Simulation-based joint estimation of body deformation and elasticity parameters for medical image analysis

Adaptation rules inducing synchronization of heterogeneous Kuramoto oscillator network with triadic couplings

A class of adaptation functions is found for which a synchronous mode with different number of phase clusters exists in a network of phase oscillators with triadic couplings. This mode is implemented in a fairly wide range of initial conditions and the maximum number of phase clusters is four. The joint influence of coupling strength and adaptation parameters on synchronization in the network has been studied. The desynchronization transition under variation of the adaptation parameter occurs abruptly and begins with the highest-frequency oscillator, spreading hierarchically to all other elements.

Using shear wave elastography to assess uterine tonicity after vaginal delivery

This study aims to evaluate the feasibility and clinical interest of shear wave elastography, by quantitatively estimating the baseline stiffness of the myometrium before and after placental expulsion. We conducted a prospective cohort study of women at term, without known risk factors for postpartum hemorrhage, who gave birth via spontaneous labor in our tertiary center. Myometrium tonicity was evaluated based on measurements of shear wave speed (SWS) in the anterior uterine corpus. All data points were collected by a single operator. Measurements were carried out at three different time points: after fetal delivery (T1), after placental delivery (T2) and 30 min after placental delivery (T3). Our primary objective was to assess the feasibility of this new imaging technique. Ten valid SWS measurements obtained at each of the three different time points were considered as a positive primary outcome. Our secondary objectives were to evaluate the difference in median myometrial shear wave velocity between each time point, as well as to determine the correlation between myometrial shear wave velocity and patients’ characteristics. 38 women were recruited during the study period, of whom 34 met the study criteria. 1017 SWS measurements were obtained. The median time to perform measurements was 16 s for one value, and 2 min 56 s for ten. For 11 women (32%) it was not possible to achieve ten SWS at T1 as placental expulsion immediately followed the birth of the newborn. One patient experienced placental retention and only measurements at T1 were performed. For all other patients, we were successfully able to obtain all measures as intended. There was no difference in the mean shear wave speed between the three time points. After adjustments for confounders, we observed a significant correlation for total blood loss (correlation coefficient = − 0.26, p < 0.001, units of oxytocin (correlation coefficient = − 0.34, p = 0.03), and newborn weight (correlation coefficient = − 0.08, p = 0.001). It is feasible to assess uterine tonicity by shear wave imaging, after placental expulsion. We did not observe a variance in uterine tonicity between the three time points. Women who had higher blood loss, received more units of oxytocin and/or those with newborns of a higher weight exhibited lower shear wave speed measures.

Disrupted metabolic connectivity in dopaminergic and cholinergic networks at different stages of dementia from 18F-FDG PET brain persistent homology network

Dementia is related to the cellular accumulation of β-amyloid plaques, tau aggregates, or α-synuclein aggregates, or to neurotransmitter deficiencies in the dopaminergic and cholinergic pathways. Cellular and neurochemical changes are both involved in dementia pathology. However, the role of dopaminergic and cholinergic networks in metabolic connectivity at different stages of dementia remains unclear. The altered network organisation of the human brain characteristic of many neuropsychiatric and neurodegenerative disorders can be detected using persistent homology network (PHN) analysis and algebraic topology. We used 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging data to construct dopaminergic and cholinergic metabolism networks, and used PHN analysis to track the evolution of these networks in patients with different stages of dementia. The sums of the network distances revealed significant differences between the network connectivity evident in the Alzheimer's disease and mild cognitive impairment cohorts. A larger distance between brain regions can indicate poorer efficiency in the integration of information. PHN analysis revealed the structural properties of and changes in the dopaminergic and cholinergic metabolism networks in patients with different stages of dementia at a range of thresholds. This method was thus able to identify dysregulation of dopaminergic and cholinergic networks in the pathology of dementia.

Stability of synchronization in simplicial complexes

Various systems in physics, biology, social sciences and engineering have been successfully modeled as networks of coupled dynamical systems, where the links describe pairwise interactions. This is, however, too strong a limitation, as recent studies have revealed that higher-order many-body interactions are present in social groups, ecosystems and in the human brain, and they actually affect the emergent dynamics of all these systems. Here, we introduce a general framework to study coupled dynamical systems accounting for the precise microscopic structure of their interactions at any possible order. We show that complete synchronization exists as an invariant solution, and give the necessary condition for it to be observed as a stable state. Moreover, in some relevant instances, such a necessary condition takes the form of a Master Stability Function. This generalizes the existing results valid for pairwise interactions to the case of complex systems with the most general possible architecture.

Maturational delay and asymmetric information flow of brain connectivity in SHR model of ADHD revealed by topological analysis of metabolic networks

Attention-deficit hyperactivity disorder (ADHD) is a complex brain development disorder characterized by hyperactivity/impulsivity and inattention. A major hypothesis of ADHD is a lag of maturation, which is supported mainly by anatomical studies evaluating cortical thickness. Here, we analyzed changes of topological characteristics of whole-brain metabolic connectivity in twelve SHR rats selected as ADHD-model rats by confirming behavior abnormalities using the marble burying test, open field test, and delay discounting task and 12 Wistar Kyoto rats as the control group, across development from 4 weeks old (childhood) and 6 weeks old (entry of puberty). A topological approach based on graph filtrations revealed a lag in the strengthening of limbic-cortical/subcortical connections in ADHD-model rats. This in turn related to impaired modularization of memory and reward-motivation associated regions. Using mathematical network analysis techniques such as single linkage hierarchical clustering and volume entropy, we observed left-lateralized connectivity in the ADHD-model rats at 6 weeks old. Our findings supported the maturational delay of metabolic connectivity in the SHR model of ADHD, and also suggested the possibility of impaired and compensative reconfiguration of information flow over the brain network.

Laser-speckle-contrast projection tomography for three-dimensional shear wave imaging

Laser-speckle-contrast shear wave (LSC-SW) imaging is an optical method for tracking the propagation of a transient shear wave. With high spatial resolution and sensitivity in detecting displacements, this method is suitable for performing mechanical measurements in vitro. Here, we present a LSC-SW tomographic imaging system for visualizing the propagating shear wave wavefront in four dimensions [i.e., three-dimensional (3D) space plus time]. The volumetric elasticity distribution of a sample is constructed by estimating the speeds of the shear waves propagating along multiple paths at different angles. The proposed method enables multidirectional estimations of the shear wave speed. The capabilities of the imaging system are demonstrated by evaluating isotropy (both homogeneous and heterogeneous) and anisotropy in semiturbid phantoms. The proposed system is suitable for the mechanical characterization of a 3D cell culture system, such as monitoring changes in fiber orientation during the remodeling of the extracellular matrix that is known to be strongly associated with the progression and characterization of tumors.

Persistent homology of unweighted complex networks via discrete Morse theory

Topological data analysis can reveal higher-order structure beyond pairwise connections between vertices in complex networks. We present a new method based on discrete Morse theory to study topological properties of unweighted and undirected networks using persistent homology. Leveraging on the features of discrete Morse theory, our method not only captures the topology of the clique complex of such graphs via the concept of critical simplices, but also achieves close to the theoretical minimum number of critical simplices in several analyzed model and real networks. This leads to a reduced filtration scheme based on the subsequence of the corresponding critical weights, thereby leading to a significant increase in computational efficiency. We have employed our filtration scheme to explore the persistent homology of several model and real-world networks. In particular, we show that our method can detect differences in the higher-order structure of networks, and the corresponding persistence diagrams can be used to distinguish between different model networks. In summary, our method based on discrete Morse theory further increases the applicability of persistent homology to investigate the global topology of complex networks.

Simplicial models of social contagion

Complex networks have been successfully used to describe the spread of diseases in populations of interacting individuals. Conversely, pairwise interactions are often not enough to characterize social contagion processes such as opinion formation or the adoption of novelties, where complex mechanisms of influence and reinforcement are at work. Here we introduce a higher-order model of social contagion in which a social system is represented by a simplicial complex and contagion can occur through interactions in groups of different sizes. Numerical simulations of the model on both empirical and synthetic simplicial complexes highlight the emergence of novel phenomena such as a discontinuous transition induced by higher-order interactions. We show analytically that the transition is discontinuous and that a bistable region appears where healthy and endemic states co-exist. Our results help explain why critical masses are required to initiate social changes and contribute to the understanding of higher-order interactions in complex systems.

Clinical Personal Connectomics Using Hybrid PET/MRI

Brain connectivity can now be studied with topological analysis using persistent homology. It overcame the arbitrariness of thresholding to make binary graphs for comparison between disease and normal control groups. Resting-state fMRI can yield personal interregional brain connectivity based on perfusion signal on MRI on individual subject bases and FDG PET produces the topography of glucose metabolism. Assuming metabolism perfusion coupling and disregarding the slight difference of representing time of metabolism (before image acquisition) and representing time of perfusion (during image acquisition), topography of brain metabolism on FDG PET and topologically analyzed brain connectivity on resting-state fMRI might be related to yield personal connectomics of individual subjects and even individual patients. The work of association of FDG PET/resting-state fMRI is yet to be warranted; however, the statistics behind the group comparison of connectivity on FDG PET or resting-state MRI was already developed. Before going further into the connectomics construction using directed weighted brain graphs of FDG PET or resting-state fMRI, I detailed in this review the plausibility of using hybrid PET/MRI to enable the interpretation of personal connectomics which can lead to the clinical use of brain connectivity in the near future.

High-speed collagen fiber modeling and orientation quantification for optical coherence tomography imaging

Quantifying collagen fiber architecture has clinical and scientific relevance across a variety of tissue types and adds functionality to otherwise largely qualitative imaging modalities. Optical coherence tomography (OCT) is uniquely suited for this task due to its ability to capture the collagen microstructure over larger fields of view than traditional microscopy. Existing image processing techniques for quantifying fiber architecture, while accurate and effective, are very slow for processing large datasets and tend to lack structural specificity. We describe here a computationally efficient method for quantifying and visualizing collagen fiber organization. The algorithm is demonstrated on swine atria, bovine anterior cruciate ligament, and human cervical tissue samples. Additionally, we show an improved performance for images with crimped fiber textures and low signal to noise when compared to similar methods.

Disrupted Resting State Network of Fibromyalgia in Theta frequency

Fibromyalgia (FM), chronic widespread pain, exhibits spontaneous pain without external stimuli and is associated with altered brain activities during resting state. To understand the topological features of brain network in FM, we employed persistent homology which is a multiple scale network modeling framework not requiring thresholding. Spontaneous magnetoencephalography (MEG) activity was recorded in 19 healthy controls (HCs) and 18 FM patients. Barcode, single linkage dendrogram and single linkage matrix were generated based on the proposed modeling framework. In theta band, the slope of decrease in the number of connected components in barcodes showed steeper in HC, suggesting FM patients had decreased global connectivity. FM patients had reduced connectivity within default mode network, between middle/inferior temporal gyrus and visual cortex. The longer pain duration was correlated with reduced connectivity between inferior temporal gyrus and visual cortex. Our findings demonstrated that the aberrant resting state network could be associated with dysfunction of sensory processing in chronic pain. The spontaneous nature of FM pain may accrue to disruption of resting state network.

Alteration in the Local and Global Functional Connectivity of Resting State Networks in Parkinson's Disease

OBJECTIVE

Parkinson's disease (PD) is a neurodegenerative disorder that mainly leads to the impairment of patients' motor function, as well as of cognition, as it progresses. This study tried to investigate the impact of PD on the resting state functional connectivity of the default mode network (DMN), as well as of the entire brain.

METHODS

Sixty patients with PD were included and compared to 60 matched normal control (NC) subjects. For the local connectivity analysis, the resting state fMRI data were analyzed by seed-based correlation analyses, and then a novel persistent homology analysis was implemented to examine the connectivity from a global perspective.

RESULTS

The functional connectivity of the DMN was decreased in the PD group compared to the NC, with a stronger difference in the medial prefrontal cortex. Moreover, the results of the persistent homology analysis indicated that the PD group had a more locally connected and less globally connected network compared to the NC.

CONCLUSION

Our findings suggest that the DMN is altered in PD, and persistent homology analysis, as a useful measure of the topological characteristics of the networks from a broader perspective, was able to identify changes in the large-scale functional organization of the patients' brain.

Disrupted brain metabolic connectivity in a 6-OHDA-induced mouse model of Parkinson's disease examined using persistent homology-based analysis

Movement impairments in Parkinson’s disease (PD) are caused by the degeneration of dopaminergic neurons and the consequent disruption of connectivity in the cortico-striatal-thalamic loop. This study evaluated brain metabolic connectivity in a 6-Hydroxydopamine (6-OHDA)-induced mouse model of PD using ¹⁸F-fluorodeoxy glucose positron emission tomography (FDG PET). Fourteen PD-model mice and ten control mice were used for the analysis. Voxel-wise t-tests on FDG PET results yielded no significant regional metabolic differences between the PD and control groups. However, the PD group showed lower correlations between the right caudoputamen and the left caudoputamen and right visual cortex. Further network analyses based on the threshold-free persistent homology framework revealed that brain networks were globally disrupted in the PD group, especially between the right auditory cortex and bilateral cortical structures and the left caudoputamen. In conclusion, regional glucose metabolism of PD was preserved, but the metabolic connectivity of the cortico-striatal-thalamic loop was globally impaired in PD.

MaterialCloning: Acquiring Elasticity Parameters from Images for Medical Applications

We present a practical approach for automatically estimating the material properties of soft bodies from two sets of images, taken before and after deformation. We reconstruct 3D geometry from the given sets of multiple-view images; we use a coupled simulation-optimization-identification framework to deform one soft body at its original, non-deformed state to match the deformed geometry of the same object in its deformed state. For shape correspondence, we use a distance-based error metric to compare the estimated deformation fields against the actual deformation field from the reconstructed geometry. The optimal set of material parameters is thereby determined by minimizing the error metric function. This method can simultaneously recover the elasticity parameters of multiple types of soft bodies using Finite Element Method-based simulation (of either linear or nonlinear materials undergoing large deformation) and particle-swarm optimization methods. We demonstrate this approach on real-time interaction with virtual organs in patient-specific surgical simulation, using parameters acquired from low-resolution medical images. We also highlight the results on physics-based animation of virtual objects using sketches from an artist's conception.

Mapped Chebyshev pseudo-spectral method for simulating the shear wave propagation in the plane of symmetry of a transversely isotropic viscoelastic medium

Shear wave elastography is a versatile technique that is being applied to many organs. However, in tissues that exhibit anisotropic material properties, special care must be taken to estimate shear wave propagation accurately and efficiently. A two-dimensional simulation method is implemented to simulate the shear wave propagation in the plane of symmetry in transversely isotropic viscoelastic media. The method uses a mapped Chebyshev pseudo-spectral method to calculate the spatial derivatives and an Adams-Bashforth-Moulton integrator with variable step sizes for time marching. The boundaries of the two-dimensional domain are surrounded by perfectly matched layers to approximate an infinite domain and minimize reflection errors. In an earlier work, we proposed a solution for estimating the apparent shear wave elasticity and viscosity of the spatial group velocity as a function of rotation angle through a low-frequency approximation by a Taylor expansion. With the solver implemented in MATLAB, the simulated results in this paper match well with the theory. Compared to the finite element method simulations we used before, the pseudo-spectral solver consumes less memory and is faster and achieves better accuracy.

Effects of using the unloaded configuration in predicting the in vivo diastolic properties of the heart

Computational models are increasingly being used to investigate the mechanical properties of cardiac tissue. While much insight has been gained from these studies, one important limitation associated with computational modeling arises when using in vivo images of the heart to generate the reference state of the model. An unloaded reference configuration is needed to accurately represent the deformation of the heart. However, it is rare for a beating heart to actually reach a zero-pressure state during the cardiac cycle. To overcome this, a computational technique was adapted to determine the unloaded configuration of an in vivo porcine left ventricle (LV). In the current study, in vivo measurements were acquired using magnetic resonance images (MRI) and synchronous pressure catheterization in the LV (N = 5). The overall goal was to quantify the effects of using early-diastolic filling as the reference configuration (common assumption used in modeling) versus using the unloaded reference configuration for predicting the in vivo properties of LV myocardium. This was accomplished by using optimization to minimize the difference between MRI measured and finite element predicted strains and cavity volumes. The results show that when using the unloaded reference configuration, the computational method predicts material properties for LV myocardium that are softer and less anisotropic than when using the early-diastolic filling reference configuration. This indicates that the choice of reference configuration could have a significant impact on capturing the realistic mechanical response of the heart.

DTI template-based estimation of cardiac fiber orientations from 3D ultrasound

PURPOSE

Cardiac muscle fibers directly affect the mechanical, physiological, and pathological properties of the heart. Patient-specific quantification of cardiac fiber orientations is an important but difficult problem in cardiac imaging research. In this study, the authors proposed a cardiac fiber orientation estimation method based on three-dimensional (3D) ultrasound images and a cardiac fiber template that was obtained from magnetic resonance diffusion tensor imaging (DTI).

METHODS

A DTI template-based framework was developed to estimate cardiac fiber orientations from 3D ultrasound images using an animal model. It estimated the cardiac fiber orientations of the target heart by deforming the fiber orientations of the template heart, based on the deformation field of the registration between the ultrasound geometry of the target heart and the MRI geometry of the template heart. In the experiments, the animal hearts were imaged by high-frequency ultrasound, T1-weighted MRI, and high-resolution DTI.

RESULTS

The proposed method was evaluated by four different parameters: Dice similarity coefficient (DSC), target errors, acute angle error (AAE), and inclination angle error (IAE). Its ability of estimating cardiac fiber orientations was first validated by a public database. Then, the performance of the proposed method on 3D ultrasound data was evaluated by an acquired database. Their average values were 95.4% ± 2.0% for the DSC of geometric registrations, 21.0° ± 0.76° for AAE, and 19.4° ± 1.2° for IAE of fiber orientation estimations. Furthermore, the feasibility of this framework was also performed on 3D ultrasound images of a beating heart.

CONCLUSIONS

The proposed framework demonstrated the feasibility of using 3D ultrasound imaging to estimate cardiac fiber orientation of in vivo beating hearts and its further improvements could contribute to understanding the dynamic mechanism of the beating heart and has the potential to help diagnosis and therapy of heart disease.

Material stiffness parameters as potential predictors of presence of left ventricle myocardial infarction: 3D echo-based computational modeling study

Background

Ventricle material properties are difficult to obtain under in vivo conditions and are not readily available in the current literature. It is also desirable to have an initial determination if a patient had an infarction based on echo data before more expensive examinations are recommended. A noninvasive echo-based modeling approach and a predictive method were introduced to determine left ventricle material parameters and differentiate patients with recent myocardial infarction (MI) from those without.

Methods

Echo data were obtained from 10 patients, 5 with MI (Infarct Group) and 5 without (Non-Infarcted Group). Echo-based patient-specific computational left ventricle (LV) models were constructed to quantify LV material properties. All patients were treated equally in the modeling process without using MI information. Systolic and diastolic material parameter values in the Mooney-Rivlin models were adjusted to match echo volume data. The equivalent Young’s modulus (YM) values were obtained for each material stress–strain curve by linear fitting for easy comparison. Predictive logistic regression analysis was used to identify the best parameters for infract prediction.

Results

The LV end-systole material stiffness (ES-YM_f) was the best single predictor among the 12 individual parameters with an area under the receiver operating characteristic (ROC) curve of 0.9841. LV wall thickness (WT), material stiffness in fiber direction at end-systole (ES-YM_f) and material stiffness variation (∆YM_f) had positive correlations with LV ejection fraction with correlation coefficients r = 0.8125, 0.9495 and 0.9619, respectively. The best combination of parameters WT + ∆YM_f was the best over-all predictor with an area under the ROC curve of 0.9951.

Conclusion

Computational modeling and material stiffness parameters may be used as a potential tool to suggest if a patient had infarction based on echo data. Large-scale clinical studies are needed to validate these preliminary findings.

3D surface-based registration of ultrasound and histology in prostate cancer imaging

Several transrectal ultrasound (TRUS)-based techniques aiming at accurate localization of prostate cancer are emerging to improve diagnostics or to assist with focal therapy. However, precise validation prior to introduction into clinical practice is required. Histopathology after radical prostatectomy provides an excellent ground truth, but needs accurate registration with imaging. In this work, a 3D, surface-based, elastic registration method was developed to fuse TRUS images with histopathologic results. To maximize the applicability in clinical practice, no auxiliary sensors or dedicated hardware were used for the registration. The mean registration errors, measured in vitro and in vivo, were 1.5±0.2 and 2.1±0.5mm, respectively.

Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury

Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis.

Multiresolution persistent homology for excessively large biomolecular datasets

Although persistent homology has emerged as a promising tool for the topological simplification of complex data, it is computationally intractable for large datasets. We introduce multiresolution persistent homology to handle excessively large datasets. We match the resolution with the scale of interest so as to represent large scale datasets with appropriate resolution. We utilize flexibility-rigidity index to access the topological connectivity of the data set and define a rigidity density for the filtration analysis. By appropriately tuning the resolution of the rigidity density, we are able to focus the topological lens on the scale of interest. The proposed multiresolution topological analysis is validated by a hexagonal fractal image which has three distinct scales. We further demonstrate the proposed method for extracting topological fingerprints from DNA molecules. In particular, the topological persistence of a virus capsid with 273 780 atoms is successfully analyzed which would otherwise be inaccessible to the normal point cloud method and unreliable by using coarse-grained multiscale persistent homology. The proposed method has also been successfully applied to the protein domain classification, which is the first time that persistent homology is used for practical protein domain analysis, to our knowledge. The proposed multiresolution topological method has potential applications in arbitrary data sets, such as social networks, biological networks, and graphs.

Multidimensional persistence in biomolecular data

Persistent homology has emerged as a popular technique for the topological simplification of big data, including biomolecular data. Multidimensional persistence bears considerable promise to bridge the gap between geometry and topology. However, its practical and robust construction has been a challenge. We introduce two families of multidimensional persistence, namely pseudomultidimensional persistence and multiscale multidimensional persistence. The former is generated via the repeated applications of persistent homology filtration to high-dimensional data, such as results from molecular dynamics or partial differential equations. The latter is constructed via isotropic and anisotropic scales that create new simiplicial complexes and associated topological spaces. The utility, robustness, and efficiency of the proposed topological methods are demonstrated via protein folding, protein flexibility analysis, the topological denoising of cryoelectron microscopy data, and the scale dependence of nanoparticles. Topological transition between partial folded and unfolded proteins has been observed in multidimensional persistence. The separation between noise topological signatures and molecular topological fingerprints is achieved by the Laplace-Beltrami flow. The multiscale multidimensional persistent homology reveals relative local features in Betti-0 invariants and the relatively global characteristics of Betti-1 and Betti-2 invariants.

Quantitative evaluation of atrial radio frequency ablation using intracardiac shear-wave elastography

PURPOSE

Radio frequency catheter ablation (RFCA) is a well-established clinical procedure for the treatment of atrial fibrillation (AF) but suffers from a low single-procedure success rate. Recurrence of AF is most likely attributable to discontinuous or nontransmural ablation lesions. Yet, despite this urgent clinical need, there is no clinically available imaging modality that can reliably map the lesion transmural extent in real time. In this study, the authors demonstrated the feasibility of shear-wave elastography (SWE) to map quantitatively the stiffness of RFCA-induced thermal lesions in cardiac tissues in vitro and in vivo using an intracardiac transducer array.

METHODS

SWE was first validated in ex vivo porcine ventricular samples (N = 5). Both B-mode imaging and SWE were performed on normal cardiac tissue before and after RFCA. Areas of the lesions were determined by tissue color change with gross pathology and compared against the SWE stiffness maps. SWE was then performed in vivo in three sheep (N = 3). First, the stiffness of normal atrial tissues was assessed quantitatively as well as its variation during the cardiac cycle. SWE was then performed in atrial tissue after RFCA.

RESULTS

A large increase in stiffness was observed in ablated ex vivo regions (average shear modulus across samples in normal tissue: 22 ± 5 kPa, average shear-wave speed (ct): 4.5 ± 0.4 m s(-1) and in determined ablated zones: 99 ± 17 kPa, average ct: 9.0 ± 0.5 m s(-1) for a mean shear modulus increase ratio of 4.5 ± 0.9). In vivo, a threefold increase of the shear modulus was measured in the ablated regions, and the lesion extension was clearly visible on the stiffness maps.

CONCLUSIONS

By its quantitative and real-time capabilities, Intracardiac SWE is a promising intraoperative imaging technique for the evaluation of thermal ablation during RFCA.

High-frame rate four dimensional optoacoustic tomography enables visualization of cardiovascular dynamics and mouse heart perfusion

Functional imaging of mouse models of cardiac health and disease provides a major contribution to our fundamental understanding of the mammalian heart. However, imaging murine hearts presents significant challenges due to their small size and rapid heart rate. Here we demonstrate the feasibility of high-frame-rate, noninvasive optoacoustic imaging of the murine heart. The temporal resolution of 50 three-dimensional frames per second provides functional information at important phases of the cardiac cycle without the use of gating or other motion-reduction methods. Differentiation of the blood oxygenation state in the heart chambers was enabled by exploiting the wavelength dependence of optoacoustic signals. Real-time volumetric tracking of blood perfusion in the cardiac chambers was also evaluated using indocyanine green. Taken together, the newly-discovered capacities offer a unique tool set for in-vivo structural and functional imaging of the whole heart with high spatio-temporal resolution in all three dimensions.

Simultaneous Estimation of Elasticity for Multiple Deformable Bodies

Material property has great importance in deformable body simulation and medical robotics. The elasticity parameters, such as Young's modulus of the deformable bodies, are important to make realistic animations. Further in medical applications the (recovered) elasticity parameters can assist surgeons to perform better pre-op surgical planning and enable medical robots to carry out personalized surgical procedures. Previous elasticity parameters estimation methods are limited to recover one elasticity parameter of one deformable body at a time. In this paper, we propose a novel elasticity parameter estimation algorithm that can recover the elasticity parameters of multiple deformable bodies or multiple regions of one deformable body simultaneously from (at least two sets of) images. We validate our algorithm with both synthetic test cases and real patient CT images.

Registration by interactive inverse simulation: application for adaptive radiotherapy

PURPOSE

This paper introduces a new methodology for semi-automatic registration of anatomical structure deformations. The contribution is to use an interactive inverse simulation of physics-based deformable model, computed in real time.

METHODS

The method relies on nonlinear finite element method (FEM) within a constraint-based framework. Given a set of few registered points provided by the user, a real-time optimization adapts the boundary conditions and(/or) some parameters of the FEM in order to obtain the adequate geometrical deformations. To dramatically fasten the process, the method relies on a projection of the model in the space of the optimization variables. In this reduced space, a quadratic programming problem is formulated and solved very quickly. The method is validated with numerical examples for retrieving some unknown parameters such as the Young's modulus and some pressures on the boundaries of the model.

RESULTS

The approach is employed in the context of radiotherapy of the neck where weight loss during the 7 weeks of the therapy modifies the volume of the anatomical structures and induces large deformations. Indeed, sensitive structures such as the parotid glands may cross the target volume due to these deformations which leads to adverse effects for the patient. We thus apply the approach for the registration of the parotid glands during the radiotherapy of the head and neck cancer.

CONCLUSIONS

The results show how the method could be used in a clinical routine and be employed in the planning in order to limit the radiations of these glands.

Brain networks engaged in audiovisual integration during speech perception revealed by persistent homology-based network filtration

The human brain naturally integrates audiovisual information to improve speech perception. However, in noisy environments, understanding speech is difficult and may require much effort. Although the brain network is supposed to be engaged in speech perception, it is unclear how speech-related brain regions are connected during natural bimodal audiovisual or unimodal speech perception with counterpart irrelevant noise. To investigate the topological changes of speech-related brain networks at all possible thresholds, we used a persistent homological framework through hierarchical clustering, such as single linkage distance, to analyze the connected component of the functional network during speech perception using functional magnetic resonance imaging. For speech perception, bimodal (audio-visual speech cue) or unimodal speech cues with counterpart irrelevant noise (auditory white-noise or visual gum-chewing) were delivered to 15 subjects. In terms of positive relationship, similar connected components were observed in bimodal and unimodal speech conditions during filtration. However, during speech perception by congruent audiovisual stimuli, the tighter couplings of left anterior temporal gyrus-anterior insula component and right premotor-visual components were observed than auditory or visual speech cue conditions, respectively. Interestingly, visual speech is perceived under white noise by tight negative coupling in the left inferior frontal region-right anterior cingulate, left anterior insula, and bilateral visual regions, including right middle temporal gyrus, right fusiform components. In conclusion, the speech brain network is tightly positively or negatively connected, and can reflect efficient or effortful processes during natural audiovisual integration or lip-reading, respectively, in speech perception.

Effect of material property heterogeneity on biomechanical modeling of prostate under deformation

Biomechanical model based deformable image registration has been widely used to account for prostate deformation in various medical imaging procedures. Biomechanical material properties are important components of a biomechanical model. In this study, the effect of incorporating tumor-specific material properties in the prostate biomechanical model was investigated to provide insight into the potential impact of material heterogeneity on the prostate deformation calculations. First, a simple spherical prostate and tumor model was used to analytically describe the deformations and demonstrate the fundamental effect of changes in the tumor volume and stiffness in the modeled deformation. Next, using a clinical prostate model, a parametric approach was used to describe the variations in the heterogeneous prostate model by changing tumor volume, stiffness, and location, to show the differences in the modeled deformation between heterogeneous and homogeneous prostate models. Finally, five clinical prostatectomy examples were used in separately performed homogeneous and heterogeneous biomechanical model based registrations to describe the deformations between 3D reconstructed histopathology images and ex vivo magnetic resonance imaging, and examine the potential clinical impact of modeling biomechanical heterogeneity of the prostate. The analytical formulation showed that increasing the tumor volume and stiffness could significantly increase the impact of the heterogeneous prostate model in the calculated displacement differences compared to the homogeneous model. The parametric approach using a single prostate model indicated up to 4.8 mm of displacement difference at the tumor boundary compared to a homogeneous model. Such differences in the deformation of the prostate could be potentially clinically significant given the voxel size of the ex vivo MR images (0.3  ×  0.3  ×  0.3 mm). However, no significant changes in the registration accuracy were observed using heterogeneous models for the limited number of clinical prostatectomy patients modeled and evaluated in this study.

Persistent homology for the quantitative prediction of fullerene stability

Persistent homology is a relatively new tool often used for qualitative analysis of intrinsic topological features in images and data originated from scientific and engineering applications. In this article, we report novel quantitative predictions of the energy and stability of fullerene molecules, the very first attempt in using persistent homology in this context. The ground-state structures of a series of small fullerene molecules are first investigated with the standard Vietoris-Rips complex. We decipher all the barcodes, including both short-lived local bars and long-lived global bars arising from topological invariants, and associate them with fullerene structural details. Using accumulated bar lengths, we build quantitative models to correlate local and global Betti-2 bars, respectively with the heat of formation and total curvature energies of fullerenes. It is found that the heat of formation energy is related to the local hexagonal cavities of small fullerenes, while the total curvature energies of fullerene isomers are associated with their sphericities, which are measured by the lengths of their long-lived Betti-2 bars. Excellent correlation coefficients (>0.94) between persistent homology predictions and those of quantum or curvature analysis have been observed. A correlation matrix based filtration is introduced to further verify our findings.

Introducing interactive inverse FEM simulation and its application for adaptive radiotherapy

We introduce a new methodology for semi-automatic deformable registration of anatomical structures, using interactive inverse simulations. The method relies on non-linear real-time Finite Element Method (FEM) within a constraint-based framework. Given a set of few registered points provided by the user, a real-time optimization adapts the boundary conditions and(/or) some parameters of the FEM in order to obtain the adequate geometrical deformations. To dramatically fasten the process, the method relies on a projection of the model in the space of the optimization variables. In this reduced space, a quadratic programming problem is formulated and solved very quickly. The method is validated with numerical examples for retrieving Young's modulus and some pressures on the boundaries. Then, we apply the approach for the registration of the parotid glands during the radiotherapy of the head and neck cancer. Radiotherapy treatment induces weight loss that modifies the shape and the positions of these structures and they eventually intersect the target volume. We show how we could adapt the planning to limit the radiation of these glands.

Estimating passive mechanical properties in a myocardial infarction using MRI and finite element simulations

Myocardial infarction (MI) triggers a series of maladaptive events that lead to structural and functional changes in the left ventricle. It is crucial to better understand the progression of adverse remodeling, in order to develop effective treatment. In addition, being able to assess changes in vivo would be a powerful tool in the clinic. The goal of the current study is to quantify the in vivo material properties of infarcted and remote myocardium 1 week after MI, as well as the orientation of collagen fibers in the infarct. This will be accomplished by using a combination of magnetic resonance imaging (MRI), catheterization, finite element modeling, and numerical optimization to analyze a porcine model ([Formula: see text]) of posterolateral myocardial infarction. Specifically, properties will be determined by minimizing the difference between in vivo strains and volume calculated from MRI and finite element model predicted strains and volume. The results indicate that the infarct region is stiffer than the remote region and that the infarct collagen fibers become more circumferentially oriented 1 week post-MI. These findings are consistent with previous studies, which employed ex vivo techniques. The proposed methodology will ultimately provide a means of predicting remote and infarct mechanical properties in vivo at any time point post-MI.

3D static elastography at the micrometer scale using Full Field OCT

Full-Field OCT (FF-OCT) is able to image biological tissues in 3D with micrometer resolution. In this study we add elastographic contrast to the FF-OCT modality. By combining FF-OCT with elastography, we create a virtual palpation map at the micrometer scale. We present here a proof of concept on multi-layer phantoms and preliminary results on ex vivo biological samples such as porcine cornea, human breast tissues and rat heart. The 3D digital volume correlation that is used in connection with the 3D stack of images allows to access to the full 3D strain tensor and to reveal stiffness anisotropy.

Breakdown of brain connectivity between normal aging and Alzheimer's disease: a structural k-core network analysis

Brain connectivity analyses show considerable promise for understanding how our neural pathways gradually break down in aging and Alzheimer's disease (AD). Even so, we know very little about how the brain's networks change in AD, and which metrics are best to evaluate these changes. To better understand how AD affects brain connectivity, we analyzed anatomical connectivity based on 3-T diffusion-weighted images from 111 subjects (15 with AD, 68 with mild cognitive impairment, and 28 healthy elderly; mean age, 73.7±7.6 SD years). We performed whole brain tractography based on the orientation distribution functions, and compiled connectivity matrices showing the proportions of detected fibers interconnecting 68 cortical regions. We computed a variety of measures sensitive to anatomical network topology, including the structural backbone--the so-called "k-core"--of the anatomical network, and the nodal degree. We found widespread network disruptions, as connections were lost in AD. Among other connectivity measures showing disease effects, network nodal degree, normalized characteristic path length, and efficiency decreased with disease, while normalized small-worldness increased, in the whole brain and left and right hemispheres individually. The normalized clustering coefficient also increased in the whole brain; we discuss factors that may cause this effect. The proportions of fibers intersecting left and right cortical regions were asymmetrical in all diagnostic groups. This asymmetry may intensify as disease progressed. Connectivity metrics based on the k-core may help understand brain network breakdown as cognitive impairment increases, revealing how degenerative diseases affect the human connectome.

Extracting three-dimensional orientation and tractography of myofibers using optical coherence tomography

Abnormal changes in orientation of myofibers are associated with various cardiac diseases such as arrhythmia, irregular contraction, and cardiomyopathy. To extract fiber information, we present a method of quantifying fiber orientation and reconstructing three-dimensional tractography of myofibers using optical coherence tomography (OCT). A gradient based algorithm was developed to quantify fiber orientation in three dimensions and particle filtering technique was employed to track myofibers. Prior to image processing, three-dimensional image data set were acquired from all cardiac chambers and ventricular septum of swine hearts using OCT system without optical clearing. The algorithm was validated through rotation test and comparison with manual measurements. The experimental results demonstrate that we are able to visualize three-dimensional fiber tractography in myocardium tissues.