[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128032-en":3,"doc-seo-128032-105":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},128032,962084928904,"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 - Dissertation","Machine learning strategies are developed to improve reconstruction and classification performance in diffuse optical tomography (DOT) and photoacoustic tomography (PAT) for ovarian and breast cancer applications. The work introduces models that incorporate physical constraints for DOT, combines ultrasound-enhanced learning to enable quantitative PAT of ovarian lesions, and proposes a neural radiance field framework (PA-NeRF) to reconstruct 3D PAT from limited B-scan data. Quantitative evaluations, phantom experiments, and clinical examples assess image quality, recovered anatomical parameters, and diagnostic relevance across scenarios.","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. 1106.  \n[https://openscholarship.wustl.edu/eng_etds/1](https://openscholarship.wustl.edu/eng_etds/1)106  \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 [digital@wumail.wustl.edu](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 ........................... 30  \n3.3.1 Ultrasound-enhanced Unet Model .","cbCaioZ7ArCI2CYf","https://ap.wps.com/l/cbCaioZ7ArCI2CYf","pdf",61044252,2,1,124,"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## Discussion\n# Chapter 5: Ultrasound and Diffused Optical Tomography-Transformer Model for Assessing Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer","[{\"question\":\"What imaging modalities and cancer types are covered in the dissertation?\",\"answer\":\"The dissertation focuses on diffuse optical tomography and photoacoustic tomography for ovarian cancer and breast cancer applications.\"},{\"question\":\"How does the work improve DOT reconstruction using machine learning?\",\"answer\":\"It proposes machine learning models that embed physical constraints, including a Born-CGD method and an ML-PC approach, and evaluates them quantitatively.\"},{\"question\":\"What is the purpose of the PA-NeRF model in photoacoustic tomography?\",\"answer\":\"PA-NeRF is presented as a neural radiance field model to enable 3D photoacoustic tomography reconstruction from limited B-scan data, supported by simulations, phantom results, and clinical examples.\"}]","Machine Learning Strategies for Enhancing Reconstruction and Classification in Diffuse Optical Tomography and Photoacoustic Tomography for Ovarian and Breast Cancer - 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