Clinical observation of diminished bone quality and quantity through longitudinal HR-pQCT-derived remodeling and mechanoregulation.

High resolution peripheral quantitative computed tomography (HR-pQCT) provides methods for quantifying volumetric bone mineral density and microarchitecture necessary for early diagnosis of bone disease. When combined with a longitudinal imaging protocol and finite element analysis, HR-pQCT can be used to assess bone formation and resorption (i.e., remodeling) and the relationship between this remodeling and mechanical loading (i.e., mechanoregulation) at the tissue level. Herein, 25 patients with a contralateral distal radius fracture were imaged with HR-pQCT at baseline and 9-12 months follow-up: 16 patients were prescribed vitamin D3 with/without calcium supplement based on a blood biomarker measures of bone metabolism and dual-energy X-ray absorptiometry image-based measures of normative bone quantity which indicated diminishing (n = 9) or poor (n = 7) bone quantity and 9 were not. To evaluate the sensitivity of this imaging protocol to microstructural changes, HR-pQCT images were registered for quantification of bone remodeling and image-based micro-finite element analysis was then used to predict local bone strains and derive rules for mechanoregulation. Remodeling volume fractions were predicted by both average values of trabecular and cortical thickness and bone mineral density (R2 > 0.8), whereas mechanoregulation was affected by dominance of the arm and group classification (p < 0.05). Overall, longitudinal, extended HR-pQCT analysis enabled the identification of changes in bone quantity and quality too subtle for traditional measures

Bone remodeling simulations: challenges, problems and applications

La remodelación ósea es el mecanismo que regula la relación entre la morfología del hueso y sus cargas mecánicas externas. Se basa en el hecho de que el hueso se adapta a las condiciones mecánicas a las que está expuesto. Varios factores mecánicos y bioquímicos pueden regular la respuesta final de la remodelación ósea. De hecho, se considera que la remodelación ósea pretende alcanzar varios objetivos mecánicos: reparar el daño para reducir el riesgo de fractura y optimizar la rigidez y resistencia con el mínimo peso. Durante las últimas décadas, se han propuesto un gran número de leyes matemáticas implementadas numéricamente, pero la mayoría de ellas presentan diferentes problemas como la estabilidad, la convergencia o la dependencia de las condiciones iniciales. Por tanto, el objetivo principal de esta tesis es estudiar los modelos de remodelación ósea, mostrando sus retos, su problemática y su aplicación en el ámbito clínico. En primer lugar, se han revisado dos teorías clásicas de la remodelación ósea (conocidas como modelo de Stanford y modelo de Doblaré y García). En ambos casos, se propone un aspecto novedoso planteando que el estímulo homeostático de referencia no es constante, sino que depende localmente de la historia de carga que cada punto local está soportando. Como consecuencia directa de esta hipótesis, se demuestra que las inestabilidades numéricas que normalmente presentan estos algoritmos, pueden quedar resueltas, mejorando claramente los resultados finales. Esta metodología se aplicó a un modelo de elementos finitos 2D/3D mejorando la convergencia de la solución y asegurando su estabilidad numérica a largo plazo. Por otra parte, en un intento de dilucidar las características de adaptación mecánica del hueso en diferentes escalas, se plantea una relación a nivel órgano y a nivel de tejido que depende de un cambio en el estímulo homeostático de referencia acorde con la densidad aparente, mientras que se considera que la densidad de energía de deformación a nivel de tejido permanece invariante. Esta hipótesis mejora la unicidad de la solución y la hace independiente de las condiciones iniciales, ayudando también a su estabilidad numérica. Además, en esta tesis se aborda el modelado de paciente específico que es un tema que está adquiriendo cada vez más importancia. Una de las principales dificultades en la creación de modelos de paciente específico, es la determinación de las cargas que el hueso está realmente soportando. Los datos relativos a pacientes específicos, como la geometría ósea y la distribución de la densidad ósea, puede ser utilizados para determinar estas cargas. Por lo tanto, se ha estudiado la estimación de la cargas con tres diferentes técnicas matemáticas: regresión lineal, redes neuronales artificiales y máquinas de soporte vector. Estas técnicas se han aplicado a un ejemplo teórico para obtener las cargas a través de la densidad aparente que se predice con los modelos de remodelación ósea. Para concluir, la metodología desarrollada que combina modelos de remodelación ósea con redes neuronales se ha aplicado a la predicción de las cargas de cinco tibias de pacientes. Para ello, se han determinado la geometría y la distribución de la densidad a partir de un TAC y se han introducido los valores de densidad en el modelo previamente desarrollado, obteniendo así, las cargas específicas de las tibias de los pacientes. Con el fin de validar la capacidad de esta novedosa técnica, se han comparado las cargas obtenidas de la técnica propuesta con las cargas obtenidas en un análisis de marcha de dichos pacientes. Los errores obtenidos en las predicciones han sido menores de un 6 %. Por lo tanto, se puede concluir que la metodología aquí propuesta, permite determinar de forma aproximada las cargas que un hueso específico soporta.Bone remodeling is the mechanism that regulates the relationship between bone morphology and its external mechanical loads. It is based on the fact that bone adapts itself to the mechanical conditions to which it is exposed. Several mechanical and biochemical factors may regulate the final bone remodeling response. In fact, bone remodeling is hypothesized to achieve several mechanical objectives: repair damage to reduce the risk of fracture and optimize stiffness and strength with minimum weight. During recent decades, a great number of numerically implemented mathematical laws have been proposed, but most of them present different problems as stability, convergence or dependence of the initial conditions. Thus, the main scope of this Thesis is to study bone remodeling models, showing their challenges, their problematic and their applicability in the clinical setting. Firstly, we revisit two classical bone remodeling theories (Stanford model and Doblaré and García model). In both of them, the reference homeostatic stimulus is hypothesized that is not constant, but it is locally dependent on the loading history that each local point is effectively supporting. As a direct consequence of this assumption, we demonstrate that the numerical instabilities that all these algorithms normally present can be solved, clearly improving the final results. For this reason, we applied this methodology to 2D/3D finite element models. This contribution improves the convergence of the solution, leading to its numerical stability in the long-term. In an attempt to elucidate the features of bone adaptation at the di erent scales, we hypothesize that the relationship between the organ level and tissue level depends on the reference homeostatic stimulus changes according to the density and the tissue effective energy remains unchanged. This assumption improves the uniqueness of the solution, independently of the initial conditions selected and clearly helps in its numerical stability. In addition, patient-specific modeling is becoming increasingly important. One of the most challenging diffculties in creating patient-specific models is the determination of the specific load that the bone is really supporting. Real information related to specific patients, such as bone geometry and bone density distribution, can be used to determine patient loads. Therefore, we studied three different mathematical techniques: linear regression, artificial neural networks (ANN) and support vector machines (SVM). These techniques have been applied to a theoretical femur to obtain the load through the density that came from many bone remodeling simulations. Finally, the application of this novel methodology has been applied for the loading prediction of five real tibias. We are able to determine the subject-specific forces from CT data, from which we obtain bone geometry and density distribuviition of the five tibias. Then, the density values at certain bone regions have been introduced in the methodology developed that combines bone remodeling models and artificial neuronal networks (ANN) for obtaining the predicted subject-specific loads. Finally, in order to validate this novel technique for tibia loading predictions, we compare predicted loads with the loads obtained from the patientspecific musculoskeletal model. The errors between both loads were lower tan 6%. Therefore, the methodology proposed has been validate

Delivering computationally-intensive digital patient applications to the clinic: An exemplar solution to predict femoral bone strength from CT data

Background and objective:Whilst fragility hip fractures commonly affect elderly people, often causing permanent disability or death, they are rarely addressed in advance through preventive techniques. Quantification of bone strength can help to identify subjects at risk, thus reducing the incidence of fractures in the population. In recent years, researchers have shown that finite element models (FEMs) of the hip joint, derived from computed tomography (CT) images, can predict bone strength more accurately than other techniques currently used in the clinic. The specialised hardware and trained personnel required to perform such analyses, however, limits the widespread adoption of FEMs in clinical contexts. In this manuscript we present CT2S (Computed Tomography To Strength), a system developed in collaboration between The University of Sheffield and Sheffield Teaching Hospitals, designed to streamline access to this complex workflow for clinical end-users. Methods:The system relies on XNAT and makes use of custom apps based on open source software. Available through a website, it allows doctors in the healthcare environment to benefit from FE based bone strength estimation without being exposed to the technical aspects, which are concealed behind a user-friendly interface. Clinicians request the analysis of CT scans of a patient through the website. Using XNAT functionality, the anonymised images are automatically transferred to the University research facility, where an operator processes them and estimates the bone strength through FEM using a combination of open source and commercial software. Following the analysis, the doctor is provided with the results in a structured report. Results:The platform, currently available for research purposes, has been deployed and fully tested in Sheffield, UK. The entire analysis requires processing times ranging from 3.5 to 8 h, depending on the available computational power. Conclusions:The short processing time makes the system compatible with current clinical workflows. The use of open source software and the accurate description of the workflow given here facilitates the deployment in other centres

Accurate volume alignment of arbitrarily oriented tibiae based on a mutual attention network for osteoarthritis analysis

Damage to cartilage is an important indicator of osteoarthritis progression, but manual extraction of cartilage morphology is time-consuming and prone to error. To address this, we hypothesize that automatic labeling of cartilage can be achieved through the comparison of contrasted and non-contrasted Computer Tomography (CT). However, this is non-trivial as the pre-clinical volumes are at arbitrary starting poses due to the lack of standardized acquisition protocols. Thus, we propose an annotation-free deep learning method, D-net, for accurate and automatic alignment of pre- and post-contrasted cartilage CT volumes. D-Net is based on a novel mutual attention network structure to capture large-range translation and full-range rotation without the need for a prior pose template. CT volumes of mice tibiae are used for validation, with synthetic transformation for training and tested with real pre- and post-contrasted CT volumes. Analysis of Variance (ANOVA) was used to compare the different network structures. Our proposed method, D-net, achieves a Dice coefficient of 0.87, and significantly outperforms other state-of-the-art deep learning models, in the real-world alignment of 50 pairs of pre- and post-contrasted CT volumes when cascaded as a multi-stage network

Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning

[EN] A great challenge in osteoporosis clinical assessment is identifying patients at higher risk of hip fracture. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold-standard, but its classification accuracy is limited to 65%. DXA-based Finite Element (FE) models have been developed to predict the mechanical failure of the bone. Yet, their contribution has been modest. In this study, supervised machine learning (ML) is applied in conjunction with clinical and computationally driven mechanical attributes. Through this multi-technique approach, we aimed to obtain a predictive model that outperforms BMD and other clinical data alone, as well as to identify the best-learned ML classifier within a group of suitable algorithms. A total number of 137 postmenopausal women (81.4 +/- 6.95 years) were included in the study and separated into a fracture group (n = 89) and a control group (n = 48). A semi-automatic and patient-specific DXA-based FE model was used to generate mechanical attributes, describing the geometry, the impact force, bone structure and mechanical response of the bone after a sideways-fall. After preprocessing the whole dataset, 19 attributes were selected as predictors. Support Vector Machine (SVM) with radial basis function (RBF), Logistic Regression, Shallow Neural Networks and Random Forest were tested through a comprehensive validation procedure to compare their predictive performance. Clinical attributes were used alone in another experimental setup for the sake of comparison. SVM was confirmed to generate the best-learned algorithm for both experimental setups, including 19 attributes and only clinical attributes. The first, generated the best-learned model and outperformed BMD by 14pp. The results suggests that this approach could be easily integrated for effective prediction of hip fracture without interrupting the actual clinical workflow.This study was partially funded by two grants Catedra UPVFundacion Quaes, obtained by Eduardo Villamor Medina and Antonio Cutillas Pardines, and one FPI grant (FPI-SP20170111) from the Universitat Politecnica de Valencia obtained by Eduardo Villamor Medina.Villamor, E.; Monserrat Aranda, C.; Del Río, L.; Romero-Martín, J.; Rupérez Moreno, MJ. (2020). Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning. Computer Methods and Programs in Biomedicine. 193:1-11. https://doi.org/10.1016/j.cmpb.2020.105484S111193Holt, G., Smith, R., Duncan, K., Hutchison, J. D., & Reid, D. (2009). Changes in population demographics and the future incidence of hip fracture. Injury, 40(7), 722-726. doi:10.1016/j.injury.2008.11.004Cooper, C., Campion, G., & Melton, L. J. (1992). Hip fractures in the elderly: A world-wide projection. Osteoporosis International, 2(6), 285-289. doi:10.1007/bf01623184Cooper, C., Atkinson, E. J., Jacobsen, S. J., O’Fallon, W. M., & Melton, L. J. (1993). Population-Based Study of Survival after Osteoporotic Fractures. American Journal of Epidemiology, 137(9), 1001-1005. doi:10.1093/oxfordjournals.aje.a116756Geusens, P., van Geel, T., & van den Bergh, J. (2010). Can hip fracture prediction in women be estimated beyond bone mineral density measurement alone? Therapeutic Advances in Musculoskeletal Disease, 2(2), 63-77. doi:10.1177/1759720x09359541El Maghraoui, A., & Roux, C. (2008). DXA scanning in clinical practice. QJM, 101(8), 605-617. doi:10.1093/qjmed/hcn022Chevalley, T., Rizzoli, R., Nydegger, V., Slosman, D., Tkatch, L., Rapin, C.-H., … Bonjour, J.-P. (1991). Preferential low bone mineral density of the femoral neck in patients with a recent fracture of the proximal femur. Osteoporosis International, 1(3), 147-154. doi:10.1007/bf01625444Li, N., Li, X., Xu, L., Sun, W., Cheng, X., & Tian, W. (2013). Comparison of QCT and DXA: Osteoporosis Detection Rates in Postmenopausal Women. International Journal of Endocrinology, 2013, 1-5. doi:10.1155/2013/895474Fountoulis, G., Kerenidi, T., Kokkinis, C., Georgoulias, P., Thriskos, P., Gourgoulianis, K., … Vlychou, M. (2016). Assessment of Bone Mineral Density in Male Patients with Chronic Obstructive Pulmonary Disease by DXA and Quantitative Computed Tomography. International Journal of Endocrinology, 2016, 1-6. doi:10.1155/2016/6169721Yang, L., Palermo, L., Black, D. M., & Eastell, R. (2014). Prediction of Incident Hip Fracture with the Estimated Femoral Strength by Finite Element Analysis of DXA Scans in the Study of Osteoporotic Fractures. Journal of Bone and Mineral Research, 29(12), 2594-2600. doi:10.1002/jbmr.2291Dall’Ara, E., Eastell, R., Viceconti, M., Pahr, D., & Yang, L. (2016). Experimental validation of DXA-based finite element models for prediction of femoral strength. Journal of the Mechanical Behavior of Biomedical Materials, 63, 17-25. doi:10.1016/j.jmbbm.2016.06.004Enns-Bray, W. S., Bahaloo, H., Fleps, I., Pauchard, Y., Taghizadeh, E., Sigurdsson, S., … Helgason, B. (2019). Biofidelic finite element models for accurately classifying hip fracture in a retrospective clinical study of elderly women from the AGES Reykjavik cohort. Bone, 120, 25-37. doi:10.1016/j.bone.2018.09.014Terzini, M., Aldieri, A., Rinaudo, L., Osella, G., Audenino, A. L., & Bignardi, C. (2019). Improving the Hip Fracture Risk Prediction Through 2D Finite Element Models From DXA Images: Validation Against 3D Models. Frontiers in Bioengineering and Biotechnology, 7. doi:10.3389/fbioe.2019.00220Nguyen, N. D., Frost, S. A., Center, J. R., Eisman, J. A., & Nguyen, T. V. (2008). Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks. Osteoporosis International, 19(10), 1431-1444. doi:10.1007/s00198-008-0588-0Kanis, J. A., Oden, A., Johansson, H., Borgström, F., Ström, O., & McCloskey, E. (2009). FRAX® and its applications to clinical practice. Bone, 44(5), 734-743. doi:10.1016/j.bone.2009.01.373Bolland, M. J., Siu, A. T., Mason, B. H., Horne, A. M., Ames, R. W., Grey, A. B., … Reid, I. R. (2011). Evaluation of the FRAX and Garvan fracture risk calculators in older women. Journal of Bone and Mineral Research, 26(2), 420-427. doi:10.1002/jbmr.215Kruse, C., Eiken, P., & Vestergaard, P. (2016). Clinical fracture risk evaluated by hierarchical agglomerative clustering. Osteoporosis International, 28(3), 819-832. doi:10.1007/s00198-016-3828-8Nishiyama, K. K., Ito, M., Harada, A., & Boyd, S. K. (2013). Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis. Osteoporosis International, 25(2), 619-626. doi:10.1007/s00198-013-2459-6Jiang, P., Missoum, S., & Chen, Z. (2015). Fusion of clinical and stochastic finite element data for hip fracture risk prediction. Journal of Biomechanics, 48(15), 4043-4052. doi:10.1016/j.jbiomech.2015.09.044Naylor, K. E., McCloskey, E. V., Eastell, R., & Yang, L. (2013). Use of DXA-based finite element analysis of the proximal femur in a longitudinal study of hip fracture. Journal of Bone and Mineral Research, 28(5), 1014-1021. doi:10.1002/jbmr.1856Maas, S. A., Ellis, B. J., Ateshian, G. A., & Weiss, J. A. (2012). FEBio: Finite Elements for Biomechanics. Journal of Biomechanical Engineering, 134(1). doi:10.1115/1.4005694Rossman, T., Kushvaha, V., & Dragomir-Daescu, D. (2015). QCT/FEA predictions of femoral stiffness are strongly affected by boundary condition modeling. Computer Methods in Biomechanics and Biomedical Engineering, 19(2), 208-216. doi:10.1080/10255842.2015.1006209Si, H. (2015). TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator. ACM Transactions on Mathematical Software, 41(2), 1-36. doi:10.1145/2629697Yang, L., Peel, N., Clowes, J. A., McCloskey, E. V., & Eastell, R. (2009). Use of DXA-Based Structural Engineering Models of the Proximal Femur to Discriminate Hip Fracture. Journal of Bone and Mineral Research, 24(1), 33-42. doi:10.1359/jbmr.080906Schileo, E., Dall’Ara, E., Taddei, F., Malandrino, A., Schotkamp, T., Baleani, M., & Viceconti, M. (2008). An accurate estimation of bone density improves the accuracy of subject-specific finite element models. Journal of Biomechanics, 41(11), 2483-2491. doi:10.1016/j.jbiomech.2008.05.017Morgan, E. F., & Keaveny, T. M. (2001). Dependence of yield strain of human trabecular bone on anatomic site. Journal of Biomechanics, 34(5), 569-577. doi:10.1016/s0021-9290(01)00011-2Morgan, E. F., Bayraktar, H. H., & Keaveny, T. M. (2003). Trabecular bone modulus–density relationships depend on anatomic site. Journal of Biomechanics, 36(7), 897-904. doi:10.1016/s0021-9290(03)00071-xBayraktar, H. H., Morgan, E. F., Niebur, G. L., Morris, G. E., Wong, E. K., & Keaveny, T. M. (2004). Comparison of the elastic and yield properties of human femoral trabecular and cortical bone tissue. Journal of Biomechanics, 37(1), 27-35. doi:10.1016/s0021-9290(03)00257-4Ün, K., Bevill, G., & Keaveny, T. M. (2006). The effects of side-artifacts on the elastic modulus of trabecular bone. Journal of Biomechanics, 39(11), 1955-1963. doi:10.1016/j.jbiomech.2006.05.012Schileo, E., Balistreri, L., Grassi, L., Cristofolini, L., & Taddei, F. (2014). To what extent can linear finite element models of human femora predict failure under stance and fall loading configurations? Journal of Biomechanics, 47(14), 3531-3538. doi:10.1016/j.jbiomech.2014.08.024Wirtz, D. C., Schiffers, N., Pandorf, T., Radermacher, K., Weichert, D., & Forst, R. (2000). Critical evaluation of known bone material properties to realize anisotropic FE-simulation of the proximal femur. Journal of Biomechanics, 33(10), 1325-1330. doi:10.1016/s0021-9290(00)00069-5Eckstein, F., Wunderer, C., Boehm, H., Kuhn, V., Priemel, M., Link, T. M., & Lochmüller, E.-M. (2003). Reproducibility and Side Differences of Mechanical Tests for Determining the Structural Strength of the Proximal Femur. Journal of Bone and Mineral Research, 19(3), 379-385. doi:10.1359/jbmr.0301247Orwoll, E. S., Marshall, L. M., Nielson, C. M., Cummings, S. R., Lapidus, J., … Cauley, J. A. (2009). Finite Element Analysis of the Proximal Femur and Hip Fracture Risk in Older Men. Journal of Bone and Mineral Research, 24(3), 475-483. doi:10.1359/jbmr.081201Choi, W. J., Cripton, P. A., & Robinovitch, S. N. (2014). Effects of hip abductor muscle forces and knee boundary conditions on femoral neck stresses during simulated falls. Osteoporosis International, 26(1), 291-301. doi:10.1007/s00198-014-2812-4Van den Kroonenberg, A. J., Hayes, W. C., & McMahon, T. A. (1995). Dynamic Models for Sideways Falls From Standing Height. Journal of Biomechanical Engineering, 117(3), 309-318. doi:10.1115/1.2794186Robinovitch, S. N., Hayes, W. C., & McMahon, T. A. (1991). Prediction of Femoral Impact Forces in Falls on the Hip. Journal of Biomechanical Engineering, 113(4), 366-374. doi:10.1115/1.2895414Robinovitch, S. N., McMahon, T. A., & Hayes, W. C. (1995). Force attenuation in trochanteric soft tissues during impact from a fall. Journal of Orthopaedic Research, 13(6), 956-962. doi:10.1002/jor.1100130621Dufour, A. B., Roberts, B., Broe, K. E., Kiel, D. P., Bouxsein, M. L., & Hannan, M. T. (2011). The factor-of-risk biomechanical approach predicts hip fracture in men and women: the Framingham Study. Osteoporosis International, 23(2), 513-520. doi:10.1007/s00198-011-1569-2Schileo, E., Taddei, F., Cristofolini, L., & Viceconti, M. (2008). Subject-specific finite element models implementing a maximum principal strain criterion are able to estimate failure risk and fracture location on human femurs tested in vitro. Journal of Biomechanics, 41(2), 356-367. doi:10.1016/j.jbiomech.2007.09.009Mautalen, C. A., Vega, E. M., & Einhorn, T. A. (1996). Are the etiologies of cervical and trochanteric hip fractures different? Bone, 18(3), S133-S137. doi:10.1016/8756-3282(95)00490-4Yang, S., Leslie, W. D., Luo, Y., Goertzen, A. L., Ahmed, S., Ward, L. M., … Lix, L. M. (2017). Automated DXA-based finite element analysis for hip fracture risk stratification: a cross-sectional study. Osteoporosis International, 29(1), 191-200. doi:10.1007/s00198-017-4232-8Testi, D., Viceconti, M., Cappello, A., & Gnudi, S. (2002). Prediction of Hip Fracture Can Be Significantly Improved by a Single Biomedical Indicator. Annals of Biomedical Engineering, 30(6), 801-807. doi:10.1114/1.1495866Langton, C. M., Pisharody, S., & Keyak, J. H. (2008). Generation of a 3D proximal femur shape from a single projection 2D radiographic image. Osteoporosis International, 20(3), 455-461. doi:10.1007/s00198-008-0665-4Humbert, L., Martelli, Y., Fonolla, R., Steghofer, M., Di Gregorio, S., Malouf, J., … Barquero, L. M. D. R. (2017). 3D-DXA: Assessing the Femoral Shape, the Trabecular Macrostructure and the Cortex in 3D from DXA images. IEEE Transactions on Medical Imaging, 36(1), 27-39. doi:10.1109/tmi.2016.2593346Keyak, J. H., Sigurdsson, S., Karlsdottir, G., Oskarsdottir, D., Sigmarsdottir, A., Zhao, S., … Lang, T. F. (2011). Male–female differences in the association between incident hip fracture and proximal femoral strength: A finite element analysis study. Bone, 48(6), 1239-1245. doi:10.1016/j.bone.2011.03.682Lobo, E., Marcos, G., Santabárbara, J., Salvador-Rosés, H., Lobo-Escolar, L., De la Cámara, C., … Lobo-Escolar, A. (2017). Gender differences in the incidence of and risk factors for hip fracture: A 16-year longitudinal study in a southern European population. Maturitas, 97, 38-43. doi:10.1016/j.maturitas.2016.12.00

Towards Individualized Transcranial Electric Stimulation Therapy through Computer Simulation

Transkranielle Elektrostimulation (tES) beschreibt eine Gruppe von Hirnstimulationstechniken, die einen schwachen elektrischen Strom über zwei nicht-invasiv am Kopf angebrachten Elektroden applizieren. Handelt es sich dabei um einen Gleichstrom, spricht man von transkranieller Gleichstromstimulation, auch tDCS abgekürzt. Die allgemeine Zielstellung aller Hirnstimulationstechniken ist Hirnfunktion durch ein Verstärken oder Dämpfen von Hirnaktivität zu beeinflussen. Unter den Stimulationstechniken wird die transkranielle Gleichstromstimulation als ein adjuvantes Werkzeug zur Unterstützung der mikroskopischen Reorganisation des Gehirnes in Folge von Lernprozessen und besonders der Rehabilitationstherapie nach einem Schlaganfall untersucht. Aktuelle Herausforderungen dieser Forschung sind eine hohe Variabilität im erreichten Stimulationseffekt zwischen den Probanden sowie ein unvollständiges Verständnis des Zusammenspiels der der Stimulation zugrundeliegenden Mechanismen. Als Schlüsselkomponente für das Verständnis der Stimulationsmechanismen wird das zwischen den Elektroden im Kopf des Probanden aufgebaute elektrische Feld erachtet. Einem grundlegenden Konzept folgend wird angenommen, dass Hirnareale, die einer größeren elektrischen Feldstärke ausgesetzt sind, ebenso einen höheren Stimulationseffekt erfahren. Damit kommt der Positionierung der Elektroden eine entscheidende Rolle für die Stimulation zu. Allerdings verteilt sich das elektrische Feld wegen des heterogenen elektrischen Leitfähigkeitsprofil des menschlichen Kopfes nicht uniform im Gehirn der Probanden. Außerdem ist das Verteilungsmuster auf Grund anatomischer Unterschiede zwischen den Probanden verschieden. Die triviale Abschätzung der Ausbreitung des elektrischen Feldes anhand der bloßen Position der Stimulationselektroden ist daher nicht ausreichend genau für eine zielgerichtete Stimulation. Computerbasierte, biophysikalische Simulationen der transkraniellen Elektrostimulation ermöglichen die individuelle Approximation des Verteilungsmusters des elektrischen Feldes in Probanden basierend auf deren medizinischen Bildgebungsdaten. Sie werden daher zunehmend verwendet, um tDCS-Anwendungen zu planen und verifizieren, und stellen ein wesentliches Hilfswerkzeug auf dem Weg zu individualisierter Schlaganfall-Rehabilitationstherapie dar. Softwaresysteme, die den dahinterstehenden individualisierten Verarbeitungsprozess erleichtern und für ein breites Feld an Forschern zugänglich machen, wurden in den vergangenen Jahren für den Anwendungsfall in gesunden Erwachsenen entwickelt. Jedoch bleibt die Simulation von Patienten mit krankhaftem Hirngewebe und strukturzerstörenden Läsionen eine nicht-triviale Aufgabe. Daher befasst sich das hier vorgestellte Projekt mit dem Aufbau und der praktischen Anwendung eines Arbeitsablaufes zur Simulation transkranieller Elektrostimulation. Dabei stand die Anforderung im Vordergrund medizinische Bildgebungsdaten insbesondere neurologischer Patienten mit krankhaft verändertem Hirngewebe verarbeiten zu können. Der grundlegende Arbeitsablauf zur Simulation wurde zunächst für gesunde Erwachsene entworfen und validiert. Dies umfasste die Zusammenstellung medizinischer Bildverarbeitungsalgorithmen zu einer umfangreichen Verarbeitungskette, um elektrisch relevante Strukturen in den Magnetresonanztomographiebildern des Kopfes und des Oberkörpers der Probanden zu identifizieren und zu extrahieren. Die identifizierten Strukturen mussten in Computermodelle überführt werden und das zugrundeliegende, physikalische Problem der elektrischen Volumenleitung in biologischen Geweben mit Hilfe numerischer Simulation gelöst werden. Im Verlauf des normalen Alterns ist das Gehirn strukturellen Veränderungen unterworfen, unter denen ein Verlust des Hirnvolumens sowie die Ausbildung mikroskopischer Veränderungen seiner Nervenfaserstruktur die Bedeutendsten sind. In einem zweiten Schritt wurde der Arbeitsablauf daher erweitert, um diese Phänomene des normalen Alterns zu berücksichtigen. Die vordergründige Herausforderung in diesem Teilprojekt war die biophysikalische Modellierung der veränderten Hirnmikrostruktur, da die resultierenden Veränderungen im Leitfähigkeitsprofil des Gehirns bisher noch nicht in der Literatur quantifiziert wurden. Die Erweiterung des Simulationsablauf zeichnete sich vorrangig dadurch aus, dass mit unsicheren elektrischen Leitfähigkeitswerten gearbeitet werden konnte. Damit war es möglich den Einfluss der ungenau bestimmbaren elektrischen Leitfähigkeit der verschiedenen biologischen Strukturen des menschlichen Kopfes auf das elektrische Feld zu ermitteln. In einer Simulationsstudie, in der Bilddaten von 88 Probanden einflossen, wurde die Auswirkung der veränderten Hirnfaserstruktur auf das elektrische Feld dann systematisch untersucht. Es wurde festgestellt, dass sich diese Gewebsveränderungen hochgradig lokal und im Allgemeinen gering auswirken. Schließlich wurden in einem dritten Schritt Simulationen für Schlaganfallpatienten durchgeführt. Ihre großen, strukturzerstörenden Läsionen wurden dabei mit einem höheren Detailgrad als in bisherigen Arbeiten modelliert und physikalisch abermals mit unsicheren Leitfähigkeiten gearbeitet, was zu unsicheren elektrischen Feldabschätzungen führte. Es wurden individuell berechnete elektrische Felddaten mit der Hirnaktivierung von 18 Patienten in Verbindung gesetzt, unter Berücksichtigung der inhärenten Unsicherheit in der Bestimmung der elektrischen Felder. Das Ziel war zu ergründen, ob die Hirnstimulation einen positiven Einfluss auf die Hirnaktivität der Patienten im Kontext von Rehabilitationstherapie ausüben und so die Neuorganisierung des Gehirns nach einem Schlaganfall unterstützen kann. Während ein schwacher Zusammenhang hergestellt werden konnte, sind weitere Untersuchungen nötig, um diese Frage abschließend zu klären.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms BibliographyTranscranial electric current stimulation (tES) denotes a group of brain stimulation techniques that apply a weak electric current over two or more non-invasively, head-mounted electrodes. When employing a direct-current, this method is denoted transcranial direct current stimulation (tDCS). The general aim of all tES techniques is the modulation of brain function by an up- or downregulation of brain activity. Among these, transcranial direct current stimulation is investigated as an adjuvant tool to promote processes of the microscopic reorganization of the brain as a consequence of learning and, more specifically, rehabilitation therapy after a stroke. Current challenges of this research are a high variability in the achieved stimulation effects across subjects and an incomplete understanding of the interplay between its underlying mechanisms. A key component to understanding the stimulation mechanism is considered the electric field, which is exerted by the electrodes and distributes in the subjects' heads. A principle concept assumes that brain areas exposed to a higher electric field strength likewise experience a higher stimulation. This attributes the positioning of the electrodes a decisive role for the stimulation. However, the electric field distributes non-uniformly across subjects' brains due to the heterogeneous electrical conductivity profile of the human head. Moreover, the distribution pattern is variable between subjects due to their individual anatomy. A trivial estimation of the distribution of the electric field solely based on the position of the stimulating electrodes is, therefore, not precise enough for a well-targeted stimulation. Computer-based biophysical simulations of transcranial electric stimulation enable the individual approximation of the distribution pattern of the electric field in subjects based on their medical imaging data. They are, thus, increasingly employed for the planning and verification of tDCS applications and constitute an essential tool on the way to individualized stroke rehabilitation therapy. Software pipelines facilitating the underlying individualized processing for a wide range of researchers have been developed for use in healthy adults over the past years, but, to date, the simulation of patients with abnormal brain tissue and structure disrupting lesions remains a non-trivial task. Therefore, the presented project was dedicated to establishing and practically applying a tES simulation workflow. The processing of medical imaging data of neurological patients with abnormal brain tissue was a central requirement in this process. The basic simulation workflow was first designed and validated for the simulation of healthy adults. This comprised compiling medical image processing algorithms into a comprehensive workflow to identify and extract electrically relevant physiological structures of the human head and upper torso from magnetic resonance images. The identified structures had to be converted to computational models. The underlying physical problem of electric volume conduction in biological tissue was solved by means of numeric simulation. Over the course of normal aging, the brain is subjected to structural alterations, among which a loss of brain volume and the development of microscopic alterations of its fiber structure are the most relevant. In a second step, the workflow was, thus, extended to incorporate these phenomena of normal aging. The main challenge in this subproject was the biophysical modeling of the altered brain microstructure as the resulting alterations to the conductivity profile of the brain were so far not quantified in the literature. Therefore, the augmentation of the workflow most notably included the modeling of uncertain electrical properties. With this, the influence of the uncertain electrical conductivity of the biological structures of the human head on the electric field could be assessed. In a simulation study, including imaging data of 88 subjects, the influence of the altered brain fiber structure on the electric field was then systematically investigated. These tissue alterations were found to exhibit a highly localized and generally low impact. Finally, in a third step, tDCS simulations of stroke patients were conducted. Their large, structure-disrupting lesions were modeled in a more detailed manner than in previous stroke simulation studies, and they were physically, again, modeled by uncertain electrical conductivity resulting in uncertain electric field estimates. Individually simulated electric fields were related to the brain activation of 18 patients, considering the inherently uncertain electric field estimations. The goal was to clarify whether the stimulation exerts a positive influence on brain function in the context of rehabilitation therapy supporting brain reorganization following a stroke. While a weak correlation could be established, further investigation will be necessary to answer that research question.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms Bibliograph

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf