3D bone texture analysis as a potential predictor of radiationinduced insufficiency fractures

Background: The aim of our work is to assess the potential role of texture analysis (TA), applied to computed tomography (CT) simulation scans, in relation to the development of insuffciency fractures (IFs) in patients undergoing radiation therapy (RT) for pelvic malignancies. Methods: We analyzed patients undergoing pelvic RT from Jan-2010 to Dec-2016, 31 of whom had developed IFs of the pelvis. We analyzed CT simulation scans using LifeX Software, and in particular we selected three regions of interest (ROI): L5 body, the sacrum and both the femoral heads. The ROI were automatically contoured using the treatment planning software Raystation. TA parameters included parameters from the gray-level histogram, indices from sphericity and from the matrix of GLCM (gray level co-occurrence matrix). The IFs patients were matched (1:1 ratio) with control patients who had not developed IFs, and were matched for age, sex, type of tumor, menopausal status, RT dose and use of chemotherapy. Univariate and multivariate analyses (logistic regression) were used for statistical analysis. Results: Signifcant TA parameters on univariate analysis included both parameters from the histogram distribution, as well from the matrix of GLCM. On logistic regression analysis the signifcant parameters were L5-energy [P=0.033, odds ratio (OR): 1.997, 95% CI: 1.0593.767] and FH-Skewness (P=0.014, OR: 2.338, 95% CI: 1.1914.591), with a R2: 0.268. A ROC curve was generated from the binary logistic regression, and the AUC was 0.741 (95% CI: 0.6270.855, P=0.001, S.E.: 0.058). Conclusions: In our experience, 3D-bone CT TA can be used to stratify the risk of the patients to develop radiation-induced IFs. A prospective study will be conducted to validate these fndings

Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio

New Imaging Modalities in Bone

The digital era has witnessed an exponential growth in bone imaging as new modalities and analytic techniques improve the potential for noninvasive study of bone anatomy, physiology, and pathophysiology. Bone imaging very much lends itself to input across medical and engineering disciplines. It is in part a reflection of this multidisciplinary input that developments in the field of bone imaging over the past 30 years have in some respects outshone those in many other fields of medicine. These developments have resulted in much deeper knowledge of bone macrostructure and microstructure in osteoporosis and a much better understanding of the subtle changes that occur with age, concurrent disease, and treatment. This new knowledge is already being translated into improved day-to day clinical care with better recognition, treatment, and monitoring of the osteoporotic process. As “the more you know, the more you know you don’t know” certainly holds true with osteoporosis and bone disease, there is little doubt that further advances in bone imaging and analytical techniques will continue to hold center stage in osteoporosis and related research

Compression failure characterization of cancellous bone combining experimental testing, digital image correlation and finite element modeling

[EN] Cancellous bone yield strain has been reported in the literature to be relatively constant and independent from microstructure and apparent density, while fracture strain shows higher scattering. The objective of this work is to assess this hypothesis, characterizing the compression fracture in cancellous bone from a numerical approach and relating it to morphological parameters. Quasi-static compression fractures of cancellous bone samples are modeled using high-resolution image-based finite elements, correlating the numerical models and experimental results. The yield strain and the strain at fracture are inferred from the micro-CT-based finite element models by inverse analysis. The validation of the fracture models is carried out through digital image correlation (DIC). To develop this work, cancellous bone parallelepiped-shaped specimens were prepared and micro-CT scanned at 22 mu m spatial resolution. A morphometric analysis was carried out for each specimen in order to characterize its microstructure. Quasi-static compression tests were conducted, recording the force-displacement response and a sequence of images during testing for the application of the DIC technique. This was applied without the need of a speckle pattern benefiting from the irregular microstructure of cancellous bone. The finite element models are also used to simulate the local fracture of trabeculae at the micro level using a combination of continuum damage mechanics and the element deletion technique. Equivalent strain, computed both from DIC and micro-FE, was the best predictor of the compression fracture pattern. The procedure followed in this work permits the estimation of failure parameters that are difficult to measure experimentally, which can be used in numerical models.This work was supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades grant numbers DPI2013-46641-R and DPI2017-89197-C2-2-R and the Generalitat Valenciana (Programme PROMETEO 2016/007). The micro-CT acquisitions were performed at CENIEH facilities with the collaboration of CENIEH staff. The authors also gratefully acknowledge the collaboration of Ms. Lucia Gomez.Belda, R.; Palomar-Toledano, M.; Peris Serra, JL.; Vercher Martínez, A.; Giner Maravilla, E. (2020). Compression failure characterization of cancellous bone combining experimental testing, digital image correlation and finite element modeling. International Journal of Mechanical Sciences. 165:1-12. https://doi.org/10.1016/j.ijmecsci.2019.105213S112165Gold, D. T. (2001). The Nonskeletal Consequences of Osteoporotic Fractures. Rheumatic Disease Clinics of North America, 27(1), 255-262. doi:10.1016/s0889-857x(05)70197-6Keaveny, T. M., Morgan, E. F., Niebur, G. L., & Yeh, O. C. (2001). Biomechanics of Trabecular Bone. Annual Review of Biomedical Engineering, 3(1), 307-333. doi:10.1146/annurev.bioeng.3.1.307Rho, J.-Y., Kuhn-Spearing, L., & Zioupos, P. (1998). Mechanical properties and the hierarchical structure of bone. Medical Engineering & Physics, 20(2), 92-102. doi:10.1016/s1350-4533(98)00007-1Currey, J. D. (2011). The structure and mechanics of bone. Journal of Materials Science, 47(1), 41-54. doi:10.1007/s10853-011-5914-9Gupta, H. S., & Zioupos, P. (2008). Fracture of bone tissue: The ‘hows’ and the ‘whys’. Medical Engineering & Physics, 30(10), 1209-1226. doi:10.1016/j.medengphy.2008.09.007Nagaraja, S., Couse, T. L., & Guldberg, R. E. (2005). Trabecular bone microdamage and microstructural stresses under uniaxial compression. Journal of Biomechanics, 38(4), 707-716. doi:10.1016/j.jbiomech.2004.05.013Garcia, D., Zysset, P. K., Charlebois, M., & Curnier, A. (2008). A three-dimensional elastic plastic damage constitutive law for bone tissue. Biomechanics and Modeling in Mechanobiology, 8(2), 149-165. doi:10.1007/s10237-008-0125-2Ridha, H., & Thurner, P. J. (2013). Finite element prediction with experimental validation of damage distribution in single trabeculae during three-point bending tests. Journal of the Mechanical Behavior of Biomedical Materials, 27, 94-106. doi:10.1016/j.jmbbm.2013.07.005Hambli, R. (2012). A quasi-brittle continuum damage finite element model of the human proximal femur based on element deletion. Medical & Biological Engineering & Computing, 51(1-2), 219-231. doi:10.1007/s11517-012-0986-5Fan, R., Gong, H., Zhang, X., Liu, J., Jia, Z., & Zhu, D. (2016). Modeling the Mechanical Consequences of Age-Related Trabecular Bone Loss by XFEM Simulation. Computational and Mathematical Methods in Medicine, 2016, 1-12. doi:10.1155/2016/3495152Vellwock, A. E., Vergani, L., & Libonati, F. (2018). A multiscale XFEM approach to investigate the fracture behavior of bio-inspired composite materials. Composites Part B: Engineering, 141, 258-264. doi:10.1016/j.compositesb.2017.12.062Hambli, R. (2010). Multiscale prediction of crack density and crack length accumulation in trabecular bone based on neural networks and finite element simulation. International Journal for Numerical Methods in Biomedical Engineering, 27(4), 461-475. doi:10.1002/cnm.1413Hambli, R. (2011). Apparent damage accumulation in cancellous bone using neural networks. Journal of the Mechanical Behavior of Biomedical Materials, 4(6), 868-878. doi:10.1016/j.jmbbm.2011.03.002Lemaitre, J. (1985). A Continuous Damage Mechanics Model for Ductile Fracture. Journal of Engineering Materials and Technology, 107(1), 83-89. doi:10.1115/1.3225775Turner, C. H., & Burr, D. B. (1993). Basic biomechanical measurements of bone: A tutorial. Bone, 14(4), 595-608. doi:10.1016/8756-3282(93)90081-kBay, B. K. (1995). Texture correlation: A method for the measurement of detailed strain distributions within trabecular bone. Journal of Orthopaedic Research, 13(2), 258-267. doi:10.1002/jor.1100130214Peters, W. H., & Ranson, W. F. (1982). Digital Imaging Techniques In Experimental Stress Analysis. Optical Engineering, 21(3). doi:10.1117/12.7972925Sutton, M., Wolters, W., Peters, W., Ranson, W., & McNeill, S. (1983). Determination of displacements using an improved digital correlation method. Image and Vision Computing, 1(3), 133-139. doi:10.1016/0262-8856(83)90064-1Pan, B., Qian, K., Xie, H., & Asundi, A. (2009). Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review. Measurement Science and Technology, 20(6), 062001. doi:10.1088/0957-0233/20/6/062001Khoo, S.-W., Karuppanan, S., & Tan, C.-S. (2016). A Review of Surface Deformation and Strain Measurement Using Two-Dimensional Digital Image Correlation. Metrology and Measurement Systems, 23(3), 461-480. doi:10.1515/mms-2016-0028Palanca, M., Tozzi, G., & Cristofolini, L. (2015). The use of digital image correlation in the biomechanical area: a review. International Biomechanics, 3(1), 1-21. doi:10.1080/23335432.2015.1117395Grassi, L., & Isaksson, H. (2015). Extracting accurate strain measurements in bone mechanics: A critical review of current methods. Journal of the Mechanical Behavior of Biomedical Materials, 50, 43-54. doi:10.1016/j.jmbbm.2015.06.006Bayraktar, 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)00257-4Carretta, R., Stüssi, E., Müller, R., & Lorenzetti, S. (2013). Within subject heterogeneity in tissue-level post-yield mechanical and material properties in human trabecular bone. Journal of the Mechanical Behavior of Biomedical Materials, 24, 64-73. doi:10.1016/j.jmbbm.2013.04.014Linde, F., & Sørensen, H. C. F. (1993). The effect of different storage methods on the mechanical properties of trabecular bone. Journal of Biomechanics, 26(10), 1249-1252. doi:10.1016/0021-9290(93)90072-mLinde, F., & Hvid, I. (1987). Stiffness behaviour of trabecular bone specimens. Journal of Biomechanics, 20(1), 83-89. doi:10.1016/0021-9290(87)90270-3Keaveny, T. M., Borchers, R. E., Gibson, L. J., & Hayes, W. C. (1993). Theoretical analysis of the experimental artifact in trabecular bone compressive modulus. Journal of Biomechanics, 26(4-5), 599-607. doi:10.1016/0021-9290(93)90021-6Keaveny, T. M., Guo, X. E., Wachtel, E. F., McMahon, T. A., & Hayes, W. C. (1994). Trabecular bone exhibits fully linear elastic behavior and yields at low strains. Journal of Biomechanics, 27(9), 1127-1136. doi:10.1016/0021-9290(94)90053-1Keaveny, T. M., Pinilla, T. P., Crawford, R. P., Kopperdahl, D. L., & Lou, A. (1997). Systematic and random errors in compression testing of trabecular bone. Journal of Orthopaedic Research, 15(1), 101-110. doi:10.1002/jor.1100150115Correlated Solutions. VIC-2d v6 reference manual. 2016. http://www.correlatedsolutions.com/supportcontent/Vic-2D-v6-Manual.pdf.Whitehouse, W. J. (1974). The quantitative morphology of anisotropic trabecular bone. Journal of Microscopy, 101(2), 153-168. doi:10.1111/j.1365-2818.1974.tb03878.xKabel, J., van Rietbergen, B., Dalstra, M., Odgaard, A., & Huiskes, R. (1999). The role of an effective isotropic tissue modulus in the elastic properties of cancellous bone. Journal of Biomechanics, 32(7), 673-680. doi:10.1016/s0021-9290(99)00045-7Nalla, R. K., Kinney, J. H., & Ritchie, R. O. (2003). Mechanistic fracture criteria for the failure of human cortical bone. Nature Materials, 2(3), 164-168. doi:10.1038/nmat832Taylor, D. (2003). A crack growth model for the simulation of fatigue in bone. International Journal of Fatigue, 25(5), 387-395. doi:10.1016/s0142-1123(02)00165-2Burr, D. B., & Stafford, T. (1990). Validity of the Bulk-Staining Technique to Separate Artifactual From In Vivo Bone Microdamage. Clinical Orthopaedics and Related Research, 260, 305-308. doi:10.1097/00003086-199011000-00047Keaveny, T. M., & Hayes, W. C. (1993). A 20-Year Perspective on the Mechanical Properties of Trabecular Bone. Journal of Biomechanical Engineering, 115(4B), 534-542. doi:10.1115/1.2895536Wolfram, U., Wilke, H.-J., & Zysset, P. K. (2011). Damage accumulation in vertebral trabecular bone depends on loading mode and direction. Journal of Biomechanics, 44(6), 1164-1169. doi:10.1016/j.jbiomech.2011.01.018Kopperdahl, D. L., & Keaveny, T. M. (1998). Yield strain behavior of trabecular bone. Journal of Biomechanics, 31(7), 601-608. doi:10.1016/s0021-9290(98)00057-8Hara, T., Tanck, E., Homminga, J., & Huiskes, R. (2002). The influence of microcomputed tomography threshold variations on the assessment of structural and mechanical trabecular bone properties. Bone, 31(1), 107-109. doi:10.1016/s8756-3282(02)00782-2Parkinson, I. H., Badiei, A., & Fazzalari, N. L. (2008). Variation in segmentation of bone from micro-CT imaging: implications for quantitative morphometric analysis. Australasian Physics & Engineering Sciences in Medicine, 31(2), 160-164. doi:10.1007/bf03178592Wachtel, E. F., & Keaveny, T. M. (1997). Dependence of trabecular damage on mechanical strain. Journal of Orthopaedic Research, 15(5), 781-787. doi:10.1002/jor.1100150522Nazarian, A., Meier, D., Müller, R., & Snyder, B. D. (2009). Functional dependence of cancellous bone shear properties on trabecular microstructure evaluated using time-lapsed micro-computed tomographic imaging and torsion testing. Journal of Orthopaedic Research, 27(12), 1667-1674. doi:10.1002/jor.20931Schwiedrzik, J., Taylor, A., Casari, D., Wolfram, U., Zysset, P., & Michler, J. (2017). Nanoscale deformation mechanisms and yield properties of hydrated bone extracellular matrix. Acta Biomaterialia, 60, 302-314. doi:10.1016/j.actbio.2017.07.030Bevill, G., Eswaran, S. K., Gupta, A., Papadopoulos, P., & Keaveny, T. M. (2006). Influence of bone volume fraction and architecture on computed large-deformation failure mechanisms in human trabecular bone. Bone, 39(6), 1218-1225. doi:10.1016/j.bone.2006.06.016Althouse, A. D. (2016). Adjust for Multiple Comparisons? It’s Not That Simple. The Annals of Thoracic Surgery, 101(5), 1644-1645. doi:10.1016/j.athoracsur.2015.11.02

A physically-based muscle and skin model for facial animation

Facial animation is a popular area of research which has been around for over thirty years, but even with this long time scale, automatically creating realistic facial expressions is still an unsolved goal. This work furthers the state of the art in computer facial animation by introducing a new muscle and skin model and a method of easily transferring a full muscle and bone animation setup from one head mesh to another with very little user input. The developed muscle model allows muscles of any shape to be accurately simulated, preserving volume during contraction and interacting with surrounding muscles and skin in a lifelike manner. The muscles can drive a rigid body model of a jaw, giving realistic physically-based movement to all areas of the face. The skin model has multiple layers, mimicking the natural structure of skin and it connects onto the muscle model and is deformed realistically by the movements of the muscles and underlying bones. The skin smoothly transfers underlying movements into skin surface movements and propagates forces smoothly across the face. Once a head model has been set up with muscles and bones, moving this muscle and bone set to another head is a simple matter using the developed techniques. The developed software employs principles from forensic reconstruction, using specific landmarks on the head to map the bone and muscles to the new head model and once the muscles and skull have been quickly transferred, they provide animation capabilities on the new mesh within minutes

Magnetic resonance imaging radiomic feature analysis of radiation-induced femoral head changes in prostate cancer radiotherapy

Background and Purpose: As a feasible approach, radiotherapy has a great role in prostate cancer (Pca) management. However, Pca patients have an increased risk of femoral head damages including fractures after radiotherapy. The mechanisms of these complications are unknown and time of manifestations is too long; however, they may be predicted by early imaging. The main purpose of this study was to assess the early changes in femoral heads in Pca patients treated with intensity-modulated radiation therapy (IMRT) using multiparametric magnetic resonance imaging (mpMRI) radiomic feature analysis. Materials and Methods: Thirty Pca patients treated with IMRT were included in the study. All patients underwent two mpMRI pre- and postradiotherapy. Thirty-four robust radiomic features were extracted from T1, T2, and apparent diffusion coefficient (ADC) obtained from diffusion-weighted images. Wilcoxon signed-rank test was performed to assess the significance of the change in the mean T1, T2, and ADC radiomic features postradiotherapy relative to preradiotherapy values. The percentage change values were normalized based on the natural logarithm base ten. Features were also ranked based on their median changes. Results: Sixty femoral heads were analyzed. All radiomic features have undergone changes. Significant postradiotherapy radiomic feature changes were observed in 20 and 5 T1- and T2-weighted radiomic features, respectively (P < 0.05). ADC features did not vary significantly postradiotherapy. The mean radiation dose received by femoral heads was 40 Gy. No fractures were observed within the follow-up time. Different features were found as high ranked among T1, T2, and ADC images. Conclusion: Early structural change analysis using radiomic features may contribute to predict postradiotherapy fracture in Pca patients. These features can be identified as being potentially important imaging biomarkers for predicting radiotherapy-induced femoral changes. © 2019 Journal of Cancer Research and Therapeutics | Published by Wolters Kluwer - Medknow

Deep Segmentation of the Mandibular Canal: a New 3D Annotated Dataset of CBCT Volumes

Inferior Alveolar Nerve (IAN) canal detection has been the focus of multiple recent works in dentistry and maxillofacial imaging. Deep learning-based techniques have reached interesting results in this research field, although the small size of 3D maxillofacial datasets has strongly limited the performance of these algorithms. Researchers have been forced to build their own private datasets, thus precluding any opportunity for reproducing results and fairly comparing proposals. This work describes a novel, large, and publicly available mandibular Cone Beam Computed Tomography (CBCT) dataset, with 2D and 3D manual annotations, provided by expert clinicians. Leveraging this dataset and employing deep learning techniques, we are able to improve the state of the art on the 3D mandibular canal segmentation. The source code which allows to exactly reproduce all the reported experiments is released as an open-source project, along with this article

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Medical image enhancement

Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, “deblurring” an image to obtain better quality is an important issue in medical image processing. In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified to improve the visibility of the corresponding details. The stronger image density variations make a major contribution to the overall dynamic range, and have large coefficient values. These values can be reduced without much information loss