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Background: Identification of serum proteins that track with disease course in sarcoidosis may have clinical and pathologic importance. We previously identified up-regulated transcripts for interferon-inducible chemokines CXCL9, and CXCL10, in blood of sar

Background: Rapidly improving acute respiratory distress syndrome (RIARDS) is an increasingly appreciated subgroup of ARDS in which hypoxemia improves within 24 h after initiation of mechanical ventilation. Detailed clinical and biological features of RIARDS have not been clearly defined, and it is unknown whether RIARDS is associated with the hypoinflammatory or hyperinflammatory phenotype of ARDS. The purpose of this study was to define the clinical and biological features of RIARDS and its association with inflammatory subphenotypes., Methods: We analyzed data from 215 patients who met Berlin criteria for ARDS (endotracheally intubated) and were enrolled in a prospective observational cohort conducted at two sites, one tertiary care center and one urban safety net hospital. RIARDS was defined according to previous studies as improvement of hypoxemia defined as (i) PaO 2 :FiO 2  > 300 or (ii) SpO2: FiO 2  > 315 on the day following diagnosis of ARDS (day 2) or (iii) unassisted breathing by day 2 and for the next 48 h (defined as absence of endotracheal intubation on day 2 through day 4). Plasma biomarkers were measured on samples collected on the day of study enrollment, and ARDS phenotypes were allocated as previously described., Results: RIARDS accounted for 21% of all ARDS participants. Patients with RIARDS had better clinical outcomes compared to those with persistent ARDS, with lower hospital mortality (13% vs. 57%; p value < 0.001) and more ICU-free days (median 24 vs. 0; p value < 0.001). Plasma levels of interleukin-6, interleukin-8, and plasminogen activator inhibitor-1 were significantly lower among patients with RIARDS. The hypoinflammatory phenotype of ARDS was more common among patients with RIARDS (78% vs. 51% in persistent ARDS; p value = 0.001)., Conclusions: This study identifies a high prevalence of RIARDS in a multicenter observational cohort and confirms the more benign clinical course of these patients. We report the novel finding that RIARDS is characterized by lower concentrations of plasma biomarkers of inflammation compared to persistent ARDS, and that hypoinflammatory ARDS is more prevalent among patients with RIARDS. Identification and exclusion of RIARDS could potentially improve prognostic and predictive enrichment in clinical trials., (© 2024. The Author(s).)

Background: Deep brain stimulation (DBS) is a highly efficient, evidence-based therapy to alleviate symptoms and improve quality of life in movement disorders such as Parkinson's disease, essential tremor, and dystonia, which is also being applied in several psychiatric disorders, such as obsessive-compulsive disorder and depression, when they are otherwise resistant to therapy., Summary: At present, DBS is clinically applied in the so-called open-loop approach, with fixed stimulation parameters, irrespective of the patients' clinical state(s). This approach ignores the brain states or feedback from the central nervous system or peripheral recordings, thus potentially limiting its efficacy and inducing side effects by stimulation of the targeted networks below or above the therapeutic level., Key Messages: The currently emerging closed-loop (CL) approaches are designed to adapt stimulation parameters to the electrophysiological surrogates of disease symptoms and states. CL-DBS paves the way for adaptive personalized DBS protocols. This review elaborates on the perspectives of the CL technology and discusses its opportunities as well as its potential pitfalls for both clinical and research use in neuropsychiatric disorders., (© 2023 The Author(s). Published by S. Karger AG, Basel.)

Background: In sepsis and acute respiratory distress syndrome (ARDS), heterogeneity has contributed to difficulty identifying effective pharmacotherapies. In ARDS, two molecular phenotypes (hypoinflammatory and hyperinflammatory) have consistently been identified, with divergent outcomes and treatment responses. In this study, we sought to derive molecular phenotypes in critically ill adults with sepsis, determine their overlap with previous ARDS phenotypes, and evaluate whether they respond differently to treatment in completed sepsis trials., Methods: We used clinical data and plasma biomarkers from two prospective sepsis cohorts, the Validating Acute Lung Injury biomarkers for Diagnosis (VALID) study (N=1140) and the Early Assessment of Renal and Lung Injury (EARLI) study (N=818), in latent class analysis (LCA) to identify the optimal number of classes in each cohort independently. We used validated models trained to classify ARDS phenotypes to evaluate concordance of sepsis and ARDS phenotypes. We applied these models retrospectively to the previously published Prospective Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis and Septic Shock (PROWESS-SHOCK) trial and Vasopressin and Septic Shock Trial (VASST) to assign phenotypes and evaluate heterogeneity of treatment effect., Findings: A two-class model best fit both VALID and EARLI (p<0·0001). In VALID, 804 (70·5%) of the 1140 patients were classified as hypoinflammatory and 336 (29·5%) as hyperinflammatory; in EARLI, 530 (64·8%) of 818 were hypoinflammatory and 288 (35·2%) hyperinflammatory. We observed higher plasma pro-inflammatory cytokines, more vasopressor use, more bacteraemia, lower protein C, and higher mortality in the hyperinflammatory than in the hypoinflammatory phenotype (p<0·0001 for all). Classifier models indicated strong concordance between sepsis phenotypes and previously identified ARDS phenotypes (area under the curve 0·87-0·96, depending on the model). Findings were similar excluding participants with both sepsis and ARDS. In PROWESS-SHOCK, 1142 (68·0%) of 1680 patients had the hypoinflammatory phenotype and 538 (32·0%) had the hyperinflammatory phenotype, and response to activated protein C differed by phenotype (p=0·0043). In VASST, phenotype proportions were similar to other cohorts; however, no treatment interaction with the type of vasopressor was observed (p=0·72)., Interpretation: Molecular phenotypes previously identified in ARDS are also identifiable in multiple sepsis cohorts and respond differently to activated protein C. Molecular phenotypes could represent a treatable trait in critical illness beyond the patient's syndromic diagnosis., Funding: US National Institutes of Health., Competing Interests: Declaration of interests PS reports funding from the US National Institutes of Health (NIH) and National Institute of General Medical Sciences; and consulting fees from AstraZeneca. LBW reports funding from NIH, Department of Defense (DoD), Genentech, Boehringer Ingelheim, and CSL Behring; consulting fees from Akebia Therapeutics, Santhera, Global Blood Therapeutics, and Boehringer Ingelheim; and stock options in Virtuoso Surgical. CSC reports funding from NIH; research grants from Roche Genentech and Quantum Leap Healthcare Collaborative; consulting fees from Vasomune Therapeutics, GEn1E Lifesciences, NGM Bio, Cellenkos, and Janssen; and a patent on metagenomic sequencing for sepsis diagnosis (co-recipient). MAM reports funding from Roche Genentech, Quantum Therapeutics, NIH/National Heart, Lung, and Blood Institute/National Institute of Allergy and Infectious Diseases, DoD, and California Institute for Regenerative Medicine; and consulting fees from Johnson & Johnson, Gilead Sciences, and Novartis. MMC reports funding from NIH and DoD; and intellectual property royalties from an issued patent (#11 410 777). JAR reports an investigator-initiated grant from Grifols provided to and administered by the University of British Columbia, Canadian Institutes of Health Research; three grants from the St Paul's Foundation; patents owned by the University of British Columbia related to the use of PCSK9 inhibitor(s) in sepsis and the use of vasopressin in septic shock, and by Ferring Pharmaceuticals for use of selepressin in septic shock; formerly being a founder, Director, and shareholder in Cyon Therapeutics (now closed); being a shareholder in Molecular You; receiving consulting fees in the last 3 years from SIB, Ferring Pharmaceuticals, and Par Pharmaceutical; and having been a funded member of the Data and Safety Monitoring Board of an NIH-sponsored trial of plasma in COVID-19 (PASS-IT-ON). KDL reports grants from NIH: National Institute of Diabetes and Digestive and Kidney Diseases; consulting fees from bioMérieux, UpToDate, SeaStar Medical, AM-Pharma, and Baxter; and stock or stock options in Amgen. All other authors declare no competing interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Angiopoietin-2 (Ang-2) is associated with vascular endothelial injury and permeability in the acute respiratory distress syndrome (ARDS) and sepsis. Elevated circulating Ang-2 levels may identify critically ill patients with distinct pathobiology amenable to targeted therapy. We hypothesized that plasma Ang-2 measured shortly after hospitalization among patients with sepsis would be associated with the development of ARDS and poor clinical outcomes. To test this hypothesis, we measured plasma Ang-2 in a cohort of 757 patients with sepsis, including 267 with ARDS, enrolled in the emergency department or early in their ICU course before the COVID-19 pandemic. Multivariable models were used to test the association of Ang-2 with the development of ARDS and 30-day morality. We found that early plasma Ang-2 in sepsis was associated with higher baseline severity of illness, the development of ARDS, and mortality risk. The association between Ang-2 and mortality was strongest among patients with ARDS and sepsis as compared to those with sepsis alone (OR 1.81 vs. 1.52 per log Ang-2 increase). These findings might inform models testing patient risk prediction and strengthen the evidence for Ang-2 as an appealing biomarker for patient selection for novel therapeutic agents to target vascular injury in sepsis and ARDS., (© 2023. The Author(s).)

Intelligible speech is produced by creating varying internal local muscle groupings-i.e., functional units-that are generated in a systematic and coordinated manner. There are two major challenges in characterizing and analyzing functional units. First, due to the complex and convoluted nature of tongue structure and function, it is of great importance to develop a method that can accurately decode complex muscle coordination patterns during speech. Second, it is challenging to keep identified functional units across subjects comparable due to their substantial variability. In this work, to address these challenges, we develop a new deep learning framework to identify common and subject-specific functional units of tongue motion during speech. Our framework hinges on joint deep graph-regularized sparse non-negative matrix factorization (NMF) using motion quantities derived from displacements by tagged Magnetic Resonance Imaging. More specifically, we transform NMF with sparse and graph regularizations into modular architectures akin to deep neural networks by means of unfolding the Iterative Shrinkage-Thresholding Algorithm to learn interpretable building blocks and associated weighting map. We then apply spectral clustering to common and subject-specific weighting maps from which we jointly determine the common and subject-specific functional units. Experiments carried out with simulated datasets show that the proposed method achieved on par or better clustering performance over the comparison methods.Experiments carried out with in vivo tongue motion data show that the proposed method can determine the common and subject-specific functional units with increased interpretability and decreased size variability., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier B.V.)

Brain movement during an impact can elicit a traumatic brain injury, but tissue kinematics vary from person to person and knowledge regarding this variability is limited. This study examines spatio-temporal brain-skull displacement and brain tissue deformation across groups of subjects during a mild impact in vivo . The heads of two groups of participants were imaged while subjected to a mild (less than 350 rad s -2 ) impact during neck extension (NE, n = 10) and neck rotation (NR, n = 9). A kinematic atlas of displacement and strain fields averaged across all participants was constructed and compared against individual participant data. The atlas-derived mean displacement magnitude was 0.26 ± 0.13 mm for NE and 0.40 ± 0.26 mm for NR, which is comparable to the displacement magnitudes from individual participants. The strain tensor from the atlas displacement field exhibited maximum shear strain (MSS) of 0.011 ± 0.006 for NE and 0.017 ± 0.009 for NR and was lower than the individual MSS averaged across participants. The atlas illustrates common patterns, containing some blurring but visible relationships between anatomy and kinematics. Conversely, the direction of the impact, brain size, and fluid motion appear to underlie kinematic variability. These findings demonstrate the biomechanical roles of key anatomical features and illustrate common features of brain response for model evaluation.

Background: Alternative tobacco product (ATP) use has bee linked to critical illness, however, few studies have examined the use of these substances in critically ill populations. We sought to examine ATP use within critically ill patients and to define barriers in accurately assessing use within this population. Methods: We prospectively studied 533 consecutive patients from the Early Assessment of Renal and Lung Injury study, enrolled between 2013 and 2016 at a tertiary referral center and a safety-net hospital. ATP use information (electronic cigarettes, cigars, pipes, hookahs/waterpipes, and snus/chewing tobacco) was obtained from the patient or surrogate using a detailed survey. Reasons for non-completion of the survey were recorded, and differences between survey responders vs. non-responders, self- vs. surrogate responders, and ATP users vs. non-users were explored. Results: Overall, 80% ( n = 425) of subjects (56% male) completed a tobacco product use survey. Of these, 12.2% ( n = 52) reported current ATP use, while 5.6% reported using multiple ATP products. When restricted to subjects who were self-responders, 17% reported ATP use, while 10% reported current cigarette smoking alone. The mean age of ATP users was 57 ± 17 years. Those who did not complete a survey were sicker and more likely to have died during admission. Subjects who completed the survey as self-responders reported higher levels of ATP use than ones with surrogate responders ( p < 0.0001). Conclusion: ATP use is common among critically ill patients despite them being generally older than traditional users. Survey self-responders were more likely than surrogate responders to report use. These findings highlight the importance of improving our current methods of surveillance of ATP use in older adults in the outpatient setting.

The rapid deformation of brain tissue in response to head impact can lead to traumatic brain injury. In vivo measurements of brain deformation during non-injurious head impacts are necessary to understand the underlying mechanisms of traumatic brain injury and compare to computational models of brain biomechanics. Using tagged magnetic resonance imaging (MRI), we obtained measurements of three-dimensional strain tensors that resulted from a mild head impact after neck rotation or neck extension. Measurements of maximum principal strain (MPS) peaked shortly after impact, with maximal values of 0.019-0.053 that correlated strongly with peak angular velocity. Subject-specific patterns of MPS were spatially heterogeneous and consistent across subjects for the same motion, though regions of high deformation differed between motions. The largest MPS values were seen in the cortical gray matter and cerebral white matter for neck rotation and the brainstem and cerebellum for neck extension. Axonal fiber strain (Ef) was estimated by combining the strain tensor with diffusion tensor imaging data. As with MPS, patterns of Ef varied spatially within subjects, were similar across subjects within each motion, and showed group differences between motions. Values were highest and most strongly correlated with peak angular velocity in the corpus callosum for neck rotation and in the brainstem for neck extension. The different patterns of brain deformation between head motions highlight potential areas of greater risk of injury between motions at higher loading conditions. Additionally, these experimental measurements can be directly compared to predictions of generic or subject-specific computational models of traumatic brain injury.

This study investigates mechanical cooperation among tongue muscles. Five volunteers were imaged using tagged magnetic resonance imaging to quantify spatiotemporal kinematics while speaking. Waveforms of strain in the line of action of fibers (SLAF) were estimated by projecting strain tensors onto a model of fiber directionality. SLAF waveforms were temporally aligned to determine consistency across subjects and correlation across muscles. The cohort exhibited consistent patterns of SLAF, and muscular extension-contraction was correlated. Volume-preserving tongue movement in speech generation can be achieved through multiple paths, but the study reveals similarities in motion patterns and muscular action-despite anatomical (and other) dissimilarities.

Head impact can cause traumatic brain injury (TBI) through axonal overstretch or subsequent inflammation and understanding the biomechanics of the impact event is useful for TBI prevention research. Tagged magnetic resonance imaging (MRI) acquired during a mild-acceleration impact has enabled measurement and visualization of brain deformation in vivo. However, measurements using MRI are subject to error, and having independent validation while imaging in vivo is very difficult. Thus, characterizing the accuracy of these measurements needs to be done in a separate experiment using a phantom where a gold standard is available. This study describes a method for error quantification using a calibration phantom compatible with MRI and high-speed video (the gold standard). During linear acceleration, the maximum shear strain (MSS) in the phantom ranged from 0 to 12%, which is similar to in vivo brain deformation at a similar acceleration. The mean displacement error against video was 0.3±0.3 mm, and the MSS error was 1.4±0.3%. To match resolutions, video data was filtered temporally using an averaging filter. Compared to the unfiltered results, resolution matching improved the agreement between MRI and video results by 15%. In conclusion, tagged MRI analysis compares well to video data provided that resolutions are matched-a finding that is also applicable when using MRI to validate simulations.

Purpose Anterior tongue shape during /s/ production is often described as "tip-up" or apical, versus "tip-down" or laminal. Typically, this is determined by observing the shape of the anterior midline tongue. The purpose of this study was to identify methods of curvature calculation that quantify the observed shape differences and to examine whether the shape differences were affected by palate shape. Previous work shows that palate height has some effect (Grimm et al., 2017). Method Four curvature-based measures were applied to a series of points selected along the tongue surface in midsagittal cine magnetic resonance images during speech. The measures were minimal curvature, averaged largest curvature (ALC), normalized ALC, and interpolated normalized ALC. These measures were compared to visual judgments of apical and laminal /s/. Anterior palate shape was measured from dental casts. Results The apical /s/ contained a flat or concave region in the anterior tongue, while the laminal /s/ had a convex shape along the entire tongue. Thus, the laminal shape was less complex than the apical. The last 2 metrics, based on averages of multiple normalized curvatures, captured this complexity difference. Subjects with a more steeply sloped anterior palate tended to use laminal /s/. Conclusions The tongue shape for the 2 /s/ types was best defined by complexity of the shape, rather than local anterior shape. Statistical quantities that measured curvature in multiple locations, and normalized across subjects, were best at distinguishing the 2 /s/ shapes. Interpolating additional points between the manually selected ones did not improve the method. Supplemental Material https://doi.org/10.23641/asha.9733709.

Objective: To obtain dense spatiotemporal measurements of brain deformation from two distinct but complementary head motion experiments: linear and rotational accelerations., Methods: This study introduces a strategy for integrating harmonic phase analysis of tagged magnetic resonance imaging (MRI) and finite-element models to extract mechanically representative deformation measurements. The method was calibrated using simulated as well as experimental data, demonstrated in a phantom including data with image artifacts, and used to measure brain deformation in human volunteers undergoing rotational and linear acceleration., Results: Evaluation methods yielded a displacement error of 1.1 mm compared to human observers and strain errors between [Formula: see text] for linear acceleration and [Formula: see text] for rotational acceleration. This study also demonstrates an approach that can reduce error by 86% in the presence of corrupted data. Analysis of results shows consistency with 2-D motion estimation, agreement with external sensors, and the expected physical behavior of the brain., Conclusion: Mechanical regularization is useful for obtaining dense spatiotemporal measurements of in vivo brain deformation under different loading regimes., Significance: The measurements suggest that the brain's 3-D response to mild accelerations includes distinct patterns observable using practical MRI resolutions. This type of measurement can provide validation data for computer models for the study of traumatic brain injury.

Background Cardiovascular intensive care units ( CICUs ) have evolved from coronary care wards into distinct units for critically ill patients with primary cardiac diseases, often suffering from illnesses that cross multiple disciplines. Mounting evidence has demonstrated improved survival with the incorporation of dedicated CICU providers with expertise in critical care medicine ( CCM ). This is the first study to systematically survey dual certified physicians in order to assess the relevance of CCM training to contemporary CICU care. Methods and Results Utilizing American Board of Internal Medicine data through 2014, 397 eligible physicians had obtained initial certification in both cardiovascular disease and CCM . A survey to delineate the role of critical care training in the CICU was provided to these physicians. Among those surveyed, 120 physicians (30%) responded. Dual certified physicians reported frequent use of their CCM skills in the CICU , highlighting ventilator management, multiorgan dysfunction management, end-of-life care, and airway management. The majority (85%) cited these skills as the reason CCM training should be prioritized by future CICU providers. Few (17%) agreed that general cardiology fellowship alone is currently sufficient to care for patients in the modern CICU . Furthermore, there was a consensus that there is an unmet need for cardiologists trained in CCM (70%) and that CICU s should adopt a level system similar to trauma centers (61%). Conclusions Citing specific skills acquired during CCM training, dual certified critical care cardiologists reported that their additional critical care experience was necessary in their practice to effectively deliver care in the modern CICU .

Muscle coordination patterns of lingual behaviors are synergies generated by deforming local muscle groups in a variety of ways. Functional units are functional muscle groups of local structural elements within the tongue that compress, expand, and move in a cohesive and consistent manner. Identifying the functional units using tagged-magnetic resonance imaging (MRI) sheds light on the mechanisms of normal and pathological muscle coordination patterns, yielding improvement in surgical planning, treatment, or rehabilitation procedures. In this paper, to mine this information, we propose a matrix factorization and probabilistic graphical model framework to produce building blocks and their associated weighting map using motion quantities extracted from tagged-MRI. Our tagged-MRI imaging and accurate voxel-level tracking provide previously unavailable internal tongue motion patterns, thus revealing the inner workings of the tongue during speech or other lingual behaviors. We then employ spectral clustering on the weighting map to identify the cohesive regions defined by the tongue motion that may involve multiple or undocumented regions. To evaluate our method, we perform a series of experiments. We first use two-dimensional images and synthetic data to demonstrate the accuracy of our method. We then use three-dimensional synthetic and in vivo tongue motion data using protrusion and simple speech tasks to identify subject-specific and data-driven functional units of the tongue in localized regions.

Strain tensor fields quantify tissue deformation and are important for functional analysis of moving organs such as the heart and the tongue. Strain data can be readily obtained using medical imaging. However, quantification of similarity between different data sets is difficult. Strain patterns vary in space and time, and are inherently multidimensional. Also, the same type of mechanical deformation can be applied to different shapes; hence, automatic quantification of similarity should be unaffected by the geometry of the objects being deformed. This work introduces the application of global distributions used to classify shapes and vector fields in the pattern recognition literature, in the context of tensorial strain data. In particular, the distribution of mechanical properties of a field are approximated using a 3D histogram, and the Wasserstein distance from optimal transport theory is used to measure the similarity between histograms. To measure the method's consistency in matching deformations across different objects, the proposed approach was evaluated by sorting strain fields according to their similarity. Performance was compared to sorting via maximum shear distribution (a 1D histogram) and tensor residual magnitude (in perfectly registered objects). The technique was also applied to correlate muscle activation to muscular contraction observed via tagged MRI. The results show that the proposed approach accurately matches deformation regardless of the shape of the object being deformed. Sorting accuracy surpassed 1D shear distribution and was on par with residual magnitude, but without the need for registration between objects.

Mechanical modeling of tongue deformation plays a significant role in the study of breathing, swallowing, and speech production. In the absence of internal joints, fiber orientations determine the direction of sarcomeric contraction and have great influence over real and simulated tissue motion. However, subject-specific experimental observations of fiber distribution are difficult to obtain; thus, models of fiber distribution are generally used in mechanical simulations. This paper describes modeling of fiber distribution using solutions of Laplace equations and compares the effectiveness of this approach against tractography from diffusion tensor magnetic resonance imaging. The experiments included qualitative comparison of streamlines from the fiber model against experimental tractography, as well as quantitative differences between biomechanical simulations focusing in the region near the genioglossus. The model showed good overall agreement in terms of fiber directionality and muscle positioning when compared to subject-specific imaging results and the literature. The angle between the fiber distribution model against tractography in the genioglossus and geniohyoid muscles averaged [Formula: see text] likely due to experimental noise. However, kinematic responses were similar between simulations with modeled fibers versus experimentally obtained fibers; average discrepancy in surface displacement ranged from 1 to 7 mm, and average strain residual magnitude ranged from [Formula: see text] to 0.2. The results suggest that, for simulation purposes, the modeled fibers can act as a reasonable approximation for the tongue's fiber distribution. Also, given its agreement with the global tongue anatomy, the approach may be used in model-based reconstruction of displacement tracking and diffusion results.

The tongue's deformation during speech can be measured using tagged magnetic resonance imaging, but there is no current method to directly measure the pattern of muscles that activate to produce a given motion. In this paper, the activation pattern of the tongue's muscles is estimated by solving an inverse problem using a random forest. Examples describing different activation patterns and the resulting deformations are generated using a finite-element model of the tongue. These examples form training data for a random forest comprising 30 decision trees to estimate contractions in 262 contractile elements. The method was evaluated on data from tagged magnetic resonance data from actual speech and on simulated data mimicking flaps that might have resulted from glossectomy surgery. The estimation accuracy was modest (5.6% error), but it surpassed a semi-manual approach (8.1% error). The results suggest that a machine learning approach to contraction pattern estimation in the tongue is feasible, even in the presence of flaps.

Harmonic phase analysis has been used to perform noninvasive organ motion and strain estimation using tagged magnetic resonance imaging (MRI). The filtering process, which is used to produce harmonic phase images used for tissue tracking, influences the estimation accuracy. In this work, we evaluated different filtering approaches, and propose a novel high-pass filter for volumes tagged in individual directions. Testing was done using an open benchmarking dataset and synthetic images obtained using a mechanical model. We compared estimation results from our filtering approach with results from the traditional filtering approach. Our results indicate that 1) the proposed high-pass filter outperforms the traditional filtering approach reducing error by as much as 50% and 2) the accuracy improvements are especially marked in complex deformations.

Tagged magnetic resonance imaging has been used for decades to observe and quantify motion and strain of deforming tissue. It is challenging to obtain 3-D motion estimates due to a tradeoff between image slice density and acquisition time. Typically, interpolation methods are used either to combine 2-D motion extracted from sparse slice acquisitions into 3-D motion or to construct a dense volume from sparse acquisitions before image registration methods are applied. This paper proposes a new phase-based 3-D motion estimation technique that first computes harmonic phase volumes from interpolated tagged slices and then matches them using an image registration framework. The approach uses several concepts from diffeomorphic image registration with a key novelty that defines a symmetric similarity metric on harmonic phase volumes from multiple orientations. The material property of harmonic phase solves the aperture problem of optical flow and intensity-based methods and is robust to tag fading. A harmonic magnitude volume is used in enforcing incompressibility in the tissue regions. The estimated motion fields are dense, incompressible, diffeomorphic, and inverse-consistent at a 3-D voxel level. The method was evaluated using simulated phantoms, human brain data in mild head accelerations, human tongue data during speech, and an open cardiac data set. The method shows comparable accuracy to three existing methods while demonstrating low computation time and robustness to tag fading and noise.

Right ventricular failure (RVF) is a lethal condition in diverse pathologies. Pressure overload is the most common etiology of RVF, but our understanding of the tissue structure remodeling and other biomechanical factors involved in RVF is limited. Some remodeling patterns are interpreted as compensatory mechanisms including myocyte hypertrophy, extracellular fibrosis, and changes in fiber orientation. However, the specific implications of these changes, especially in relation to clinically observable measurements, are difficult to investigate experimentally. In this computational study, we hypothesized that, with other variables constant, fiber orientation alteration provides a quantifiable and distinct compensatory mechanism during RV pressure overload (RVPO). Numerical models were constructed using a rabbit model of chronic pressure overload RVF based on intraventricular pressure measurements, CINE magnetic resonance imaging (MRI), and diffusion tensor MRI (DT-MRI). Biventricular simulations were conducted under normotensive and hypertensive boundary conditions using variations in RV wall thickness, tissue stiffness, and fiber orientation to investigate their effect on RV pump function. Our results show that a longitudinally aligned myocardial fiber orientation contributed to an increase in RV ejection fraction (RVEF). This effect was more pronounced in response to pressure overload. Likewise, models with longitudinally aligned fiber orientation required a lesser contractility for maintaining a target RVEF against elevated pressures. In addition to increased wall thickness and material stiffness (diastolic compensation), systolic mechanisms in the forms of myocardial fiber realignment and changes in contractility are likely involved in the overall compensatory responses to pressure overload.

Noninvasive measurements of tissue deformation provide bio-mechanical insights of an organ, which can be used as clinical functional biomarkers or experimental data for validating computational simulations. However, acquisition of 3D displacement information is susceptible to experimental inconsistency and limited scan time. In this research, we describe the process of tracking tagged magnetic resonance imaging (MRI) as enforcing harmonic phase conservation in finite-element (FE) models. This concept is demonstrated as a tool for motion estimation in a brain motion phantom, the heart, and the tongue. Our results demonstrate that the new methodology offers robustness to edge and large-displacement artifacts, and that it can be seamlessly coupled with numerical simulations for estimating fiber stretch in residually stressed tissue, or for inverse identification of muscle activation.

Noninvasive analysis of motion has important uses as qualitative markers for organ function and to validate biomechanical computer simulations relative to experimental observations. Tagged MRI is considered the gold standard for noninvasive tissue motion estimation in the heart, and this has inspired multiple studies focusing on other organs, including the brain under mild acceleration and the tongue during speech. As with other motion estimation approaches, using tagged MRI to measure 3D motion includes several preprocessing steps that affect the quality and accuracy of estimation. Benchmarks, or test suites, are datasets of known geometries and displacements that act as tools to tune tracking parameters or to compare different motion estimation approaches. Because motion estimation was originally developed to study the heart, existing test suites focus on cardiac motion. However, many fundamental differences exist between the heart and other organs, such that parameter tuning (or other optimization) with respect to a cardiac database may not be appropriate. Therefore, the objective of this research was to design and construct motion benchmarks by adopting an "image synthesis" test suite to study brain deformation due to mild rotational accelerations, and a benchmark to model motion of the tongue during speech. To obtain a realistic representation of mechanical behavior, kinematics were obtained from finite-element (FE) models. These results were combined with an approximation of the acquisition process of tagged MRI (including tag generation, slice thickness, and inconsistent motion repetition). To demonstrate an application of the presented methodology, the effect of motion inconsistency on synthetic measurements of head-brain rotation and deformation was evaluated. The results indicated that acquisition inconsistency is roughly proportional to head rotation estimation error. Furthermore, when evaluating non-rigid deformation, the results suggest that inconsistent motion can yield "ghost" shear strains, which are a function of slice acquisition viability as opposed to a true physical deformation.

Purpose: To seek a better understanding of the effect of organized capillary flow on the MR diffusion-weighted signal., Methods: A theoretical framework was proposed to describe the diffusion-weighted MR signal, which was then validated both numerically using a realistic model of capillary network and experimentally in an animal model of isolated perfused heart preparation with myocardial blood flow verified by means of direct arterial spin labeling measurements., Results: Microcirculation in organized tissues gave rise to an MR signal that could be described as a combination of the bi-exponential behavior of conventional intravoxel incoherent motion (IVIM) theory and diffusion tensor imaging (DTI) -like anisotropy of the vascular signal, with the flow-related pseudo diffusivity represented as the linear algebraic product between the encoding directional unit vector and an appropriate tensor entity. Very good agreement between theoretical predictions and both numerical and experimental observations were found., Conclusion: These findings suggest that the DTI formalism of anisotropic spin motion can be incorporated into the classical IVIM theory to describe the MR signal arising from diffusion and microcirculation in organized tissues. Magn Reson Med 76:1252-1262, 2016. © 2015 Wiley Periodicals, Inc., (© 2015 Wiley Periodicals, Inc.)

Diffusion tensor imaging (DTI) has emerged as a promising method for noninvasive quantification of myocardial microstructure. However, the origin and behavior of DTI measurements during myocardial normal development and remodeling remain poorly understood. In this work, conventional and bicompartmental DTI in addition to three-dimensional histological correlation were performed in a sheep model of myocardial development from third trimester to postnatal 5 months of age. Comparing the earliest time points in the third trimester with the postnatal 5 month group, the scalar transverse diffusivities preferentially increased in both left ventricle (LV) and right ventricle (RV): secondary eigenvalues D2 increased by 54% (LV) and 36% (RV), whereas tertiary eigenvalues D3 increased by 85% (LV) and 67% (RV). The longitudinal diffusivity D1 changes were small, which led to a decrease in fractional anisotropy by 41% (LV) and 33% (RV) in 5 month versus fetal hearts. Histological analysis suggested that myocardial development is associated with hyperplasia in the early stages of the third trimester followed by myocyte growth in the later stages up to 5 months of age (increased average myocyte width by 198%, myocyte length by 128%, and decreased nucleus density by 70% between preterm and postnatal 5 month hearts.) In a few histological samples (N = 6), correlations were observed between DTI longitudinal diffusivity and myocyte length (r = 0.86, P < 0.05), and transverse diffusivity and myocyte width (r = 0.96, P < 0.01). Linear regression analysis showed that transverse diffusivities are more affected by changes in myocyte size and nucleus density changes than longitudinal diffusivities, which is consistent with predictions of classical models of diffusion in porous media. Furthermore, primary and secondary DTI eigenvectors during development changed significantly. Collectively, the findings demonstrate a role for DTI to monitor and quantify myocardial development, and potentially cardiac disease. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Magnetic resonance diffusion tensor imaging (DTI) has greatly facilitated detailed quantifications of myocardial structures. However, structural patterns, such as the distinctive transmural rotation of the fibers, remain incompletely described. To investigate the validity and practicality of pattern-based analysis, 3D DTI was performed on 13 fixed mouse hearts and fiber angles in the left ventricle were transformed and fitted to parametric expressions constructed from elementary functions of the prolate spheroidal spatial variables. It was found that, on average, the myocardial fiber helix angle could be represented to 6.5° accuracy by the equivalence of a product of 10th-order polynomials of the radial and longitudinal variables, and 17th-order Fourier series of the circumferential variable. Similarly, the fiber imbrication angle could be described by 10th-order polynomials and 24th-order Fourier series, to 5.6° accuracy. The representations, while relatively concise, did not adversely affect the information commonly derived from DTI datasets including the whole-ventricle mean fiber helix angle transmural span and atlases constructed for the group. The unique ability of parametric models for predicting the 3D myocardial fiber structure from finite number of 2D slices was also demonstrated. These findings strongly support the principle of parametric modeling for characterizing myocardial structures in the mouse and beyond.

Background: Heart failure is a leading cause of death but very little is known about right ventricular (RV) failure (RVF) and right ventricular recovery (RVR). A robust animal model of reversible, RVF does not exist, which currently limits research opportunities and clinical progress. We sought to develop an animal model of reversible, pressure-overload RVF to study RVF and RVR., Materials and Methods: Fifteen New Zealand rabbits underwent implantation of a fully implantable, adjustable, pulmonary artery band. Animals were assigned to the control, RVF, and RVR groups (n = 5 for each). For the RVF and RVR groups, the pulmonary artery bands were serially tightened to create RVF and released for RVR. Echocardiographic, cardiac magnetic resonance imaging, and histologic analysis were performed., Results: RV chamber size and wall thickness increased during RVF and regressed during RVR. RV volumes were 1023 μL ± 123 for control, 2381 μL ± 637 for RVF, and 635 μL ± 549 for RVR, and RV wall thicknesses were 0.98 mm ± 0.12 for controls (P = 0.05), 1.72 mm ± 0.60 for RVF, and 1.16 mm ± 0.03 for RVR animals (P = 0.04), respectively. Similarly, heart weight, liver weight, cardiomyocyte size, and the degree of cardiac and hepatic fibrosis increased with RVF and decreased during RVR., Conclusions: We report an animal model of chronic, reversible, pressure-overload RVF to study RVF and RVR. This model will be used for preclinical studies that improve our understanding of the mechanisms of RVF and that develop and test RV protective and RVR strategies to be studied later in humans., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: Detailed mechanical information of the vein is important to better understand remodeling of the vessel in disease states, but has been difficult to obtain due to its thinness, unique geometry, and limitations of mechanical testing. This study presents a novel method for characterizing deformation of the intact explanted vein under physiological loads and determining its material properties by combining high-resolution imaging and computational analysis., Methods: High-resolution CT (microCT) was used to image an iodine-stained, excised porcine internal jugular vein sample under extension to 100% and 120% of in situ length, and inflation and 2, 10, 20 mmHg of pressure, inside a microCT-compatible hydrostatic loading chamber. Regional strains were measured with the finite element (FE) image registration method known as Hyperelastic Warping. Material properties were approximated with inverse FE characterization by optimizing stiffness-related coefficients so to match simulated strains to the experimental measurements., Results: The observed morphology and regional strain of the vein were found to be relatively heterogeneous. The regional variability in the measured strain was primarily driven by geometry. Although iodine treatment may result in tissue stiffening, which requires additional investigation, it is effective in allowing detailed detection of vein geometry., Conclusions: The feasibility and utility of using microCT and computational analysis to characterize mechanical responses and material properties of the vein were demonstrated. The presented method is a promising alternative or addition to mechanical testing for characterizing veins or other similarly delicate vessels in their native anatomical configuration under a wide range of realistic or simulated environmental and loading conditions.

Displacement fields are important to analyze deformation, which is associated with functional and material tissue properties often used as indicators of health. Magnetic resonance imaging (MRI) techniques like DENSE and image registration methods like Hyperelastic Warping have been used to produce pixel-level deformation fields that are desirable in high-resolution analysis. However, DENSE can be complicated by challenges associated with image phase unwrapping, in particular offset determination. On the other hand, Hyperelastic Warping can be hampered by low local image contrast. The current work proposes a novel approach for measuring tissue displacement with both DENSE and Hyperelastic Warping, incorporating physically accurate displacements obtained by the latter to improve phase characterization in DENSE. The validity of the proposed technique is demonstrated using numerical and physical phantoms, and in vivo small animal cardiac MRI.

Background: Identification of serum proteins that track with disease course in sarcoidosis may have clinical and pathologic importance. We previously identified up-regulated transcripts for interferon-inducible chemokines CXCL9, and CXCL10, in blood of sarcoidosis patients compared to controls. The objective of this study was to determine whether proteins encoded by these transcripts were elevated in serum and identified patients with remitting vs. chronic progressive sarcoidosis longitudinally., Methods: Serum levels of CXCL9, CXCL10, and proteins associated with inflammation and/or disease activity (sIL2R, ACE, ESR and CRP) were measured in a prospective cohort of sarcoidosis subjects and controls. Comparisons were made between groups and clinical course using pulmonary function measures and a severity score developed by Wasfi et al., Results: In a cross-sectional analysis of 36 non-immunosuppressed sarcoidosis subjects, serum CXCL9, CXCL10, and sIL2R were significantly elevated compared to 46 controls (p < 0.0001). CXCL9 and CXCL10 were strongly inter-correlated (p = 0.0009). CXCL10 and CXCL9 were inversely correlated with FVC% predicted and DLCO% predicted, respectively. CXCL10 and CXCL9 significantly correlated with sarcoidosis severity score. sIL2R, ESR, CRP, and ACE serum levels did not correlate with pulmonary function measures or severity score. In the longitudinal analysis of 26 subjects, changes in serum CXCL10 level over time corresponded with progression versus remission of disease., Conclusions: Interferon-γ-inducible chemokines, CXCL9 and CXCL10, are elevated in sarcoidosis and inter-correlated with each other. Chemokine levels correlated with measures of disease severity. Serial measurements of CXCL10 corresponded to clinical course.