[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128033-en":3,"doc-seo-128033-105":31,"detail-sidebar-cat-0-en-105":92},{"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":28,"seo_description":14,"update_tm":29,"read_time":30},128033,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Strategies for Enhancing Reconstruction and Classification in Diffuse Optical Tomography and Photoacoustic Tomography for Ovarian and Breast Cancer","This dissertation develops machine learning strategies to improve reconstruction quality and diagnostic classification for diffuse optical tomography and photoacoustic tomography in ovarian and breast cancer imaging. It introduces a physical-constraint learning model for diffuse optical tomography, with quantitative evaluation on reconstructed target geometry and absorption-related parameters using phantom and clinical data. For photoacoustic tomography, it proposes ultrasound-enhanced U-Net for quantitative ovarian lesion assessment and a PA-NeRF neural radiance field approach for 3D reconstruction from limited B-scan measurements, supported by simulation, phantom, and clinical experiments.","Washington University in St. Louis  \nWashington University Open Scholarship  \n\n| McKelvey School of Engineering Theses & Dissertations | McKelvey School of Engineering |\n| --- | --- |\n| 10-22-2024\u003Cbr>Machine Learning Strategies for Enhancing Reconstruction and Classification in Diffuse Optical Tomography and Photoacoustic Tomography for Ovarian and Breast Cancer\u003Cbr>Yun Zou\u003Cbr>Washington University – McKelvey School of Engineering\u003Cbr>Follow this and additional works at: [https://openscholarship.wustl.edu/eng_etds](https://openscholarship.wustl.edu/eng_etds) |  |\n\nRecommended Citation  \nZou, Yun, \"Machine Learning Strategies for Enhancing Reconstruction and Classification in Diffuse Optical Tomography and Photoacoustic Tomography for Ovarian and Breast Cancer\" (2024) . McKelvey School of Engineering Theses & Dissertations. 1123.  \n[https://openscholarship.wustl.edu/eng_etds/1](https://openscholarship.wustl.edu/eng_etds/1)123  \nThis Dissertation is brought to you for free and open access by the McKelvey School of Engineering at Washington University Open Scholarship. It has been accepted for inclusion in McKelvey School of Engineering Theses & Dissertations by an authorized administrator of Washington University Open Scholarship. For more information, [please contact](please contact digital@wumail.wustl.edu)[ digital@wumail.wustl.edu](please contact digital@wumail.wustl.edu).  \nWASHINGTON UNIVERSITY IN ST. LOUIS  \nMcKelvey School of Engineering  \nDepartment of Biomedical Engineering  \nDissertation Examination Committee:  \nQuing Zhu, Chair  \nAdam Bauer  \nSong Hu  \nAbhinav K. Jha  \nNeal Patwari  \nMachine Learning Strategies for Enhancing Reconstruction and Classification in Diffuse Optical Tomography and Photoacoustic Tomography for Ovarian and Breast Cancer  \nby  \nYun Zou  \nA dissertation presented to  \nthe McKelvey School of Engineering  \nof Washington University in  \npartial fulfillment of the  \nrequirements for the degree  \nof Doctor of Philosophy  \nDecember 2024  \nSt. Louis, Missouri  \n© 2024, Yun Zou  \nTable of Contents  \nList of Figures ..................................... v  \nList of Tables ...................................... x  \nAcknowledgments ................................... xi  \nAbstract ......................................... xii  \nChapter 1: Introduction ............................... 1  \n1.1 Motivation ..................................... 1  \n1.2 Optical Imaging and Deep Learning Techniques ................ 3  \n1.3 Organization of the Dissertation ......................... 4  \nChapter 2: Machine Learning Model with Physical Constraints for Diffuse Optical Tomography ................................ 6  \n2.1 Background .................................... 6  \n2.2 Dataset and System ................................ 8  \n2.2.1 Simulation Data .............................. 9  \n2.2.2 DOT system, phantom and clinical data ................ 9  \n2.3 Born-CGD method ................................ 11  \n2.4 ML-PC model ................................... 13  \n2.4.1 Training .................................. 14  \n2.4.2 Testing and Physical Constraints for ML-PC .............. 15  \n2.5 Quantitative Evaluation ............................. 17  \n2.5.1 Reconstructed Target Diameter ..................... 17  \n2.5.2 Reconstructed Depth Profile ....................... 17  \n2.5.3 Reconstructed Maximum Absorption Coefficient ............ 18  \n2.6 Results ....................................... 18  \n2.6.1 Phantom Reconstruction Results .................... 18  \n2.6.2 Phantom Quantitative Results ...................... 20  \n2.6.3 Clinical Examples ............................. 22  \n2.7 Conclusions .................................... 24  \nChapter 3: Ultrasound-enhanced Unet model for quantitative photoacoustic tomography of ovarian lesions .......................... 27  \n3.1 Abstract ...................................... 27  \n3.2 Introduction .................................... 28  \n3.3 Methodology and Materials .............","cbCaigSYj0OEpUj7","https://ap.wps.com/l/cbCaigSYj0OEpUj7","pdf",61046518,3,1,124,"English","en",105,"# List of Figures\n# List of Tables\n# Acknowledgments\n# Abstract\n# Chapter 1: Introduction\n## Motivation\n## Optical Imaging and Deep Learning Techniques\n## Organization of the Dissertation\n# Chapter 2: Machine Learning Model with Physical Constraints for Diffuse Optical Tomography\n## Background\n## Dataset and System\n## Born-CGD method\n## ML-PC model\n## Quantitative Evaluation\n## Results\n## Conclusions\n# Chapter 3: Ultrasound-enhanced Unet model for quantitative photoacoustic tomography of ovarian lesions\n## Introduction\n## Methodology and Materials\n## Results and Analysis\n## Summary and Discussion\n# Chapter 4: PA-NeRF, a neural radiance field model for 3D photoacoustic tomography reconstruction from limited Bscan data\n## Introduction\n## Methodology\n## Results\n## Simulation Results\n## Clinical Examples\n## Ablation Study\n## Discussion","[{\"question\":\"What imaging modalities and cancer targets are studied in this dissertation?\",\"answer\":\"The work focuses on diffuse optical tomography and photoacoustic tomography, with applications to ovarian lesions and breast cancer-related reconstruction and classification goals.\"},{\"question\":\"How does the dissertation improve diffuse optical tomography using machine learning?\",\"answer\":\"It introduces a machine learning model that incorporates physical constraints, trained and tested on simulation, phantom, and clinical data, followed by quantitative evaluation of reconstructed targets and absorption-related measures.\"},{\"question\":\"What methods are proposed for photoacoustic tomography reconstruction and quantification?\",\"answer\":\"It presents an ultrasound-enhanced U-Net for quantitative assessment of ovarian lesions and a PA-NeRF neural radiance field model for 3D photoacoustic reconstruction from limited B-scan data.\"}]","Machine Learning Strategies for Enhancing Reconstruction and Classification in Diffuse Optical Tomography and Photoacoustic Tomography for Ovarian and Breast Cancer | PDF",1785944218,312,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-strategies-for-enhancing-reconstruction-and-classification-in-diffuse-optical-tomography-and-photoacoustic-tomography-for-ovarian-and-breast-cancer","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-strategies-for-enhancing-reconstruction-and-classification-in-diffuse-optical-tomography-and-photoacoustic-tomography-for-ovarian-and-breast-cancer/128033/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What imaging modalities and cancer targets are studied in this dissertation?","Question",{"text":76,"@type":77},"The work focuses on diffuse optical tomography and photoacoustic tomography, with applications to ovarian lesions and breast cancer-related reconstruction and classification goals.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the dissertation improve diffuse optical tomography using machine learning?",{"text":81,"@type":77},"It introduces a machine learning model that incorporates physical constraints, trained and tested on simulation, phantom, and clinical data, followed by quantitative evaluation of reconstructed targets and absorption-related measures.",{"name":83,"@type":74,"acceptedAnswer":84},"What methods are proposed for photoacoustic tomography reconstruction and quantification?",{"text":85,"@type":77},"It presents an ultrasound-enhanced U-Net for quantitative assessment of ovarian lesions and a PA-NeRF neural radiance field model for 3D photoacoustic reconstruction from limited B-scan data.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]