Collaborative Research: Physics-Based Modeling and Simulation for Post-Mastectomy Breast Reconstructive Surgery

合作研究:基于物理的乳房切除术后乳房重建手术建模与仿真

基本信息

  • 批准号:
    0402591
  • 负责人:
  • 金额:
    $ 27.25万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2004
  • 资助国家:
    美国
  • 起止时间:
    2004-09-01 至 2007-08-31
  • 项目状态:
    已结题

项目摘要

0402591KakadiarisBreast cancer is the second most frequent cancer diagnosis among American women today, after skin cancer. Post-mastectomy breast reconstruction is the third most frequently performed reconstructive procedure, superseded only by tumor removal and hand surgery. The psychological distress that accompanies a lump found in the breast, and the subsequent mastectomy is compounded by the limitations of contemporary reconstructive procedures; without a trusted reconstruction procedure, the specter of a disfiguring operation adds to the fear. Conventional procedures for reconstructing breast, or other soft tissue defects requiring adipose tissue, involve "robbing Peter to pay Paul". That is, tissue from a donor site on the patient is used to reconstruct the receiving site. However, it is difficult to predict exactly how the breast will be changed by a specific procedure in any particular patient. What size of donor tissue will yield the desired breast shape? How large a tension will be generated in the skin? Is there enough tissue to recreate a breast that would meet the expectations of the patient? Currently, there are no methods that would predict the outcome of the surgery.The goal of this proposal is to develop a predictive methodology to replace the trial and error method currently practiced. The proposed method is based on the large deformation analysis of mechanics; the skin covering the breast is modeled as a nonlinear elastic membrane or shell whose mechanical properties must be determined. After the shape, the size, and the mass of implant tissue and the skin on the breast are known, a nonlinear analysis will be used to calculate the resulting end shape. Conversely, if the end shape is prescribed, the analysis should yield the amount of implant tissue, and the shape and size of the skin necessary. The specific aims of this project were: 1) to develop an analytical model and numerical simulations to predict the breast shape for known initial conditions using generic data as well as physical and computational phantoms. This is based on the mechanics of finitely deforming bodies, 2) to develop a parametric deformable model that describes the shape of a female breast mathematically and 3) to experimentally verify these methods using phantoms and to demonstrate their utility for breast reconstructive surgery. Successful completion of this research has the potential to improve the quality of life for individuals recovering from post-mastectomy reconstruction operations. The predictive methodology will also lead to reduction in surgical time. The models that will be developed will apply to other organs and tissues as well and thus will increase understanding of soft tissue modeling and surgical simulation.
0402591 Kakadikov乳腺癌是当今美国女性中第二常见的癌症诊断,仅次于皮肤癌。乳房切除术后乳房重建是第三个最常见的重建程序,仅次于肿瘤切除和手外科手术。伴随乳房肿块的心理痛苦,以及随后的乳房切除术,由于当代重建手术的局限性而变得更加复杂;没有可靠的重建手术,毁容手术的幽灵增加了恐惧。重建乳房或其他需要脂肪组织的软组织缺陷的传统手术涉及“拆东墙补西墙”。也就是说,来自患者上的供体部位的组织用于重建接收部位。然而,很难准确预测任何特定患者的特定手术将如何改变乳房。什么样的供体组织可以获得理想的乳房形状?皮肤会产生多大的张力?是否有足够的组织来再造一个符合患者期望的乳房?目前,还没有方法可以预测手术的结果。本提案的目标是开发一种预测方法来取代目前实践的试错法。所提出的方法是基于大变形力学分析,皮肤覆盖的乳房被建模为一个非线性弹性膜或壳,其机械性能必须确定。在已知植入物组织和乳房皮肤的形状、尺寸和质量后,将使用非线性分析来计算最终形状。相反,如果规定了最终形状,则分析应产生植入物组织的量以及所需皮肤的形状和尺寸。该项目的具体目标是:1)开发一个分析模型和数值模拟,使用通用数据以及物理和计算幻影预测已知初始条件下的乳房形状。这是基于变形体的力学,2)开发一个参数化的可变形模型,该模型在数学上描述了女性乳房的形状,3)使用体模实验验证这些方法,并证明它们在乳房重建手术中的实用性。这项研究的成功完成有可能提高从乳房切除术后重建手术中恢复的个人的生活质量。预测方法也将导致手术时间的减少。将开发的模型也将适用于其他器官和组织,从而增加对软组织建模和手术模拟的理解。

项目成果

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Ioannis Kakadiaris其他文献

AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis
冠状动脉钙 (CAC) 扫描 (AI-CAC) 中支持 AI 的心室容量和钙化斑块特征显着改善 Agatston CAC 评分,用于预测所有心血管事件:动脉粥样硬化的多种族研究
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    M. Naghavi;A. Reeves;K. Atlas;Chenyu Zhang;T. Atlas;C. Henschke;D. Yankelevitz;M. Budoff;Dong Li;Sion Roy;Khurram Nasir;Jagat Narula;Ioannis Kakadiaris;S. Molloi;Zahi Fayad;David Maron;Michael McConnell;Kim Williams;Daniel Levy;Nathan S Wong
  • 通讯作者:
    Nathan S Wong
Introduction to the special issue on human modeling, analysis, and synthesis
  • DOI:
    10.1007/s00138-003-0122-5
  • 发表时间:
    2003-09-01
  • 期刊:
  • 影响因子:
    2.300
  • 作者:
    Ioannis Kakadiaris;Rajeev Sharma;Mohammed Yeasin
  • 通讯作者:
    Mohammed Yeasin
Developing a healthy food access index (HFAI): Web-based mapping and future directions for AI integrations
开发健康食品获取指数(HFAI):基于网络的绘图以及人工智能集成的未来方向
  • DOI:
    10.1016/j.cities.2025.105908
  • 发表时间:
    2025-06-01
  • 期刊:
  • 影响因子:
    6.600
  • 作者:
    Junfeng Jiao;Kijin Seong;Marcus Sammer;Ryan Hardesty Lewis;Alison Reese;Norma Olvera;Susie L. Gronseth;Elizabeth Anderson-Fletcher;Ioannis Kakadiaris
  • 通讯作者:
    Ioannis Kakadiaris
Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction
人工智能应用于冠状动脉钙扫描(AI-CAC)可显著改善心血管事件预测
  • DOI:
    10.1038/s41746-024-01308-0
  • 发表时间:
    2024-11-05
  • 期刊:
  • 影响因子:
    15.100
  • 作者:
    Morteza Naghavi;Anthony P. Reeves;Kyle Atlas;Chenyu Zhang;Thomas Atlas;Claudia I. Henschke;David F. Yankelevitz;Matthew J. Budoff;Dong Li;Sion K. Roy;Khurram Nasir;Sabee Molloi;Zahi Fayad;Michael V. McConnell;Ioannis Kakadiaris;David J. Maron;Jagat Narula;Kim Williams;Prediman K. Shah;Daniel Levy;Nathan D. Wong
  • 通讯作者:
    Nathan D. Wong

Ioannis Kakadiaris的其他文献

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{{ truncateString('Ioannis Kakadiaris', 18)}}的其他基金

NSF Convergence Accelerator Track J: Artificial-Intelligence-Based Decision Support for Equitable Food and Nutrition Security in the Houston Area
NSF 融合加速器轨道 J:基于人工智能的决策支持,实现休斯顿地区公平的粮食和营养安全
  • 批准号:
    2236305
  • 财政年份:
    2022
  • 资助金额:
    $ 27.25万
  • 项目类别:
    Standard Grant
D-ISN/Collaborative Research: Financial and Network Disruptions in Counterfeit and Illegal Medicines Trade
D-ISN/合作研究:假冒和非法药品贸易中的财务和网络中断
  • 批准号:
    2146335
  • 财政年份:
    2022
  • 资助金额:
    $ 27.25万
  • 项目类别:
    Standard Grant
SCC-CIVIC-FA Track B: Artificial-Intelligence-Based Decision Support for Equitable and Resilient Food Distribution during Pandemics and Extreme Weather Events
SCC-CIVIC-FA 轨道 B:基于人工智能的决策支持,在大流行和极端天气事件期间实现公平和有弹性的粮食分配
  • 批准号:
    2133352
  • 财政年份:
    2021
  • 资助金额:
    $ 27.25万
  • 项目类别:
    Standard Grant
SCC-CIVIC-PG Track B: Equitable Food-Security: Disaster-resilient supply chains for pandemics and extreme weather events
SCC-CIVIC-PG 轨道 B:公平粮食安全:应对流行病和极端天气事件的抗灾供应链
  • 批准号:
    2043988
  • 财政年份:
    2021
  • 资助金额:
    $ 27.25万
  • 项目类别:
    Standard Grant
Supporting Student Development Activities at the International Joint Conference on Biometrics (IJCB2020)
在国际生物识别联合会议(IJCB2020)上支持学生发展活动
  • 批准号:
    2038085
  • 财政年份:
    2020
  • 资助金额:
    $ 27.25万
  • 项目类别:
    Standard Grant
D-ISN: TRACK 2: Collaborative Research: Financial Network Disruptions in Illicit and Counterfeit Medicines (FIND-M)
D-ISN:轨道 2:合作研究:非法和假冒药品的金融网络中断 (FIND-M)
  • 批准号:
    2039946
  • 财政年份:
    2020
  • 资助金额:
    $ 27.25万
  • 项目类别:
    Standard Grant
I-Corps: Exploiting matching score distributions to improve biometric recognition
I-Corps:利用匹配分数分布来提高生物特征识别
  • 批准号:
    1561151
  • 财政年份:
    2015
  • 资助金额:
    $ 27.25万
  • 项目类别:
    Standard Grant
Segmentation of 3D Tubular Structures
3D 管状结构的分割
  • 批准号:
    0638875
  • 财政年份:
    2006
  • 资助金额:
    $ 27.25万
  • 项目类别:
    Standard Grant
SEI: Cardiovascular Informatics
SEI:心血管信息学
  • 批准号:
    0431144
  • 财政年份:
    2004
  • 资助金额:
    $ 27.25万
  • 项目类别:
    Standard Grant
2003 Workshop on Robotics and Computer Vision: PI Meeting
2003 年机器人和计算机视觉研讨会:PI 会议
  • 批准号:
    0334822
  • 财政年份:
    2003
  • 资助金额:
    $ 27.25万
  • 项目类别:
    Standard Grant

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