scholarly journals Massively Multidimensional Diffusion-Relaxation Correlation MRI

2022 ◽  
Vol 9 ◽  
Author(s):  
Omar Narvaez ◽  
Leo Svenningsson ◽  
Maxime Yon ◽  
Alejandra Sierra ◽  
Daniel Topgaard
Keyword(s):  
Diffusion Tensor ◽  
Ex Vivo ◽  
Biological Tissues ◽  
Relaxation Rates ◽  
Inversion Algorithm ◽  
Monte Carlo Data ◽  
Distinct Component ◽  
Frequency Dependent ◽  
Diffusion Relaxation ◽  
Multidimensional Diffusion

Diverse approaches such as oscillating gradients, tensor-valued encoding, and diffusion-relaxation correlation have been used to study microstructure and heterogeneity in healthy and pathological biological tissues. Recently, acquisition schemes with free gradient waveforms exploring both the frequency-dependent and tensorial aspects of the encoding spectrum b(ω) have enabled estimation of nonparametric distributions of frequency-dependent diffusion tensors. These “D(ω)-distributions” allow investigation of restricted diffusion for each distinct component resolved in the diffusion tensor trace, anisotropy, and orientation dimensions. Likewise, multidimensional methods combining longitudinal and transverse relaxation rates, R1 and R2, with (ω-independent) D-distributions capitalize on the component resolution offered by the diffusion dimensions to investigate subtle differences in relaxation properties of sub-voxel water populations in the living human brain, for instance nerve fiber bundles with different orientations. By measurements on an ex vivo rat brain, we here demonstrate a “massively multidimensional” diffusion-relaxation correlation protocol joining all the approaches mentioned above. Images acquired as a function of the magnitude, normalized anisotropy, orientation, and frequency content of b(ω), as well as the repetition time and echo time, yield nonparametric D(ω)-R1-R2-distributions via a Monte Carlo data inversion algorithm. The obtained per-voxel distributions are converted to parameter maps commonly associated with conventional lower-dimensional methods as well as unique statistical descriptors reporting on the correlations between restriction, anisotropy, and relaxation.

scholarly journals Combination of histochemical analyses and micro-MRI reveals regional changes of the murine cervix in preparation for labor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Antara Chatterjee ◽  
Rojan Saghian ◽  
Anna Dorogin ◽  
Lindsay S. Cahill ◽  
John G. Sled ◽  
...  
Keyword(s):  
Smooth Muscle ◽  
Smooth Muscle Cells ◽  
Diffusion Tensor ◽  
Ex Vivo ◽  
Muscle Cells ◽  
Pregnant Mouse ◽  
Collagen Fibers ◽  
Muscular Layer ◽  
Tissue Organization ◽  
Picrosirius Red

AbstractThe cervix is responsible for maintaining pregnancy, and its timely remodeling is essential for the proper delivery of a baby. Cervical insufficiency, or “weakness”, may lead to preterm birth, which causes infant morbidities and mortalities worldwide. We used a mouse model of pregnancy and term labor, to examine the cervical structure by histology (Masson Trichome and Picrosirius Red staining), immunohistochemistry (Hyaluronic Acid Binding Protein/HABP), and ex-vivo MRI (T2-weighted and diffusion tensor imaging), focusing on two regions of the cervix (i.e., endocervix and ectocervix). Our results show that mouse endocervix has a higher proportion of smooth muscle cells and collagen fibers per area, with more compact tissue structure, than the ectocervix. With advanced gestation, endocervical changes, indicative of impending delivery, are manifested in fewer smooth muscle cells, expansion of the extracellular space, and lower presence of collagen fibers. MRI detected three distinctive zones in pregnant mouse endocervix: (1) inner collagenous layer, (2) middle circular muscular layer, and (3) outer longitudinal muscular layer. Diffusion MRI images detected changes in tissue organization as gestation progressed suggesting the potential application of this technique to non-invasively monitor cervical changes that precede the onset of labor in women at risk for preterm delivery.

scholarly journals Optical signatures of radiofrequency ablation in biological tissues

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pranav Lanka ◽  
Kalloor Joseph Francis ◽  
Hindrik Kruit ◽  
Andrea Farina ◽  
Rinaldo Cubeddu ◽  
...  
Keyword(s):  
Radiofrequency Ablation ◽  
Treatment Time ◽  
Ex Vivo ◽  
Biological Tissues ◽  
Tissue Response ◽  
Optical Monitoring ◽  
Diffuse Optical Spectroscopy ◽  
Spectral Changes ◽  
Scattering Spectra ◽  
Optical Behavior

AbstractAccurate monitoring of treatment is crucial in minimally-invasive radiofrequency ablation in oncology and cardiovascular disease. We investigated alterations in optical properties of ex-vivo bovine tissues of the liver, heart, muscle, and brain, undergoing the treatment. Time-domain diffuse optical spectroscopy was used, which enabled us to disentangle and quantify absorption and reduced scattering spectra. In addition to the well-known global (1) decrease in absorption, and (2) increase in reduced scattering, we uncovered new features based on sensitive detection of spectral changes. These absorption spectrum features are: (3) emergence of a peak around 840 nm, (4) redshift of the 760 nm deoxyhemoglobin peak, and (5) blueshift of the 970 nm water peak. Treatment temperatures above 100 °C led to (6) increased absorption at shorter wavelengths, and (7) further decrease in reduced scattering. This optical behavior provides new insights into tissue response to thermal treatment and sets the stage for optical monitoring of radiofrequency ablation.

scholarly journals Applications of Vibrational Spectroscopy for Analysis of Connective Tissues

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 922
Author(s):  
William Querido ◽  
Shital Kandel ◽  
Nancy Pleshko
Keyword(s):  
Raman Spectroscopy ◽  
Vibrational Spectroscopy ◽  
Near Infrared ◽  
Ex Vivo ◽  
Biological Tissues ◽  
Label Free ◽  
Connective Tissues ◽  
Bone Properties ◽  
Composition And Structure

Advances in vibrational spectroscopy have propelled new insights into the molecular composition and structure of biological tissues. In this review, we discuss common modalities and techniques of vibrational spectroscopy, and present key examples to illustrate how they have been applied to enrich the assessment of connective tissues. In particular, we focus on applications of Fourier transform infrared (FTIR), near infrared (NIR) and Raman spectroscopy to assess cartilage and bone properties. We present strengths and limitations of each approach and discuss how the combination of spectrometers with microscopes (hyperspectral imaging) and fiber optic probes have greatly advanced their biomedical applications. We show how these modalities may be used to evaluate virtually any type of sample (ex vivo, in situ or in vivo) and how “spectral fingerprints” can be interpreted to quantify outcomes related to tissue composition and quality. We highlight the unparalleled advantage of vibrational spectroscopy as a label-free and often nondestructive approach to assess properties of the extracellular matrix (ECM) associated with normal, developing, aging, pathological and treated tissues. We believe this review will assist readers not only in better understanding applications of FTIR, NIR and Raman spectroscopy, but also in implementing these approaches for their own research projects.

Human Brain Diffusion Tensor Imaging Visualization With Virtual Reality

2010 ◽  
Author(s):  
Bin Chen ◽  
John Moreland
Keyword(s):  
Virtual Reality ◽  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Biological Tissues ◽  
Head Tracking ◽  
Fiber Tractography ◽  
Brain White Matter ◽  
Diffusion Tensors ◽  
Visualization Techniques

Magnetic resonance diffusion tensor imaging (DTI) is sensitive to the anisotropic diffusion of water exerted by its macromolecular environment and has been shown useful in characterizing structures of ordered tissues such as the brain white matter, myocardium, and cartilage. The water diffusivity inside of biological tissues is characterized by the diffusion tensor, a rank-2 symmetrical 3×3 matrix, which consists of six independent variables. The diffusion tensor contains much information of diffusion anisotropy. However, it is difficult to perceive the characteristics of diffusion tensors by looking at the tensor elements even with the aid of traditional three dimensional visualization techniques. There is a need to fully explore the important characteristics of diffusion tensors in a straightforward and quantitative way. In this study, a virtual reality (VR) based MR DTI visualization with high resolution anatomical image segmentation and registration, ROI definition and neuronal white matter fiber tractography visualization and fMRI activation map integration is proposed. The VR application will utilize brain image visualization techniques including surface, volume, streamline and streamtube rendering, and use head tracking and wand for navigation and interaction, the application will allow the user to switch between different modalities and visualization techniques, as well making point and choose queries. The main purpose of the application is for basic research and clinical applications with quantitative and accurate measurements to depict the diffusivity or the degree of anisotropy derived from the diffusion tensor.

A robust ex vivo method to evaluate the diffusion properties of agents in biological tissues

2019 ◽  
Vol 12 (4) ◽  
pp. e201800333 ◽  
Author(s):  
Isa Carneiro ◽  
Sónia Carvalho ◽  
Rui Henrique ◽  
Luís M. Oliveira ◽  
Valery V. Tuchin
Keyword(s):  
Ex Vivo ◽  
Biological Tissues ◽  
Diffusion Properties

scholarly journals Simulation models of current density imaging in studying cardiac states

2021 ◽  
Author(s):  
Mohammadali Beheshti
Keyword(s):  
Cardiac Function ◽  
Electrical Activity ◽  
Current Density ◽  
Diffusion Tensor ◽  
Model Simulation ◽  
Correlation Coefficients ◽  
Biological Tissues ◽  
Density Maps ◽  
Current Density Imaging ◽  
Rms Error

Electro-mechanical disorders in cardiac function result in arrhythmias. Due to the non-stationary nature of these arrhythmias and, owing to lethality associated with certain type of arrhythmias, they are challenging to study. Most of the existing studies are limited in that they extract electrical activity from surface intracardiac electrical activity, either through the use of electrical or optical mapping. One way of studying current pathways inside and through biological tissues is by using Magnetic Resonance Imaging (MRI) based Low Frequency Current Density Imaging (LFCDI). For the first time CDI was used to study ex-vivo beating hearts in different cardiac states. It should be said that; this approach involves heavy logistical and procedural complexity, hence, it would be beneficial to adapt existing electrophysiological computer models to investigate and simulate current density maps specific to studying cardiac function. In achieving this, the proposed work presents an approach to model the current density maps in 3D and study the current distributions in different electrophysiological states of the heart. The structural and fiber orientation of the heart used in this study were extracted using MRI-based Diffusion Tensor Imaging. The monodomain and bidomain Aliev-Panfilov electrophysiological models were used for CDI modeling, and the results indicate that different states were distinguishable using range and correlation of simulated current density maps. The obtained results through modeling were corroborated with actual experimental CDI data from porcine hearts. Individually and comparatively, the experimental and simulation results for various states have the same trend in terms of variations (trend correlation coefficients ≥ 0.98) and state correlations (trend correlation coefficients ≥ 0.89). The results also show that the root mean square (RMS) error in average range ratios between bidomain CDI model results and real CDI data is 0.1972 and the RMS error in state correlations between bidomain CDI model results and real CDI data is 0.2833. These results indicate, as expected, the proposed bidomain model simulation of CDI corroborates well with experimental data and can serve as a valuable tool for studying lethal cardiac arrhythmias under different simulation conditions that are otherwise not possible or difficult in a real-world experimental setup.

scholarly journals Simulation models of current density imaging in studying cardiac states

2021 ◽  
Author(s):  
Mohammadali Beheshti
Keyword(s):  
Cardiac Function ◽  
Electrical Activity ◽  
Current Density ◽  
Diffusion Tensor ◽  
Model Simulation ◽  
Correlation Coefficients ◽  
Biological Tissues ◽  
Density Maps ◽  
Current Density Imaging ◽  
Rms Error

Electro-mechanical disorders in cardiac function result in arrhythmias. Due to the non-stationary nature of these arrhythmias and, owing to lethality associated with certain type of arrhythmias, they are challenging to study. Most of the existing studies are limited in that they extract electrical activity from surface intracardiac electrical activity, either through the use of electrical or optical mapping. One way of studying current pathways inside and through biological tissues is by using Magnetic Resonance Imaging (MRI) based Low Frequency Current Density Imaging (LFCDI). For the first time CDI was used to study ex-vivo beating hearts in different cardiac states. It should be said that; this approach involves heavy logistical and procedural complexity, hence, it would be beneficial to adapt existing electrophysiological computer models to investigate and simulate current density maps specific to studying cardiac function. In achieving this, the proposed work presents an approach to model the current density maps in 3D and study the current distributions in different electrophysiological states of the heart. The structural and fiber orientation of the heart used in this study were extracted using MRI-based Diffusion Tensor Imaging. The monodomain and bidomain Aliev-Panfilov electrophysiological models were used for CDI modeling, and the results indicate that different states were distinguishable using range and correlation of simulated current density maps. The obtained results through modeling were corroborated with actual experimental CDI data from porcine hearts. Individually and comparatively, the experimental and simulation results for various states have the same trend in terms of variations (trend correlation coefficients ≥ 0.98) and state correlations (trend correlation coefficients ≥ 0.89). The results also show that the root mean square (RMS) error in average range ratios between bidomain CDI model results and real CDI data is 0.1972 and the RMS error in state correlations between bidomain CDI model results and real CDI data is 0.2833. These results indicate, as expected, the proposed bidomain model simulation of CDI corroborates well with experimental data and can serve as a valuable tool for studying lethal cardiac arrhythmias under different simulation conditions that are otherwise not possible or difficult in a real-world experimental setup.

A study of the dielectric properties of biological tissues: Ex-vivo vs preserved samples

2017 ◽  
Author(s):  
Irina L. Alborova ◽  
Julian Bonello ◽  
Lourdes Farrugia ◽  
Charles V. Sammut ◽  
Lesya N. Anishchenko
Keyword(s):  
Dielectric Properties ◽  
Ex Vivo ◽  
Biological Tissues

Intraocular Pressure Estimation Method Based on Vibration Propagation Characteristics According to Structure Contact

2021 ◽  
Vol 263 (1) ◽  
pp. 5552-5554
Author(s):  
Kim Deukha ◽  
Seongwook Jeon ◽  
Won June Lee ◽  
Junhong Park
Keyword(s):  
Intraocular Pressure ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Ex Vivo ◽  
Estimation Method ◽  
Biological Tissues ◽  
Response To Treatment ◽  
Deep Learning Algorithm ◽  
Cross Correlation Analysis ◽  
Porcine Eyes

Intraocular pressure (IOP) measurement is one of the basic tests performed in ophthalmology and is known to be an important risk factor for the development and progression of glaucoma. Measurement of IOP is important for assessing response to treatment and monitoring the progression of the disease in glaucoma. In this study, we investigate a method for measuring IOP using the characteristics of vibration propagation generated when the structure is in contact with the eyeball. The response was measured using an accelerometer and a force sensitive resistor to determine the correlation between the IOP. Experiment was performed using ex-vivo porcine eyes. To control the IOP, a needle of the infusion line connected with the water bottle was inserted into the porcine eyes through the limbus. A cross correlation analysis between the accelerometer and the force sensitive resistor was performed to derive a vibration factor that indicate the change in IOP. In order to analyze the degree of influence of biological tissues such as the eyelid, silicon was placed between the structure and the eyeball. The Long Short-Term Memory (LSTM) deep learning algorithm was used to predict IOP based on the vibration factor.

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