Inverse Problems in Medical Imaging

医学成像中的反问题

基本信息

  • 批准号:
    RGPIN-2014-06233
  • 负责人:
  • 金额:
    $ 1.68万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2015
  • 资助国家:
    加拿大
  • 起止时间:
    2015-01-01 至 2016-12-31
  • 项目状态:
    已结题

项目摘要

This research program explores the broad area of medical image processing and provides solutions to various associated inverse problems such as medical image registration. In general, many real-world inverse problems are ill-posed, mainly because of the lack of existence of a unique solution. The procedure of providing acceptable unique solutions to such problems is known as regularization. Indeed, much of the recent progress in imaging has been due to advances in the formulation and practice of regularization. This, coupled with progress in optimization and numerical analysis, has yielded much improvement in computational methods of solving inverse imaging problems. Image registration, the process of aligning different sets of data into one coordinate system, is a key challenge in many medical imaging applications, e.g., tumour detection and surgery planning, that can be modelled as an inverse problem. Given a template (or moving) image and a reference (or fixed) image, the goal is to find a reasonable transformation that maximizes a predetermined similarity measure between the reference and the transformed template image. Solving this inverse problem is specifically challenging when images of highly deformable tissue, e.g., breast, is considered. Some of the short- and medium-term research objectives of this proposal in this direction are outlined below. 1-Breast Magnetic Resonance Imaging (MRI) is frequently performed prior to breast conserving surgery in order to assess the location and extent of the lesion. Ideally, the surgeon should be able to use the pre-surgery image information during surgery to guide the excision. This requires the prone pre-surgical MR image to be aligned or co-registered to conform to the patient's supine position on the operating table. The future breast Computer Assisted Surgery (CAS) technology will demand novel efficient alignment algorithms that employ the surface information of the breast in the operating room along with the intensity information of the pre-surgical images. 2-Recently, Dynamic Contrast-Enhanced (DCE) imaging has emerged as a powerful screening tool. Accurate registration of DCE images is valuable for proper identification of the lesions. This requires defining regularization expressions that directly incorporate the underlying physical process of this inverse problem. In addition, developing relevant efficient computational schemes is necessary to address the problem. 3-In image registration, researchers generally rely on transformations to describe the alignment process relating two images. However, in the clinical setting, there are many situations where the deformation may have discontinuities. Given the limitations of current approaches to image registration, an open problem is to develop a method that would enable deformable registration in the presence of various types of large-scale discontinuities. The proposed multidisciplinary research program will not only foster scientific advances valuable to the academic community, but also directly benefit the society. The developed computational methods are of significant importance that can shape the future of computer assisted surgery, diagnosis, and treatment planning technologies of highly deformable tissue. Revision surgeries due to misplacement or misdiagnosis impose a huge financial burden on the Canadian healthcare system. This research program is directed towards affordable alternatives using multidisciplinary techniques in collaboration with leading Canadian research institutions. In addition, the proposed program will prepare students in this demanding research field and place them in a more competitive position in academia or industry.
这项研究计划探索医学图像处理的广泛领域,并提供各种相关逆问题的解决方案,如医学图像配准。一般来说,许多真实世界的反问题都是不适定的,主要是因为缺乏唯一解的存在。为此类问题提供可接受的独特解决方案的过程称为正规化。事实上,最近成像方面的许多进展都归功于正规化的制定和实践方面的进步。这一点,再加上优化和数值分析的进步,使得求解反成像问题的计算方法有了很大的改进。 图像配准是将不同的数据集对齐到一个坐标系中的过程,在许多医学成像应用中,例如肿瘤检测和手术规划,图像配准是一个关键挑战,可以建模为逆问题。在给定模板(或运动)图像和参考(或固定)图像的情况下,目标是找到使参考图像和变换后的模板图像之间的预定相似性度量最大化的合理变换。当考虑高度变形的组织(例如,乳房)的图像时,解决这个逆问题特别具有挑战性。该提案在这方面的一些短期和中期研究目标概述如下。 乳房核磁共振成像(MRI)经常在保乳手术前进行,以评估病变的位置和范围。理想情况下,外科医生应该能够在手术过程中使用术前图像信息来指导切除手术。这要求手术前俯卧位的MR图像要与患者在手术台上的仰卧位置一致或共同配准。未来的乳房计算机辅助手术(CAS)技术需要新的高效的配准算法,该算法利用手术室中乳房的表面信息和术前图像的亮度信息。 2-最近,动态对比度增强(DCE)成像已成为一种强大的筛查工具。DCE图像的准确配准对于正确识别病变是很有价值的。这需要定义直接包含该逆问题的基本物理过程的正则化表达式。此外,有必要开发相关的高效计算格式来解决这一问题。 在图像配准中,研究人员通常依靠变换来描述两个图像之间的对齐过程。然而,在临床环境中,有许多情况下变形可能会有不连续。考虑到当前图像配准方法的局限性,一个悬而未决的问题是开发一种方法,该方法能够在存在各种类型的大规模不连续的情况下实现可变形配准。 拟议的多学科研究计划不仅将促进对学术界有价值的科学进步,而且还将直接造福社会。开发的计算方法具有重要意义,可以塑造高度变形组织的计算机辅助手术、诊断和治疗计划技术的未来。由于错位或误诊而进行的翻修手术给加拿大医疗保健系统带来了巨大的财政负担。这项研究计划旨在与加拿大领先的研究机构合作,使用多学科技术来寻找负担得起的替代方案。此外,拟议的计划将使学生在这一要求苛刻的研究领域做好准备,并使他们在学术界或行业处于更具竞争力的地位。

项目成果

期刊论文数量(0)
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专利数量(0)

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Ebrahimi, Mehran其他文献

Optimal Design of Continuum Robots With Reachability Constraints
  • DOI:
    10.1109/lra.2021.3066978
  • 发表时间:
    2021-04-01
  • 期刊:
  • 影响因子:
    5.2
  • 作者:
    Cheong, Hyunmin;Ebrahimi, Mehran;Duggan, Timothy
  • 通讯作者:
    Duggan, Timothy
Using surface markers for MRI guided breast conserving surgery: a feasibility survey
  • DOI:
    10.1088/0031-9155/59/7/1589
  • 发表时间:
    2014-04-07
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    Ebrahimi, Mehran;Siegler, Peter;Martel, Anne L.
  • 通讯作者:
    Martel, Anne L.
A case report of transmural rectosigmoid ischemia in an elderly patient.
  • DOI:
    10.1016/j.ijscr.2023.108372
  • 发表时间:
    2023-06
  • 期刊:
  • 影响因子:
    0.6
  • 作者:
    Ebrahimi, Mehran;Arabi, Akram;Dabiri, Shahriar;Razavinasab, Seyed Ali;Pasandi, Abbas Pour;Zeidabadi, Ali
  • 通讯作者:
    Zeidabadi, Ali
Greening Remote SMEs: The Case of Small Regional Airports
  • DOI:
    10.1007/s10551-017-3447-0
  • 发表时间:
    2019-02-01
  • 期刊:
  • 影响因子:
    6.1
  • 作者:
    Boiral, Olivier;Ebrahimi, Mehran;Talbot, David
  • 通讯作者:
    Talbot, David

Ebrahimi, Mehran的其他文献

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{{ truncateString('Ebrahimi, Mehran', 18)}}的其他基金

Inverse Problems in Medical Image Processing
医学图像处理中的反问题
  • 批准号:
    DDG-2020-00031
  • 财政年份:
    2022
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Development Grant
Inverse Problems in Medical Image Processing
医学图像处理中的反问题
  • 批准号:
    DDG-2020-00031
  • 财政年份:
    2021
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Development Grant
Inverse Problems in Medical Image Processing
医学图像处理中的反问题
  • 批准号:
    DDG-2020-00031
  • 财政年份:
    2020
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Development Grant
Inverse Problems in Medical Imaging
医学成像中的反问题
  • 批准号:
    RGPIN-2014-06233
  • 财政年份:
    2019
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
Inverse Problems in Medical Imaging
医学成像中的反问题
  • 批准号:
    RGPIN-2014-06233
  • 财政年份:
    2018
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
Inverse Problems in Medical Imaging
医学成像中的反问题
  • 批准号:
    RGPIN-2014-06233
  • 财政年份:
    2017
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
Inverse Problems in Medical Imaging
医学成像中的反问题
  • 批准号:
    RGPIN-2014-06233
  • 财政年份:
    2016
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
Inverse Problems in Medical Imaging
医学成像中的反问题
  • 批准号:
    RGPIN-2014-06233
  • 财政年份:
    2014
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
Development of a New Metaheuristic Optimization Algorithm and its Application in Multidisciplinary Design Optimization of an Automotive Cross-Car Beam Assembly
一种新的元启发式优化算法的开发及其在汽车横梁总成多学科设计优化中的应用
  • 批准号:
    464804-2014
  • 财政年份:
    2014
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Alexander Graham Bell Canada Graduate Scholarships - Master's

相似海外基金

Inverse Problems in Medical Image Processing
医学图像处理中的反问题
  • 批准号:
    DDG-2020-00031
  • 财政年份:
    2022
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Development Grant
Inverse Problems in Medical Imaging: Development and assessment of novel imaging applications for diagnosis and therapy.
医学成像中的逆问题:用于诊断和治疗的新型成像应用的开发和评估。
  • 批准号:
    RGPIN-2017-05503
  • 财政年份:
    2021
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
Inverse Problems in Medical Image Processing
医学图像处理中的反问题
  • 批准号:
    DDG-2020-00031
  • 财政年份:
    2021
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Development Grant
Inverse Problems in Medical Imaging: Development and assessment of novel imaging applications for diagnosis and therapy.
医学成像中的逆问题:用于诊断和治疗的新型成像应用的开发和评估。
  • 批准号:
    RGPIN-2017-05503
  • 财政年份:
    2020
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
Inverse Problems in Medical Image Processing
医学图像处理中的反问题
  • 批准号:
    DDG-2020-00031
  • 财政年份:
    2020
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Development Grant
Inverse Problems in Medical Imaging: Development and assessment of novel imaging applications for diagnosis and therapy.
医学成像中的逆问题:用于诊断和治疗的新型成像应用的开发和评估。
  • 批准号:
    RGPIN-2017-05503
  • 财政年份:
    2019
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
Inverse Problems in Medical Imaging
医学成像中的反问题
  • 批准号:
    RGPIN-2014-06233
  • 财政年份:
    2019
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
Inverse Problems in Medical Imaging
医学成像中的反问题
  • 批准号:
    RGPIN-2014-06233
  • 财政年份:
    2018
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
Inverse Problems in Medical Imaging: Development and assessment of novel imaging applications for diagnosis and therapy.
医学成像中的逆问题:用于诊断和治疗的新型成像应用的开发和评估。
  • 批准号:
    RGPIN-2017-05503
  • 财政年份:
    2018
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
Inverse Problems in Medical Imaging: Development and assessment of novel imaging applications for diagnosis and therapy.
医学成像中的逆问题:用于诊断和治疗的新型成像应用的开发和评估。
  • 批准号:
    RGPIN-2017-05503
  • 财政年份:
    2017
  • 资助金额:
    $ 1.68万
  • 项目类别:
    Discovery Grants Program - Individual
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