[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85969-en":3,"doc-seo-85969-105":30,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85969,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Quantum Compressed Sensing CT Reconstruction Algorithm Based on Penalized Weighted Least Squares and Guided Total Variation","Objective. Develop a QUBO-based sparse-view computed tomography (CT) reconstruction framework that models photon-counting statistics and anatomical heterogeneity while maintaining a quadratic form suitable for annealing optimization. Approach. A quantum compressed-sensing CT method combines penalized weighted least squares (PWLS) and guided total variation (GTV): PWLS weights residuals by photon reliability, and GTV uses gradients from a prior reconstruction to limit smoothing near boundaries and strengthen denoising in homogeneous regions. Binary encoding merges both terms into a unified QUBO. Experiments with four CT images (10-view fan-beam, Poisson noise) compare multiple baselines including classical solvers and a D-Wave hybrid optimizer. Results show PWLS-GTV gives the best quality, outperforming unweighted or uniformly regularized QUBO models; continuous optimization fails under strong quantization, while simulated annealing and the hybrid solver yield similar reconstructions.","Journal Name  \nCrossmark  \nRECEIVED  \ndd Month yyyy  \nREVISED  \ndd Month yyyy  \narXiv :2607 . 10566v1 [ cs .CV] 12 Jul 2026  \nPAPER  \nQuantum Compressed Sensing CT Reconstruction Algorithm Based on Penalized Weighted Least Squares and Guided Total Variation  \nYuwen Zhang 1 ,†, Yujie Liu 1 ,†, Ao Wang 1, Yikuang Yuluo 1, Shuangyang Zhong 1, Haijun Yu 1 and Yixing Huang 1 ,∗  \n1 Department of Medical Imaging Technology, Peking University Health Science Center, Beijing, China † These authors contributed equally to this work. ∗ Author to whom any correspondence should be addressed.  \nE-mail: [huangyx@pku.edu.cn](huangyx@pku.edu.cn)  \nKeywords: computed tomography, image reconstruction, quantum annealing, QUBO, penalized weighted least squares, guided total variation, sparse-view CT, compressed sensing  \nAbstract  \nObjective. To develop a QUBO-based sparse-view computed tomography (CT) reconstruction framework that accounts for both photon-counting statistics and anatomical heterogeneity while preserving the quadratic structure required for annealing-based optimization. Approach. We propose a quantum compressed-sensing CT framework combining penalized weighted least squares (PWLS) and guided total variation (GTV) . PWLS weights projection residuals according to photon-count reliability, while GTV uses gradients from a prior reconstruction to reduce smoothing near structural boundaries and enhance noise suppression in homogeneous regions. After binary encoding, both terms are integrated into a unified QUBO model. Experiments used four CT images under a 10-view fan-beam geometry with Poisson noise and compared conventional algorithms, several QUBO variants, continuous gradient descent, classical simulated annealing, and a D-Wave hybrid quantum–classical solver. Main results. PWLS-GTV achieved the best reconstruction quality among the evaluated methods and consistently outperformed QUBO models based on unweighted least squares or uniform regularization. Classical simulated annealing and the D-Wave hybrid solver produced similar reconstructions, whereas direct continuous optimization was ineffective under the strongly quantized sparse-view setting. Significance. Photon-statistical weighting and prior-guided spatial regularization can be incorporated into QUBO-based CT reconstruction without changing its quadratic structure, providing a basis for further quantum-assisted CT reconstruction research.  \n1 Introduction  \nComputed tomography (CT) has become one of the most important medical imaging modalities due to its ability to provide high-resolution cross-sectional anatomical information. In CT imaging, image reconstruction aims to recover the spatial distribution of attenuation coefficients from measured projection data. Conventional reconstruction algorithms, such as filtered back projection (FBP) and algebraic iterative reconstruction methods including the algebraic reconstruction technique (ART), the simultaneous algebraic reconstruction technique (SART), and the simultaneous iterative reconstruction technique (SIRT) [7, 8], have achieved considerable success under sufficiently sampled projection conditions. However, increasing the number of projection views is usually accompanied by higher radiation exposure to patients. To reduce radiation dose, sparse-view and low-dose CT imaging have attracted extensive attention in recent years. Under such conditions, CT reconstruction becomes a severely ill-posed inverse problem because the available projection information is incomplete and contaminated by noise. Although iterative reconstruction methods can partially alleviate these difficulties, achieving accurate and robust image reconstruction from sparse and noisy measurements remains a challenging optimization problem.  \nIn recent years, quantum computing has emerged as a promising paradigm for complex optimization [1, 2 , 3] . Among its frameworks, quantum annealing is particularly suited to problems  \nposed as quadratic unconstrained b","cbCaiveDOU85JySu","https://ap.wps.com/l/cbCaiveDOU85JySu","pdf",4752938,3,1,14,"English","en",105,"# Abstract\n# 1 Introduction\n## Sparse-view CT and ill-posed reconstruction\n## Quantum annealing and QUBO formulation\n## Prior quantum CT reconstruction methods and limitations\n## Photon-count statistics and weighted data fidelity","[{\"question\":\"What is the objective of the proposed framework?\",\"answer\":\"To build a QUBO-based sparse-view CT reconstruction method that incorporates photon-counting statistics and anatomical heterogeneity while keeping the quadratic structure required for annealing-based optimization.\"},{\"question\":\"How do PWLS and GTV contribute to reconstruction quality?\",\"answer\":\"PWLS weights projection residuals according to photon-count reliability, while GTV uses gradients from a prior reconstruction to reduce smoothing near structural boundaries and enhance noise suppression in homogeneous regions.\"},{\"question\":\"Which evaluation methods were compared, and what were the main findings?\",\"answer\":\"The experiments compared conventional algorithms, several QUBO variants, continuous gradient descent, classical simulated annealing, and a D-Wave hybrid quantum–classical solver. PWLS-GTV achieved the best quality, simulated annealing and the hybrid solver were similar, and direct continuous optimization performed poorly under strongly quantized sparse-view settings.\"}]",1784207466,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"quantum-compressed-sensing-ct-reconstruction-algorithm-based-on-penalized-weighted-least-squares-and-guided-total-variation","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/quantum-compressed-sensing-ct-reconstruction-algorithm-based-on-penalized-weighted-least-squares-and-guided-total-variation/85969/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","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 is the objective of the proposed framework?","Question",{"text":75,"@type":76},"To build a QUBO-based sparse-view CT reconstruction method that incorporates photon-counting statistics and anatomical heterogeneity while keeping the quadratic structure required for annealing-based optimization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do PWLS and GTV contribute to reconstruction quality?",{"text":80,"@type":76},"PWLS weights projection residuals according to photon-count reliability, while GTV uses gradients from a prior reconstruction to reduce smoothing near structural boundaries and enhance noise suppression in homogeneous regions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which evaluation methods were compared, and what were the main findings?",{"text":84,"@type":76},"The experiments compared conventional algorithms, several QUBO variants, continuous gradient descent, classical simulated annealing, and a D-Wave hybrid quantum–classical solver. PWLS-GTV achieved the best quality, simulated annealing and the hybrid solver were similar, and direct continuous optimization performed poorly under strongly quantized sparse-view settings.","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":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]