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Results for "disease diagnosis": 21 found

23na Magnetic Resonance Imaging k-Space Denoising

Authors: Lorenzo Arsini, Andrea Ciardiello, Fabio Massimo D'Amore, Stefano Giagu, Federico Giove, Carlo Mancini-Terracciano, Cecilia Voena

Affiliation: Istituto Superiore di Sanità, Sapienza University of Rome, Università Sapienza Roma, Magnetic Resonance for Brain Investigation Laboratory, Museo Storico della Fisica e Centro di Studi e Ricerche Enrico Fermi

Abstract Preview: Purpose: To leverage newly developed heteronuclear magnetic resonance imaging (MRI) techniques, particularly sodium (23Na) imaging, for identifying potential biomarkers of Alzheimer's disease—such as ...

AI in Disease Diagnosis

Authors: Tessa Cook

Affiliation: Penn Medicine

Abstract Preview: N/A...

Artificial Intelligence-Powered Conventional Energy Integrating Detector-Based Coronary CT Angiography: Learning High-Resolution and Multi-Energy Imaging from Photon-Counting Detector CT

Authors: Shaojie Chang, Thomas A. Foley, Hao Gong, Emily Koons, Shuai Leng, Cynthia H. McCollough, Eric E. Williamson

Affiliation: Mayo Clinic

Abstract Preview: Purpose: To enhance coronary CT angiography (cCTA) capabilities on conventional energy integrating detector CT (EID-CT) using artificial intelligence (AI). The AI framework incorporates high-resolutio...

Assessing Low Iodine Concentrations in Liver Lesions with Dual Energy CT: Impact of Beam Choices

Authors: Xinhua Li, Vu Nguyen, Yifang (Jimmy) Zhou

Affiliation: Cedars-Sinai Medical Center

Abstract Preview: Purpose: Assessing iodine concentration in liver lesions is essential for evaluating contrast enhancement in multi-phase liver CT and for accurate disease diagnosis. This study aims to evaluate the as...

Chat with Oncology Information System Via Large Language Model

Authors: Michael Dohopolski, Xuejun Gu, Hao Jiang, Steve B. Jiang, Christopher Kabat, Jingying Lin, Weiguo Lu, Michael Tang

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Neuralrad LLC, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine

Abstract Preview: Purpose: To streamline access to clinical data stored in Oncology Information Systems such as MOSAIQ or ARIA, we developed an AI-powered chatbot capable of querying, summarizing, and interactively ans...

Early Imaging Identification of Osteoradionecrosis and Classification Using the Novel Clinrad System

Authors: Serageldin Attia, Zayne Belal, Cem Dede, Clifton David Fuller, Andrew Hope, Laia Humbert Vidan, Kate Hutcheson, Zaphanlene Kaffey, Stephen Y. Lai, Abdallah Mohamed, Amy Moreno, Jillian Rigert, Erin Watson

Affiliation: Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Princess Margaret Cancer Centre, University Health Network, 610 University Ave., The University of Texas MD Anderson Cancer Center, UT MD Anderson, Princess Margaret Cancer Centre, UT MD Anderson Cancer Center, Hospital of the University of Pennsylvania, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Department of Head and Neck Surgery, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology

Abstract Preview: Purpose: Osteoradionecrosis (ORN) of the jaw is a debilitating radiation-induced toxicity lacking standardized classification criteria or treatment guidelines. Early identification of tissue injury co...

Feasibility of Extracting Diagnosis and Staging at Scale from Clinical Notes Via a Real-World Data Warehouse

Authors: Melissa Bronson, Elizabeth L. Covington, Robert T. Dess, Joseph R. Evans, William C. Jackson, Charles S. Mayo, Michelle L. Mierzwa, Benjamin S. Rosen, VG Vinod Vydiswaran, Grant Weyburne, Zheng Zhang, Henry Zocher

Affiliation: PCORnet®, The National Patient-Centered Clinical Research Network, University of Michigan

Abstract Preview: Purpose: Diagnosis and staging are an integral part of cancer care, but this information is often scattered across various electronic medical records. The fragmentation increases overall documentation...

Functional Liver Image Guided Radiation Planning Using MRI with a Contrast Agent

Authors: Kenneth L. Homann, Natalie A Lockney, Hong Zhang

Affiliation: Department of Radiation Oncology, Vanderbilt University Medical Center, Vanderbilt University Medical Center

Abstract Preview: Purpose: The aim of this study is to develop a treatment planning methodology utilizing liver functional imaging via contrast-enhanced Magnetic Resonance Imaging (MRI) in patients undergoing stereotac...

Generating Brain Pseudo-CT from PET-Only Images Using Deep Learning Method

Authors: Pouya Azarbar, Nima Kasraie, Mahsa Shahrbabki Mofrad, Peyman Sheikhzadeh

Affiliation: UT Southwestern Medical Center, Shahid Beheshti University of Medical science, Imam Khomeini Hospital Complex,Tehran University of Medical Sciences, Tehran University of Medical Science

Abstract Preview: Purpose: PET imaging become crucial in diagnosing and managing various diseases, but its key limitation is the lack of detailed anatomical information. Integrating CT-scans with PET images enhances cl...

Generation of Virtual Lung PET Images from CT Data Via Deep Learning for Accelerated Tumor Detection and Preliminary Diagnosis

Authors: Pouya Azarbar, Nima Kasraie, Peyman Sheikhzadeh

Affiliation: UT Southwestern Medical Center, Shahid Beheshti University of Medical science, Imam Khomeini Hospital Complex,Tehran University of Medical Sciences

Abstract Preview: Purpose: Positron Emission Tomography (PET) is crucial for diagnosing and monitoring diseases due to its functional imaging capabilities. However, its high cost, significant radiation exposure, and li...

Integrating Foundation Model with Self-Supervised Learning for Brain Lesion Segmentation with Multimodal and Diverse MRI Datasets

Authors: Zong Fan, Fan Lam, Hua Li, Rita Huan-Ting Peng, Yuan Yang

Affiliation: University of Illinois at Urbana Champaign, University of Illinois at Urbana-Champaign, Washington University School of Medicine, University of Illinois Urbana-Champaign

Abstract Preview: Purpose: Accurate lesion segmentation in MRI is critical for early diagnosis, treatment planning, and monitoring disease progression in various neurological disorders. Cross-site MRI data can alleviat...

Investigating CT-Based Lung Ventilation Heterogeneity As a Biomarker for COPD Severity

Authors: Shiho Amster, Ryan Andosca, Igor Barjaktarevic, Michael Vincent Lauria, Daniel A. Low, Dylan P. O'Connell, Ann Raldow, Brad Stiehl

Affiliation: Department of Pulmonology, University of California Los Angeles, Department of Radiation Oncology, University of California, Los Angeles, Cedars-Sinai Medical Center

Abstract Preview: Purpose: Image-based biomarkers could be useful for disease diagnosis and prognosis, especially in heterogeneous lung diseases like COPD. The purpose of this study is to assess the feasibility of usin...

Knee Image Generation Based on Fine-Tuning Stable Diffusion Model

Authors: Xiangli Cui, Zilei Fu, Man Hu, Wanli Huo, Xiaoqing Wu, Jianguang Zhang, Yingying Zhang

Affiliation: Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University, Departments of Radiation Oncology, Zibo Wanjie Cancer Hospital, Department of Oncology, Xiangya Hospital, Central South University, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences

Abstract Preview: Purpose:
Using Stable Diffusion to generate images of the knee in different disease states can enrich the medical imaging database and inject new vitality into the field of medical imaging analysis...

Latent Diffusion Model-Driven Semi-Supervised Semantic Segmentation of Cell Nuclei

Authors: Mark Anastasio, Hua Li, Zhuchen Shao

Affiliation: Washington University School of Medicine, University of Illinois Urbana-Champaign

Abstract Preview: Purpose: Automated semantic segmentation of cell nuclei in microscopic images is vital for disease diagnosis and tissue microenvironment analysis. However, obtaining large annotated datasets for train...

Memory-Efficient Deep Learning for Volumetric Cone-Beam CT Image Reconstruction

Authors: Ziqi Gao, Lei Xing, Siqi Ye, S. Kevin Zhou

Affiliation: Department of Radiation Oncology, Stanford University, University of Science and Technology of China (USTC)

Abstract Preview: Purpose: To address the challenge of high memory usage in volumetric cone-beam CT (CBCT) imaging, we propose a method that combines joint reconstruction and super-resolution for sparsely sampled CBCT ...

Optimizing Vessel Wall Visualization Using a Novel Black-Blood CT Technique on Craniocervical CT Angiography

Authors: Xiaohu Li, Jianjun Shen, Guozhi Zhang, Sihua Zhong, Jingjie Zhou

Affiliation: United Imaging Healthcare

Abstract Preview: Purpose:
Visualization of carotid artery vessel wall on computed tomography angiography (CTA) imaging is challenging. This study aims to develop a novel post-processing technique, black-blood compu...

Performance Comparison of Artificial Intelligence-Based Auto-Segmentation Software on Pediatric CT Image Datasets for the Creation of Patient Specific Computational Phantoms

Authors: Wesley E. Bolch, Emily L. Marshall, Dhanashree Rajderkar, Wyatt Smither

Affiliation: University of Florida

Abstract Preview: Purpose: To determine the accuracy of TotalSegmentator, an AI-based automatic segmentation toolkit, on pediatric CT scans as the original software was trained on adult image datasets with a mean patie...

Quantifying Uncertainties in Radiation Risk and Performance-Based Clinical Risk Assessment in Clinical Computed Tomography

Authors: Em Harkness, Francesco Ria, Ehsan Samei

Affiliation: Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System

Abstract Preview: Purpose:
A recently introduced mathematical method quantifies performance-based clinical risk to create a risk-to-risk assessment with radiation risk, rendering the so-called total risk. However, t...

Region-Specific Structure-Function Coupling Alterations in Parkinson’s Disease: Insights from Multi-Modal MRI

Authors: Yifei Hao, Ting Huang, Wenxuan Li, Xiang Li, Manju Liu, Rong Liu, Tao Peng, Yulu Wu, Fang-Fang Yin, Lei Zhang, Yaogong Zhang, Jiangtao Zhu

Affiliation: Duke University, Department of Radiology, The Second Affiliated Hospital of Soochow University, School of Future Science and Engineering, Soochow University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: This study investigates the alterations in structure-function coupling (SC-FC) networks in Parkinson’s disease (PD) patients, focusing on region-specific disruptions and compensatory mechanis...

Simultaneous Synthesis of Lung Perfusion and Ventilation Images from CT Using a Dual-Decoder Residual Attention Network for Lung Disease Diagnosis

Authors: Li-Sheng Geng, David Huang, Haoze Li, Xi Liu, Meng Wang, Tianyu Xiong, Ruijie Yang, Weifang Zhang, Meixin Zhao

Affiliation: School of Physics, Beihang University, Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Department of Radiation Oncology, Peking University Third Hospital, Department of Nuclear Medicine, Peking University Third Hospital, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: This study aimed to develop a deep learning-based framework for simultaneously generating lung perfusion and ventilation images from three-dimensional computed tomography (3D CT) images.
M...

Towards Achieving Quantitative Attenuation Values in CT with Energy-Integrated and Photon-Counting Detectors

Authors: Zijia Guo, Viktor Haase, Michael F. McNitt-Gray, Frederic Noo

Affiliation: Siemens Healthineers, University of Utah, David Geffen School of Medicine at UCLA

Abstract Preview: Purpose: The attenuation values in CT hold strong potential for disease diagnosis. However, they lack reliability, which has limited their use to clinical trials where variability can be controlled. S...