Tissue Acoustic Properties Research Articles

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for large-scale problems. Deep learning-based image-to-image learned reconstruction (IILR) methods can offer computationally efficient alternatives. This study investigates the impact of the chosen input modalities on IILR methods for high-resolution SOS reconstruction in USCT. The selected modalities are traveltime tomography (TT) and reflection tomography (RT), which produce a low-resolution SOS map and a reflectivity map, respectively. These modalities have been chosen for their lower computational cost relative to FWI and their capacity to provide complementary information: TT offers a direct SOS measure, while RT reveals tissue boundary information. Systematic analyses were facilitated by employing a virtual USCT imaging system with anatomically realistic numerical breast phantoms. Within this testbed, a supervised convolutional neural network (CNN) was trained to map dual-channel (TT and RT images) to a high-resolution SOS map. Single-input CNNs were trained separately using inputs from each modality alone (TT or RT) for comparison. The accuracy of the methods was systematically assessed using normalized root mean squared error (NRMSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). For tumor detection performance, receiver operating characteristic analysis was employed. The dual-channel IILR method was also tested on clinical human breast data. Ensemble average of the NRMSE, SSIM, and PSNR evaluated on this clinical dataset were 0.2355, 0.8845, and 28.33 dB, respectively.

Pulse-echo ultrasound attenuation tomography

Objective. We present the first fully two-dimensional attenuation imaging technique developed for pulse-echo ultrasound systems. Unlike state-of-the-art techniques, which use line-by-line acquisitions, our method uses steered emissions to constrain attenuation values at each location with multiple crossing wave paths, essential to resolve the spatial variations of this tissue property. Approach. At every location, we compute normalized cross-correlations between the beamformed images that are obtained from emissions at different steering angles. We demonstrate that their log-amplitudes provide the changes between attenuation-induced amplitude losses undergone by the different incident waves. This allows us to formulate a linear tomographic problem, which we efficiently solve via a Tikhonov-regularized least-squares approach. Main results. The performance of our tomography technique is first validated in numerical examples and then experimentally demonstrated in custom-made tissue-mimicking phantoms with inclusions of varying size, echogenicity, and attenuation. We show that this technique is particularly good at resolving lateral variations in tissue attenuation and remains accurate in media with varying echogenicity. Significance. Based on a similar principle, this method can be easily combined with computed ultrasound tomography in echo mode for speed-of-sound imaging, paving the way towards a multi-modal ultrasound tomography framework characterizing multiple acoustic tissue properties simultaneously.

Mapping acoustic properties of tissues at low temperatures for ultrasound rewarming

Cryopreservation of large volumes of cells and tissues, followed by successful rewarming, promises to revolutionize organ transplantation by extending organ preservation times. This technology addresses a critical issue: over 6,000 individuals in the UK are awaiting organ transplants, yet an alarming 60% of donated organs go unused due to current preservation time limitations. A significant challenge lies in providing a rapid and uniform rewarming method for cryopreserved tissues. Ultrasound, capable of converting sound power into heat as it propagates through tissue, offers a promising solution. Nonetheless, before tapping into ultrasound’s potential for rewarming experiments, a fundamental understanding of the acoustic properties of tissues, which vary with temperature and during phase change, is essential. This study employs the multiple-reflection method (MRM), with buffer-rod positioned between the transducer and sample, to measure the acoustic attenuation coefficient and sound speed in biological relevant materials and the primary components of tissues, such as water and lipids, across a temperature range of 20 to −100ºC. Concurrently, the acoustic properties of commonly used cryopreservation solutions were assessed. These data will facilitate computational simulation of acoustic power delivery and the resulting temperature distributions, enabling planning and monitoring of tissue rewarming, which is critical to its success.

Computationally efficient deep learning-based image reconstruction for sound speed estimation in ultrasound computed tomography

Ultrasound computed tomography (USCT) has great potential for breast cancer imaging due to its ability to quantify acoustic tissue properties such as the speed of sound (SOS). Full-waveform inversion (FWI)-based image reconstruction methods can produce high spatial resolution SOS images, but these methods are computationally demanding, especially when formulated in the time-domain. Deep learning (DL)-based methods can be computationally efficient and provide high-quality object estimates as studied in various medical image reconstruction problems. However, there remains a further need to develop and assess the DL-based image reconstruction methods for USCT for use in medical imaging applications. Here, we present a supervised DL approach for USCT image reconstruction that is formulated as an image-to-image translation problem. Specifically, a low-resolution SOS image obtained via traveltime tomography and a high-resolution reflection tomography image are used as inputs to a U-Net, which is trained to estimate a high-resolution SOS map. These input images are complementary, providing both quantitative information about SOS values and information about tissue boundaries. Numerical studies that employ realistic numerical breast phantoms demonstrated that the proposed method produced high-quality SOS estimates based on image quality metrics such as NMSE, SSIM, and PSNR.

Effects of human tissue acoustic properties, abdominal wall shape, and respiratory motion on ultrasound-mediated hyperthermia for targeted drug delivery to pancreatic tumors

Background PanDox is a Phase-1 trial of chemotherapeutic drug delivery to pancreatic tumors using ultrasound-mediated hyperthermia to release doxorubicin from thermally sensitive liposomes. This report describes trial-related hyperthermia simulations featuring: (i) new ultrasonic properties of human pancreatic tissues, (ii) abdomen deflections imposed by a water balloon, and (iii) respiration-driven organ motion. Methods Pancreas heating simulations were carried out using three patient body models. Pancreas acoustic properties were varied between values found in the literature and those determined from our human tissue study. Acoustic beam distortion was assessed with and without balloon-induced abdomen deformation. Target heating was assessed for static, normal respiratory, and jet-ventilation-controlled pancreas motion. Results Human pancreatic tumor attenuation is 63% of the literature values, so that pancreas treatments require commensurately higher input intensity to achieve adequate hyperthermia. Abdominal wall deformation decreased the peak field pressure by as much as 3.5 dB and refracted the focal spot by as much as 4.5 mm. These effects were thermally counteracted by sidelobe power deposition, so the net impact on achieving mild hyperthermia was small. Respiratory motion during moving beam hyperthermia produced localized regions overheated by more than 8.0 °C above the 4.0 °C volumetric goal. The use of jet ventilation reduced this excess to 0.7 °C and yielded temperature field uniformity that was nearly identical to having no respiratory motion. Conclusion Realistic modeling of the ultrasonic propagation environment is critical to achieving adequate mild hyperthermia without the use of real time thermometry for targeted drug delivery in pancreatic cancer patients.

SIMPA: an open-source toolkit for simulation and image processing for photonics and acoustics.

.SignificanceOptical and acoustic imaging techniques enable noninvasive visualisation of structural and functional properties of tissue. The quantification of measurements, however, remains challenging due to the inverse problems that must be solved. Emerging data-driven approaches are promising, but they rely heavily on the presence of high-quality simulations across a range of wavelengths due to the lack of ground truth knowledge of tissue acoustical and optical properties in realistic settings.AimTo facilitate this process, we present the open-source simulation and image processing for photonics and acoustics (SIMPA) Python toolkit. SIMPA is being developed according to modern software design standards.ApproachSIMPA enables the use of computational forward models, data processing algorithms, and digital device twins to simulate realistic images within a single pipeline. SIMPA’s module implementations can be seamlessly exchanged as SIMPA abstracts from the concrete implementation of each forward model and builds the simulation pipeline in a modular fashion. Furthermore, SIMPA provides comprehensive libraries of biological structures, such as vessels, as well as optical and acoustic properties and other functionalities for the generation of realistic tissue models.ResultsTo showcase the capabilities of SIMPA, we show examples in the context of photoacoustic imaging: the diversity of creatable tissue models, the customisability of a simulation pipeline, and the degree of realism of the simulations.ConclusionsSIMPA is an open-source toolkit that can be used to simulate optical and acoustic imaging modalities. The code is available at: https://github.com/IMSY-DKFZ/simpa, and all of the examples and experiments in this paper can be reproduced using the code available at: https://github.com/IMSY-DKFZ/simpa_paper_experiments.

DeepUCT: Complex cascaded deep learning network for improved ultrasound tomography

Ultrasound computed tomography is an inexpensive and radiation-free medical imaging technique used to quantify the tissue acoustic properties for advanced clinical diagnosis. Image reconstruction in ultrasound tomography is often modeled as an optimization scheme solved by iterative methods like full-waveform inversion. These iterative methods are computationally expensive, while the optimization problem is ill-posed and nonlinear. To address this problem, we propose to use deep learning to overcome the computational burden and ill-posedness, and achieve near real-time image reconstruction in ultrasound tomography. We aim to directly learn the mapping from the recorded time-series sensor data to a spatial image of acoustical properties. To accomplish this, we develop a deep learning model using two cascaded convolutional neural networks with an encoder–decoder architecture. We achieve a good representation by first extracting the intermediate mapping-knowledge and later utilizing this knowledge to reconstruct the image. This approach is evaluated on synthetic phantoms where simulated ultrasound data are acquired from a ring of transducers surrounding the region of interest. The measurement data is acquired by forward modeling the wave equation using the k-wave toolbox. Our simulation results demonstrate that our proposed deep-learning method is robust to noise and significantly outperforms the state-of-the-art traditional iterative method both quantitatively and qualitatively. Furthermore, our model takes substantially less computational time than the conventional full-wave inversion method.

3-D Stochastic Numerical Breast Phantoms for Enabling Virtual Imaging Trials of Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) is an emerging imaging modality for breast imaging that can produce quantitative images that depict the acoustic properties of tissues. Computer-simulation studies, also known as virtual imaging trials, provide researchers with an economical and convenient route to systematically explore imaging system designs and image reconstruction methods. When simulating an imaging technology intended for clinical use, it is essential to employ realistic numerical phantoms that can facilitate the objective, or task-based, assessment of image quality (IQ). Moreover, when computing objective IQ measures, an ensemble of such phantoms should be employed, which displays the variability in anatomy and object properties that are representative of the to-be-imaged patient cohort. Such stochastic phantoms for clinically relevant applications of USCT are currently lacking. In this work, a methodology for producing realistic 3-D numerical breast phantoms for enabling clinically relevant computer-simulationstudies of USCT breast imaging is presented. By extending and adapting an existing stochastic 3-D breast phantom for use with USCT, methods for creating ensembles of numerical acoustic breast phantoms are established. These breast phantoms will possess clinically relevant variations in breast size, composition, acoustic properties, tumor locations, and tissue textures. To demonstrate the use of the phantoms in virtual USCT studies, two brief case studies are presented, which addresses the development and assessment of image reconstruction procedures. Examples of breast phantoms produced by use of the proposed methods and a collection of 52 sets of simulated USCT measurement data have been made open source for use in image reconstruction development.

PD56-11 RAW MICRO-ULTRASOUND TISSUE CHARACTERIZATION USING CONVOLUTION NEURAL NETWORKS TO DIFFERENTIATION BENIGN TISSUE FROM CLINICALLY SIGNIFICANT PROSTATE CANCER

You have accessJournal of UrologyProstate Cancer: Detection & Screening VI (PD56)1 Sep 2021PD56-11 RAW MICRO-ULTRASOUND TISSUE CHARACTERIZATION USING CONVOLUTION NEURAL NETWORKS TO DIFFERENTIATION BENIGN TISSUE FROM CLINICALLY SIGNIFICANT PROSTATE CANCER Ahmed El Kaffas, Brian Wodlinger, Mirabela Rusu, Aya Kamaya, Richard Fan, and Geoffrey Sonn Ahmed El KaffasAhmed El Kaffas More articles by this author , Brian WodlingerBrian Wodlinger More articles by this author , Mirabela RusuMirabela Rusu More articles by this author , Aya KamayaAya Kamaya More articles by this author , Richard FanRichard Fan More articles by this author , and Geoffrey SonnGeoffrey Sonn More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002090.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Micro-ultrasound is a promising new technique offering high-resolution images to identify prostate cancer. However, clinical interpretation of the images remains challenging. The underlying raw radio-frequency (RF) ultrasound data acquired during imaging is a unique source of tissue acoustic properties, potentially ideal to identify suspicious areas to target at biopsy. Here, we develop a convolutional neural network (3D CNN) to classify tissue as benign vs clinically significant prostate cancer (csPCa) using the raw RF data. METHODS: 837 male patients (median age 63, IQR 57-68) undergoing biopsy for suspected prostate cancer were recruited. After IRB approval, data was acquired from 29MHz micro-ultrasound imaging (ExactVu, Exact Imaging, Markham, Canada) during targeted biopsy at 5 collaborating sites. Patients across all sites were scanned using consistent acquisition presets; all data was saved in raw digital RF format. One RF image was obtained at each biopsy location (up to 12 images per patient). Histopathological analysis of each biopsy sample served as the clinical standard of benign vs clinically significant cancer (grade group ≥2). Samples containing grade group 1 cancer were not analyzed. Data was processed to maintain both spatial and frequency information for images. A shallow 3D CNN model was trained on images (patient stratified); a similar network was also trained with the addition of PSA for each patient as a feature. Training used 5220 patient images. Testing was carried out on a set-aside dataset of 1310 images. The area under the receiver operator curve (ROC-AUC) was the main evaluation metric. We also explored whether our models could differentiate cancer from benign on a patient-level. RESULTS: Our model yielded a ROC-AUC of 82% in differentiating benign vs. csPSa at an image-level. Including the PSA into our model during training increased the ROC-AUC to 85%. On a patient-level, an 81% ROC-AUC was achieved in differentiating patients with benign vs. csPSa. Including PSA in the model increased the ROC-AUC to 87%. CONCLUSIONS: Our CNN model accurately differentiated cancerous from benign tissue using raw RF data obtained during clinical micro-ultrasound imaging. Source of Funding: NA © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1008-e1008 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ahmed El Kaffas More articles by this author Brian Wodlinger More articles by this author Mirabela Rusu More articles by this author Aya Kamaya More articles by this author Richard Fan More articles by this author Geoffrey Sonn More articles by this author Expand All Advertisement Loading ...

Deployable ultrasound applicators for endoluminal delivery of volumetric hyperthermia

Purpose To investigate the design of an endoluminal deployable ultrasound applicator for delivering volumetric hyperthermia to deep tissue sites as a possible adjunct to radiation and chemotherapy. Method This study considers an ultrasound applicator consisting of two tubular transducers situated at the end of a catheter assembly, encased within a distensible conical shaped balloon-based reflector that redirects acoustic energy distally into the tissue. The applicator assembly can be inserted endoluminally or laparoscopically in a compact form and expanded after delivery to the target site. Comprehensive acoustic and biothermal simulations and parametric studies were employed in generalized 3D and patient-specific pancreatic head and body tumor models to characterize the acoustic performance and evaluate heating capabilities of the applicator by investigating the device at a range of operating frequencies, tissue acoustic and thermal properties, transducer configurations, power modulation, applicator positioning, and by analyzing the resultant 40, 41, and 43 °C isothermal volumes and penetration depth of the heating volume. Intensity distributions and volumetric temperature contours were calculated to define moderate hyperthermia boundaries. Results Parametric studies demonstrated the frequency selection to control volume and depth of therapeutic heating from 62 to 22 cm3 and 4 to 2.6 cm as frequency ranges from 1 MHz to 4.7 MHz, respectively. Width of the heating profile tracks closely with the aperture. Water cooling within the reflector balloon was effective in controlling temperature to 37 °C maximum within the luminal wall. Patient-specific studies indicated that applicators with extended OD in the range of 3.6–6.2 cm with 0.5–1 cm long and 1 cm OD transducers can heat volumes of 1.1–7 cm3, 3–26 cm3, and 3.3–37.4 cm3 of pancreatic body and head tumors above 43, 41, and 40 °C, respectively. Conclusion In silico studies demonstrated the feasibility of combining endoluminal ultrasound with an integrated expandable balloon reflector for delivering volumetric hyperthermia in regions adjacent to body lumens and cavities.

Abstract PO-022: Automated liver tissue segmentation in point of care ultrasound b-mode images using U-Net

Abstract Introduction/Purpose: We have recently developed and demonstrated that tissue acoustic properties can facilitate early detection of liver cancer during period abdominal ultrasound surveillance exams in high risk patients. Automated segmentation of the liver is a necessary step in increasing the accuracy, sensitivity, and specificity of Machine Learning Models which detect cancer using the lower quality data generated from Point-of-Care Ultrasound (PoCUS). We have developed deep learning algorithms which can successfully segment the data generated by these PoCUS systems. Methods: A total of 1000 patients from 4 collaboration sites were recruited, though for this study only 25 of which were manually segmented were used.. For this HIPPA compliant study, all data was obtained through IRBs approved at the local data acquisition site; all patients were consented before data acquisition. The patient's mean age and BMI were 43(11 STD) and 23 (5 STD). All patients were scanned using the same acquisition protocol, capturing the same 5 scan visualization windows of the liver using a PoCUS system, with capabilities to capture raw signal data. We use a deep learning algorithm to automatically segment out the liver. The initial version of the U-net Deep learning architecture was trained on a subset of our data containing B-mode images from 20 patients (500 frames) and tested on a dataset with 5 patients (125 frames). The Regions of interest that served as training and testing labels for the algorithm were outlined on an image-by-image basis by expert sonographers. U-Net is a fully convolutional neural network that consists of a block of contraction layers followed by a block of expansion layers. The architecture contains feature channels to relay context information to higher resolution layers. U-Net has been used at multiple medical segmentations tasks in which it performs particularly well even with small datasets. Results: The architecture successfully segmented the images both in the train and test datasets with a great degree of accuracy and generalization. Test accuracies surpassed 86% with a sensitivity of 80% and specificity of 95% and results can be further improved by applying thresholds to the algorithm’s output. Conclusion: This automated segmentation algorithm is a clear next step in the improvement of any automated disease detection method in the liver. Once in place it will greatly aid in the early screening of all liver diseases and improve the outcomes for at risk patients. Citation Format: Raul Blazquez Garcia, Adi Lightstone, Miriam Naim Ibrahim, Ahmed El Kaffas. Automated liver tissue segmentation in point of care ultrasound b-mode images using U-Net [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-022.

Abstract PO-025: Differentiation of benign from clinically significant prostate cancer tissues using convolution neural networks on raw micro-ultrasound data

Abstract Purpose: Micro-ultrasound is a promising new technique offering high-resolution images to identify prostate cancer. However, clinical interpretation of the images remains challenging. The underlying raw radio-frequency ultrasound is a unique source of tissue acoustic properties, potentially ideal to identify tissues-of-interest in images for targeted biopsies. Here, we develop a convolutional neural network (3D CNN) to classify tissue as benign vs clinically significant prostate cancer (csPCa) using the raw RF data. Materials and Methods: A total of 837 male patients (median age 63, IQR 57-68) suspected of prostate cancer and undergoing a prostate biopsy were recruited. All data was obtained through an IRB approved at the local data acquisition site (5 collaborating sites); all patients were consented before 29MHz micro-ultrasound (ExactVu, Exact Imaging, Markham, Canada) data acquisition during targeted biopsy. Patients across all sites were scanned using consistent acquisition presets, and all data was saved in raw IQ format. Up to 12 IQ images were obtained for each patient, representing targeted biopsy locations. Histopathological analysis of each biopsy sample served as the clinical standard of benign vs clinically significant cancer (Gleason score (GS) ≥7). Samples containing clinically insignificant cancer (GS6) were not analyzed. Data was processed to maintain both spatial and frequency information for images. A shallow 3D CNN model was trained on patient images (train-test split based on patients); a similar network was also trained with the addition of Prostate Specific Antigen (PSA) level for each patient as a feature. Training used 5220 patient images (4520 benign vs. 700 GS7+;80% of the data); testing was carried out on a set-aside data set of 1310 patient images (1131 benign vs. 179 GS7+ images; 20% of data). The area area under the receiver operator curve (ROC-AUC) was the main evaluation metric. We also explored whether our models could help classify data on a patient level of benign vs. csPSa patients. Results: Preliminary results suggest that our model yields a ROC-AUC of 82% on an image-level to differentiate between benign and csPSa-confirmed images. Including the PSA into our model during training resulted in ROC-AUCs of up to 85%. On a patient level, up to 81% ROC-AUC was achievable in differentiating patients with benign vs. csPSa. Including the PSA on a patient-level resulted in an 87% ROC-AUC. Conclusion: CNNs can help capture unique tissue acoustic properties in micro-ultrasound images to help identify areas suspicious for prostate cancer that warrant targeted biopsy. This offers the potential to improve cancer diagnosis while reducing the number of biopsy cores required. Citation Format: Ahmed El Kaffas, Adi Lightstone, Raul Garcia, Brian Woodlinger, Miriam Ibrahim, Richard Fan, Geoffrey Sonn. Differentiation of benign from clinically significant prostate cancer tissues using convolution neural networks on raw micro-ultrasound data [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-025.

An Improved Strategy for Detection and Localization of Nodules in Liver Tissues by a 16 MHz Needle Ultrasonic Probe Mounted on a Robotic Platform.

This study presents an improved strategy for the detection and localization of small size nodules (down to few mm) of agar in excised pork liver tissues via pulse-echo ultrasound measurements performed with a 16 MHz needle probe. This work contributes to the development of a new generation of medical instruments to support robotic surgery decision processes that need information about cancerous tissues in a short time (minutes). The developed ultrasonic probe is part of a scanning platform designed for the automation of surgery-associated histological analyses. It was coupled with a force sensor to control the indentation of tissue samples placed on a steel plate. For the detection of nodules, we took advantage of the property of nodules of altering not only the acoustical properties of tissues producing ultrasound attenuation, but also of developing patterns at their boundary that can modify the shape and the amplitude of the received echo signals from the steel plate supporting the tissues. Besides the Correlation Index Amplitude (CIA), which is linked to the overall amplitude changes of the ultrasonic signals, we introduced the Correlation Index Shape (CIS) linked to their shape changes. Furthermore, we applied AND-OR logical operators to these correlation indices. The results were found particularly helpful in the localization of the irregular masses of agar we inserted into some excised liver tissues, and in the individuation of the regions of major interest over which perform the vertical dissections of tissues in an automated analysis finalized to histopathology. We correctly identified up to 89% of inclusions, with an improvement of about 14% with respect to the result obtained (78%) from the analysis performed with the CIA parameter only.

The Impact of Acoustic Clutter on Large Array Abdominal Imaging.

Abdominal imaging suffers from a particularly difficult acoustic environment-targets are located deep and overlying tissue layers with varying properties generate acoustic clutter. Increasing array size can overcome the penetration and lateral resolution problems in ideal conditions, but how the impact of clutter scales with increasing array extent is unknown and may limit the benefits in vivo. Previous ex vivo experimental work showed the promise of large arrays but was technically limited to a length of 6.4 cm and to only partial sampling of the array elements. We present an extension of those studies using the Fullwave simulation tool to create a 10 cm ×2 cm matrix array with full lateral element sampling. We used a numerical model of the abdomen based on the maps of tissue acoustical properties and found that propagation through the modeled abdominal layers generated on average 25.4 ns of aberration and 0.74 cm of reverberation clutter across the array extent. Growing the full aperture from 2 to 10 cm improved contrast by 8.6 dB and contrast-to-noise ratio by 22.9% in addition to significantly improving target resolution. Alternative array strategies that may be useful for implementation-mismatched aperture sizes or a swept synthetic aperture-also produced improved quality with growing aperture size. These results motivate the development of larger diagnostic imaging arrays for the purpose of high-resolution imaging in challenging environments.

Effect of Bifidobacterium-induced changes in tumor tissue acoustic properties on efficacy of high-intensity focused ultrasound ablation

To investigate the effects of Bifidobacterium on the acoustic characteristics of tumor tissue and how such acoustic changes affect the efficacy of high-intensity focused ultrasound (HIFU) ablation in nude mice. Forty mice bearing human breast cancer cell (MDA-MB-231) xenograft were randomized into experimental group (n=20) and control group (n=20) for intravenous injection of Bifidobacterium suspension (200 μL, 4 × 108 cfu/mL) and PBS (200 μL) for 3 consecutive days, respectively. Before and at 3 and 7 days after the first injection, shear wave elastography was used to evaluate the hardness of the tumor tissue. On day 7 after the first injection, 10 mice from each group were sacrificed and the sound velocity and sound attenuation of the tumor tissues were measured. The changes in the collagen fibers in the tumors were evaluated using Masson staining, and neovascularization in the tumor was assessed with immunohistochemistry for platelet endothelial cell adhesion molecule-1 (PECAM-1/CD31). The remaining 10 tumor-bearing mice in each group were subjected to HIFU ablation, and the ablation efficiency was evaluated by assessing the changes in irradiation gray values, coagulative necrosis volume, energy efficiency factor (EEF) and irradiation area and by pathological examination with HE staining. In the experimental group, the collagen fibers in the tumor tissues were strong and densely aligned, and the tumors contained fewer new blood vessels showing strip-or spot-like morphologies. In the control group, the collagen fibers in the tumors were thin and loosely arranged, and the tumors showed abundant elongated or round new blood vessels. Bifidobacterium colonized in the tumor 7 days after the injection, and the tumor hardness was significantly greater in the experimental group than in the control group (P=0.01); the acoustic velocity (P=0.001) and the acoustic attenuation (P=0.000) of the tumor tissues were also greater in the experimental group. HIFU irradiation resulted in significantly greater changes in the gray scale of tumor (P=0.0006) and larger coagulative necrosis volume (P=0.0045) in the experimental group than in the control group, and the EEF was significantly smaller in the experimental group (P=0.0134). Bifidobacterium can cause changes in collagen fiber content, acoustic velocity and attenuation in the tumor tissue and reduce the EEF of HIFU irradiation, thereby improving the efficacy of HIFU irradiation.

A Mechatronic Platform for Computer Aided Detection of Nodules in Anatomopathological Analyses via Stiffness and Ultrasound Measurements.

This study presents a platform for ex-vivo detection of cancer nodules, addressing automation of medical diagnoses in surgery and associated histological analyses. The proposed approach takes advantage of the property of cancer to alter the mechanical and acoustical properties of tissues, because of changes in stiffness and density. A force sensor and an ultrasound probe were combined to detect such alterations during force-regulated indentations. To explore the specimens, regardless of their orientation and shape, a scanned area of the test sample was defined using shape recognition applying optical background subtraction to the images captured by a camera. The motorized platform was validated using seven phantom tissues, simulating the mechanical and acoustical properties of ex-vivo diseased tissues, including stiffer nodules that can be encountered in pathological conditions during histological analyses. Results demonstrated the platform’s ability to automatically explore and identify the inclusions in the phantom. Overall, the system was able to correctly identify up to 90.3% of the inclusions by means of stiffness in combination with ultrasound measurements, paving pathways towards robotic palpation during intraoperative examinations.

Irreversible shifts in optical autofluorescence spectra applied to the assessment of thermal lesion formation under high intensity focused ultrasound

High-intensity focused ultrasound (HIFU) is often used to create lesions, or regions of tissue destruction due to heating and cavitation activity, most often in tumors or other diseased tissues. However, the acoustic properties of tissues denatured by heat are not very different from those of untreated tissue, making lesion detection and quantification difficult by ultrasound alone. Autofluorescence refers to the broadband emission of light within materials that are stimulated by narrowband incident light, typically from a laser. It is a common characteristic of lipids, proteins, and other biomolecules, and the “autofluorescence spectrum” is a function of the state of the material. We have examined irreversible shifts in the dominant autofluorescence spectra of proteins denatured by HIFU heating. These shifts appear to be related to protein conformational changes due to denaturation. We report on the feasibility of using optical autofluorescence as a means of quantifying in vitro lesion formation by HIFU for optically accessible tissues.High-intensity focused ultrasound (HIFU) is often used to create lesions, or regions of tissue destruction due to heating and cavitation activity, most often in tumors or other diseased tissues. However, the acoustic properties of tissues denatured by heat are not very different from those of untreated tissue, making lesion detection and quantification difficult by ultrasound alone. Autofluorescence refers to the broadband emission of light within materials that are stimulated by narrowband incident light, typically from a laser. It is a common characteristic of lipids, proteins, and other biomolecules, and the “autofluorescence spectrum” is a function of the state of the material. We have examined irreversible shifts in the dominant autofluorescence spectra of proteins denatured by HIFU heating. These shifts appear to be related to protein conformational changes due to denaturation. We report on the feasibility of using optical autofluorescence as a means of quantifying in vitro lesion formation by HIFU ...

Mitigating acoustic heterogeneous effects in microwave-induced breast thermoacoustic tomography using multi-physical K-means clustering

Microwave-induced thermoacoustic tomography shows great potential for early-stage breast tumor detection, but imaging quality usually suffers due to acoustic heterogeneity of breast tissue. To mitigate this problem, conventional methods estimate the distribution of speeds of sound but at a heavy cost of system complexity or computation burden. We propose an imaging reconstruction method that incorporates dielectric and acoustic properties of tissues as a-priori information and reformulates the velocity estimation problem as a data clustering problem. The proposed method is validated by imaging anatomically realistic numerical breast phantoms and real biological tissues. Both simulation and experimental results demonstrate that the proposed method is robust and significantly improves image fidelity with less computational burden than conventional methods. These results make our approach a promising candidate for clinical breast cancer detection.

Temperature dependence of speed of sound and attenuation of porcine left ventricular myocardium

The temperature dependence of soft tissue acoustic properties is relevant for monitoring tissue hyperthermia. In the current work, the propagation speed and attenuation of healthy porcine left ventricular myocardium (N=5) was investigated in a frequency range relevant for clinical diagnostic imaging, i.e. 2.5–13.0MHz. Each tissue sample was held in a water bath at a temperature T=25°C, heated to 45°C, and allowed to cool back down to 25°C. Due to initial tissue swelling, the data for decreasing temperatures was considered more reliable. In this case, the slope of the phase velocity versus temperature relation was measured to be 1.10±0.04m/s/°C, and the slope of the attenuation was −0.11±0.04dB/cm/°C at 10MHz, or −0.0041±0.0015dB/cm/MHz1.4336/°C as a function of frequency.

Noninvasive and localized acoustic micropumping—An in vitro study of an ultrasound method that enhances drug distribution through a physiologically-relevant material

Acoustic streaming—the displacement of fluid by sound—has been proposed as the mechanism for therapeutic effects, such as drug distribution enhancement, yet there have been no direct observation or characterization of this effect in soft tissue, making it difficult to optimize and control. We aimed to directly observe ultrasound-induced streaming during sonication in a tissue-mimicking material. We hypothesized that existing ultrasound phantoms (e.g., polyacrylamide (PAA) and gelatin) mimic the acoustic properties of tissue, but not the tissue microenvironment. Scanning electron microscopy revealed that gelatin and PAA had closed pores suggesting that they can’t support acoustic streaming. In contrast, macroporous acrylamide (MPPA), a new phantom we created, had interconnected pores resembling the interstitial space of soft tissue. The focal point of a focused ultrasound (FUS) transducer was placed at the distal surface of the MPPA phantom. A model drug (Bromophenol blue) was injected in and around the focal region and different FUS parameters were evaluated. Dye clearance at the hydrogel surface was observed with a video camera. Increasing ultrasound pressure produced higher streaming. Pulsed ultrasound having the same total energy as continuous wave ultrasound produced higher streaming. Using MPPA, we were able to study acoustic streaming under different parameters. Our future work is to optimize ultrasound parameters to maximize acoustic streaming in this material and in in-vivo tissue for enhancing drug distribution.