Properties Of Tissue Research Articles

Brillouin scattering from biomedical samples: the challenge of heterogeneity.

Abstract Brillouin light scattering (BLS), a non-destructive and non-contact technique, offers a powerful tool for probing the micromechanical properties of biological tissues. However, the inherent heterogeneity of biological tissues can pose significant challenges in interpreting BLS spectra. In this study, we introduce a novel method that harnesses the intensity information within a single BLS spectrum to directly estimate the Voigt average of the longitudinal modulus. Additionally, we use a method to determine the ratio of the squared Pockels coefficients for photoelastically heterogeneous samples, based on global analysis of a 2D BLS map. This method is shown to effectively determine the photoelastic ratio of soft and hard components of human bone tissues, enabling the calculation of the average elastic moduli. Furthermore, it has the remarkable ability to generate maps of the filling factor of the scattering volume, shedding valuable light on the intricate structure and topography of rough surfaces under BLS mapping.

3D Ultrasound Shear Wave Elastography for Musculoskeletal Tissue Assessment Under Compressive Load: A Feasibility Study.

Given its real-time capability to quantify mechanical tissue properties, ultrasound shear wave elastography holds significant promise in clinical musculoskeletal imaging. However, existing shear wave elastography methods fall short in enabling full-limb analysis of 3D anatomical structures under diverse loading conditions, and may introduce measurement bias due to sonographer-applied force on the transducer. These limitations pose numerous challenges, particularly for 3D computational biomechanical tissue modeling in areas like prosthetic socket design. In this feasibility study, a clinical linear ultrasound transducer system with integrated shear wave elastography capabilities was utilized to scan both a calibrated phantom and human limbs in a water tank imaging setup. By conducting 2D and 3D scans under varying compressive loads, this study demonstrates the feasibility of volumetric ultrasound shear wave elastography of human limbs. Our preliminary results showcase a potential method for evaluating 3D spatially varying tissue properties, offering future extensions to computational biomechanical modeling of tissue for various clinical scenarios.

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.

3D bioartificial stretchable scaffolds mimicking the mechanical hallmarks of human cardiac fibrotic tissue

Human cardiac fibrotic tissues are characterized by a higher stiffness relative to healthy cardiac tissues. These tissues are unable to spontaneously contract and are subjected to passive mechanical stimulation during heart functionality. Moreover, scaffolds that can recapitulate the in vivo mechanical properties of the cardiac fibrotic tissues are lacking. Herein, this study aimed to design and fabricate mechanically stretchable bioartificial scaffolds with biomimetic composition and stiffness comparable to human cardiac fibrotic tissues. Poly(ε-caprolactone) (PCL) scaffolds with a stretchable mesh architecture were initially designed through structural and finite element method (FEM) analyses and subsequently fabricated by melt extrusion additive manufacturing (MEX). Scaffolds were surface functionalized by 3,4-dihydroxy-DL-phenylalanine (DOPA) polymerization (polyDOPA) to improve their interaction with natural polymers. Scaffold pores were then filled with different concentrations (5%, 7%, and 10% w/v) of gelatin methacryloyl (GelMA) hydrogels to support in vitro human cardiac fibroblasts (HCFs) 3D culture, thereby producing bioartificial PCL/GelMA scaffolds. Uniaxial tensile mechanical tests in static and dynamic conditions (1 Hz; 22% maximum strain) demonstrated that the bioartificial scaffolds had in vivo-like stretchability and similar stiffness to that of pathological cardiac tissue (tailored as a function of the number of PCL scaffold layers and GelMA hydrogel concentration). In vitro cell tests on bioartificial scaffolds using HCF-embedded GelMA hydrogels under static conditions displayed increasing cell viability, spreading, and cytoskeleton organization with decreasing GelMA hydrogel concentration. Moreover, α-smooth muscle actin (α-SMA)-positive cells were detected after 7 days of culture in static conditions followed by another 7 days of culture in dynamic conditions and not in HCF-loaded scaffolds cultured in static conditions for 14 days. These results suggested that in vitro culture under cyclic mechanical stimulations could induce an HCF phenotypic switch into myofibroblasts.

Optical coherence elastography under homolateral parallel acoustic radiation force excitation for ocular elasticity quantification.

Alteration in the elastic properties of biological tissues may indicate changes in the structure and components. Acoustic radiation force optical coherence elastography (ARF-OCE) can assess the elastic properties of the ocular tissues non-invasively. However, coupling the ultrasound beam and the optical beam remains challenging. In this Letter, we proposed an OCE method incorporating homolateral parallel ARF excitation for measuring the elasticity of the ocular tissues. An acoustic-optic coupling unit was established to reflect the ultrasound beam while transmitting the light beam. The ARF excited the ocular tissue in the direction parallel to the light beam from the same side of the light beam. We demonstrated the method on the agar phantoms, the porcine cornea, and the porcine retina. The results show that the ARF-OCE method can measure the elasticity of the cornea and the retina, resulting in higher detection sensitivity and a more extensive scanning range.

A new method for determining the ogden parameters of soft materials using indentation experiments

A full understanding of the material properties of skin tissue is crucial for exploring its tribo-mechanical behaviour. It has been widely accepted that the mechanical behaviour of skin tissue for both small and large deformations can be accurately described using a hyperelastic model, such as the one developed by Ogden. However, obtaining these Ogden parameters for in-vivo skin by in-vivo experiments no matter the indentation or suction tests is a significant challenge. The mathematical model used to describe the material behaviour during the test should consider not only the material nonlinearity but also the geometrical confinement of the tissue, the large deformations induced, and the fact that the specimens are relatively thin.A range of contact models is available to describe the contact behaviour during the indentation test. However, none of them can be used for hyperelastic materials with small thickness under large deformations. Simultaneously explaining material nonlinearity and geometric nonlinearity, either through theoretical equations or numerical calculations, poses a significant challenge.In this research, we propose a pragmatic method to obtain Ogden parameters for in-vivo skin tissue by combining experimental indentation results and numerical simulations. The indentation tests were used to obtain the force-indentation depth curves, while the numerical simulations were used to obtain the strain fields. The method assumes the material behaviour of specimens can be linearized in each small deformation increment, and the contact model developed by Hayes can be applied to accommodate each increment. Then, the linear elastic behaviour in each increment can be described by the elastic modulus E which were obtained using Hayes model, and the principal stresses in each increment were subsequently obtained using Hooke's law. By combining all stress fields, overall stress-strain curves can be constructed, from which the hyperelastic Ogden parameters can be obtained. A second numerical simulation of the hyperelastic indentation was then performed using the obtained Ogden parameters, allowing a comparison of the experimental and simulated relationships between force and indentation.

Cell Migration Chip (3DCM-chip): a New Tool Toward the Modelling of 3D Cellular Complex Systems.

3D hydrogel-based cell cultures provide models for studying cell behavior and can efficiently replicate the physiologic environment. Hydrogels can be tailored to mimic mechanical and biochemical properties of specific tissues and allow to produce gel-in-gel models. In this system, microspheres encapsulating cells are embedded in an outer hydrogel matrix, where cells are able to migrate. To enhance the efficiency of such studies, we design a lab-on-a-chip named 3D Cell Migration-chip (3DCM-chip) that offers substantial advantages over traditional methods. 3DCM-chip facilitates the analysis of biochemical and physical stimuli effects on cell migration/invasion in different cell types, including stem, normal and tumor cells. 3DCM-chip provides a smart platform for developing more complex cell co-cultures systems. Herein we investigate the impact of human fibroblasts on MDA-MB 231 breast cancer cells invasiveness. Moreover, how the presence of different cellular lines, including mesenchymal stem cells (hMSCs), normal human dermal fibroblasts (NHDFs), and human umbilical vein endothelial cells (HUVECs), affects the invasive behavior of cancer cells is investigated using 3DCM-chip and producing predictive tumoroid models with a more complex network of interactions between cells and microenvironment. 3DCM-chip moves us closer to creating in vitro systems that can potentially replicate key aspects of the physiological tumor microenvironment. This article is protected by copyright. All rights reserved.

Automated prediction of isthmus areas in scar-related atrial tachycardias using artificial intelligence.

Ablation of scar-related reentrant atrial tachycardia (SRRAT) involves identification and ablation of a critical isthmus. A graph convolutional network (GCN) is a machine learning structure that is well-suited to analyze the irregularly-structured data obtained in mapping procedures and may be used to identify potential isthmuses. Electroanatomic maps from 29 SRRATs were collected, and custom electrogram features assessing key tissue and wavefront properties were calculated for each point. Isthmuses were labeled off-line. Training data was used to determine the optimal GCN parameters and train the final model. Putative isthmus points were predicted in the training and test populations and grouped into proposed isthmus areas based on density and distance thresholds. The primary outcome was the distance between the centroids of the true and closest proposed isthmus areas. A total of 193 821 points were collected. Thirty isthmuses were detected in 29 tachycardias among 25 patients (median age 65.0, 5 women). The median (IQR) distance between true and the closest proposed isthmus area centroids was 8.2 (3.5, 14.4) mm in the training and 7.3 (2.8, 16.1) mm in the test group. The mean overlap in areas, measured by the Dice coefficient, was 11.5 ± 3.2% in the training group and 13.9 ± 4.6% in the test group. A GCN can be trained to identify isthmus areas in SRRATs and may help identify critical ablation targets.

Current challenges in imaging the mechanical properties of tissue engineered grafts

The mechanical performance of tissue-engineered grafts is crucial in determining their functional properties, integration with native tissue and long-term repair outcome post-implantation. To date, most approaches for testing the mechanical properties of tissue-engineered grafts are non-sterile and invasive. There is an urgent need to develop novel sterile approaches for online monitoring mechanical properties of engineered tissues in order to ensure these engineered products meet the desired mechanical strength prior to implantation. In this paper, we overview various approaches for mechanical testing of engineered tissues, which span from traditional methods to medical imaging concepts in magnetic resonance elastography, ultrasound elastography, and optical coherence elastography. We focused on the applicability of these methods to the manufacturing of tissue-engineered products online, e.g., if such approach provides a sterile monitoring capacity and is capable of defining mechanical heterogeneity in engineered tissues throughout their growth in vitro in real-time. The review delves into various imaging modalities that employ distinct methods for inducing displacement within the sample, utilizing either strain-based or shear wave-based approaches. This displacement can be achieved through external stimulation or by harnessing ambient vibrations. Subsequently, the imaging process captures and visualizes the resultant displacement. We specifically highlight the advantages of novel non-invasive imaging methods such as ultrasound elastography and optical coherence elastography to assess the mechanical properties of engineered tissues in vitro, as well as their potential applications in cancer study, drug screening and the in vivo evaluation of the engineered tissues.

Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index.

For effective optimization of MR fingerprinting (MRF) pulse sequences, estimating and minimizing errors from actual scan conditions are crucial. Although virtual-scan simulations offer an approximation to these errors, their computational demands become expensive for high-dimensional MRF frameworks, where interactions between more than two tissue properties are considered. This complexity makes sequence optimization impractical. We introduce a new mathematical model, the systematic error index (SEI), to address the scalability challenges for high-dimensional MRF sequence design. By eliminating the need to perform dictionary matching, the SEI model approximates quantification errors with low computational costs. The SEI model was validated in comparison with virtual-scan simulations. The SEI model was further applied to optimize three high-dimensional MRF sequences that quantify two to four tissue properties. The optimized scans were examined in simulations and healthy subjects. The proposed SEI model closely approximated the virtual-scan simulation outcomes while achieving hundred- to thousand-times acceleration in the computational speed. In both simulation and in vivo experiments, the optimized MRF sequences yield higher measurement accuracy with fewer undersampling artifacts at shorter scan times than the heuristically designed sequences. We developed an efficient method for estimating real-world errors in MRF scans with high computational efficiency. Our results illustrate that the SEI model could approximate errors both qualitatively and quantitatively. We also proved the practicality of the SEI model of optimizing sequences for high-dimensional MRF frameworks with manageable computational power. The optimized high-dimensional MRF scans exhibited enhanced robustness against undersampling and system imperfections with faster scan times.

Passive mechanical properties of adipose tissue and skeletal muscle from C57BL/6J mice

Skeletal muscle and adipose tissue are characterized by unique structural features finely tuned to meet specific functional demands. In this study, we investigated the passive mechanical properties of soleus (SOL), extensor digitorum longus (EDL) and diaphragm (DIA) muscles, as well as subcutaneous (SAT), visceral (VAT) and brown (BAT) adipose tissues from 13 C57BL/6J mice. Thereto, alongside stress-relaxation assessments we subjected isolated muscles and adipose tissues (ATs) to force-extension tests up to 10% and 30% of their optimal length, respectively. Peak passive stress was highest in the DIA, followed by the SOL and lowest in the EDL (p < 0.05). SOL displayed also the highest Young's modulus and hysteresis among muscles (p < 0.05). BAT demonstrated highest peak passive stress and Young's modulus followed by VAT (p < 0.05), while SAT showed the highest hysteresis (p < 0.05). When comparing data across all six biological specimens at fixed passive force intervals (i.e., 20–40 and 50–70 mN), skeletal muscles exhibited significantly higher peak stresses and strains than ATs (p < 0.05). Young's modulus was higher in skeletal muscles than in ATs (p < 0.05). Muscle specimens exhibited slower force relaxation in the first phase compared to ATs (p < 0.05), while there was no significant difference in behavior between muscles and AT in the second phase of relaxation. The study revealed distinctive mechanical behaviors specific to different tissues, and even between different muscles and ATs. These variations in mechanical properties are likely such to optimize the specific functions performed by each biological tissue.

Heart-on-a-Chip Model of Epicardial-Myocardial Interaction in Ischemia Reperfusion Injury.

Epicardial cells (EPIs) form the outer layer of the heart and play an important role in development and disease. Current heart-on-a-chip platforms still do not fully mimic the native cardiac environment due to the absence of relevant cell types, such as EPIs. Here, using the Biowire II platform, engineered cardiac tissues with an epicardial outer layer and inner myocardial structure are constructed, and an image analysis approach is developed to track the EPI cell migration in a beating myocardial environment. Functional properties of EPI cardiac tissues improve over two weeks in culture. In conditions mimicking ischemia reperfusion injury (IRI), the EPI cardiac tissues experience less cell death and a lower impact on functional properties. EPI cell coverage is significantly reduced and more diffuse under normoxic conditions compared to the post-IRI conditions. Upon IRI, migration of EPI cells into the cardiac tissue interior is observed, with contributions to alpha smooth muscle actin positive cell population. Altogether, a novel heart-on-a-chip model is designed toincorporate EPIs through a formation process that mimics cardiac development, and this work demonstrates that EPI cardiac tissues respond to injury differently than epicardium-free controls, highlighting the importance of including EPIs in heart-on-a-chip constructs that aim to accurately mimic the cardiac environment.

Angular Integral Autocorrelation for Speed Estimation in Shear-Wave Elastography

The utilization of a reverberant shear-wave field in shear-wave elastography has emerged as a promising technique for achieving robust shear-wave speed (SWS) estimation. However, many types of estimators cannot accurately measure SWS within such a complicated 3D wave field. This study introduces an advanced autocorrelation estimator based on angular integration known as the angular integral autocorrelation (AIA) approach to address this issue. The AIA approach incorporates all the autocorrelation data from various angles during measurements, resulting in enhanced robustness to both noise and imperfect distributions in SWS estimation. The effectiveness of the AIA estimator for SWS estimation is first validated using a k-Wave simulation of a stiff branching tube in a uniform background. Furthermore, the AIA estimator is applied to ultrasound elastography experiments, magnetic resonance imaging (MRI) experiments, and optical coherence tomography (OCT) studies across a range of different excitation frequencies on tissues and phantoms, including in vivo scans. The results verify the capacity of the AIA approach to enhance the accuracy of SWS estimation and the signal-to-noise ratio (SNR), even within an imperfect reverberant shear-wave field. Compared to simple autocorrelation approaches, the AIA approach can also successfully visualize and define lesions while significantly improving the estimated SWS and SNR in homogeneous background materials and providing improved elastic contrast between structures within the scans. These findings demonstrate the robustness and effectiveness of the AIA approach across a wide range of applications, including ultrasound elastography, magnetic resonance elastography (MRE), and optical coherence elastography (OCE), for accurately identifying the elastic properties of biological tissues in diverse excitation scenarios.

Patient-specific temperature distribution prediction in laser interstitial thermal therapy: single-irradiation data-driven method

Laser interstitial thermal therapy (LITT) is popular for treating brain tumours and epilepsy. The strict control of tissue thermal damage extent is crucial for LITT. Temperature prediction is useful for predicting thermal damage extent. Accurately predicting in vivo brain tissue temperature is challenging due to the temperature dependence and the individual variations in tissue properties. Considering these factors is essential for improving the temperature prediction accuracy. Objective. To present a method for predicting patient-specific tissue temperature distribution within a target lesion area in the brain during LITT. Approach. A magnetic resonance temperature imaging (MRTI) data-driven estimation model was constructed and combined with a modified Pennes bioheat transfer equation (PBHE) to predict patient-specific temperature distribution. In the PBHE for temperature prediction, the individual specificity and temperature dependence of thermal tissue properties and blood perfusion, as well as the individual specificity of optical tissue properties were considered. Only MRTI data during one laser irradiation were required in the method. This enables the prediction of patient-specific temperature distribution and the resulting thermal damage region for subsequent ablations. Main results. Patient-specific temperature prediction was evaluated based on clinical data acquired during LITT in the brain, using intraoperative MRTI data as the reference standard. Our method significantly improved the prediction performance of temperature distribution and thermal damage region. The average root mean square error was decreased by 69.54%, the average intraclass correlation coefficient was increased by 37.5%, the average Dice similarity coefficient was increased by 43.14% for thermal damage region prediction. Significance. The proposed method can predict temperature distribution and thermal damage region at an individual patient level during LITT, providing a promising approach to assist in patient-specific treatment planning for LITT in the brain.

Nano-ZnO modified hydroxyapatite whiskers with enhanced osteoinductivity for bone defect repair

Abstract Hydroxyapatite (HA) whisker (HAw) represents a distinct form of HA characterized by its high aspect ratio, offering significant potential for enhancing the mechanical properties of bone tissue engineering scaffolds. However, the limited osteoinductivity of HAw hampers its widespread application. In this investigation, we observed HAw punctured osteoblast membranes and infiltrated the cell body, resulting in mechanical damage to cells that adversely impacted osteoblast proliferation and differentiation. To address this challenge, we developed nano-zinc oxide particle-modified HAw (nano-ZnO/HAw). Acting as a reinforcing and toughening agent, nano-ZnO/HAw augmented the compressive strength and ductility of the matrix materials. At the same time, the surface modification with nano-ZnO particles improved osteoblast differentiation by reducing the mechanical damage from HAw to cells and releasing zinc ion, the two aspects collectively promoted the osteoinductivity of HAw. Encouragingly, the osteoinductive potential of 5%nano-ZnO/HAw and 10%nano-ZnO/HAw was validated in relevant rat models, demonstrating the efficacy of this approach in promoting new bone formation in vivo. Our findings underscore the role of nano-ZnO particle surface modification in enhancing the osteoinductivity of HAw from a physical standpoint, offering valuable insights into the development of bone substitutes with favorable osteoinductive properties while simultaneously bolstering matrix material strength and toughness.

An emerging role for tissue plasticity in developmental precision.

Reproducible tissue morphology is a fundamental feature of embryonic development. To ensure such robustness during tissue morphogenesis, inherent noise in biological processes must be buffered. While redundant genes, parallel signaling pathways and intricate network topologies are known to reduce noise, over the last few years, mechanical properties of tissues have been shown to play a vital role. Here, taking the example of somite shape changes, I will discuss how tissues are highly plastic in their ability to change shapes leading to increased precision and reproducibility.

Region specific anisotropy and rate dependence of Göttingen minipig brain tissue.

Traumatic brain injury is a major cause of morbidity in civilian as well as military populations. Computational simulations of injurious events are an important tool to understanding the biomechanics of brain injury and evaluating injury criteria and safety measures. However, these computational models are highly dependent on the material parameters used to represent the brain tissue. Reported material properties of tissue from the cerebrum and cerebellum remain poorly defined at high rates and with respect to anisotropy. In this work, brain tissue from the cerebrum and cerebellum of male Göttingen minipigs was tested in one of three directions relative to axon fibers in oscillatory simple shear over a large range of strain rates from 0.025 to 250s-1. Brain tissue showed significant direction dependence in both regions, each with a single preferred loading direction. The tissue also showed strong rate dependence over the full range of rates considered. Transversely isotropic hyper-viscoelastic constitutive models were fit to experimental data using dynamic inverse finite element models to account for wave propagation observed at high strain rates. The fit constitutive models predicted the response in all directions well at rates below 100s-1, after which they adequately predicted the initial two loading cycles, with the exception of the 250s-1 rate, where models performed poorly. These constitutive models can be readily implemented in finite element packages and are suitable for simulation of both conventional and blast injury in porcine, especially Göttingen minipig, models.

Longitudinal in vivo cationic contrast-enhanced computed tomography classifies equine articular cartilage injury and repair.

Cationic contrast-enhanced computed tomography (CECT) capitalizes on increased contrast agent affinity to the charged proteoglycans in articular cartilage matrix to provide quantitative assessment of proteoglycan content with enhanced images. While high resolution microCT has demonstrated success, we investigate cationic CECT use in longitudinal in vivo imaging at clinical resolution. We hypothesize that repeated administration of CA4+ will have no adverse side effects or complications, and that sequential in vivo imaging assessments will distinguish articular cartilage repair tissue from early degenerative and healthy cartilage in critically sized chondral defects. In an established equine translational preclinical model, lameness and synovial effusion scores are similar to controls after repeated injections of CA4+ (eight injections over 16 weeks) compared to controls. Synovial fluid total protein, leukocyte concentration, and sGAG and PGE2 concentrations and articular cartilage and synovial membrane scores are also equivalent to controls. Longitudinal in vivo cationic CECT attenuation in repair tissue is significantly lower than peripheral to (adjacent) and distantly from defects (remote sites) by 4 weeks (p < 0.001), and this difference persists until 16 weeks. At the 6- and 8-week time points, the adjacent locations exhibit significantly lower cationic CECT attenuation compared with the remote sites, reflecting peri-defect degeneration (p < 0.01). Cationic CECT attenuation at clinical resolution significantly correlates with cationic CECT (microCT) (r = 0.69, p < 0.0001), sGAG (r = 0.48, p < 0.0001), and ICRS II histology score (r = 0.63, p < 0.0001). In vivo cationic CECT imaging at clinical resolution distinguishes fibrous repair tissue from degenerative and healthy hyaline cartilage and correlates with molecular tissue properties of articular cartilage.

A hybrid boundary element-finite element approach for solving the EEG forward problem in brain modeling.

This article introduces a hybrid BE-FE method for solving the EEG forward problem, leveraging the strengths of both the Boundary Element Method (BEM) and Finite Element Method (FEM). FEM accurately models complex and anisotropic tissue properties for realistic head geometries, while BEM excels in handling isotropic tissue regions and dipolar sources efficiently. The proposed hybrid method divides regions into homogeneous boundary element (BE) regions that include sources and heterogeneous anisotropic finite element (FE) regions. So, BEM models the brain, including dipole sources, and FEM models other head layers. Validation includes inhomogeneous isotropic/anisotropic three- and four-layer spherical head models, and a four-layer MRI-based realistic head model. Results for six dipole eccentricities and two orientations are computed using BEM, FEM, and hybrid BE-FE method. Statistical analysis, comparing error criteria of RDM and MAG, reveals notable improvements using the hybrid FE-BE method. In the spherical head model, the hybrid BE-FE method compared with FEM demonstrates enhancements of at least 1.05 and 38.31% in RDM and MAG criteria, respectively. Notably, in the anisotropic four-layer head model, improvements reach a maximum of 88.3% for RDM and 93.27% for MAG over FEM. Moreover, in the anisotropic four-layer realistic head model, the proposed hybrid method exhibits 55.4% improvement in RDM and 89.3% improvement in MAG compared to FEM. These findings underscore the proposed method is a promising approach for solving the realistic EEG forward problems, advancing neuroimaging techniques and enhancing understanding of brain function.

Abstract PO3-07-08: Breast density estimation with a microwave-frequency imaging system

Abstract Background: Microwave imaging has been proposed as an alternative method of breast imaging that is low-cost and comfortable for women as it avoids excessive compression. Microwave properties of tissues relate to water content and behavior (Gabriel et al, 1996); specifically, fatty tissues have lower properties and glandular tissues have greater properties (Lazebnik et al, 2007). These differences in microwave signatures of fatty and glandular tissues provide the opportunity to map the composition of the breast and create a density score without a mammogram. This density score may find utility in risk stratification or monitoring interventions aimed at decreasing breast density (Salazar et a, 2020). Purpose: We examine the feasibility of developing a density score based on microwave images that correlates to mammographic breast density (VOLPARA score) in a pilot study with healthy volunteers. Imaging System: We have developed a microwave imaging system that facilitates scanning of large groups of patients, as well as comparison to x-ray mammography (Mojabi et al, 2023). The system consists of two plates which are placed in contact with the breast. Microwave transmitters and receivers are embedded in the plates; signals transmitted through the breast are used to estimate microwave frequency properties of tissues, and maps of these estimates form a 2D image. Methods: 50 patients provided informed consent (study approved by Health Ethics Research Board of Alberta CC-21-0082). Both breasts of each volunteer were scanned. Previously performed mammograms were available for 21 of the volunteers. The number of volunteers with VOLPARA scores A, B, C, and D is 2, 8, 6, and 5, respectively. The percent density reported with the VOLPARA score is also available for these volunteers. Microwave images were formed for each scan and analyzed to predict density with three approaches: (1) average permittivity, (2) segmented regions, and (3) pixel-based intensities. The results demonstrate that the average permittivity of the breast typically increases from VOLPARA A to D, with some overlap in average values observed between the density categories. A correlation between average permittivity and percent breast density was observed. Regions representing glandular tissues are segmented from microwave images; the average values of these regions clearly differentiate between VOLPARA A and D, however do not show consistent ranges for VOLPARA scores B and C. Finally, the microwave breast density estimated using pixel-based intensities shows good correlation with the percentage density calculations from mammograms. Conclusions: Microwave images contain features related to the glandular tissues embedded in fat. By analyzing the composition of the breast in these images, density scores are created. While average permittivity appears to correlate to percent density calculated from mammograms, area or pixel-based approaches appear to have greater potential for categorizing into density classes. Expanding the number of participants, identifying biomarkers, and exploring deep learning techniques for density prediction are considered for future work Citation Format: Elise Fear, Jeremie Bourqui, Pedram Mojabi, Bobbie-Jo Docktor, Anita Garland, Danielle Deutscher, Zahra Lasemiimeni, Kathleen McMahon, Brendon Besler, Roger Tsang. Breast density estimation with a microwave-frequency imaging system [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-08.