Deformation Metrics Research Articles

CARDIAC ULTRASOUND ALTERATIONS IN INDIVIDUALS WITH PNEUMONIA RELATED TO COVID-19 INFECTION

A wide range of cardiac effects are observed during the acute phase of COVID-19. Interestingly, troponin levels are raised in 8–28% of instances, while systolic myocardial dysfunction symptoms are not always evident. Subclinical myocardial dysfunction is more common and is commonly defined as a decrease in the left ventricle's global longitudinal strain (LV GLS). Up to 80% of individuals exhibit this alteration while they are in the hospital. LV GLS is a crucial component in risk assessment for follow-up care and a strong independent predictor of in-hospital mortality. The first results on the long-term cardiovascular effects of COVID-19 one year after hospitalization are presented in this study. One year after discharge, we found that patients with COVID-19 pneumonia had a negative trend in LV GLS and deformation metrics in the apical and certain middle segments of the LV myocardium, as compared to data collected three months after discharge. The purpose of the study is to look at the prevalence and clinical significance of long-term heart injury following COVID-19. In order to do this, we examined patient data and categorized patients a year following hospital discharge according to their LV GLS values. This method aids in comprehending the clinical ramifications of long-term cardiac injury after COVID-19.

In vivo human knee varus-valgus loading apparatus for analysis of MRI-based intratissue strain and relaxometry

The diagnosis of early-stage osteoarthritis remains as an unmet challenge in medicine and a roadblock to evaluating the efficacy of disease-modifying treatments. Recent studies demonstrate that unique patterns of intratissue cartilage deformation under cyclic loading can serve as potential biomarkers to detect early disease pathogenesis. However, a workflow to obtain deformation, strain maps, and quantitative MRI metrics due to the loading of articular cartilage in vivo has not been fully developed. In this study, we characterize and demonstrate an apparatus that is capable of applying a varus-valgus load to the human knee in vivo within an MRI environment to enable the measurement of cartilage structure and mechanical function. The apparatus was first tested in a lab environment, then the functionality and utility of the apparatus were examined during varus loading in a clinical 3T MRI system for human imaging. We found that the device enables quantitative MRI metrics for biomechanics and relaxometry data acquisition during joint loading leading to compression of the medial knee compartment. Integration with spiral DENSE MRI during cyclic loading provided time-dependent displacement and strain maps within the tibiofemoral cartilage. The results from these procedures demonstrate that the performance of this loading apparatus meets the design criteria and enables a simple and practical workflow for future studies of clinical cohorts, and the identification and validation of imaging-based biomechanical biomarkers.

Viscoelastic circular cell honeycomb helmet liners for reducing head rotation and brain strain in oblique impacts

Rotational head motion is one of the major contributors to brain tissue strain during head impacts, which damages axons and vessels and leads to traumatic brain injury. Helmet technologies have come to market promising enhanced protection against such rotational head motion. We recently introduced novel air-filled viscoelastic cell arrays and showed that their shear response under oblique impacts can be tailored through altering the cell wall curvature. We found that concave cells provide shear stiffness that is a few folds larger than that of convex cells. Here we test whether altering the cell curvature can reduce head rotational kinematics and brain strain and whether the viscoelastic cell arrays outperform the reference EPS foam-based liner. To test these hypotheses, we incorporate the viscoelastic cell arrays in a bicycle helmet liner. We use validated finite element models of the helmet and replace the liner with validated finite element models of the cellular cell arrays. We simulate oblique impacts at different locations to represent a wide range of real-world bicycle head impacts. In all cases, the head kinematics and brain deformation metrics indicate significant improvements with the novel cell arrays over the conventional EPS liner. We show that the shear-compliant cell arrays can reduce head rotational acceleration by as much as 64 % and brain strain by 69 %, but not in all impact locations. Cell arrays with similar axial stiffness yet lower shear stiffness often bottomed out, indicating that a considerable amount of energy is dissipated via cell shearing around the impact zone. Our results show that placement of cells with varying amounts of shear stiffness should be optimised, with the most shear-compliant cells near the crown and the least near the temples. This study shows the promising performance of viscoelastic cell arrays in protecting the head and brain under oblique impacts and provides avenues for optimising the distribution of their compressive-shear properties.

Biotransport in human phonation: Porous vocal fold tissue and fluid–structure interaction

Human phonation involves the flow-induced vibrations of the vocal folds (VFs) that result from the interaction with airflow through the larynx. Most voice dysfunctions correspond with the fluid–structure interaction (FSI) features as well as the local changes in perfusion within the VF tissue. This study aims to develop a multiphysics computational framework to simulate the interstitial fluid flow dynamics in vibrating VFs using a biphasic description of the tissue and FSI methodology. The integration of FSI and a permeable VF model presents a novel approach to capture phonation physics' complexity and investigate VF tissue's porous nature. The glottal airflow is modeled by the unsteady, incompressible Navier–Stokes equations, and the Brinkman equation is employed to simulate the flow through the saturated porous medium of the VFs. The computational model provides a prediction of tissue deformation metrics and pulsatile glottal flow, in addition to the interstitial fluid velocity and flow circulation within the porous structure. Furthermore, the model is used to characterize the effects of variation in subglottal lung pressure and VF permeability coefficient by conducting parametric studies. Subsequent investigations to quantify the relationships between these input variables, flow perfusion, pore pressure, and vibration amplitude are presented. A linear relationship is found between the vibration amplitude, pore pressure, and filtration flow with subglottal pressure, whereas a nonlinear dependence between the filtration velocity and VF permeability coefficient is detected. The outcomes highlight the importance of poroelasticity in phonation models.

WarpPINN: Cine-MR image registration with physics-informed neural networks.

The diagnosis of heart failure usually includes a global functional assessment, such as ejection fraction measured by magnetic resonance imaging. However, these metrics have low discriminate power to distinguish different cardiomyopathies, which may not affect the global function of the heart. Quantifying local deformations in the form of cardiac strain can provide helpful information, but it remains a challenge. In this work, we introduce WarpPINN, a physics-informed neural network to perform image registration to obtain local metrics of heart deformation. We apply this method to cine magnetic resonance images to estimate the motion during the cardiac cycle. We inform our neural network of the near-incompressibility of cardiac tissue by penalizing the Jacobian of the deformation field. The loss function has two components: an intensity-based similarity term between the reference and the warped template images, and a regularizer that represents the hyperelastic behavior of the tissue. The architecture of the neural network allows us to easily compute the strain via automatic differentiation to assess cardiac activity. We use Fourier feature mappings to overcome the spectral bias of neural networks, allowing us to capture discontinuities in the strain field. The algorithm is tested on synthetic examples and on a cine SSFP MRI benchmark of 15 healthy volunteers, where it is trained to learn the deformation mapping of each case. We outperform current methodologies in landmark tracking and provide physiological strain estimations in the radial and circumferential directions. WarpPINN provides precise measurements of local cardiac deformations that can be used for a better diagnosis of heart failure and can be used for general image registration tasks. Source code is available at https://github.com/fsahli/WarpPINN.

A quasi-objective single-buoy approach for understanding Lagrangian coherent structures and sea ice dynamics

Abstract. Sea ice drift and deformation, namely sea ice dynamics, play a significant role in atmosphere–ice–ocean coupling. Deformation patterns in sea ice can be observed over a wide range of spatial and temporal scales, though high-resolution objective quantification of these features remains difficult. In an effort to better understand local deformation of sea ice, we adapt the trajectory-stretching exponents (TSEs), quasi-objective measures of Lagrangian stretching in continuous media, to sea ice buoy data and develop a temporal analysis of TSE time series. Our work expands on previous ocean current studies that have shown TSEs provide an approximation of Lagrangian coherent structure diagnostics when only sparse trajectory data are available. As TSEs do not require multiple buoys, we find they have an expanded range of use when compared with traditional Eulerian buoy-array deformation metrics and provide local-stretching information below the length scales possible when averaging over buoy arrays. We verify the ability of TSEs to temporally and spatially identify dynamic features for three different sea ice datasets. The ability of TSEs to quantify trajectory stretching is verified by concurrent ice fracture in buoy neighborhoods ranging from tens to hundreds of kilometers in diameter, as well as the temporal concurrence of significant storm events.

Evidence for multiple early impacts on the H chondrite parent body from electron backscatter diffraction analysis

AbstractWe examined H4 chondrites Beaver Creek, Forest Vale, Quenggouk, Ste. Marguerite, and Sena with the electron backscatter diffraction (EBSD) techniques of Ruzicka and Hugo (2018) to determine if there is evidence for shock metamorphism consistent with the previously inferred histories of their early impact excavation or lack thereof. We find that all samples have EBSD data consistent with a history of synmetamorphic impact shock (i.e., shock during thermal metamorphism), followed by postshock annealing. Petrographic analysis of Sena, Quenggouk, and Ste. Marguerite found exsolved Cu and irregular troilite within Fe metal, features consistent with shock metamorphism. All samples have a spatial variability in grain deformation consistent with shock processes, though Forest Vale, Quenggouk, and Ste. Marguerite may have relict signatures of accretional deformation as indicated by variability in their olivine deformation metrics. Within the context of previous workers' geochemical observations, a more complex history is inferred for each sample. The “slow‐cooled” samples, Quenggouk and Sena, were subject to synmetamorphic shock without excavation and annealed at depth. The same is true of the “fast‐cooled” samples, Beaver Creek, Forest Vale, and Ste. Marguerite. However, after annealing, these rocks were excavated by a secondary impact or impacts around 5.2–6.5 Ma post‐CAI formation and were left to cool rapidly on the surface of the H chondrite parent body. These interpreted histories are best compatible with a model of an impact‐battered but intact onion shell for the earliest history of the H parent body. However, the EBSD evidence does not preclude a parent body disruption after 7 Ma post‐CAI formation.

Supine magnetic resonance image registration for breast surgery: insights on material mechanics.

Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, is being explored since it more closely represents the surgical presentation compared to conventional diagnostic imaging positions. Despite this preoperative imaging position, deformation is still present between the supine imaging and surgical state. As a result, a fast and accurate image-to-physical registration approach is needed to realize image-guided breast surgery. In this study, three registration methods were investigated on healthy volunteers' breasts ( ) with the supine arm-down position simulating preoperative imaging and supine arm-up position simulating intraoperative presentation. The registration methods included (1)point-based rigid registration using synthetic fiducials, (2)nonrigid biomechanical model-based registration using sparse data, and (3)a data-dense three-dimensional diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. Additionally, deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field to better understand breast material mechanics. The average target registration errors (TRE) were , , and (mean ± standard deviation) and the average fiducial registration errors (FRE) were , , and for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. The mechanics-based deformation metrics revealed an overall anisotropic tissue behavior and a statistically significant difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy. Overall, registration accuracy significantly improved with increasingly flexible and data-dense registration methods. Analysis of these outcomes may inform the future development of image guidance systems for lumpectomy procedures.

Quantification of left ventricular strain and torsion by joint analysis of 3D tagging and cine MR images.

Cardiovascular magnetic resonance (CMR) imaging is the gold standard for the non-invasive assessment of left-ventricular (LV) function. Prognostic value of deformation metrics extracted directly from regular SSFP CMR images has been shown by numerous studies in the clinical setting, but with some limitations to detect torsion of the myocardium. Tagged CMR introduces trackable features in the myocardium that allow for the assessment of local myocardial deformation, including torsion; it is, however, limited in the quantification of radial strain, which is a decisive metric for assessing the contractility of the heart. In order to improve SSFP-only and tagged-only approaches, we propose to combine the advantages of both image types by fusing global shape motion obtained from SSFP images with the local deformation obtained from tagged images. To this end, tracking is first performed on SSFP images, and subsequently, the resulting motion is utilized to mask and track tagged data. Our implementation is based on a recent finite element-based motion tracking tool with mechanical regularization. Joint SSFP and tagged images registration performance is assessed based on deformation metrics including LV strain and twist using human and in-house porcine datasets. Results show that joint analysis of SSFP and 3DTAG images provides better quantification of LV strain and twist as either data source alone.

A Laboratory and Field Universal Estimation Method for Tire–Pavement Interaction Noise (TPIN) Based on 3D Image Technology

Tire–pavement interaction noise (TPIN) accounts mainly for traffic noise, a sensitive parameter affecting the eco-based maintenance decision outcome. Consistent methods or metrics for lab and field pavement texture evaluation are lacking. TPIN prediction based on pavement structural and material characteristics is not yet available. This paper used 3D point cloud data scanned from specimens and road pavement to conduct correlation and clustering analysis based on representative 3D texture metrics. We conducted an influence analysis to exclude macroscope pavement detection metrics and macro deformation metrics’ effects (international roughness index, IRI, and mean profile depth, MPD). The cluster analysis results verified the feasibility of texture metrics for evaluating lab and field pavement wear, differentiating the wear states. TPIN prediction accuracy based on texture indicators was high (R2 = 0.9958), implying that it is feasible to predict the TPIN level using 3D texture metrics. The effects of pavement texture changes on TPIN can be simulated by laboratory wear.

Quantifying the Effect of Sex and Neuroanatomical Biomechanical Features on Brain Deformation Response in Finite Element Brain Models.

Recent automotive epidemiology studies have concluded that females have significantly higher odds of sustaining a moderate brain injury or concussion than males in a frontal crash after controlling for multiple crash and occupant variables. Differences in neuroanatomical features, such as intracranial volume (ICV), have been shown between male and female subjects, but how these sex-specific neuroanatomical differences affect brain deformation is unknown. This study used subject-specific finite element brain models, generated via registration-based morphing using both male and female magnetic resonance imaging scans, to investigate sex differences of a variety of neuroanatomical features and their effect on brain deformation; additionally, this study aimed to determine the relative importance of these neuroanatomical features and sex on brain deformation metrics for a single automotive loading environment. Based on the Bayesian linear mixed models, sex had a significant effect on ICV, white matter volume and gray matter volume, as well as a section of cortical gray matter regions' thicknesses and volumes; however, after these neuroanatomical features were accounted for in the statistical model, sex was not a significant factor in predicting brain deformation. ICV had the highest relative effect on the brain deformation metrics assessed. Therefore, ICV should be considered when investigating both brain injury biomechanics and injury risk.

Breast image registration for surgery: Insights on material mechanics modeling.

Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, more closely represents the surgical presentation compared to conventional diagnostic pendant positioning. Optimal utilization for surgical guidance, however, requires a fast and accurate image-to-physical registration from preoperative imaging to intraoperative surgical presentation. In this study, three registration methods were investigated on healthy volunteers' breasts (n=11) with the arm-down position simulating preoperative imaging and arm-up position simulating intraoperative data. The registration methods included: (1) point-based rigid registration using synthetic fiducials, (2) non-rigid biomechanical model-based registration using sparse data, and (3) a data-dense 3D diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. The average target registration errors (TRE) were 10.4 ± 2.3, 6.4 ± 1.5, and 2.8 ± 1.3 mm (mean ± standard deviation) and the average fiducial registration errors (FRE) were 7.8 ± 1.7, 2.5 ± 1.1, and 3.1 ± 1.1 mm (mean ± standard deviation) for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. Additionally, common mechanics-based deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field. The average metrics revealed anisotropic tissue behavior and a statistical difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy. Overall, registration accuracy significantly improved with increasingly flexible registration methods, which may inform future development of image guidance systems for lumpectomy procedures.

Sea Ice Rheology Experiment (SIREx): 1. Scaling and Statistical Properties of Sea‐Ice Deformation Fields

AbstractAs the sea‐ice modeling community is shifting to advanced numerical frameworks, developing new sea‐ice rheologies, and increasing model spatial resolution, ubiquitous deformation features in the Arctic sea ice are now being resolved by sea‐ice models. Initiated at the Forum for Arctic Modeling and Observational Synthesis, the Sea Ice Rheology Experiment (SIREx) aims at evaluating state‐of‐the‐art sea‐ice models using existing and new metrics to understand how the simulated deformation fields are affected by different representations of sea‐ice physics (rheology) and by model configuration. Part 1 of the SIREx analysis is concerned with evaluation of the statistical distribution and scaling properties of sea‐ice deformation fields from 35 different simulations against those from the RADARSAT Geophysical Processor System (RGPS). For the first time, the viscous‐plastic (and the elastic‐viscous‐plastic variant), elastic‐anisotropic‐plastic, and Maxwell‐elasto‐brittle rheologies are compared in a single study. We find that both plastic and brittle sea‐ice rheologies have the potential to reproduce the observed RGPS deformation statistics, including multi‐fractality. Model configuration (e.g., numerical convergence, atmospheric representation, spatial resolution) and physical parameterizations (e.g., ice strength parameters and ice thickness distribution) both have effects as important as the choice of sea‐ice rheology on the deformation statistics. It is therefore not straightforward to attribute model performance to a specific rheological framework using current deformation metrics. In light of these results, we further evaluate the statistical properties of simulated Linear Kinematic Features in a SIREx Part 2 companion paper.

Investigating Strike‐Slip Faulting Parallel to the Icelandic Plate Boundary Using Boundary Element Models

AbstractMost faults in Iceland strike roughly parallel to the divergent plate boundary, a part of the North American‐Eurasian plate boundary, which would be expected to lead to primarily normal faulting. However, several studies have observed a significant component of rift‐parallel strike‐slip faulting in Iceland. To investigate these fault kinematics, we use the boundary element method to model fault slip and crustal stress patterns of the Icelandic tectonic system, including a spherical hotspot and uniaxial stress that represents rifting. On a network of faults, we estimate the slip required to relieve traction imposed by hotspot inflation and remote rifting stress and compare the model results with observed slip kinematics, crustal seismicity, and geodetic data. We note a good fit between model‐predicted and observed deformation metrics, with both indicating significant components of normal and strike‐slip faulting and consistency between recent data and longer‐term records of geologic fault slip. Possible stress permutations between steeply plunging σ1 and σ2 axes are common in our models, suggesting that localized stress perturbations may impact strike‐slip faulting. Some increases in model complexity, including older hotspot configurations and allowing fault opening to simulate dike intrusion, show improvement to model fit in select regions. This work provides new insight into the physical mechanisms driving faulting styles within Iceland away from the current active plate boundary, implying that a significant portion of observed strike‐slip faulting is likely caused by the combined effects of tectonic rifting, hotspot impacts, and mechanical interactions across the fault network.

Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions

Industry 4.0 solutions are composed of autonomous engineered systems where heterogeneous agents act in a choreographed manner to create complex workflows. Agents work at low-level in a flexible and independent manner, and their actions and behaviour may be sparsely manipulated. Besides, agents such as humans tend to show a very dynamic behaviour and processes may be executed in a very anarchic, but correct way. Thus, innovative, and more flexible control techniques are required. In this work, supervisory control techniques are employed to guarantee a correct execution of distributed and choreographed processes in Industry 4.0 scenarios. At prosumer level, processes are represented using soft models where logic rules and deformation indicators are used to analyse the correctness of executions. These logic rules are verified using specific engines at business level. These engines are fed with deformation metrics obtained through tensor deformation functions at production level. To apply deformation functions, processes are represented as discrete flexible solids in a phase space, under external forces representing the variations in every task’s inputs. The proposed solution presents two main novelties and original contributions. On the one hand, the innovative use of soft models and deformation indicators allows the implementation of this control solution not only in traditional industrial scenarios where rigid procedures are followed, but also in other future engineered applications. On the other hand, the original integration of logic rules and events makes possible to control any kind of device, including those which do not have an explicit control plane or interface. Finally, to evaluate the performance of the proposed solution, an experimental validation using a real pervasive computing infrastructure is carried out.

Deformation Precursors to Catastrophic Failure in Rocks

AbstractForecasting the timing of catastrophic failure, such as crustal earthquakes, has been a central concern for centuries. Such forecasting requires identifying signals that evolve or accelerate in the precursory phase leading to failure, and the subset of signals that may be detected in the crust. We develop machine learning models to predict the proximity of catastrophic failure in synchrotron X‐ray tomography triaxial compression experiments on rocks using characteristics of evolving fracture networks. We then examine the characteristics that most strongly influence the model results, and thus may be considered the best predictors of the proximity of macroscopic failure. The resulting suite of predictive parameters underscores the importance of dilation in the precursory phase leading to catastrophic failure. The results indicate that the evolution of the strain energy density field may provide more robust predictions of the proximity of failure than other existing metrics of rock deformation.

Magnitude Calculation without Saturation from Strong-Motion Waveforms

ABSTRACTAfter destructive earthquakes, it is a challenge to estimate magnitude rapidly and accurately for dissemination to emergency responders and the public. Here, we propose criteria to calculate peak ground displacement (PGD) from strong-motion records, which can be used to calculate unsaturated event magnitude. Using collocated strong-motion and Global Navigation Satellite Systems observations of five major earthquakes in Japan, we demonstrate the effectiveness and accuracy of our strategy. Our results show that, with the right filtering criteria, PGD estimated from strong-motion acceleration waveforms is consistent with geodetic estimates. The methodology, however, does not allow for calculation of reliable estimates of coseismic deformation or other ground displacement metrics. We demonstrate a simulated real-time magnitude estimation that suggests it is feasible to generate an unsaturated magnitude estimate in real time from near-field strong-motion records. These findings have important implications for early warning and emergency response in seismically active areas, especially where real-time strong-motion data are more widely available than geodetic measurements.

Dynamic recovery observed in distinct grains within a polycrystalline nickel-based superalloy during cyclic high temperature fatigue via high energy X-ray diffraction microscopy

Elastic micromechanical fields are tracked for a nickel-based superalloy polycrystal via high energy X-ray diffraction microscopy (HEDM) and corresponding intragranular deformation metrics are determined with peak broadening analysis during cyclic high temperature loading. A low solvus, high refractory (LSHR) sample with 252 grains was subjected to uniaxial tension and held under fixed displacement while thermally cycling between 460 °C and 770 °C with intermittent HEDM characterization to study the grain average response to thermo-mechanical deformation. Elevated temperatures were found to allow for heterogeneous amounts of recovery amongst distinct grains within the sampled region, indicating complex grain interactions; the extent of recovery was greater at higher temperatures.

Prediction of patellofemoral joint kinematics and contact through co-simulation of rigid body dynamics and nonlinear finite element analysis

Joint-level rigid body dynamics simulations, when coupled with tissue-level finite element analyses, can simultaneously provide movement and tissue deformation metrics to understand mechanical interactions within the joint on a multi-scale level. In this study, a co-simulation workflow of a joint-level rigid body model that predicts the relative motion as a function of the non-linear cartilage response predicted by a non-linear implicit finite element solver is presented. Predictions are compared to in-vitro measurements (The Open Knee(s) project) in terms of the mean error and level-of-agreement: pressureerror = 0.46 MPa (level-of-agreement, −0.23 – 1.1 MPa); areaerror = −89 mm2 (level-of-agreement, −280 – 98 mm2) and contact forceerror = 93 N (level-of-agreement, 7.8 – 180 N). The automated co-simulation control algorithm enables multiscale coupling between joint and tissue-level models with real-time two-way communication as opposed to the traditional feed-forward approach of multi-scale models.

Clinical feasibility of MR-assisted CT-based cervical brachytherapy using MR-to-CT deformable image registration

PurposeThe purpose of this study is to evaluate the feasibility of using deformable image registration algorithms to improve high-dose-rate high-risk clinical target volume (HR-CTV) delineation between preapplicator implantation MRI (pre-MRI) and postapplicator implantation CT (post-CT) in the treatment of locally advanced cervical cancer (LACC). Method and materialsTwenty-six patients were identified for the study. Regions of interest were segmented on MRI and CT. A HR-CTV was delineated on pre-MRI and compared with the previously contoured HR-CTV on the post-CT. Two commercially available algorithms, ANACONDA (anatomically constrained) and MORFEUS (biomechanical model based) with various controlling structure settings, including the cervix, uterus, etc., were used to deform pre-MRI to post-CT. MRI-to-CT deformed targets are denoted as HR-CTV’. Quantitative deformation metrics include Dice index, distance to agreement, and center of mass displacement. Qualitative clinical usefulness of deformations was scored based on HR-CTV identification on CT images. ResultsFor ANACONDA and MORFEUS deformations, using a cervix controlling region of interest resulted in the highest Dice, lowest distance to agreement, and lowest center of mass displacement for HR-CTV′. With MORFEUS deformations, the deformed HR-CTV’ proved clinically useful in 23 patients. ConclusionsPrebrachytherapy implantation MRI can aid target contours for CT-based brachytherapy through ANACONDA or MORFEUS algorithms with appropriate parameter selection for LACC patients.