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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=567677
• 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.

本研究计划探索医学图像处理的广阔领域，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，许多现实世界的逆问题是不适定的，主要是因为缺乏唯一解的存在。为这类问题提供可接受的唯一解的过程称为正则化。事实上，最近成像的许多进展都是由于正则化的制定和实践的进步。再加上优化和数值分析方面的进展，使得求解逆成像问题的计算方法有了很大的改进。图像配准是将不同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。给定一个模板（或移动）图像和一个参考（或固定）图像，目标是找到一个合理的转换，使参考和转换后的模板图像之间预定的相似性度量最大化。当考虑高度变形组织（例如乳房）的图像时，解决这个反问题特别具有挑战性。本提案在这方面的一些短期和中期研究目标概述如下。乳房磁共振成像（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中使用术前图像信息来指导切除。这就要求术前俯卧位的MR图像对齐或共同注册，以符合患者在手术台上的仰卧位。未来的乳腺计算机辅助手术（CAS）技术将需要新颖有效的对齐算法，该算法将利用手术室中乳房的表面信息以及术前图像的强度信息。最近，动态对比增强（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有价值的。这需要定义正则化表达式，这些表达式直接包含这个逆问题的底层物理过程。此外，开发相关的高效计算方案是解决这一问题的必要条件。在图像配准中，研究人员通常依靠变换来描述两幅图像的对齐过程。然而，在临床环境中，有许多情况下，变形可能有不连续。鉴于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，使各种类型的大规模不连续存在下的可变形配准成为可能。提出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。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。由于错位或误诊的翻修手术给加拿大医疗保健系统带来了巨大的经济负担。该研究项目旨在与加拿大领先的研究机构合作，利用多学科技术开发经济实惠的替代品。此外，该计划将为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工业界更具竞争力。