[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85735-en":3,"doc-seo-85735-105":29,"detail-sidebar-cat-0-en-105":91},{"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":20,"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},85735,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Robustness and Stability Analysis of Differentiable Shift-Variant FBP for Cone-Beam CT","Differentiable shift-variant filtered backprojection (SV-FBP) is used for cone-beam CT to learn redundancy weights from data, avoiding analytically derived weighting schemes for general source trajectories. The study systematically evaluates robustness and adaptability under challenging acquisition settings, showing stable reconstruction across highly irregular and discontinuous trajectories. Results indicate performance depends more on the spatial distribution of sampling points than on trajectory ordering or continuity, supporting reliable use in difficult CBCT data acquisition regimes.","Machine Learning for Biomedical Imaging  \nRobustness and Stability Analysis of Differentiable Shift-Variant FBP for Cone-Beam CT under Challenging Acquisition Settings  \nChengze Ye , Linda-Sophie Schneider , Yipeng Sun , Mareike Thies, Siyuan Mei , Paula Andrea Prez-Toro  , Siming Bayer , Andreas Maier   \nPattern Recognition Lab, Friedrich-Alexander-Universit t Erlangen-N rnberg, Erlangen, Germany  \nAbstract  \nThe differentiable shift-variant filtered backprojection (SV-FBP) framework enables data-driven estimation of redundancy weights for cone-beam CT reconstruction under general source trajectories, removing the need for analytically derived weighting schemes. In this work, we present a systematic study of the robustness and adaptability of differentiable SV-FBP under challenging acquisition settings. We show that the framework remains stable across highly irregular and discontinuous trajectories, indicating that reconstruction performance is largely insensitive to trajectory ordering or continuity. Instead, the spatial distribution of sampling points plays a more dominant role. Under sparse-view  \nVolume 2026, Received: 2025-07-15, Published 2026-07-02  \nCorresponding author: [chengze.ye@fau.de](chengze.ye@fau.de)  \nSpecial issue: MELBA–BVM 2025 Special Issue  \nGuest editors: Andreas Maier, Thomas Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Katharina Breininger  \n1. Introduction  \nC one beam Computed tomography (CBCT) is a  \nwidely used imaging modality in operating rooms, enabling the acquisition of three-dimensional (3D)  \nimages within a single rotation at a relatively low radiation dose. Its compact design and rapid acquisition capability make it particularly well suited for image-guided interventions such as angiography and spine surgery. Nevertheless, maintaining high image quality in these dynamic settings remains challenging, particularly in the presence of metal implants, which frequently cause artifacts and reconstruction errors .  \nIn recent years, the advent of robotic C-arm systems  \nhas enabled the exploration of scanning trajectories beyond conventional circular scans. These systems can follow non-circular paths tailored to patient anatomy and specific clinical requirements (Hatamikia et al. , 2022) . For example, sinusoidal trajectories and other carefully designed acquisition paths have been shown to significantly reduce metal artifacts (Gang et al. , 2020a,b) . Optimized trajectories can also improve image quality while reducing the number of required projections (Herl et al. , 2020) . Saddle trajectories, in particular, have proven effective in mitigating cone-beam artifacts and enabling reliable reconstruction even in the presence of axial truncation (Pack et al. , 2004) .  \nHowever, non-circular trajectories also pose substantial  \narXiv :2607 .09828v1  \nchallenges for image reconstruction. The Feldkamp-DavisKress (FDK) algorithm (Feldkamp et al., 1984), one of the most widely used reconstruction methods, assumes a circular orbit and therefore degrades markedly when this assumption is violated. Iterative approaches such as model-based iterative reconstruction (MBIR) (Liu, 2014) can achieve high reconstruction accuracy for more general trajectories, but they typically require substantial computational resources . In addition, the design of suitable regularization terms for MBIR is often challenging, which further limits its practical applicability.  \nTo address this issue, Defrise and Clack proposed a shiftvariant filtered backprojection (FBP) algorithm that adapts to varying scanning geometries (Defrise and Clack, 1994) . Although this approach considerably reduces reconstruction time compared with iterative methods, it still requires the estimation of trajectory-dependent weights. Importantly, the analytical form of these weights depends on the derivative of the scanning trajectory, which makes the method difficult to apply to discontinuous or piecewise-defined acquisit","cbCaimOT2bAISGvG","https://ap.wps.com/l/cbCaimOT2bAISGvG","pdf",2673698,1,13,"English","en",105,"# Abstract\n# Introduction\n## CBCT motivation and challenges with metal artifacts\n## Trajectory design beyond circular scans\n## Limitations of FDK and iterative methods\n## Shift-variant FBP and differentiable SV-FBP background\n## Goals and evaluation scope of this study","[{\"question\":\"What problem does differentiable SV-FBP address in cone-beam CT reconstruction?\",\"answer\":\"It estimates redundancy weights data-driven for general source trajectories, removing reliance on analytically derived weights that are hard to apply to discontinuous or piecewise scanning paths.\"},{\"question\":\"How does the method behave under irregular or discontinuous trajectories?\",\"answer\":\"Reconstruction remains stable across highly irregular and discontinuous trajectories, with performance largely insensitive to trajectory ordering or continuity.\"},{\"question\":\"What factor is more important for reconstruction performance in this framework?\",\"answer\":\"The spatial distribution of sampling points is more dominant than trajectory ordering or continuity.\"}]",1784205899,33,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":27},"robustness-and-stability-analysis-of-differentiable-shift-variant-fbp-for-cone-beam-ct","",{"@graph":35,"@context":85},[36,53,68],{"@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/robustness-and-stability-analysis-of-differentiable-shift-variant-fbp-for-cone-beam-ct/85735/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does differentiable SV-FBP address in cone-beam CT reconstruction?","Question",{"text":75,"@type":76},"It estimates redundancy weights data-driven for general source trajectories, removing reliance on analytically derived weights that are hard to apply to discontinuous or piecewise scanning paths.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method behave under irregular or discontinuous trajectories?",{"text":80,"@type":76},"Reconstruction remains stable across highly irregular and discontinuous trajectories, with performance largely insensitive to trajectory ordering or continuity.",{"name":82,"@type":73,"acceptedAnswer":83},"What factor is more important for reconstruction performance in this framework?",{"text":84,"@type":76},"The spatial distribution of sampling points is more dominant than trajectory ordering or 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