[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85654-en":3,"doc-seo-85654-105":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},85654,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Reliability-Aware CT–MRI Registration Quality Engineering Framework with Stability Analysis and Risk Classification","Multimodal CT–MRI registration underpins image-guided radiotherapy, surgical navigation, and diagnostic workflows, yet common pipelines provide only aggregate quality and no per-case reliability signal to guide clinical decisions. A plausible local optimum may score well while failing silently. A reliability-aware framework converts continuous per-case registration quality into Green/Yellow/Red risk categories using data-learned thresholds. Automatic acceptance, expert-review triggering, and rejection/retry are integrated into a single pipeline. Rigid and affine CT-to-T1 MRI registration are validated on 90 slices across 18 patients.","Reliability-Aware CT–MRI Registration: A Quality Engineering Framework with Stability Analysis and Risk Classification  \nNisreen Albzour  \nSchool of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USA  \nORCID: [https://orcid.org/0009-0000-9317-340X](https://orcid.org/0009-0000-9317-340X)  \nAbstract  \nMultimodal CT–MRI registration underpins image-guided radiotherapy, surgical navigation, and diagnostic workflows, yet existing pipelines report only aggregate quality metrics and provide no per-case reliability signal to support clinical decisions. A registration that converges to a plausible but incorrect local optimum can still score well on conventional post-hoc metrics, creating a silent failure mode. We propose a reliability-aware framework that converts continuous per-case registration quality into actionable Green/Yellow/Red risk categories using data-learned thresholds, enabling automated acceptance, expert-review triggering, and rejection within one pipeline. CT was registered to T1-weighted MRI using rigid and affine transformations on 90 paired slices from 18 patients (14 training, 4 held-out test) across brain (n=9), abdominal (n=8), and neck (n=1) anatomies. Reliability was assessed through quality improvement (ΔNMI,ΔSSIM), spatial overlap (Dice), registration stability across repeated initialisations, and inverse consistency error, combined into a single score R. Thresholds learned from training patients were applied unchanged to test patients. Affine registration significantly outperformed rigid on NMI (Cohen’s d = 0.76, p \u003C 10⁻¹³) and SSIM (d = 0.36, p \u003C 0.01), yielding 44% Green (automatically acceptable) classifications versus 33% for rigid. Reliability-filtered registrations surpassed unfiltered methods on all metrics. Per-anatomy performance varied markedly (abdominal 73% Green, brain 22%), and weight-sensitivity analysis identified Dice overlap as the dominant reliability component. This interpretable, data-driven framework provides a reproducible foundation for quality-controlled multimodal registration and applies to any rigid or affine algorithm. Risk thresholds reflect statistical rather than clinical validation, which should precede clinical deployment.  \nKeywords: Image registration, CT–MRI, quality assurance, reliability engineering, risk stratification, inverse consistency, registration stability  \n1. Introduction  \nComputed tomography (CT) and magnetic resonance imaging (MRI) provide complementary anatomical information: CT offers high spatial resolution and bone contrast, while MRI excels at soft-tissue differentiation. Combining these modalities through image registration is foundationalto radiotherapy planning, surgical navigation, and multimodal diagnostic workflows [1]. However, registration is itself an estimation problem with no ground truth available at deployment time, anda misregistered image pair can silently propagate errors into downstream clinical decisions [2] .  \nDespite decades of methodological development in image registration—from intensity-based similarity metrics [3] to deep-learning-based deformable registration [4]—the great majority of registration pipelines report only aggregate quality metrics (e.g., mean mutual information or Dice score across a dataset) without providing a per-case indication of whether a specific registration result can be trusted [5]. This is a critical gap from a quality-engineering perspective: a registration pipeline deployed in practice does not have access to ground-truth alignment, and therefore needs an internal signal for when a result is reliable enough to accept automatically, when it warrants expert review, and when it should be rejected and re-attempted [6] .  \nReliability engineering principles—which emphasise quantifying confidence in a system's output rather than reporting only point estimates—have been increasingly recognised as essential for trustworthy deployment of imaging algorithms [7] . Yet regist","cbCaiuRN4CiFpAO1","https://ap.wps.com/l/cbCaiuRN4CiFpAO1","pdf",1253944,1,24,"English","en",105,"# Introduction\n## Reliability engineering for imaging algorithms\n## Proposed reliability-aware CT–MRI registration framework\n# Methods\n## Data and registration settings\n## Reliability diagnostics and score R\n## Risk classification with learned thresholds\n# Results\n## Rigid vs affine performance\n## Reliability-filtered registration impact\n## Per-anatomy reliability differences\n# Discussion and limitations\n## Interpretability and deployment considerations","[{\"question\":\"Why is a per-case reliability signal needed for CT–MRI registration?\",\"answer\":\"Conventional pipelines report only aggregate quality metrics, so misregistrations can fail silently when no ground truth alignment exists at deployment time. A reliability signal supports deciding whether to accept automatically, seek expert review, or reject and retry.\"},{\"question\":\"What diagnostics are combined to form the reliability score R?\",\"answer\":\"R integrates four diagnostics: quality improvement (ΔNMI and ΔSSIM), spatial overlap (Dice), registration stability from repeated initialisations, and inverse consistency error from forward/backward registration discrepancy.\"},{\"question\":\"How are the Green/Yellow/Red risk categories determined?\",\"answer\":\"Thresholds learned from training patients are applied unchanged to held-out test patients. The resulting reliability score R is mapped into three tiers (Green automatically acceptable, Yellow review/attention, Red rejection/retry).\"}]",1784205381,60,{"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},"reliability-aware-ctmri-registration-quality-engineering-framework-with-stability-analysis-and-risk-classification","",{"@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/reliability-aware-ctmri-registration-quality-engineering-framework-with-stability-analysis-and-risk-classification/85654/",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},"Why is a per-case reliability signal needed for CT–MRI registration?","Question",{"text":75,"@type":76},"Conventional pipelines report only aggregate quality metrics, so misregistrations can fail silently when no ground truth alignment exists at deployment time. A reliability signal supports deciding whether to accept automatically, seek expert review, or reject and retry.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What diagnostics are combined to form the reliability score R?",{"text":80,"@type":76},"R integrates four diagnostics: quality improvement (ΔNMI and ΔSSIM), spatial overlap (Dice), registration stability from repeated initialisations, and inverse consistency error from forward/backward registration discrepancy.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the Green/Yellow/Red risk categories determined?",{"text":84,"@type":76},"Thresholds learned from training patients are applied unchanged to held-out test patients. The resulting reliability score R is mapped into three tiers (Green automatically acceptable, Yellow review/attention, Red rejection/retry).","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":28,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]