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

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
批准号:
0402591
负责人:
Ioannis Kakadiaris
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
项目状态:
已结题
起止时间:
2004-09-01 至 2007-08-31

项目摘要

项目成果

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中文摘要
翻译
乳腺癌是当今美国女性中第二大最常见的癌症诊断,仅次于皮肤癌。乳房再造术是乳房切除术后第三种最常见的再造术,仅次于肿瘤切除和手部手术。在乳房中发现肿块和随后的乳房切除术所带来的心理困扰,由于当代重建手术的局限性而加剧;没有可靠的重建程序,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损的传统方法是“拆东墙补西墙”。也就是说,患者的供体部位的组织被用来重建接受部位。然而,很难准确预测某一特定患者的乳房会因特定的手术而发生怎样的改变。多大的供体组织能产生理想的乳房形状?皮肤会产生多大的张力?有足够的组织来重建一个符合病人期望的乳房吗?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,以取代目前实践的试错法。该方法基于力学大变形分析;将覆盖乳房的皮肤建模为非线性弹性膜或壳,其力学性能必须确定。在确定了植入组织和乳房皮肤的形状、大小和质量之后,将使用非线性分析来计算最终的形状。相反,如果规定了最终形状,则分析应得出植入组织的数量,以及所需皮肤的形状和大小。该项目的具体目标是:1)开发一个分析模型和数值模拟,利用一般数据以及物理和计算模型来预测已知初始条件下的乳房形状。这是基于有限变形体的力学,2)开发一个参数化的可变形模型,以数学方式描述女性乳房的形状,3)通过实验验证这些方法,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改善乳房切除术后重建手术患者的生活质量。预测方法也将减少手术时间。将开发的模型也将应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。
英文摘要
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 Post-mastectomy breast reconstruction is the third most frequently performed reconstructive procedure, superseded only by tumor removal and hand surgery.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.
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NSF Convergence Accelerator Track J: Artificial-Intelligence-Based Decision Support for Equitable Food and Nutrition Security in the Houston Area
  • 批准号:
    2236305
  • 项目类别:
    Standard Grant
  • 资助金额:
    $75.0万
  • 财政年份:
    2022
  • 负责人:
    Ioannis Kakadiaris
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  • 批准号:
    2146335
  • 项目类别:
    Standard Grant
  • 资助金额:
    $25.0万
  • 财政年份:
    2022
  • 负责人:
    Ioannis Kakadiaris
  • 依托单位:
SCC-CIVIC-FA Track B: Artificial-Intelligence-Based Decision Support for Equitable and Resilient Food Distribution during Pandemics and Extreme Weather Events
  • 批准号:
    2133352
  • 项目类别:
    Standard Grant
  • 资助金额:
    $100.0万
  • 财政年份:
    2021
  • 负责人:
    Ioannis Kakadiaris
  • 依托单位:
SCC-CIVIC-PG Track B: Equitable Food-Security: Disaster-resilient supply chains for pandemics and extreme weather events
  • 批准号:
    2043988
  • 项目类别:
    Standard Grant
  • 资助金额:
    $5.0万
  • 财政年份:
    2021
  • 负责人:
    Ioannis Kakadiaris
  • 依托单位:
国内基金
海外基金
Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    SATOSHI NAWATA
  • 依托单位:
Cell Research
Cell Research
Cell Research (细胞研究)