[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81861-en":3,"doc-seo-81861-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},81861,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","On the Validity of Using Idealised Sample Geometries for Interpreting Mechanical Tests of Very Soft Tissues","Mechanical characterization of soft tissues often uses inverse analysis to calibrate constitutive models to experimental force–displacement curves, but most studies assume idealised (nominal) sample geometries despite unavoidable deviations from imperfections during excision and mounting. The impact of these geometric simplifications on inferred material parameters remains insufficiently quantified. This work evaluates brain tissue by reconstructing real geometry via MRI and comparing finite element–based tensile, compressive, and shear tests against idealised cuboids. Idealised geometries yield consistently lower shear modulus, with discrepancies varying by loading mode and mainly driven by compressive response; thus measured geometry should be used in inverse analysis.","On the validity of using idealised sample geometries for interpreting mechanical tests of very soft tissues  \nSajjad Arzemanzadeh, Karol Miller, Adam Wittek  \nIntelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Western Australia, Australia  \nAbstract  \nMechanical characterisation of soft tissues often relies on inverse analysis of experimental data in which constitutive models are calibrated to match experimental force–displacement curves, yet the vast majority of such studies use idealised (nominal) sample geometries eventhough experimental samples unavoidably deviate from these nominal shapes because of imperfections in excision and mounting. The influence of these geometric simplifications on the material parameters determined through inverse analysis remains poorly quantified. We investigate the appropriateness of using idealised sample geometries in mechanical characterisation of brain tissue. Magnetic resonance imaging (MRI) was used to reconstruct the exact (real) geometry of each nominally cuboidal tissue sample. We determined a stress parameter (the shear modulus) by modelling, using the finite element method, tensile, compressive, and shear tests of brain tissue samples with both the MRI-based (real) and idealised cuboidal geometries, enabling a controlled comparison of geometry. Idealised geometries consistently yielded a lower stress parameter. The discrepancy in shear modulus between the real and idealised geometries varied across loading modes, averaging approximately 10% in shear and 48% under axial loading, predominantly arising from the compressive response. These discrepancies can be attributed to the inability of idealisedgeometry models to accurately represent contact interactions and predict strain distributions, particularly under compressive loading. Idealisation of sample geometry may introduce systematic bias in the mechanical characterisation of very soft tissues; therefore, the actual measured sample geometry should be used in inverse analysis to identify constitutive models and their parameters.  \nKeywords: Very soft tissue, Brain tissue, Mechanical properties, Sample geometry, Finite element method  \n1. Introduction  \nA Google Scholar search for “soft tissue biomechanics experiment” returns approximately 179,000 documents. Despite this extensive body of research, recent literature reviews report inconsistencies and even contradictions in experimentally derived mechanical properties of very soft tissues, including the brain [1-3] and liver [4, 5] . Mechanically characterising very soft tissues (and other very soft materials) is inherently difficult: their extreme compliance challenges conventional test configurations designed for stiffer materials and has prompted a range of specialised experimental techniques for measuring their properties [6-9] . Therefore, Experimental procedures for characterising very soft tissues typically follow those used for engineering materials: material samples are subjected to imposed displacements and resulting forces are measured. These measurements, combined with sample geometry, are used to infer a constitutive model and its parameters using inverse analysis. However, experimental investigation of very soft tissues typically involves large deformations, often exceeding 30% strain, such that changes in sample geometry during testing must be accounted for. This necessitates the use of geometrically non-linear formulations based on finite deformation theory. When combined with the intrinsically non-linear constitutive behaviour of very soft tissues, these factors render closed-form analytical solutions challenging and not always practical. Consequently, methods of computational mechanics, most commonly the finite element (FE) method, are employed to model very soft tissue mechanical tests and conduct inverse identification of tissue material properties [10-16] .  \nUnlike analytical methods, which require simple geometries such as cy","cbCaibnm0sN4wbEh","https://ap.wps.com/l/cbCaibnm0sN4wbEh","pdf",1178934,1,20,"English","en",105,"# Introduction\n# Materials and Methods\n## Sample Preparation","[{\"question\":\"Why do idealised sample geometries pose a problem in inverse mechanical characterization of very soft tissues?\",\"answer\":\"Because experimental specimens deviate from nominal shapes due to excision and mounting imperfections, while inverse analysis typically assumes the idealised geometry when calibrating constitutive models to force–displacement data.\"},{\"question\":\"How does the study reconstruct real brain tissue geometry for comparison?\",\"answer\":\"Magnetic resonance imaging (MRI) is used to reconstruct the exact three-dimensional geometry of each nominally cuboidal tissue sample.\"},{\"question\":\"What is the main finding about the shear modulus obtained using idealised versus real geometries?\",\"answer\":\"Idealised geometries consistently produce a lower stress parameter (shear modulus). The discrepancy varies by loading mode, averaging about 10% in shear and 48% under axial loading, largely due to the compressive response.\"}]",1784176707,50,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"on-the-validity-of-using-idealised-sample-geometries-for-interpreting-mechanical-tests-of-very-soft-tissues","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/on-the-validity-of-using-idealised-sample-geometries-for-interpreting-mechanical-tests-of-very-soft-tissues/81861/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why do idealised sample geometries pose a problem in inverse mechanical characterization of very soft tissues?","Question",{"text":74,"@type":75},"Because experimental specimens deviate from nominal shapes due to excision and mounting imperfections, while inverse analysis typically assumes the idealised geometry when calibrating constitutive models to force–displacement data.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the study reconstruct real brain tissue geometry for comparison?",{"text":79,"@type":75},"Magnetic resonance imaging (MRI) is used to reconstruct the exact three-dimensional geometry of each nominally cuboidal tissue sample.",{"name":81,"@type":72,"acceptedAnswer":82},"What is the main finding about the shear modulus obtained using idealised versus real geometries?",{"text":83,"@type":75},"Idealised geometries consistently produce a lower stress parameter (shear modulus). The discrepancy varies by loading mode, averaging about 10% in shear and 48% under axial loading, largely due to the compressive response.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,113,118,121,125,128,132],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":28,"slug":112},6,"Technology","technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":21,"slug":124},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":126,"show_sort_weight":21,"slug":127},"World Cup","world-cup",{"id":129,"doc_module":4,"doc_module_name":45,"category_name":130,"show_sort_weight":129,"slug":131},10,"Lifestyle","lifestyle",{"id":133,"doc_module":4,"doc_module_name":45,"category_name":134,"show_sort_weight":105,"slug":135},19,"General","general"]