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Ferroelectric HfO 2 -based thin films have attracted much interest in the utilization of ferroelectricity at the nanoscale for next-generation electronic devices. However, the structural origin and stabilization mechanism of the ferroelectric phase are not understood because the film is typically nanocrystalline with active yet stochastic ferroelectric domains. Here, electron microscopy is used to map the in-plane domain network structures of epitaxially grown ferroelectric Y:HfO 2 films in atomic resolution. The ferroelectricity is confirmed in free-standing Y:HfO 2 films, allowing for investigating the structural origin for their ferroelectricity by 4D-STEM, high-resolution STEM, and iDPC-STEM. At the grain boundaries of <111>-oriented Pca2 1 orthorhombic grains, a high-symmetry mixed-( R 3 m , Pnm2 1 ) phase is induced, exhibiting enhanced polarization due to in-plane compressive strain. Nanoscale Pca2 1 orthorhombic grains and their grain boundaries with mixed-( R 3 m , Pnm2 1 ) phases of higher symmetry cooperatively determine the ferroelectricity of the Y:HfO 2 film. It is also found that such ferroelectric domain networks emerge when the film thickness is beyond a finite value. Furthermore, in-plane mapping of oxygen positions overlaid on ferroelectric domains discloses that polarization is suppressed at vertical domain walls, while it is active when domains are aligned horizontally with subangstrom domain walls. In addition, randomly distributed 180° charged domain walls are confined by spacer layers.

Hollow vaterite microspheres are important materials for biomedical applications such as drug delivery and regenerative medicine owing to their biocompatibility, high specific surface area, and ability to encapsulate a large number of bioactive molecules and compounds. We demonstrated that hollow vaterite microspheres are produced by an Escherichia coli strain engineered with a urease gene cluster from the ureolytic bacteria Sporosarcina pasteurii in the presence of bovine serum albumin. We characterized the 3D nanoscale morphology of five biogenic hollow vaterite microspheres using 3D high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) tomography. Using automated high-throughput HAADF-STEM imaging across several sample tilt orientations, we show that the microspheres evolved from a smaller more ellipsoidal shape to a larger more spherical shape while the internal hollow core increased in size and remained relatively spherical, indicating that the microspheres produced by this engineered strain likely do not contain the bacteria. The statistical 3D morphology information demonstrates the potential for using biogenic calcium carbonate mineralization to produce hollow vaterite microspheres with controlled morphologies. STATEMENT OF SIGNIFICANCE: The nanoscale 3D structures of biomaterials determine their physical, chemical, and biological properties, however significant efforts are required to obtain a statistical understanding of the internal 3D morphology of materials without damaging the structures. In this study, we developed a non-destructive, automated technique that allows us to understand the nanoscale 3D morphology of many unique hollow vaterite microspheres beyond the spectroscopy methods that lack local information and microscopy methods that cannot interrogate the full 3D structure. The method allowed us to quantitatively correlate the external diameters and aspect ratios of vaterite microspheres with their hollow internal structures at the nanoscale. This work demonstrates the opportunity to use automated transmission electron microscopy to characterize nanoscale 3D morphologies of many biomaterials and validate the chemical and biological functionality of these materials., 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 © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Scanning transmission electron microscopy is a common tool used to study the atomic structure of materials. It is an inherently multimodal tool allowing for the simultaneous acquisition of multiple information channels. Despite its versatility, however, experimental workflows currently rely heavily on experienced human operators and can only acquire data from small regions of a sample at a time. Here, we demonstrate a flexible pipeline-based system for high-throughput acquisition of atomic-resolution structural data using an all-piezo sample stage applied to large-scale imaging of nanoparticles and multimodal data acquisition. The system is available as part of the user program of the Molecular Foundry at Lawrence Berkeley National Laboratory., (© 2023 IOP Publishing Ltd.)

Nanomaterials with core-shell architectures are prominent examples of strain-engineered materials. The lattice mismatch between the core and shell materials can cause strong interface strain, which affects the surface structures. Therefore, surface functional properties such as catalytic activities can be designed by fine-tuning the misfit strain at the interface. To precisely control the core-shell effect, it is essential to understand how the surface and interface strains are related at the atomic scale. Here, we elucidate the surface-interface strain relations by determining the full 3D atomic structure of Pd@Pt core-shell nanoparticles at the single-atom level via atomic electron tomography. Full 3D displacement fields and strain profiles of core-shell nanoparticles were obtained, which revealed a direct correlation between the surface and interface strain. The strain distributions show a strong shape-dependent anisotropy, whose nature was further corroborated by molecular statics simulations. From the observed surface strains, the surface oxygen reduction reaction activities were predicted. These findings give a deep understanding of structure-property relationships in strain-engineerable core-shell systems, which can lead to direct control over the resulting catalytic properties., (© 2022. The Author(s).)

Purpose: The aim was to develop a novel artificial intelligence (AI)-guided clinical decision support system, to predict radiation doses to subsites of the mandible using diagnostic computed tomography scans acquired before any planning of head and neck radiation therapy (RT)., Methods and Materials: A dose classifier was trained using RT plans from 86 patients with oropharyngeal cancer; the test set consisted of an additional 20 plans. The classifier was trained to predict whether mandible subsites would receive a mean dose >50 Gy. The AI predictions were prospectively evaluated and compared with those of a specialist head and neck radiation oncologist for 9 patients. Positive predictive value (PPV), negative predictive value (NPV), Pearson correlation coefficient, and Lin concordance correlation coefficient were calculated to compare the AI predictions to those of the physician., Results: In the test data set, the AI predictions had a PPV of 0.95 and NPV of 0.88. For 9 patients evaluated prospectively, there was a strong correlation between the predictions of the AI algorithm and physician ( P  = .72, P < .001). Comparing the AI algorithm versus the physician, the PPVs were 0.82 versus 0.25, and the NPVs were 0.94 versus 1.0, respectively. Concordance between physician estimates and final planned doses was 0.62; this was 0.71 between AI-based estimates and final planned doses., Conclusion: AI-guided decision support increased precision and accuracy of pre-RT dental dose estimates., (© 2021 The Author(s).)

Background: The difference in patient comfort with conscious sedation versus general anaesthesia for bronchoscopy has not been adequately assessed in a randomised trial. This study aimed to assess if patient comfort during bronchoscopy with conscious sedation is noninferior to general anaesthesia., Methods: 96 subjects were randomised to receive conscious sedation or general anaesthesia for bronchoscopy. The primary outcome was subject comfort. Secondary outcomes included willingness to undergo a repeat procedure if necessary and level of sedation assessed clinically and by bispectral index (BIS) monitoring., Results: There was no significant difference between subject comfort scores (difference -0.01, 95% CI -0.63-0.61 on a 10-point scale; p=0.97) or willingness to undergo a repeat procedure (97.7% versus 91.8%, 95% CI -4.8-15.5%; p=0.37). Deeper levels of sedation in the general anaesthesia cohort was confirmed with both clinical and BIS monitoring. There was no significant difference in diagnostic accuracy (conscious sedation 93.9%, 95% CI 80.4-98.3% versus general anaesthesia 86.5%, 95% CI 72.0-94.1%; p=0.43). There were more complications (29.6%, 95% CI 18.2-44.2% versus 6.1%, 95% CI 2.1-16.5%; p<0.01) in the general anaesthesia group. There was no relationship between high BIS scores and subject discomfort. BIS levels <40 during a procedure were associated with increased complications., Conclusion: Conscious sedation is not inferior to general anaesthesia in providing patient comfort during bronchoscopy, despite lighter sedation, and is associated with fewer complications and comparable diagnostic accuracy. BIS monitoring may have a role in preventing complications associated with deeper sedation., Competing Interests: Conflict of interest: T.R. Skinner has nothing to disclose. Conflict of interest: J. Churton has nothing to disclose. Conflict of interest: T.P. Edwards has nothing to disclose. Conflict of interest: F. Bashirzadeh has nothing to disclose. Conflict of interest: C. Zappala has nothing to disclose. Conflict of interest: J.T. Hundloe has nothing to disclose. Conflict of interest: H. Tan has nothing to disclose. Conflict of interest: A.J. Pattison has nothing to disclose. Conflict of interest: M. Todman has nothing to disclose. Conflict of interest: G.F. Hartel has nothing to disclose. Conflict of interest: D.I. Fielding has nothing to disclose., (Copyright ©The authors 2021.)

Background and Purpose: Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify which previously approved historical treatment plans are achievable for a new patient to aid in selection of therapy., Material and Methods: Treatment data were collected for early-stage lung and postoperative oropharyngeal cancers treated using photon (lung and head and neck) and proton (head and neck) radiotherapy. Machine-learning classifiers were constructed using patient-specific feature-sets and a library of historical plans. Model accuracy was analyzed using learning curves, and historical treatment plan matching was investigated., Results: Learning curves demonstrate that for these datasets, approximately 45, 60, and 30 patients are needed for a sufficiently accurate classification model for radiotherapy for early-stage lung, postoperative oropharyngeal photon, and postoperative oropharyngeal proton, respectively. The resulting classification model provides a database of previously approved treatment plans that are achievable for a new patient. An exemplary case, highlighting tradeoffs between the heart and chest wall dose while holding target dose constant in two historical plans is provided., Conclusions: We report on the first artificial-intelligence based clinical decision support system that connects patients to past discrete treatment plans in radiation oncology and demonstrate for the first time how this tool can enable clinicians to use past decisions to help inform current assessments. Clinicians can be informed of dose tradeoffs between critical structures early in the treatment process, enabling more time spent on finding the optimal course of treatment for individual patients., (Copyright © 2017. Published by Elsevier B.V.)

Monolayer-protected (MP) Au clusters present attractive quantum systems with a range of potential applications e.g. in catalysis. Knowledge of the atomic structure is needed to obtain a full understanding of their intriguing physical and chemical properties. Here we employed aberration-corrected scanning transmission electron microscopy (ac-STEM), combined with multislice simulations, to make a round-robin investigation of the atomic structure of chemically synthesised clusters with nominal composition Au 144 (SCH 2 CH 2 Ph) 60 provided by two different research groups. The MP Au clusters were "weighed" by the atom counting method, based on their integrated intensities in the high angle annular dark field (HAADF) regime and calibrated exponent of the Z dependence. For atomic structure analysis, we compared experimental images of hundreds of clusters, with atomic resolution, against a variety of structural models. Across the size range 123-151 atoms, only 3% of clusters matched the theoretically predicted Au 144 (SR) 60 structure, while a large proportion of the clusters were amorphous (i.e. did not match any model structure). However, a distinct ring-dot feature, characteristic of local icosahedral symmetry, was observed in about 20% of the clusters., (Copyright © 2017. Published by Elsevier B.V.)

Background: Integrating teaching and hands-on experience in quality improvement (QI) may increase the learning and the impact of resident QI work., Objective: We sought to determine the clinical and educational impact of an integrated QI curriculum., Methods: This clustered, randomized trial with early and late intervention groups used mixed methods evaluation. For almost 2 years, internal medicine residents from Dartmouth-Hitchcock Medical Center on the inpatient teams at the White River Junction VA participated in the QI curriculum. QI project effectiveness was assessed using statistical process control. Learning outcomes were assessed with the Quality Improvement Knowledge Application Tool-Revised (QIKAT-R) and through self-efficacy, interprofessional care attitudes, and satisfaction of learners. Free text responses by residents and a focus group of nurses who worked with the residents provided information about the acceptability of the intervention., Results: The QI projects improved many clinical processes and outcomes, but not all led to improvements. Educational outcome response rates were 65% (68 of 105) at baseline, 50% (18 of 36) for the early intervention group at midpoint, 67% (24 of 36) for the control group at midpoint, and 53% (42 of 80) for the late intervention group. Composite QIKAT-R scores (range, 0-27) increased from 13.3 at baseline to 15.3 at end point ( P  < .01), as did the self-efficacy composite score ( P  < .05). Satisfaction with the curriculum was rated highly by all participants., Conclusions: Learning and participating in hands-on QI can be integrated into the usual inpatient work of resident physicians., Competing Interests: The authors declare they have no competing interests.

Purpose: Descriptions of the structure of brain tissue as a porous cellular matrix support application of a poroelastic (PE) mechanical model which includes both solid and fluid phases. However, the majority of brain magnetic resonance elastography (MRE) studies use a single phase viscoelastic (VE) model to describe brain tissue behavior, in part due to availability of relatively simple direct inversion strategies for mechanical property estimation. A notable exception is low frequency intrinsic actuation MRE, where PE mechanical properties are imaged with a nonlinear inversion algorithm., Methods: This paper investigates the effect of model choice at each end of the spectrum of in vivo human brain actuation frequencies. Repeat MRE examinations of the brains of healthy volunteers were used to compare image quality and repeatability for each inversion model for both 50 Hz externally produced motion and ≈1 Hz intrinsic motions. Additionally, realistic simulated MRE data were generated with both VE and PE finite element solvers to investigate the effect of inappropriate model choice for ideal VE and PE materials., Results: In vivo, MRE data revealed that VE inversions appear more representative of anatomical structure and quantitatively repeatable for 50 Hz induced motions, whereas PE inversion produces better results at 1 Hz. Reasonable VE approximations of PE materials can be derived by equating the equivalent wave velocities for the two models, provided that the timescale of fluid equilibration is not similar to the period of actuation. An approximation of the equilibration time for human brain reveals that this condition is violated at 1 Hz but not at 50 Hz. Additionally, simulation experiments when using the "wrong" model for the inversion demonstrated reasonable shear modulus reconstructions at 50 Hz, whereas cross-model inversions at 1 Hz were poor quality. Attenuation parameters were sensitive to changes in the forward model at both frequencies, however, no spatial information was recovered because the mechanisms of VE and PE attenuation are different., Conclusions: VE inversions are simpler with fewer unknown properties and may be sufficient to capture the mechanical behavior of PE brain tissue at higher actuation frequencies. However, accurate modeling of the fluid phase is required to produce useful mechanical property images at the lower frequencies of intrinsic brain motions.

Dynamic mechanical analysis (DMA) is a common way to measure the mechanical properties of materials as functions of frequency. Traditionally, a viscoelastic mechanical model is applied and current DMA techniques fit an analytical approximation to measured dynamic motion data by neglecting inertial forces and adding empirical correction factors to account for transverse boundary displacements. Here, a finite-element (FE) approach to processing DMA data was developed to estimate poroelastic material properties. Frequency-dependent inertial forces, which are significant in soft media and often neglected in DMA, were included in the FE model. The technique applies a constitutive relation to the DMA measurements and exploits a nonlinear inversion to estimate the material properties in the model that best fit the model response to the DMA data. A viscoelastic version of this approach was developed to validate the approach by comparing complex modulus estimates to the direct DMA results. Both analytical and FE poroelastic models were also developed to explore their behavior in the DMA testing environment. All of the models were applied to tofu as a representative soft poroelastic material that is a common phantom in elastography imaging studies. Five samples of three different stiffnesses were tested from 1-14 Hz with rough platens placed on the top and bottom surfaces of the material specimen under test to restrict transverse displacements and promote fluid-solid interaction. The viscoelastic models were identical in the static case, and nearly the same at frequency with inertial forces accounting for some of the discrepancy. The poroelastic analytical method was not sufficient when the relevant physical boundary constraints were applied, whereas the poroelastic FE approach produced high quality estimates of shear modulus and hydraulic conductivity. These results illustrated appropriate shear modulus contrast between tofu samples and yielded a consistent contrast in hydraulic conductivity as well.

Poroelastic magnetic resonance elastography is an imaging technique that could recover mechanical and hydrodynamical material properties of in vivo tissue. To date, mechanical properties have been estimated while hydrodynamical parameters have been assumed homogeneous with literature-based values. Estimating spatially-varying hydraulic conductivity would likely improve model accuracy and provide new image information related to a tissue's interstitial fluid compartment. A poroelastic model was reformulated to recover hydraulic conductivity with more appropriate fluid-flow boundary conditions. Simulated and physical experiments were conducted to evaluate the accuracy and stability of the inversion algorithm. Simulations were accurate (property errors were < 2%) even in the presence of Gaussian measurement noise up to 3%. The reformulated model significantly decreased variation in the shear modulus estimate (p << 0.001) and eliminated the homogeneity assumption and the need to assign hydraulic conductivity values from literature. Material property contrast was recovered experimentally in three different tofu phantoms and the accuracy was improved through soft-prior regularization. A frequency-dependence in hydraulic conductivity contrast was observed suggesting that fluid-solid interactions may be more prominent at low frequency. In vivo recovery of both structural and hydrodynamical characteristics of tissue could improve detection and diagnosis of neurological disorders such as hydrocephalus and brain tumors.

Purpose: Breast cancer is a major public health issue for women, and early detection significantly increases survival rate. Currently, there is increased research interest in elastographic soft-tissue imaging techniques based on the correlation between pathology and mechanical stiffness. Anthropomorphic breast phantoms are critical for ex vivo validation of emerging elastographic technologies. This research develops heterogeneous breast phantoms for use in testing elastographic imaging modalities., Methods: Mechanical property estimation of eight different elastomers is performed to determine storage moduli (E') and damping ratios (ζ) using a dynamic mechanical analyzer. Dynamic compression testing was carried out isothermally at room temperature over a range of 4-50 Hz. Silicone compositions with physiologically realistic storage modulus were chosen for mimicking skin adipose, cancerous tumors, and pectoral muscles and 13 anthropomorphic breast phantoms were constructed for ex vivo trials of digital image elastotomography (DIET) breast cancer screening system. A simpler fabrication was used to assess the possibility of multiple tumor detection using magnetic resonance elastography (MRE)., Results: Silicone materials with ranges of storage moduli (E') from 2 to 570 kPa and damping ratios (ζ) from 0.03 to 0.56 were identified. The resulting phantoms were tested in two different elastographic breast cancer diagnostic modalities. A significant contrast was successfully identified between healthy tissues and cancerous tumors both in MRE and DIET., Conclusions: The phantoms presented promise aid to researchers in elastographic imaging modalities for breast cancer detection and provide a foundation for silicone based phantom materials for mimicking soft tissues of other human organs.

Many pathologies alter the mechanical properties of tissue. Magnetic resonance elastography (MRE) has been developed to noninvasively characterize these quantities in vivo. Typically, small vibrations are induced in the tissue of interest with an external mechanical actuator. The resulting displacements are measured with phase contrast sequences and are then used to estimate the underlying mechanical property distribution. Several MRE studies have quantified brain tissue properties. However, the cranium and meninges, especially the dura, are very effective at damping externally applied vibrations from penetrating deeply into the brain. Here, we report a method, termed 'intrinsic activation', that eliminates the requirement for external vibrations by measuring the motion generated by natural blood vessel pulsation. A retrospectively gated phase contrast MR angiography sequence was used to record the tissue velocity at eight phases of the cardiac cycle. The velocities were numerically integrated via the Fourier transform to produce the harmonic displacements at each position within the brain. The displacements were then reconstructed into images of the shear modulus based on both linear elastic and poroelastic models. The mechanical properties produced fall within the range of brain tissue estimates reported in the literature and, equally important, the technique yielded highly reproducible results. The mean shear modulus was 8.1 kPa for linear elastic reconstructions and 2.4 kPa for poroelastic reconstructions where fluid pressure carries a portion of the stress. Gross structures of the brain were visualized, particularly in the poroelastic reconstructions. Intra-subject variability was significantly less than the inter-subject variability in a study of six asymptomatic individuals. Further, larger changes in mechanical properties were observed in individuals when examined over time than when the MRE procedures were repeated on the same day. Cardiac pulsation, termed intrinsic activation, produces sufficient motion to allow mechanical properties to be recovered. The poroelastic model is more consistent with the measured data from brain at low frequencies than the linear elastic model. Intrinsic activation allows MRE to be performed without a device shaking the head so the patient notices no differences between it and the other sequences in an MR examination.

A signal-to-noise ratio (SNR) measure based on the octahedral shear strain (the maximum shear strain in any plane for a 3D state of strain) is presented for magnetic resonance elastography (MRE), where motion-based SNR measures are commonly used. The shear strain, γ, is directly related to the shear modulus, μ, through the definition of shear stress, τ = μγ. Therefore, noise in the strain is the important factor in determining the quality of motion data, rather than the noise in the motion. Motion and strain SNR measures were found to be correlated for MRE of gelatin phantoms and the human breast. Analysis of the stiffness distributions of phantoms reconstructed from the measured motion data revealed a threshold for both strain and motion SNR where MRE stiffness estimates match independent mechanical testing. MRE of the feline brain showed significantly less correlation between the two SNR measures. The strain SNR measure had a threshold above which the reconstructed stiffness values were consistent between cases, whereas the motion SNR measure did not provide a useful threshold, primarily due to rigid body motion effects.

The mechanical model commonly used in magnetic resonance elastography (MRE) is linear elasticity. However, soft tissue may exhibit frequency- and direction-dependent (FDD) shear moduli in response to an induced excitation causing a purely linear elastic model to provide an inaccurate image reconstruction of its mechanical properties. The goal of this study was to characterize the effects of reconstructing FDD data using a linear elastic inversion (LEI) algorithm. Linear and FDD phantoms were manufactured and LEI images were obtained from time-harmonic MRE acquisitions with variations in frequency and driving signal amplitude. LEI responses to artificially imposed uniform phase shifts in the displacement data from both purely linear elastic and FDD phantoms were also evaluated. Of the variety of FDD phantoms considered, LEI appeared to tolerate viscoelastic data-model mismatch better than deviations caused by poroelastic and anisotropic mechanical properties in terms of visual image contrast. However, the estimated shear modulus values were substantially incorrect relative to independent mechanical measurements even in the successful viscoelastic cases and the variations in mean values with changes in experimental conditions associated with uniform phase shifts, driving signal frequency and amplitude were unpredictable. Overall, use of LEI to reconstruct data acquired in phantoms with FDD material properties provided biased results under the best conditions and significant artifacts in the worst cases. These findings suggest that the success with which LEI is applied to MRE data in tissue will depend on the underlying mechanical characteristics of the tissues and/or organs systems of clinical interest.

Imaging of the mechanical properties of in vivo brain tissue could eventually lead to non-invasive diagnosis of hydrocephalus, Alzheimer's disease and other pathologies known to alter the intracranial environment. The purpose of this work is to (1) use time-harmonic magnetic resonance elastography (MRE) to estimate the mechanical property distribution of cerebral tissue in the normal feline brain and (2) compare the recovered properties of grey and white matter. Various in vivo and ex vivo brain tissue property measurement strategies have led to the highly variable results that have been reported in the literature. MR elastography is an imaging technique that can estimate mechanical properties of tissue non-invasively and in vivo. Data was acquired in 14 felines and elastic parameters were estimated using a globo-regional nonlinear image reconstruction algorithm. Results fell within the range of values reported in the literature and showed a mean shear modulus across the subject group of 7-8 kPa with all but one animal falling within 5-15 kPa. White matter was statistically stiffer (p<0.01) than grey matter by about 1 kPa on a per subject basis. To the best of our knowledge, the results reported represent the most extensive set of estimates in the in vivo brain which have been based on MRE acquisition of the three-dimensional displacement field coupled to volumetric shear modulus image reconstruction achieved through nonlinear parameter estimation. However, the inter-subject variation in mean shear modulus indicates the need for further study, including the possibility of applying more advanced models to estimate the relevant tissue mechanical properties from the data., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Purpose: Recent interest in the poroelastic behavior of tissues has led to the development of magnetic resonance poroelastography (MRPE) as an alternative to single-phase MR elastographic image reconstruction. In addition to the elastic parameters (i.e., Lamé's constants) commonly associated with magnetic resonance elastography (MRE), MRPE enables estimation of the time-harmonic pore-pressure field induced by external mechanical vibration., Methods: This study presents numerical simulations that demonstrate the sensitivity of the computed displacement and pore-pressure fields to a priori estimates of the experimentally derived model parameters. In addition, experimental data collected in three poroelastic phantoms are used to assess the quantitative accuracy of MR poroelastographic imaging through comparisons with both quasistatic and dynamic mechanical tests., Results: The results indicate hydraulic conductivity to be the dominant parameter influencing the deformation behavior of poroelastic media under conditions applied during MRE. MRPE estimation of the matrix shear modulus was bracketed by the values determined from independent quasistatic and dynamic mechanical measurements as expected, whereas the contrast ratios for embedded inclusions were quantitatively similar (10%-15% difference between the reconstructed images and the mechanical tests)., Conclusions: The findings suggest that the addition of hydraulic conductivity and a viscoelastic solid component as parameters in the reconstruction may be warranted.