Properties Of Biological Tissues Research Articles

Development of a sterilization process for amniotic membrane allograft tissue using supercritical carbon dioxide and NovaKill

Amniotic membrane is arguably one of the most popular biological wound dressings on the market today. Various growth factors and cytokines inherent to amniotic membrane tissue have been recognized as key mediators in wound healing and tissue regeneration, giving the tissue its clinical utility. Sterilization methodologies using irradiation are recognized as the gold standard in the field and routinely used to prepare tissue allografts, including amniotic membrane for transplantation. However, irradiation is not always compatible in preserving the physical structure or biochemical factors of biological materials and can potentially result in detrimental effects to the critical quality attributes of allograft tissues. Alternatively, a novel sterilization technique involving supercritical carbon dioxide (SCCO2) has been shown to have minimal effect on the inherent biophysical properties of sensitive biological tissues and tissue-derived products. At BioBridge Global, we have developed a process utilizing SCCO2 technology for the sterilization of an amniotic membrane tissue allograft product. This process, first and foremost, meets industry standards for sterilization while simultaneously maintaining the biochemical composition of the tissue. Our results show that upon SCCO2 sterilization, most of the growth factors tested were conserved, with many at quantities significantly greater than commercially available gamma and electron beam irradiated tissue. The SCCO2-sterilized amniotic membrane allograft is unique in that it is designed to overcome limitations associated with traditional tissue sterilization methodologies, namely, the conservation of key biological factors inherent to native amniotic membrane tissue. It is anticipated that by retaining these biological factors, clinical outcomes associated with the use of SCCO2-sterilized amniotic membrane will be improved.

Photoacoustic Imaging of pH-Sensitive Optical Sensors in Biological Tissues

Photoacoustic imaging is an emerging biomedical imaging technique that enables non-invasive visualization of the optical absorption properties of biological tissues in vivo. Although numerous studies have used contrast agents to achieve high-contrast imaging in deep tissues, targeting specific areas remains a challenge when using agents that are continuously activated. Recent research has focused on developing triggered contrast agents that are selectively activated in target areas. This review delves into the use of pH-triggered contrast agents in photoacoustic imaging, which take advantage of the lower pH of the tumor microenvironment compared to normal tissues. The paper discusses the mechanisms of pH-triggered contrast agents that contribute to improving depth and contrast in photoacoustic tumor imaging. In addition, the integration of functionalities, such as photothermal therapy and drug delivery monitoring, into these agents demonstrates significant potential for biomedical applications.

Real-time monitoring of bioelectrical impedance for minimizing tissue carbonization in microwave ablation of porcine liver

The charring tissue generated by the high temperature during microwave ablation can affect the therapeutic effect, such as limiting the volume of the coagulation zone and causing rejection. This paper aimed to prevent tissue carbonization while delivering an appropriate thermal dose for effective ablations by employing a treatment protocol with real-time bioelectrical impedance monitoring. Firstly, the current field response under different microwave ablation statuses is analyzed based on finite element simulation. Next, the change of impedance measured by the electrodes is correlated with the physical state of the ablated tissue, and a microwave ablation carbonization control protocol based on real-time electrical impedance monitoring was established. The finite element simulation results show that the dielectric properties of biological tissues changed dynamically during the ablation process. Finally, the relative change rule of the electrical impedance magnitude of the ex vivo porcine liver throughout the entire MWA process and the reduction of the central zone carbonization were obtained by the MWA experiment. Charring tissue was eliminated without water cooling at 40 W and significantly reduced at 50 W and 60 W. The carbonization during MWA can be reduced according to the changes in tissue electrical impedance to optimize microwave thermal ablation efficacy.

Unveiling the biological side of PET-derived biomarkers: a simulation-based approach applied to PDAC assessment.

Radiomics has revolutionized clinical research by enabling objective measurements of imaging-derived biomarkers. However, the true potential of radiomics necessitates a comprehensive understanding of the biological basis of extracted features to serve as a clinical decision support. In this work, we propose an end-to-end framework for the in silico simulation of [18F]FLT PET imaging process in Pancreatic Ductal Adenocarcinoma, accounting for the biological characterization of tissues (including perfusion and fibrosis) on tracer delivery. We thus establish a direct association between radiomics features and the underlying biological properties of tissues. We considered 4 immunohistochemically stained Whole Slide Images of pancreatic tissue of one healthy control and three patients with PDAC and/or precursor lesions. From marker-specific images, tissue-depending diffusivity properties were estimated and computational domains were built to simulate the [18F]FLT spatial-temporal uptake exploiting Partial Differential Equations and Finite Elements Method. Consequently, we simulated the imaging process obtaining surrogated PET images for the considered patients, and we performed image-derived features extraction from PET images to be mapped with biological properties via correlation estimation. The framework captured the phenotypic differences and generated Time Activity Curves reflecting the underlying tissue composition. Image-derived biomarkers were ranked in view of their association with biological characteristics of the tissue, unveiling their molecular correlative. Moreover, we showed that the proposed pipeline could serve as a digital phantom to optimize the image acquisition for lesion detection. This innovative framework holds the potential to enhance interpretability and reliability of radiomics, fostering the adoption in personalized nuclear medicine and patient care.

In-situ, real-time monitoring of thermo-mechanical properties of biological tissues undergoing laser heating and ablation.

In this work, Brillouin light-scattering spectroscopy and optical backscattering reflectometry (OBR) using Mg-silica-NP-doped distributed sensing fibers were employed for monitoring local GHz visco-elastic properties and surface temperature, respectively, during laser driven heating and ablation of chicken tissues. The spatial temperature distribution measured by OBR at various infrared laser heating powers and times was used to validate spatio-temporal local temperature variations modeled by the finite element method via solving Pennes' bioheat conduction equation. The reduction of viscosity and stiffness in chicken skin during its laser heating was attributed to water loss, protein denaturation and change in lipid phase behavior. These findings open avenues for the simultaneous real-time hybrid optical sensing of both viscoelasticity and local temperature in biological tissues undergoing denaturation and gelation during thermal ablation in clinical settings.

Recent advances in brain organoids: a comprehensive review of the last eight years

Organoids are three-dimensional cellular structures grown in vitro that can self-organize and differentiate into cell types with organ-specific functions, closely mimicking the biological properties of tissues and organs in vivo. Brain organoids, which differentiate into structures resembling brain function, serve as valuable models for medical research, including disease microenvironment simulation, brain mechanism exploration, and drug evaluation. In this review, we analyzed 808 articles retrieved from PubMed, CNKI, and Wanfang databases using the keyword "brain organoids," of which 180 were included. We summarized the research progress of brain organoids over the past eight years by categorizing and refining the findings. Our analysis shows that brain organoids have achieved significant success in simulating brain development in vitro, leading to the establishment and refinement of 3D brain organoid models for disease research. Brain organoids have been widely applied to explore disease-related mechanisms, yielding promising results and opening avenues for further research on the human brain. In this review, we summarize the progress of brain organoids in three areas: culture methods, disease-related research, and brain exploration.

Mueller matrix analysis of a biologically sourced engineered tissue construct as polarimetric phantom.

The polarimetric properties of biological tissues are often difficult to ascertain independent of their complex structural and organizational features. Conventional polarimetric tissue phantoms have well-characterized optical properties but are overly simplified. We demonstrate that an innovative, biologically sourced, engineered tissue construct better recapitulates the desired structural and polarimetric properties of native collagenous tissues, with the added benefit of potential tunability of the polarimetric response. We bridge the gap between non-biological polarimetric phantoms and native tissues. We aim to evaluate a synthesized tissue construct for its effectiveness as a phantom that mimics the polarimetric properties in typical collagenous tissues. We use a fibroblast-derived, ring-shaped engineered tissue construct as an innovative tissue phantom for polarimetric imaging. We perform polarimetry measurements and subsequent analysis using the Mueller matrix decomposition and Mueller matrix transformation methods. Scalar polarimetric parameters of the engineered tissue are analyzed at different time points for both a control group and for those treated with the transforming growth factor . Second-harmonic generation (SHG) imaging and three-dimensional collagen fiber organization analysis are also applied. We identify linear retardance and circular depolarization as the parameters that are most sensitive to the tissue culture time and the addition of . Aside from a statistically significant increase over time, the behavior of linear retardance and circular depolarization indicates that the addition of accelerates the growth of the engineered tissue, which is consistent with expectations. We also find through SHG images that collagen fiber organization becomes more aligned over time but is not susceptible to the addition of . The engineered tissue construct exhibits changes in polarimetric properties, especially linear retardance and circular depolarization, over culture time and under treatments. This tissue construct has the potential to act as a controlled modular optical phantom for polarimetric-based methods.

Orthogonal dispersion modeling of double-stage VIPA spectrometer in SBS system.

Brillouin microscopy enables the assessment of the mechanical properties of biological tissues by mapping the Brillouin shift in three-dimensional (3D), all-optical, label-free, non-contact, and subcellular resolution. The virtually imaged phased array (VIPA) etalon is widely utilized for measuring Brillouin spectra owing to its superior light throughput, large angular dispersion, and rapid signal acquisition capabilities. The VIPA-based spectrometer plays a significant role in Brillouin microscopy, but it is highly sensitive to factors such as the tilt angle, beam radius, lens focal length, and so on. Here, we propose an orthogonal dispersion model based on paraxial wave theory. This model is employed to explore the output intensity distribution and dispersion characteristics of a double-stage VIPA spectrometer. To validate the proposed model, we develop a stimulated Brillouin scattering (SBS) system by leveraging the optimal spectrometer parameters to measure the Brillouin frequency shift in pure water. We demonstrate the capabilities of this model in mitigating spectral dispersion and enhancing spectral measurement accuracy by optimizing the spectrometer's system parameters. This work is pivotal for the future deployment of the double-stage VIPA spectrometer in SBS-based microscopy applications.

Geophysics-Inspired Nonlinear Stress-Strain Law for Biological Tissues and Its Applications in Compression Optical Coherence Elastography.

We propose a nonlinear stress-strain law to describe nonlinear elastic properties of biological tissues using an analogy with the derivation of nonlinear constitutive laws for cracked rocks. The derivation of such a constitutive equation has been stimulated by the recently developed experimental technique-quasistatic Compression Optical Coherence Elastography (C-OCE). C-OCE enables obtaining nonlinear stress-strain dependences relating the applied uniaxial compressive stress and the axial component of the resultant strain in the tissue. To adequately describe nonlinear stress-strain dependences obtained with C-OCE for various tissues, the central idea is that, by analogy with geophysics, nonlinear elastic response of tissues is mostly determined by the histologically confirmed presence of interstitial gaps/pores resembling cracks in rocks. For the latter, the nonlinear elastic response is mostly determined by elastic properties of narrow cracks that are highly compliant and can easily be closed by applied compressing stress. The smaller the aspect ratio of such a gap/crack, the smaller the stress required to close it. Upon reaching sufficiently high compressive stress, almost all such gaps become closed, so that with further increase in the compressive stress, the elastic response of the tissue becomes nearly linear and is determined by the Young's modulus of the host tissue. The form of such a nonlinear dependence is determined by the distribution of the cracks/gaps over closing pressures; for describing this process, an analogy with geophysics is also used. After presenting the derivation of the proposed nonlinear law, we demonstrate that it enables surprisingly good fitting of experimental stress-strain curves obtained with C-OCE for a broad range of various tissues. Unlike empirical fitting, each of the fitting parameters in the proposed law has a clear physical meaning. The linear and nonlinear elastic parameters extracted using this law have already demonstrated high diagnostic value, e.g., for differentiating various types of cancerous and noncancerous tissues.

Magnesium oxide nanoparticle reinforced pumpkin-derived nanostructured cellulose scaffold for enhanced bone regeneration

Considering global surge in bone fracture prevalence, limitation in use of traditional healing approaches like bone grafts highlights the need for innovative regenerative strategies. Here, a novel green fabrication approach has reported for reinforcement of physicochemical performances of sustainable bioinspired extracellular matrix (ECM) based on decellularized pumpkin tissue coated with Magnesium oxide nanoparticles (hereafter called DM-Pumpkin) for enhanced bone regeneration. Compared to uncoated scaffold, DM-Pumpkin exhibited significantly improved surface roughness, mechanical stiffness, porosity, hydrophilicity, swelling, and biodegradation rate. Obtained nanoporous structure provides an ideal three-dimensional microenvironment for the attachment, migration and osteo-induction in human adipose-derived mesenchymal stem cells (h- AdMSCs). Calcium deposition and mineralization, alkaline phosphatase activity, and SEM imaging of the cells as well as increased expression of bone-related genes after 21 days incubation confirmed capability of DM-Pumpkin in mimicking the biological properties of bone tissue. The presence of MgONPs had a silencing effect on inflammatory factors and improved wound closure, verified by in vivo studies. Increased expression of collagen type I and osteocalcin in the h- AdMSCs cultured on DM-Pumpkin compared to control further corroborated gained results. Altogether, boosting physicochemical and biological properties of DM-Pumpkin due to surface modification is a promising approach for guided bone regeneration.

Microwave Frequency Analysis of Dielectric Properties in Biological Tissues: Insights into Dielectric Constant, Conductivity, and Resistivity at 9.4 GHz

This study investigates the dielectric properties of various biological tissues at microwave frequencies, specifically focusing on their dielectric constant, conductivity, resistivity, and loss tangent. Using methods such as Von Hippel’s and Yadav Gandhi’s techniques, the dielectric characteristics of heart, liver, brain, and muscle of chicken tissues were analysed at 9.4 GHz. It is found that tissues with higher water content exhibit greater dielectric constants and conductivities, while those with higher solid content, like liver, display higher resistivity. It has also been observed that tissues with lower dielectric constants and conductivities are those with greater absorption indices, which are typically associated with increased energy dissipation as heat. An increasing amount of energy dissipation may result from tissues with higher absorption rates being less effective at transferring microwave energy, as suggested by this inverse connection, which also emphasizes the complexity of electromagnetic wave interaction within tissues. This work provides critical insights into the interaction of electromagnetic waves with biological tissues, which is essential for applications in medical diagnostics, therapeutic technologies, and electromagnetic exposure safety standards.

Development and approval of a mobile test bench for the study of the mechanical properties of biological tissues

The determination of the mechanical properties of vessel walls and atherosclerotic plaques is a recurring issue in the numerical modelling of vessel sections. Testing machines are usually located in specialised laboratories outside medical facilities, and transporting samples from the clinic is difficult. Therefore, the task of developing mobile testing devices that can be installed directly in the clinic to perform tests immediately after surgery is relevant. This paper presents the results of the development and validation of a mobile test bench for performing mechanical experiments on uniaxial compression and tension of biological tissues. The purpose of the work is to perform mechanical tests on tissues in the clinic, avoiding their transport to the laboratory and/or freezing. Arduino UNO hardware platform is used as the control element of the test bench, which controls the main elements (motor, load cell), records data on SD card and displays information about the experiment on the computer screen. Structurally, the test bench is an analogue of a two-column testing machine. All mechanisms are assembled in a portable case, the control module is in the form of a remote plastic box. Verification of the test bench was carried out using an Instron 3342 universal testing machine with a 500 N load cell. A total of 121 soft tissue tests (including arterial wall and atherosclerotic plaque) were performed on the bench. The Young's modulus and ultimate strength values obtained are summarised in a table. The mean Young's modulus for the wall of the internal carotid artery was 0.32 ± 0.24 MPa, for soft plaque 0.30 ± 0.17 MPa, and for hard plaque 0.85 ± 0.39 MPa; a tensile strength of 1.12 ± 0.67 MPa was also determined for hard plaques in carotid arteries. The result is a unique database of tissue Young's moduli obtained from freshly collected material. The methodology and its implementation on the mobile test bench show good agreement with literature data.

A wideband model to evaluate the dielectric properties of biological tissues from magnetic resonance acquisitions

Objective. Aim of this work is to illustrate and experimentally validate a model to evaluate the dielectric properties of biological tissues on a wide frequency band using the magnetic resonance imaging (MRI) technique.Approach. The dielectric behaviour of biological tissues depends on frequency, according to the so-called relaxation mechanisms. The adopted model derives the dielectric properties of biological tissues in the frequency range 10 MHz-20 GHz considering the presence of two relaxation mechanisms whose parameters are determined from quantities derived from MRI acquisitions. In particular, the MRI derived quantities are the water content and the dielectric properties of the tissue under study at the frequency of the MR scanner.Main results.The model was first theoretically validated on muscle and fat using literature data in the frequency range 10 MHz-20 GHz. Results showed capabilities of reconstructing dielectric properties with errors within 16%. Then the model was applied to ex vivo muscle and liver tissues, comparing the MRI-derived properties with data measured by the open probe technique in the frequency range 10 MHz-3 GHz, showing promising results.Significance. The use of medical techniques based on the application of electromagnetic fields (EMFs) is significantly increasing. To provide safe and effective treatments, it is necessary to know how human tissues react to the applied EMF. Since this information is embedded in the dielectric properties of biological tissues, an accurate and precise dielectric characterization is needed. Biological tissues are heterogenous, and their characteristics depend on several factors. Consequently, it is necessary to characterize dielectric propertiesin vivofor each specific patient. While this aim cannot be reached with traditional measurement techniques, through the adopted model these properties can be reconstructedin vivoon a wide frequency band from non-invasive MRI acquisitions.

Application of real-time shear wave elastography to Achilles tendon hardness evaluation in older adults.

Real-time shear wave elastography (SWE) is a non-invasive imaging technique used to measure tissue stiffness by generating and tracking shear waves in real time. This advanced ultrasound-based method provides quantitative information regarding tissue elasticity, offering valuable insights into the mechanical properties of biological tissues. However, the application of real-time SWE in the musculoskeletal system and sports medicine has not been extensively studied. To explore the practical value of real-time SWE for assessing Achilles tendon hardness in older adults. A total of 60 participants were enrolled in the present study, and differences in the elastic moduli of the bilateral Achilles tendons were compared among the following categories: (1) Age: 55-60, 60-65, and 65-70-years-old; (2) Sex: Male and female; (3) Laterality: Left and right sides; (4) Tendon state: Relaxed and tense state; and (5) Tendon segment: Proximal, middle, and distal. There were no significant differences in the elastic moduli of the bilateral Achilles tendons when comparing by age or sex (P > 0.05). There were, however, significant differences when comparing by tendon side, state, or segment (P < 0.05). Real-time SWE plays a significant role compared to other examination methods in the evaluation of Achilles tendon hardness in older adults.

Design and FEA verification of high dimensional and multi-field smart soft material

Abstract We designed a sophisticated smart soft tissue material that simulates the properties of biological soft tissue and detects three-dimensional force. Inspired by electronic skin, this material uses capacitive pressure sensors and multi-layer sensor packaging. Finite element analysis and multi-field simulation were employed for structural design and verification. The study evaluated the theory and practicality of a flexible capacitive 3D force sensor for detecting pressure and shear force. Mechanical and electrical properties were confirmed through force and electric field simulations. The 3D force detection method proved feasible, achieving sensitivity levels of 10−4 kPa −1 in the X and Y directions, and 10−3 kPa −1 in the Z direction, demonstrating the effectiveness of our design.

Relationship between depth-wise refractive index and biomechanical properties of human articular cartilage.

Optical properties of biological tissues, such as refractive index (RI), are fundamental properties, intrinsically linked to the tissue's composition and structure. We hypothesize that, as the RI and the functional properties of articular cartilage (AC) are dependent on the tissue's structure and composition, the RI of AC is related to its biomechanical properties. This study aims to investigate the relationship between RI of human AC and its biomechanical properties. Human cartilage samples ( ) were extracted from the right knee joint of three cadaver donors (one female, aged 47 years, and two males, aged 64 and 68 years) obtained from a commercial biobank (Science Care, Phoenix, Arizona, United States). The samples were initially subjected to mechanical indentation testing to determine elastic [equilibrium modulus (EM) and instantaneous modulus (IM)] and dynamic [dynamic modulus (DM)] viscoelastic properties. An Abbemat 3200 automatic one-wavelength refractometer operating at 600 nm was used to measure the RI of the extracted sections. Similarly, Spearman's and Pearson's correlation coefficients were employed for non-normal and normal datasets, respectively, to determine the correlation between the depth-wise RI and biomechanical properties of the cartilage samples as a function of the collagen fibril orientation. A positive correlation with statistically significant relations ( ) was observed between the RI and the biomechanical properties (EM, IM, and DM) along the tissue depth for each zone, e.g., superficial, middle, and deep zones. Likewise, a lower positive correlation with statistically significant relations ( ) was also observed for collagen fibril orientation of all zones with the biomechanical properties. The results indicate that, although the RI exhibits different levels of correlation with different biomechanical properties, the relationship varies as a function of the tissue depth. This knowledge paves the way for optically monitoring changes in AC biomechanical properties nondestructively via changes in the RI. Thus, the RI could be a potential biomarker for assessing the mechanical competency of AC, particularly in degenerative diseases, such as osteoarthritis.

How well do 3D-printed tissue mimics represent the complex mechanics of biological soft tissues? An example study with Stratasys' cardiovascular TissueMatrix materials.

Tissue mimicking materials are designed to represent real tissue in applications such as medical device testing and surgical training. Thanks to progress in 3D-printing technology, tissue mimics can now be easily cast into arbitrary geometries and manufactured with adjustable material properties to mimic a wide variety of tissue types. However, it is unclear how well 3D-printable mimics represent real tissues and their mechanics. The objective of this work is to fill this knowledge gap using the Stratasys Digital Anatomy 3D-Printer as an example. To this end, we created mimics of biological tissues we previously tested in our laboratory: blood clots, myocardium, and tricuspid valve leaflets. We printed each tissue mimic to have the identical geometry to its biological counterpart and tested the samples using identical protocols. In our evaluation, we focused on the stiffness of the tissues and their fracture toughness in the case of blood clots. We found that the mechanical behavior of the tissue mimics often differed substantially from the biological tissues they aim to represent. Qualitatively, tissue mimics failed to replicate the traditional strain-stiffening behavior of soft tissues. Quantitatively, tissue mimics were stiffer than their biological counterparts, especially at small strains, in some cases by orders of magnitude. In those materials in which we tested toughness, we found that tissue mimicking materials were also much tougher than their biological counterparts. Thus, our work highlights limitations of at least one 3D-printing technology in its ability to mimic the mechanical properties of biological tissues. Therefore, care should be taken when using this technology, especially where tissue mimicking materials are expected to represent soft tissue properties quantitatively. Whether other technologies fare better remains to be seen.

Comparison of test-retest reproducibility of DESPOT and 3D-QALAS for water T 1 and T 2 mapping.

Relaxometry, specifically T 1 and T 2 mapping, has become an essential technique for assessing the properties of biological tissues related to various physiological and pathological conditions. Many techniques are being used to estimate T 1 and T 2 relaxation times, ranging from the traditional inversion or saturation recovery and spin-echo sequences to more advanced methods. Choosing the appropriate method for a specific application is critical since the precision and accuracy of T 1 and T 2 measurements are influenced by a variety of factors including the pulse sequence and its parameters, the inherent properties of the tissue being examined, the MRI hardware, and the image reconstruction. The aim of this study is to evaluate and compare the test-retest reproducibility of two advanced MRI relaxometry techniques (Driven Equilibrium Single Pulse Observation of T 1 and T 2, DESPOT, and 3D Quantification using an interleaved Look-Locker acquisition Sequence with a T 2 preparation pulse, QALAS), for T 1 and T 2 mapping in a healthy volunteer cohort. 10 healthy volunteers underwent brain MRI at 1.3 mm3 isotropic resolution, acquiring DESPOT and QALAS data (~11.8 and ~5 minutes duration, including field maps, respectively), test-retest with subject repositioning, on a 3.0 Tesla Philips Ingenia Elition scanner. To reconstruct the T 1 and T 2 maps, we used an equation-based algorithm for DESPOT and a dictionary-based algorithm that incorporates inversion efficiency and B 1 -field inhomogeneity for QALAS. The test-retest reproducibility was assessed using the coefficient of variation (CoV), intraclass correlation coefficient (ICC) and Bland-Altman plots. Our results indicate that both the DESPOT and QALAS techniques demonstrate good levels of test-retest reproducibility for T 1 and T 2 mapping across the brain. Higher whole-brain voxel-to-voxel ICCs are observed in QALAS for T 1 (0.84 ± 0.039) and in DESPOT for T 2 (0.897 ± 0.029). The Bland-Altman plots show smaller bias and variability of T 1 estimates for QALAS (mean of -0.02 s, and upper and lower limits of -0.14 and 0.11 s, 95% CI) than for DESPOT (mean of -0.02 s, and limits of -0.31 and 0.27 s). QALAS also showed less variability (mean 1.08 ms, limits -1.88 to 4.04 ms) for T 2 compared to DESPOT (mean of 2.56 ms, and limits -17.29 to 22.41 ms). The within-subject CoVs for QALAS range from 0.6% (T 2 in CSF) to 5.8% (T 2 in GM), while for DESPOT they range from 2.1% (T 2 in CSF) to 6.7% (T 2 in GM). The between-subject CoVs for QALAS range from 2.5% (T 2 in GM) to 12% (T 2 in CSF), and for DESPOT they range from 3.7% (T 2 in WM) to 9.3% (T 2 in CSF). Overall, QALAS demonstrated better reproducibility for T 1 and T 2 measurements than DESPOT, in addition to reduced acquisition time.

Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection

Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. These problems include deducing the unknown properties of biological structures or tissues from the observed imaging data, presenting a unique challenge in decoding intricate biological phenomena. Regarding disease detection, this technique has played a critical role in optimizing diagnostic efficiency by extracting meaningful insights from different imaging modalities like molecular imaging, MRI, and CT scans. Inverse problems contribute to uncovering subtle abnormalities by employing iterative optimization techniques and sophisticated algorithms, enabling precise and early disease detection. Deep learning (DL) solutions have emerged as robust mechanisms for addressing inverse problems in biomedical image analysis, especially in disease recognition. Inverse problems involve reconstructing unknown structures or parameters from observed data, and the DL model excels in learning complex representations and mappings. This study develops a DL Solution for Inverse Problems in the Advanced Biomedical Image Analysis on Disease Detection (DLSIP-ABIADD) technique. The DLSIP-ABIADD technique exploits the DL approach to solve inverse problems and detect the presence of diseases on biomedical images. To solve the inverse problem, the DLSIP-ABIADD technique uses a direct mapping approach. Bilateral filtering (BF) is used for image preprocessing. Besides, the MobileNetv2 model derives feature vectors from the input images. Moreover, the Henry gas solubility optimization (HGSO) method is applied for optimal hyperparameter selection of the MobileNetv2 model. Furthermore, a bidirectional long short-term memory (BiLSTM) model is deployed to identify diseases in medical images. Extensive simulations have been involved to illustrate the better performance of the DLSIP-ABIADD technique. The experimentation outcomes stated that the DLSIP-ABIADD technique performs better than other models.

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics-Informed Neural Networks.

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the trainingdata.