An interaction graph approach to gain new insights into mechanisms that modulate cerebrovascular tone.

Mechanisms to modulate cerebrovascular tone are numerous, interconnected, and spatially dependent, increasing the complexity of experimental study design, interpretation of action-effect pathways, and mechanistic modelling. This difficulty is exacerbated when there is an incomplete understanding of these pathways. We propose interaction graphs to break down this complexity, while still maintaining a holistic view of mechanisms to modulate cerebrovascular tone. These graphs highlight the competing processes of neurovascular coupling, cerebral autoregulation, and cerebral reactivity. Subsequent analysis of these interaction graphs provides new insights and suggest potential directions for research on neurovascular coupling, modelling, and dementia.

Measuring global cerebrovascular pulsatility transmission using 4D flow MRI.

Pulse wave encephalopathy (PWE) is hypothesised to initiate many forms of dementia, motivating its identification and risk assessment. As candidate pulsatility based biomarkers for PWE, pulsatility index and pulsatility damping have been studied and, currently, do not adequately stratify risk due to variability in pulsatility and spatial bias. Here, we propose a locus-independent pulsatility transmission coefficient computed by spatially tracking pulsatility along vessels to characterise the brain pulse dynamics at a whole-organ level. Our preliminary analyses in a cohort of 20 subjects indicate that this measurement agrees with clinical observations relating blood pulsatility with age, heart rate, and sex, making it a suitable candidate to study the risk of PWE. We identified transmission differences between vascular regions perfused by the basilar and internal carotid arteries attributed to the identified dependence on cerebral blood flow, and some participants presented differences between the internal carotid perfused regions that were not related to flow or pulsatility burden, suggesting underlying mechanical differences. Large populational studies would benefit from retrospective pulsatility transmission analyses, providing a new comprehensive arterial description of the hemodynamic state in the brain. We provide a publicly available implementation of our tools to derive this coefficient, built into pre-existing open-source software.

A novel tissue mechanics-based method for improved motion tracking in quasi-static ultrasound elastography.

PURPOSE: Most cancers are associated with biological and structural changes that lead to tissue stiffening. Therefore, imaging tissue stiffness using quasi-static ultrasound elastography (USE) can potentially be effective in cancer diagnosis. USE techniques developed for stiffness image reconstruction use noisy displacement data to obtain the stiffness images. In this study, we propose a technique to substantially improve the accuracy of the displacement data computed through ultrasound tissue motion tracking techniques, especially in the lateral direction. METHODS: The proposed technique uses mathematical constraints derived from fundamental tissue mechanics principles to regularize displacement and strain fields obtained using Global Ultrasound Elastography (GLUE) and Second-Order Ultrasound Elastography (SOUL) methods. The principles include a novel technique to enforce (1) tissue incompressibility using 3D Boussinesq model and (2) deformation compatibility using the compatibility differential equation. The technique was validated thoroughly using metrics pertaining to Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR) and Normalized Cross Correlation (NCC) for four tissue-mimicking phantom models and two clinical breast ultrasound elastography cases. RESULTS: The results show substantial improvement in the displacement and strain images generated using the proposed technique. The tissue-mimicking phantom study results indicate that the proposed method is superior in improving image quality compared to the GLUE and SOUL techniques as it shows an average axial strain SNR and CNR improvement of 44% and 63%, and lateral strain SNR and CNR improvement of 130% and 435%, respectively. The results of the phantom study also indicate higher accuracy of displacement images obtained using the proposed technique, including improvement ranges of 7-84% and 26-140% for axial and lateral displacement images, respectively. For the clinical cases, the results indicate average improvement of 48% and 64% in SNR and CNR, respectively, in the axial strain images, and average improvement of 40% and 41% in SNR and CNR, respectively, in the lateral strain images. CONCLUSION: The proposed method is very effective in producing improved estimate of tissue displacement and strain images, especially with the lateral displacement and strain where the improvement is highly remarkable. While the method shows promise for clinical applications, further investigation is necessary for rigorous assessment of the method's performance in the clinic.

Effect of Cardiac Phase on Cardiac Output Index Derived from Dynamic CT Myocardial Perfusion Imaging.

Purpose: The aortic time-enhancement curve obtained from dynamic CT myocardial perfusion imaging can be used to derive the cardiac output (CO) index based on the indicator dilution principle. The objective of this study was to investigate the effect of cardiac phase at which CT myocardial perfusion imaging is triggered on the CO index measurement with this approach. Methods: Electrocardiogram (ECG) gated myocardial perfusion imaging was performed on farm pigs with consecutive cardiac axial scans using a large-coverage CT scanner (Revolution, GE Healthcare) after intravenous contrast administration. Multiple sets of dynamic contrast-enhanced (DCE) cardiac images were reconstructed retrospectively from 30% to 80% R-R intervals with a 5% phase increment. The time-enhancement curve sampled from above the aortic orifice in each DCE image set was fitted with a modified gamma variate function (MGVF). The fitted curve was then normalized to the baseline data point unaffected by the streak artifact emanating from the contrast solution in the right heart chamber. The Stewart−Hamilton equation was used to calculate the CO index based on the integral of the fitted normalized aortic curve, and the results were compared among different cardiac phases. Results: The aortic time-enhancement curves sampled at different cardiac phases were different from each other, especially in the baseline portion of the curve where the effect of streak artifact was prominent. After properly normalizing and denoising with a MGVF, the integrals of the aortic curve were minimally different among cardiac phases (0.228 ± 0.001 Hounsfield Unit × second). The corresponding mean CO index was 4.031 ± 0.028 L/min. There were no statistical differences in either the integral of the aortic curve or CO index among different cardiac phases (p > 0.05 for all phases).

Characterizing regional myofiber damage post acute myocardial infarction using global optimization.

Medical imaging derived cardiac biomechanical models offer a wealth of new information to be used in diagnosis and prognosis of cardiovascular disease. A noteworthy feature of such models is the ability to predict myofiber contraction stresses during acute or chronic ischemic events. Current techniques for heterogeneous contraction models require tissue motion tracking capabilities which are neither available on all imaging modalities, nor currently used in the clinic. Proposed in this article is a proof of concept of a tissue tracking independent technique focused on shape optimization to predict the contraction stresses of in-silico left ventricle models simulating various acute myocardial infarction events. The technique involves three variables defined in the left ventricle muscle. Two of the variables represent the contraction stresses in the healthy and infarct regions while the third is a novel periinfarct variable defining a non-contracting myofiber state allowing finer classification of local myofiber damage. Results indicate that the contraction stress reconstruction errors are overall smaller than 12% when considering standard errors associated with population modelling for the new variable of interest.

Measurement of the hyperelastic properties of 72 normal homogeneous and heterogeneous ex vivo breast tissue samples.

The mechanical properties of normal soft tissues, including breast tissue, have been of interest to the biomedical research community as there are many clinical and industrial applications that can benefit from quantitative information characterizing such properties. For instance, computer assisted surgery planning, elastography for breast cancer diagnosis, and bra design can all involve biomechanical modeling of the breast to predict its deformation or stress distribution. It is known that most biological soft tissues, including breast tissue, exhibit nonlinear mechanical response over large strains. As such, it is necessary to model such tissues as hyperelastic. In this work, we used indentation testing to estimate the hyperelastic parameters of 4 models (3rd order Ogden, 5-term polynomial, Veronda-Westman and Yeoh) estimated from 72 healthy ex vivo breast tissue samples covering adipose, fibroglandular, and mixed tissue. All estimated parameter sets were confirmed to represent stable material using Drucker's stability criterion. We observed that all three tissue types were statistically similar solidifying the use of homogenous breast modelling over large strain simulation.

Superviscous properties of the in vivo brain at large scales.

There is growing awareness that brain mechanical properties are important for neural development and health. However, published values of brain stiffness differ by orders of magnitude between static measurements and in vivo magnetic resonance elastography (MRE), which covers a dynamic range over several frequency decades. We here show that there is no fundamental disparity between static mechanical tests and in vivo MRE when considering large-scale properties, which encompass the entire brain including fluid filled compartments. Using gradient echo real-time MRE, we investigated the viscoelastic dispersion of the human brain in, so far, unexplored dynamic ranges from intrinsic brain pulsations at 1 Hz to ultralow-frequency vibrations at 5, 6.25, 7.8 and 10 Hz to the normal frequency range of MRE of 40 Hz. Surprisingly, we observed variations in brain stiffness over more than two orders of magnitude, suggesting that the in vivo human brain is superviscous on large scales with very low shear modulus of 42±13 Pa and relatively high viscosity of 6.6±0.3 Pa∙s according to the two-parameter solid model. Our data shed light on the crucial role of fluid compartments including blood vessels and cerebrospinal fluid (CSF) for whole brain properties and provide, for the first time, an explanation for the variability of the mechanical brain responses to manual palpation, local indentation, and high-dynamic tissue stimulation as used in elastography.

In-vivo lung biomechanical modeling for effective tumor motion tracking in external beam radiation therapy.

Lung cancer is the most common cause of cancer-related death in both men and women. Radiation therapy is widely used for lung cancer treatment; however, respiratory motion presents challenges that can compromise the accuracy and/or effectiveness of radiation treatment. Respiratory motion compensation using biomechanical modeling is a common approach used to address this challenge. This study focuses on the development and validation of a lung biomechanical model that can accurately estimate the motion and deformation of lung tumor. Towards this goal, treatment planning 4D-CT images of lung cancer patients were processed to develop patient-specific finite element (FE) models of the lung to predict the patients' tumor motion/deformation. The tumor motion/deformation was modeled for a full respiration cycle, as captured by the 4D-CT scans. Parameters driving the lung and tumor deformation model were found through an inverse problem formulation. The CT datasets pertaining to the inhalation phases of respiration were used for validating the model's accuracy. The volumetric Dice similarity coefficient between the actual and simulated gross tumor volumes (GTVs) of the patients calculated across respiration phases was found to range between 0.80 ± 0.03 and 0.92 ± 0.01. The average error in estimating tumor's center of mass calculated across respiration phases ranged between 0.50 ± 0.10 (mm) and 1.04 ± 0.57 (mm), indicating a reasonably good accuracy of the proposed model. The proposed model demonstrates favorable accuracy for estimating the lung tumor motion/deformation, and therefore can potentially be used in radiation therapy applications for respiratory motion compensation.

4D-CT Hyper-Elastography Using a Biomechanical Model.

Low dose computed tomography (LDCT) is the current gold-standard for lung cancer diagnosis. However, accuracy of diagnosis is limited by the radiologist's ability to discern cancerous from non-cancerous nodules. To assist with diagnoses, a 4D-CT lung elastography method is proposed to distinguish nodules based on tissue stiffness properties. The technique relies on a patient-specific inverse finite element (FE) model of the lung solved using an optimization algorithm. The FE model incorporates hyperelastic material properties for tumor and healthy regions and was deformed according to respiration physiology. The tumor hyperelastic parameters and trans-pulmonary pressure were estimated using an optimization algorithm that maximizes similarity between the actual and simulated tumor and lung image data. The proposed technique was evaluated using an in-silico study where the lung tumor elastic properties were assumed. Following that evaluation, the technique was applied to clinical 4D-CT data of two lung cancer patients. Results from the evaluation study show that the elastography technique recovered known tumor parameters with only 6% error. Tumor hyperelastic properties from the clinical data are also reported. Results from this proof of concept study demonstrate the ability to perform lung elastography with 4D-CT data alone. Advancements in the technique could lead to improved diagnoses and timely treatment of lung cancer.

A Composite Material Based Neural Network for Tissue Mechanical Properties Estimation Toward Stage Assessment of Infarction.

Cardiac biomechanical modelling is a promising new tool to be used in prognostic medicine and therapy planning for patients suffering from a variety of cardiovascular diseases and injuries. In order to have an accurate biomechanical model, personalized parameters to define loading, boundary conditions and mechanical properties are required. Achieving personalized modelling parameters often requires inverse optimization which is computationally expensive; hence techniques to reduce the multivariable complexity are in need. Presented in this paper is the fundamental blueprint to create a library of scar tissue mechanical properties to be used in modelling the healing mechanics of hearts that have suffered acute myocardial infarction. This library can be used to reduce the number of variables necessary to capture the scar tissue mechanical properties down to 1. This single parameter also carries information pertaining to staging of the scar tissue healing, predict its rate, and predict its collagen density. This information can be potentially used as valuable biomarkers to adjust existing or develop new treatment plans for patients.

A Tissue Mechanics Based Method to Improve Tissue Displacement Estimation in Ultrasound Elastography.

Cancer is known to induce significant structural changes to tissue. In most cancers, including breast cancer, such changes yield tissue stiffening. As such, imaging tissue stiffness can be used effectively for cancer diagnosis. One such imaging technique, ultrasound elastography, has emerged with the aim of providing a low-cost imaging modality for effective breast cancer diagnosis. In quasi-static breast ultrasound elastography, the breast is stimulated by ultrasound probe, leading to tissue deformation. The tissue displacement data can be estimated using a pair of acquired ultrasound radiofrequency (RF) data pertaining to pre- and post-deformation states. The data can then be used within a mathematical framework to construct an image of the tissue stiffness distribution. Ultrasound RF data is known to include significant noise which lead to corruption of estimated displacement fields, especially the lateral displacements. In this study, we propose a tissue mechanics-based method aiming at improving the quality of estimated displacement data. We applied the method to RF data acquired from a tissue-mimicking phantom. The results indicated that the method is effective in improving the quality of the displacement data.

A Shape Optimization Technique to Predict Left Ventricle Ischemic Tissue Damage.

Damaged cardiac muscle tissue caused by ischemia leads to compromised cardiac function. While conventional imaging can view the ischemic tissue, currently there is no clinical way to quantitatively predict improved heart function after revascularization treatment. This increases the decision difficulty of treatment planning as there is no guarantee the heart function will improve enough to justify the cost of revascularization treatment. The complement of biomechanical modelling with conventional imaging offers an alternative method to determine the amount of ischemic tissue which can then be used as a potential predictor to estimate the range of functional improvement. A novel shape optimization technique is presented to predict the contractility of ischemic tissue in an in-silico left ventricle model that has suffered acute myocardial infarction. Preliminary results show that the proposed technique can reconstruct the damage caused by ischemic tissue within 18%. A range of minimum to maximum predicted cardiac improvement can then be given based on this error to help decide if the cost of revascularization treatment is justified.