Inverse Problems in Medical Imaging

医学成像中的反问题

• 批准号: RGPIN-2014-06233
• 负责人: Ebrahimi, Mehran
• 金额: $ 1.68万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679297
• 关键词: Inverse; Problems; Medical; Imaging

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.

该研究项目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，许多现实世界的反问题是不适定的，主要是因为缺乏一个唯一的解决方案。为这些问题提供可接受的唯一解的过程称为正则化。事实上，最近在成像方面取得的大部分进展是由于正规化的制定和实践方面的进展。这一点，再加上在优化和数值分析的进展，产生了很大的改进，在计算方法求解逆成像问题。** 图像配准是将不同的数据集对齐到一个坐标系中的过程，是许多医学成像应用中的关键挑战，例如，肿瘤检测和手术计划，可以建模为逆问题。给定模板（或移动）图像和参考（或固定）图像，目标是找到合理的变换，其最大化参考和变换的模板图像之间的预定相似性度量。当高度可变形组织的图像，例如，乳房，被认为是。下文概述了本提案在这方面的一些短期和中期研究目标。1-乳腺磁共振成像（MRI）通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期间使用术前图像信息来指导切除。这需要将俯卧的术前MR图像对准或共配准以符合患者在手术台上的仰卧位置。未来的乳房计算机辅助手术（CAS）技术将需要新颖的高效对准算法，该算法沿着手术室中的乳房的表面信息以及手术前图像的强度信息。**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图像的精确配准对于正确识别病变是有价值的。这需要定义正则化表达式，直接包含这个逆问题的底层物理过程。此外，开发相关的有效计算方案是解决这个问题的必要条件。在图像配准中，研究人员通常依赖于变换来描述与两个图像相关的对齐过程。然而，在临床环境中，存在许多变形可能具有不连续性的情况。鉴于目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，该方法将能够在存在各种类型的大规模不连续性的情况下进行可变形配准。** 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，而且将直接造福社会。所开发的计算方法具有重要意义，可以塑造高度可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的医疗保健系统带来了巨大的经济负担。该研究方案旨在与加拿大主要研究机构合作，利用多学科技术寻找负担得起的替代品。此外，拟议的计划将准备学生在这个要求苛刻的研究领域，并把他们在学术界或工业界更具竞争力的地位。