Subject-specific atlas for automatic brain tissue segmentation of neonatal magnetic resonance images.

Developing advanced systems for 3D brain tissue segmentation from neonatal magnetic resonance (MR) images is vital for newborn structural analysis. However, automatic segmentation of neonatal brain tissues is challenging due to smaller head size and inverted T1/T2 tissue contrast compared to adults. In this work, a subject-specific atlas based technique is presented for segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from neonatal MR images. It involves atlas selection, subject-specific atlas creation using random forest (RF) classifier, and brain tissue segmentation using the expectation maximization-Markov random field (EM-MRF) method. To increase the segmentation accuracy, different tissue intensity- and gradient-based features were used. Evaluation on 40 neonatal MR images (gestational age of 37-44 weeks) demonstrated an overall accuracy of 94.3% and an average Dice similarity coefficient (DSC) of 0.945 (GM), 0.947 (WM), and 0.912 (CSF). Compared to multi-atlas segmentation methods like SEGMA and EM-MRF with multiple atlases, our method improved accuracy by up to 4%, particularly in complex tissue regions. Our proposed method allows accurate brain tissue segmentation, a crucial step in brain magnetic resonance imaging (MRI) applications including brain surface reconstruction and realistic head model creation in neonates.

Musculoskeletal spine modeling in large patient cohorts: how morphological individualization affects lumbar load estimation.

Introduction: Achieving an adequate level of detail is a crucial part of any modeling process. Thus, oversimplification of complex systems can lead to overestimation, underestimation, and general bias of effects, while elaborate models run the risk of losing validity due to the uncontrolled interaction of multiple influencing factors and error propagation. Methods: We used a validated pipeline for the automated generation of multi-body models of the trunk to create 279 models based on CT data from 93 patients to investigate how different degrees of individualization affect the observed effects of different morphological characteristics on lumbar loads. Specifically, individual parameters related to spinal morphology (thoracic kyphosis (TK), lumbar lordosis (LL), and torso height (TH)), as well as torso weight (TW) and distribution, were fully or partly considered in the respective models according to their degree of individualization, and the effect strengths of these parameters on spinal loading were compared between semi- and highly individualized models. T-distributed stochastic neighbor embedding (T-SNE) analysis was performed for overarching pattern recognition and multiple regression analyses to evaluate changes in occurring effects and significance. Results: We were able to identify significant effects (p < 0.05) of various morphological parameters on lumbar loads in models with different degrees of individualization. Torso weight and lumbar lordosis showed the strongest effects on compression (ß ≈ 0.9) and anterior-posterior shear forces (ß ≈ 0.7), respectively. We could further show that the effect strength of individual parameters tended to decrease if more individual characteristics were included in the models. Discussion: The induced variability due to model individualization could only partly be explained by simple morphological parameters. Our study shows that model simplification can lead to an emphasis on individual effects, which needs to be critically assessed with regard to in vivo complexity. At the same time, we demonstrated that individualized models representing a population-based cohort are still able to identify relevant influences on spinal loading while considering a variety of influencing factors and their interactions.

Bayesian Learning of Personalized Longitudinal Biomarker Trajectory.

This work concerns the effective personalized prediction of longitudinal biomarker trajectory, motivated by a study of cancer targeted therapy for patients with chronic myeloid leukemia (CML). Continuous monitoring with a confirmed biomarker of residual disease is a key component of CML management for early prediction of disease relapse. However, the longitudinal biomarker measurements have highly heterogeneous trajectories between subjects (patients) with various shapes and patterns. It is believed that the trajectory is clinically related to the development of treatment resistance, but there was limited knowledge about the underlying mechanism. To address the challenge, we propose a novel Bayesian approach to modeling the distribution of subject-specific longitudinal trajectories. It exploits flexible Bayesian learning to accommodate complex changing patterns over time and non-linear covariate effects, and allows for real-time prediction of both in-sample and out-of-sample subjects. The generated information can help make clinical decisions, and consequently enhance the personalized treatment management of precision medicine.

3D printing of ferromagnetic passive shims for field shaping in magnetic resonance imaging.

Magnetic Resonance Imaging (MRI) often encounters image quality degradation due to magnetic field inhomogeneities. Conventional passive shimming techniques involve the manual placement of discrete magnetic materials, imposing limitations on correcting complex inhomogeneities. To overcome this, we propose a novel 3D printing method utilizing binder jetting technology to enable precise deposition of a continuous range of concentrations of ferromagnetic ink. This approach grants complete control of the magnitude of the magnetic moment within the passive shim enabling tailored corrections of B0 field inhomogeneities. By optimizing the magnetic field distribution using linear programming and an in-house written Computer-Aided Design (CAD) generation software, we printed shims with promising results in generating low spherical harmonic corrections. Experimental evaluations demonstrate feasibility of these 3D printed passive shims to induce target magnetic fields corresponding to second-order spherical harmonic, as evidenced by acquired B0 maps. The electrically insulating properties of the printed shims eliminate the risk of eddy currents and heating, thus ensuring safety. The dimensional fabrication accuracy of the printed shims surpasses previous methods, enabling more precise and localized correction of subject-specific inhomogeneities. The findings highlight the potential of binder-jetted 3D printed passive shims in MRI shimming as a versatile and efficient solution for fabricating passive shims, with the potential to enhance the quality of MRI imaging while also being applicable to other types of Magnetic Resonance systems.

How reliable are femoropelvic kinematics during deep squats? The influence of subject-specific skeletal modelling on measurement variability.

BACKGROUND: Biplanar radiography displays promising results in the production of subject-specific (S.specific) biomechanical models. However, the focus has predominantly centred on methodological investigations in gait analysis. Exploring the influence of such models on the analysis of high range of motion tasks linked to hip pathologies is warranted. The aim of this study is to investigate the effect of S.Specific modelling techniques on the reliability of deep squats kinematics in comparison to generic modelling. METHODS: 8 able-bodied male participants attended 5 motion capture sessions conducted by 3 observers and performed 5 deep squats in each. Prior to each session a biplanar scan was acquired with the reflective-markers attached. Inverse kinematics of pelvis and thigh segments were calculated based on S.specific and Generic model definition. Agreement between the two models femoropelvic orientation in standing was assessed with Bland-Altman plots and paired t- tests. Inter-trial, inter-session, inter-observer variability and observer/trial difference and ratio were calculated for squat kinematic data derived from the two modelling approaches. RESULTS: Compared to the Generic model, the S.Specific model produced a calibration trial that is significantly offset into more posterior pelvis tilt (-2.8±2.7), hip extension (-2.2±3.8), hip abduction (-1.2±3.6) and external rotation (-13.8±11.4). The S.specific model produced significantly different squat kinematics in the sagittal plane of the pelvis (entire squat cycle) and hip (between 40 % and 60 % of the squat cycle). Variability analysis indicated that the error magnitude between the two models was comparable (difference<2°). The S.specific model exhibited a lower variability in the observer/trial ratio in the sagittal pelvis and hip, the frontal hip, but showed a higher variability in the transverse hip. SIGNIFICANCE: S.specific modelling appears to introduce a calibration offset that primarily translates into an effect in the sagittal plane kinematics. However, the clinical added value of S.specific modelling in terms of reducing experimental sources of kinematic variability was limited.

Sustaining vocabulary knowledge growth through corpus-generated lists of lexical bundles and keywords in the law of contracts.

Non-native English-speaking law students and international legal practitioners who speak English as an additional language face significant challenges while pursuing legal studies at English-only institutions, participating in professional training or catering to the legal needs of an increasingly diverse clientele. One of the most difficult challenges is sustaining adequate lexical knowledge to initiate and maintain communication regarding legal subject matter. This study aims to address this issue by presenting two short lists of lexical bundles and keywords (KWs) of the Law of Contracts. Through a combination of corpus analysis and linguistics methodology, these lists are designed to provide a pedagogically useful and subject-focused source for learning academic vocabulary. Bundles are functionally classified into referentials, discourse organisers and stance markers, and their structural forms are filtered into distinct nominal, prepositional and verbal categories. KWs are POS-tagged to allow for direct instructional intervention. This research discusses the pedagogical implications of the research for teaching English for legal purposes.

Posterior tibial slope influences joint mechanics and soft tissue loading after total knee arthroplasty.

As a solution to restore knee function and reduce pain, the demand for Total Knee Arthroplasty (TKA) has dramatically increased in recent decades. The high rates of dissatisfaction and revision makes it crucially important to understand the relationships between surgical factors and post-surgery knee performance. Tibial implant alignment in the sagittal plane (i.e., posterior tibia slope, PTS) is thought to play a key role in quadriceps muscle forces and contact conditions of the joint, but the underlying mechanisms and potential consequences are poorly understood. To address this biomechanical challenge, we developed a subject-specific musculoskeletal model based on the bone anatomy and precise implantation data provided within the CAMS-Knee datasets. Using the novel COMAK algorithm that concurrently optimizes joint kinematics, together with contact mechanics, and muscle and ligament forces, enabled highly accurate estimations of the knee joint biomechanics (RMSE <0.16 BW of joint contact force) throughout level walking and squatting. Once confirmed for accuracy, this baseline modelling framework was then used to systematically explore the influence of PTS on knee joint biomechanics. Our results indicate that PTS can greatly influence tibio-femoral translations (mainly in the anterior-posterior direction), while also suggesting an elevated risk of patellar mal-tracking and instability. Importantly, however, an increased PTS was found to reduce the maximum tibio-femoral contact force and improve efficiency of the quadriceps muscles, while also reducing the patellofemoral contact force (by approximately 1.5% for each additional degree of PTS during walking). This study presents valuable findings regarding the impact of PTS variations on the biomechanics of the TKA joint and thereby provides potential guidance for surgically optimizing implant alignment in the sagittal plane, tailored to the implant design and the individual deficits of each patient.

Validation of a Finite Element Simulation for Predicting Individual Knee Joint Kinematics.

Goal: We introduce an in-vivo validated finite element (FE) simulation approach for predicting individual knee joint kinematics. Our vision is to improve clinicians' understanding of the complex individual anatomy and potential pathologies to improve treatment and restore physiological joint kinematics. Methods: Our 3D FE modeling approach for individual human knee joints is based on segmentation of anatomical structures extracted from routine static magnetic resonance (MR) images. We validate the predictive abilities of our model using static MR images of the knees of eleven healthy volunteers in dedicated knee poses, which are achieved using a customized MR-compatible pneumatic loading device. Results: Our FE simulations reach an average translational accuracy of 2 mm and an average angular accuracy of 1[Formula: see text] compared to the reference knee pose. Conclusions: Reaching high accuracy, our individual FE model can be used in the decision-making process to restore knee joint stability and functionality after various knee injuries.

Prediction of jaw opening function after mandibular reconstruction using subject-specific musculoskeletal modelling.

BACKGROUND: Mandibular reconstruction patients often suffer abnormalities in the mandibular kinematics. In silico simulations, such as musculoskeletal modelling, can be used to predict post-operative mandibular kinematics. It is important to validate the mandibular musculoskeletal model and analyse the factors influencing its accuracy. OBJECTIVES: To investigate the jaw opening-closing movements after mandibular reconstruction, as predicted by the subject-specific musculoskeletal model, and the factors influencing its accuracy. METHODS: Ten mandibular reconstruction patients were enrolled in this study. Cone-beam computed tomography images, mandibular movements, and surface electromyogram signals were recorded preoperatively. A subject-specific mandibular musculoskeletal model was established to predict surgical outcomes using patient-averaged muscle parameter changes as model inputs. Jaw bone geometry was replaced by surgical planning results, and the muscle insertion sites were registered based on the non-rigid iterative closest point method. The predicted jaw kinematic data were validated based on 6-month post-operative measurements. Correlations between the prediction accuracy and patient characteristics (age, pathology and surgical scope) were further analysed. RESULTS: The root mean square error (RMSE) for lower incisor displacement was 31.4%, and the error for peak magnitude of jaw opening was 4.9 mm. Age, post-operative infection and radiotherapy influenced the prediction accuracy. The amount of masseter detachment showed little correlation with jaw opening. CONCLUSION: The mandibular musculoskeletal model successfully predicted short-range jaw opening functions after mandibular reconstruction. It provides a novel surgical planning method to predict the risk of developing trismus.

Subject-specific material properties of the heel pad: An inverse finite element analysis.

Individuals with diabetes are at a higher risk of developing foot ulcers. To better understand internal soft tissue loading and potential treatment options, subject-specific finite element (FE) foot models have been used. However, existing models typically lack subject-specific soft tissue material properties and only utilize subject-specific anatomy. Therefore, this study determined subject-specific hindfoot soft tissue material properties from one non-diabetic and one diabetic subject using inverse FE analysis. Each subject underwent cyclic MRI experiments to simulate physiological gait and to obtain compressive force and three-dimensional soft tissue imaging data at 16 phases along the loading-unloading cycles. The FE models consisted of rigid bones and nearly-incompressible first-order Ogden hyperelastic skin, fat, and muscle (resulting in six independent material parameters). Then, calcaneus and loading platen kinematics were computed from imaging data and prescribed to the FE model. Two analyses were performed for each subject. First, the skin, fat, and muscle layers were lumped into a single generic soft tissue material and optimized to the platen force. Second, the skin, fat, and muscle material properties were individually determined by simultaneously optimizing for platen force, muscle vertical displacement, and skin mediolateral bulging. Our results indicated that compared to the individual without diabetes, the individual with diabetes had stiffer generic soft tissue behavior at high strain and that the only substantially stiffer multi-material layer was fat tissue. Thus, we suggest that this protocol serves as a guideline for exploring differences in non-diabetic and diabetic soft tissue material properties in a larger population.

Nonlocal based FISTA network for noninvasive cardiac transmembrane potential imaging.

Objective. The primary aim of our study is to advance our understanding and diagnosis of cardiac diseases. We focus on the reconstruction of myocardial transmembrane potential (TMP) from body surface potential mapping.Approach. We introduce a novel methodology for the reconstruction of the dynamic distribution of TMP. This is achieved through the integration of convolutional neural networks with conventional optimization algorithms. Specifically, we utilize the subject-specific transfer matrix to describe the dynamic changes in TMP distribution and ECG observations at the body surface. To estimate the TMP distribution, we employ LNFISTA-Net, a learnable non-local regularized iterative shrinkage-thresholding network. The coupled estimation processes are iteratively repeated until convergence.Main results. Our experiments demonstrate the capabilities and benefits of this strategy. The results highlight the effectiveness of our approach in accurately estimating the TMP distribution, thereby providing a reliable method for the diagnosis of cardiac diseases.Significance. Our approach demonstrates promising results, highlighting its potential utility for a range of applications in the medical field. By providing a more accurate and dynamic reconstruction of TMP, our methodology could significantly improve the diagnosis and treatment of cardiac diseases, thereby contributing to advancements in healthcare.

Rapid patient-specific FEM meshes from 3D smart-phone based scans.

Objective.The objective of this study was to describe and evaluate a smart-phone based method to rapidly generate subject-specific finite element method (FEM) meshes. More accurate FEM meshes should lead to more accurate thoracic electrical impedance tomography (EIT) images.Approach.The method was evaluated on an iPhone®that utilized an app called Heges, to obtain 3D scans (colored, surface triangulations), a custom belt, and custom open-source software developed to produce the subject-specific meshes. The approach was quantitatively validated via mannequin and volunteer tests using an infrared tracker as the gold standard, and qualitatively assessed in a series of tidal-breathing EIT images recorded from 9 subjects.Main results.The subject-specific meshes can be generated in as little as 6.3 min, which requires on average 3.4 min of user interaction. The mannequin tests yielded high levels of precision and accuracy at 3.2 ± 0.4 mm and 4.0 ± 0.3 mm root mean square error (RMSE), respectively. Errors on volunteers were only slightly larger (5.2 ± 2.1 mm RMSE precision and 7.7 ± 2.9 mm RMSE accuracy), illustrating the practical RMSE of the method.Significance.Easy-to-generate, subject-specific meshes could be utilized in the thoracic EIT community, potentially reducing geometric-based artifacts and improving the clinical utility of EIT.

Effect of low back pain on the kinetics and kinematics of the lumbar spine - a combined in vivo and in silico investigation.

Lifting is a significant risk factor for low back pain (LBP). Different biomechanical factors including spinal loads, kinematics, and muscle electromyography (EMG) activities have previously been investigated during lifting activities in LBP patients and asymptomatic individuals to identify their association with LBP. However, the findings were contradictory and inconclusive. Accurate and subject-specific prediction of spinal loads is crucial for understanding, diagnosing, planning tailored treatments, and preventing recurrent pain in LBP patients. Therefore, the present study aimed to estimate the L5-S1 compressive and resultant shear loads in 19 healthy and 17 non-specific chronic LBP individuals during various static load-holding tasks (holding a 10 kg box at hip, chest, and head height) using full-body and personalized musculoskeletal models driven by subject-specific in vivo kinematic/kinetic, EMG, and physiological cross-sectional areas (PCSAs) data. These biomechanical characteristics were concurrently analyzed to identify potential differences between the two groups. Statistical analyses showed that LBP had almost no significant effect on the range of motion (trunk, lumbar, pelvis), PCSA, and EMG. There were no significant differences (p > 0.05) in the predicted L5-S1 loads. However, as the task became more demanding, by elevating the hand-load from hip to head, LBP patients experienced significant increases in both compressive (33 %, p = 0.00) and shear (25 %, p = 0.02) loads, while asymptomatic individuals showed significant increases only in compressive loads (30 %, p = 0.01). This suggests that engaging in more challenging activities could potentially magnify the effect of LBP on the biomechanical factors and increase their discrimination capacity between LBP and asymptomatic individuals.

Prediction of trunk muscle activation and spinal forces in adolescent idiopathic scoliosis during simulated trunk motion: A musculoskeletal modelling study.

Due to lack of reference validation data, the common strategy in characterizing adolescent idiopathic scoliosis (AIS) by musculoskeletal modelling approach consists in adapting structure and parameters of validated body models of adult individuals with physiological alignments. Until now, only static postures have been replicated and investigated in AIS subjects. When aiming to simulate trunk motion, two critical factors need consideration: how distributing movement along the vertebral motion levels (lumbar spine rhythm), and if neglecting or accounting for the contribution of the stiffness of the motion segments (disc stiffness). The present study investigates the effect of three different lumbar spine rhythms and absence/presence of disc stiffness on trunk muscle imbalance in the lumbar region and on intervertebral lateral shear at different levels of the thoracolumbar/lumbar scoliotic curve, during simulated trunk motions in the three anatomical planes (flexion/extension, lateral bending, and axial rotation). A spine model with articulated ribcage previously developed in AnyBody software and adapted to replicate the spinal alignment in AIS subjects is employed. An existing dataset of 100 subjects with mild and moderate scoliosis is exploited. The results pointed out the significant impact of lumbar spine rhythm configuration and disc stiffness on changes in the evaluated outputs, as well as a relationship with scoliosis severity. Unfortunately, no optimal settings can be identified due to lack of reference validation data. According to that, extreme caution is recommended when aiming to adapt models of adult individuals with physiological alignments to adolescent subjects with scoliotic deformity.

Subject-specific timing adaption in time-encoded arterial spin labeling imaging.

OBJECTIVES: One challenge in arterial spin labeling (ASL) is the high variability of arterial transit times (ATT), which causes associated arterial transit delay (ATD) artifacts. In patients with pathological changes, these artifacts occur when post-labeling delay (PLD) and bolus durations are not optimally matched to the subject, resulting in difficult quantification of cerebral blood flow (CBF) and ATT. This is also true for the free lunch approach in Hadamard-encoded pseudocontinuous ASL (H-pCASL). MATERIAL AND METHODS: Five healthy volunteers were scanned with a 3 T MR-system. pCASL-subbolus timing was adjusted individually by the developed adaptive Walsh-ordered pCASL sequence and an automatic feedback algorithm. The quantification results for CBF and ATT and the respective standard deviations were compared with results obtained using recommended timings and intentionally suboptimal timings. RESULTS: The algorithm individually adjusted the pCASL-subbolus PLD for each subject within the range of recommended timing for healthy subjects, with a mean intra-subject adjustment deviation of 47.15 ms for single-shot and 44.5 ms for segmented acquisition in three repetitions. DISCUSSION: A first positive assessment of the results was performed on healthy volunteers. The extent to which the results can be transferred to patients and are of benefit must be investigated in follow-up studies.

High-Dimensional Gaussian Graphical Regression Models with Covariates.

Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks. Our framework encourages sparsity of covariate effects on both the mean and the precision matrix. In particular for the precision matrix, we stipulate simultaneous sparsity, i.e., group sparsity and element-wise sparsity, on effective covariates and their effects on network edges, respectively. We establish variable selection consistency first under the case with known mean parameters and then a more challenging case with unknown means depending on external covariates, and establish in both cases the ℓ2 convergence rates and the selection consistency of the estimated precision parameters. The utility and efficacy of our proposed method is demonstrated through simulation studies and an application to a co-expression QTL study with brain cancer patients.

Calibration of Holzapfel-Gasser-Ogden collateral ligament properties in a hybrid post-arthroplasty knee joint model for laxity testing.

Knee collateral ligaments play a vital role in providing frontal-plane stability in post-total knee arthroplasty (TKA) knees. Finite element models can utilize computationally efficient one-dimensional springs or more physiologically accurate three-dimensional continuum elements like the Holzapfel-Gasser-Ogden (HGO) formulation. However, there is limited literature defining subject-specific mechanical properties, particularly for the HGO model. In this study, we propose a co-simulation framework to obtain subject-specific material parameters for an HGO-based finite element ligament model integrated into a rigid-body model of the post-TKA knee. Our approach achieves comparable accuracy to spring formulations while significantly reducing coefficient calibration time and demonstrating improved correlation with reference knee kinematics and ligament strains throughout the tested loading range.

American Society of Biomechanics Journal of Biomechanics Award 2022: Computer models do not accurately predict human muscle passive muscle force and fiber length: Evaluating subject-specific modeling impact on musculoskeletal model predictions.

Musculoskeletal models are valuable for studying and understanding the human body in a variety of clinical applications that include surgical planning, injury prevention, and prosthetic design. Subject-specific models have proven to be more accurate and useful compared to generic models. Nevertheless, it is important to validate all models when possible. To this end, gracilis muscle-tendon parameters were directly measured intraoperatively and used to test model predictions. The aim of this study was to evaluate the benefits and limitations of systematically incorporating subject-specific variables into muscle models used to predict passive force and fiber length. The results showed that incorporating subject-specific values generally reduced errors, although significant errors still existed. Optimization of the modeling parameter "tendon slack length" was explored in two cases: minimizing fiber length error and minimizing passive force error. The results showed that using all subject-specific values yielded the most favorable outcome in both models and optimization cases. However, the trade-off between fiber length error and passive force error will depend on the specific circumstances and research objectives due to significant individual errors. Notably, individual fiber length and passive force errors were as high as 20% and 37% respectively. Finally, the modeling parameter "tendon slack length" did not correlate with any real-world anatomical length.

Comparative validation of two patient-specific modelling pipelines for predicting knee joint forces during level walking.

Over the past few years, the use of computer models and simulations tailored to the patient's physiology to assist clinical decision-making has increased enormously.While several pipelines to develop personalized models exist, their adoption on a large scale is still limited due to the required niche computational skillset and the lengthy operations required. Novel toolboxes, such as STAPLE, promise to streamline and expedite the development of image-based skeletal lower limb models. STAPLE-generated models can be rapidly generated, with minimal user input, and present similar joint kinematics and kinetics compared to models developed employing the established INSIGNEO pipeline. Yet, it is unclear how much the observed discrepancies scale up and affect joint contact force predictions. In this study, we compared image-based musculoskeletal models developed (i) with the INSIGNEO pipeline and (ii) with a semi-automated pipeline that combines STAPLE and nmsBuilder, and assessed their accuracy against experimental implant data.Our results showed that both pipelines predicted similar total knee joint contact forces between one another in terms of profiles and average values, characterized by a moderately high level of agreement with the experimental data. Nonetheless, the Student t-test revealed statistically significant differences between both pipelines. Of note, the STAPLE-based pipeline required considerably less time than the INSIGNEO pipeline to generate a musculoskeletal model (i.e., 60 vs 160 min). This is likely to open up opportunities for the use of personalized musculoskeletal models in clinical practice, where time is of the essence.

A Novel Methodology to Estimate Bone Mechanical Properties Using Dual-Energy Imaging to Improve Pedicle Screw Fixation.

OBJECTIVE: To develop a methodology to improve the representation of the mechanical properties of a vertebral finite element model (FEM) based on a new dual-energy (DE) imaging technology to improve pedicle screw fixation. METHODS: Bone-calibrated radiographs were generated with dual-energy imaging technology in order to estimate the mechanical properties of the trabecular bone. Properties were included in regions of interest in four vertebral FEMs representing heterogeneity and homogeneity, as a realistic and reference model, respectively. Biomechanical parameters were measured during screw pull-out testing to evaluate pedicle screw fixation. RESULTS: Simulations with property distributions deduced from dual-energy imaging characterization (heterogeneous models) induced an increase in biomechanical indicators versus with a homogeneous representation, implying different behaviors for the subject-specific models. CONCLUSION: The presented methodology allows a patient-specific representation of bone quality in a FEM using new DE imaging technology. Consideration of individualized bone distribution in a spinal FEM improves the perspective of orthopedic surgical planning over otherwise underestimated results using a homogeneous representation.