TR&D Project 1: Virtual Patients

TR

• 批准号: 10089802
• 负责人: William P Segars
• 金额: $ 33.23万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10089802
• 关键词: 3-Dimensional; Adult; Age; Anatomy; Blood flow; Brain; Cardiac; Characteristics; Childhood; Clinical; Computed Tomography Scanners; Computers; Contrast Media; Data; Databases; Development; Disease; Dose; Evaluation; Exhibits; Future; Heart; Heart Diseases; Height; Heterogeneity; Hospitals; Image; Image Analysis; Imaging Device; Imaging technology; Individual; Intravenous; Kidney; Laboratories; Lesion; Libraries; Liquid substance; Liver; Lung; Magnetic Resonance Imaging; Malignant Neoplasms; Mathematics; Medical Imaging; Methods; Modality; Modeling; Motion; Normal tissue morphology; Organ; Paper; Patients; Performance; Perfusion; Phase; Physiological; Population; Pregnancy; Protocols documentation; PubMed; Race; Reader; Reading; Research; Research Personnel; Research Project Grants; Resources; Series; Source; Technology; Texture; Time; Tissue Model; Tissues; Universities; Update; Variant; Weight; Work; X-Ray Computed Tomography; base; clinical application; clinically relevant; computerized; cost; design; experimental study; human model; imaging approach; individual patient; innovation; patient population; phantom model; physiologic model; radiation absorbed dose; respiratory; sex; simulation; tool; virtual; virtual imaging; virtual patient; virtual platform

ABSTRACT – TRD1: Virtual Patients The broad objective of the Center for Virtual Imaging Trials is to develop and provide a comprehensive virtual platform for assessment of a medical imaging technology or clinical application from design to use, initially in Computed Tomography (CT). Virtual imaging trials, consisting of computerized patients, scanner models, and image analysis methods, provide an efficient means with which to determine the most effective and optimized use of emerging medical imaging technologies and approaches. Experiments can be conducted quickly and cost effectively on a computer with known ground truth (exact patient anatomy and physiological conditions are known) and with complete user control of the virtual imaging device and protocol. With the growing use of CT, the need for such a toolset is at an all-time high to optimize image quality versus dose, personalizing technologies to the characteristics of individual patients. Virtual imaging trials require a realistic virtual patient population. The team previously developed a series of 60 adult and 90 pediatric 4D whole-body computational human models known as XCAT phantoms (extended cardiac-torso). The phantoms have found wide use in numerous research projects both in the team's laboratory and by others, but they are limited in that they do not cover the full range of patient anatomies indicative of the population at large, including pregnancy, and lack sufficient realism in terms of anatomical textures, motion, blood flow, and contrast perfusion. All of these factors are significant determinants of image quality as well as absorbed radiation dose. This work of TRD1 will greatly expand the coverage of the XCAT library, creating a population of whole-body 4D phantoms to represent combinations of sex, age, race, height, and weight including anatomically variable models for stages of pregnancy. Further, TRD1 will update the XCAT motion models, incorporate blood flow to simulate intra-organ contrast perfusion within vital organs, and develop additional models for the tissue heterogeneity within the body including disease-related abnormalities. Such innovations greatly enhance the realism of the phantoms, giving researchers the ability to generate patient-quality CT data in order to assess image quality as well as dose in the presence of motion and contrast. The tools will be packaged into a framework from which researchers will be able to generate virtual populations, on demand, morphing between the templates to create 1000s of realistic phantoms with user-defined anatomical characteristics, lesions, physiologic motion, and contrast. The framework to produce realistic, vast populations of customizable virtual subjects will be combined with the Center's virtual scanners (TRD2) and virtual readers (TRD3) components. Through these advances, the proposed Center will have a significant impact on the development, evaluation, and optimization of clinically informed 3D and 4D CT imaging technologies with potential future expansion to other modalities.

摘要：TRD1：虚拟患者