorithms, while the false-positive (FP) value was significantly lower in contrast to the RA and ON algorithms, showing statistically observable differences (P < 0.05); the actual resection volume of the patients in the W1 group was the closest to the ideal resection volume, which was much smaller in contrast to that of the patients in the W2 and W3 groups, showing statistical differences (P < 0.05); the positive margins of the patients in the W1 group were statistically lower than those in the W2 and W3 groups (P < 0.05). In addition, 1 patient in the W1 group was not satisfied with the cosmetic effect, 3 patients in the W2 group were not satisfied with the cosmetic effect, and 9 patients in the W3 group were not satisfied with the cosmetic effect. Finally, it was found that the ultrasound image based on the deep LDL model effectively improved the AC of tumor resection and negative margins, reduced the probability of normal tissue being removed, and improved the postoperative cosmetic effect of breast.

Speed-of-sound imaging using diverging waves

PURPOSE

Due to its safe, low-cost, portable, and real-time nature, ultrasound is a prominent imaging method in computer-assisted interventions. However, typical B-mode ultrasound images have limited contrast and tissue differentiation capability for several clinical applications.

METHODS

Recent introduction of imaging speed-of-sound (SoS) in soft tissues using conventional ultrasound systems and transducers has great potential in clinical translation providing additional imaging contrast, e.g., in intervention planning, navigation, and guidance applications. However, current pulse-echo SoS imaging methods relying on plane wave (PW) sequences are highly prone to aberration effects, therefore suboptimal in image quality. In this paper we propose using diverging waves (DW) for SoS imaging and study this comparatively to PW.

RESULTS

We demonstrate wavefront aberration and its effects on the key step of displacement tracking in the SoS reconstruction pipeline, comparatively between PW and DW on a synthetic example. We then present the parameterization sensitivity of both approaches on a set of simulated phantoms. Analyzing SoS imaging performance comparatively indicates that using DW instead of PW, the reconstruction accuracy improves by over 20% in root-mean-square-error (RMSE) and by 42% in contrast-to-noise ratio (CNR). We then demonstrate SoS reconstructions with actual US acquisitions of a breast phantom. With our proposed DW, CNR for a high contrast tumor-representative inclusion is improved by 42%, while for a low contrast cyst-representative inclusion a 2.8-fold improvement is achieved.

CONCLUSION

SoS imaging, so far only studied using a plane wave transmission scheme, can be made more reliable and accurate using DW. The high imaging contrast of DW-based SoS imaging will thus facilitate the clinical translation of the method and utilization in computer-assisted interventions such as ultrasound-guided biopsies, where B-Mode contrast is often to low to detect potential lesions.

Hybrid Spectral-IRDx: Near-IR and Ultrasound Attenuation System for Differentiating Breast Cancer From Adjacent Normal Tissue

OBJECTIVE

While performing surgical excision for breast cancer (lumpectomy), it is important to ensure a clear margin of normal tissue around the cancer to achieve complete resection. The current standard is histopathology; however, it is time-consuming and labour-intensive requiring skilled personnel.

METHOD

We describe a Hybrid Spectral-IRDx - a combination of the previously reported Spectral-IRDx tool with multimodal ultrasound and NIR spectroscopy techniques. We show how this portable, cost-effective, minimal-contact tool could provide rapid diagnosis of cancer using formalin-fixed (FF) and deparaffinized (DP) breast biopsy tissues.

RESULTS

Using this new tool, measurements were performed on cancerous/fibroadenoma and its adjacent normal tissues from the same patients (N = 14). The acoustic attenuation coefficient (α) and reduced scattering coefficient (µ'_s) (at 850, 940, and 1060 nm) for the cancerous/fibroadenoma tissues were reported to be higher compared to adjacent normal tissues, a basis of delineation. Comparing FF cancerous and adjacent normal tissue, the difference in µ'_s at 850 nm and 940 nm were statistically significant (p = 3.17e-2 and 7.94e-3 respectively). The difference in α between the cancerous and adjacent normal tissues for DP and FF tissues were also statistically significant (p = 2.85e-2 and 7.94e-3 respectively). Combining multimodal parameters α and µ'_s (at 940 nm) show highest statistical significance (p = 6.72e-4) between FF cancerous/fibroadenoma and adjacent normal tissues.

CONCLUSION

We show that Hybrid Spectral-IRDx can accurately delineate between cancerous and adjacent normal breast biopsy tissue.

SIGNIFICANCE

The results obtained establish the proof-of-principle and large-scale testing of this multimodal breast cancer diagnostic platform for core biopsy diagnosis.

Bayesian Approach for a Robust Speed-of-Sound Reconstruction Using Pulse-Echo Ultrasound

Computed ultrasound tomography in echo mode (CUTE) is a promising ultrasound (US) based multi-modal technique that allows to image the spatial distribution of speed of sound (SoS) inside tissue using hand-held pulse-echo US. It is based on measuring the phase shift of echoes when detected under varying steering angles. The SoS is then reconstructed using a regularized inversion of a forward model that describes the relation between the SoS and echo phase shift. Promising results were obtained in phantoms when using a Tikhonov-type regularization of the spatial gradient (SG) of SoS. In-vivo, however, clutter and aberration lead to an increased phase noise. In many subjects, this phase noise causes strong artifacts in the SoS image when using the SG regularization. To solve this shortcoming, we propose to use a Bayesian framework for the inverse calculation, which includes a priori statistical properties of the spatial distribution of the SoS to avoid noise-related artifacts in the SoS images. In this study, the a priori model is based on segmenting the B-Mode image. We show in a simulation and phantom study that this approach leads to SoS images that are much more stable against phase noise compared to the SG regularization. In a preliminary in-vivo study, a reproducibility in the range of 10 ms^-1 was achieved when imaging the SoS of a volunteer's liver from different scanning locations. These results demonstrate the diagnostic potential of CUTE for example for the staging of fatty liver disease.

Training Variational Networks With Multidomain Simulations: Speed-of-Sound Image Reconstruction

Speed-of-sound (SoS) has been shown as a potential biomarker for breast cancer imaging, successfully differentiating malignant tumors from benign ones. SoS images can be reconstructed from time-of-flight measurements from ultrasound images acquired using conventional handheld ultrasound transducers. Variational networks (VNs) have recently been shown to be a potential learning-based approach for optimizing inverse problems in image reconstruction. Despite earlier promising results, these methods, however, do not generalize well from simulated to acquired data, due to the domain shift. In this work, we present for the first time a VN solution for a pulse-echo SoS image reconstruction problem using diverging waves with conventional transducers and single-sided tissue access. This is made possible by incorporating simulations with varying complexity into training. We use loop unrolling of gradient descent with momentum, with an exponentially weighted loss of outputs at each unrolled iteration in order to regularize the training. We learn norms as activation functions regularized to have smooth forms for robustness to input distribution variations. We evaluate reconstruction quality on the ray-based and full-wave simulations as well as on the tissue-mimicking phantom data, in comparison with a classical iterative [limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS)] optimization of this image reconstruction problem. We show that the proposed regularization techniques combined with multisource domain training yield substantial improvements in the domain adaptation capabilities of VN, reducing the median root mean squared error (RMSE) by 54% on a wave-based simulation data set compared to the baseline VN. We also show that on data acquired from a tissue-mimicking breast phantom, the proposed VN provides improved reconstruction in 12 ms.

Improved forward model for quantitative pulse-echo speed-of-sound imaging

Computed ultrasound tomography in echo mode (CUTE) allows determining the spatial distribution of speed-of-sound (SoS) inside tissue using handheld pulse-echo ultrasound (US). This technique is based on measuring the changing phase of beamformed echoes obtained under varying transmit (Tx) and/or receive (Rx) steering angles. The SoS is reconstructed by inverting a forward model describing how the spatial distribution of SoS is related to the spatial distribution of the echo phase shift. Thanks to the straight-ray approximation, this forward model is linear and can be inverted in real-time when implemented in a state-of-the art system. Here we demonstrate that the forward model must contain two features that were not taken into account so far: (a) the phase shift must be detected between pairs of Tx and Rx angles that are centred around a set of common mid-angles, and (b) it must account for an additional phase shift induced by the offset of the reconstructed position of echoes. In a phantom study mimicking hepatic and cancer imaging, we show that both features are required to accurately predict echo phase shift among different phantom geometries, and that substantially improved quantitative SoS images are obtained compared to the model that has been used so far. The importance of the new model is corroborated by a preliminary volunteer result.