Integrated Magnetic Resonance Imaging (iMRI): Integrating Multi-sequence and Multi-visit Information to Accelerate MRI Exams and Detect Temporal Image Changes

集成磁共振成像 (iMRI)：集成多序列和多次访问信息以加速 MRI 检查并检测时间图像变化

• 批准号: RGPIN-2021-02867
• 负责人: Souza, Roberto
• 金额: $ 2.04万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752277
• 关键词: Integrated; Magnetic; Resonance; Imaging; iMRI

Magnetic resonance imaging (MRI) is a cornerstone non-invasive imaging technology that allows diagnosing several conditions and answering relevant research questions. Nearly two million MRI exams are done yearly in Canada at an average cost of $700/exam, making it a billion-dollar industry. During MRI acquisition, the scanner collects raw data, known as k-space, which is a representation of the image in the Fourier-domain. Reconstruction consists of transforming the k-space into interpretable images in the spatial domain. Traditionally, k-space is sampled following the Nyquist theorem. In this case, a simple inverse Fourier Transform operation is sufficient to reconstruct the spatial domain images. However, these fully sampled acquisitions lead to long MRI exam times (> 45 minutes/exam), which creates long wait times (> 9 weeks) compared to other imaging modalities like computed tomography. The MRI scanner collects multiple sequences during a scan session, resulting in images of the same location that have complementary information. Subjects are also often scanned numerous times (i.e., multi-visit) to monitor disease progression. Compressed Sensing (CS) and Parallel Imaging (PI) reconstruction methods reconstruct MRI from sub-Nyquist acquisitions, resulting in faster MRI exams. Conventional PI and CS methods disregard multi-sequence and multi-visit information. My research program focuses on developing methods to efficiently integrate information of different types and from different sources to advance knowledge and better support imaging applications' decision-making. In this Discovery Grant, I will investigate the integration of multi-sequence and multi-visit information to improve MRI reconstruction. The multi-visit information will also be used to detect temporal changes across images, which will expedite the analysis of MRI by the radiologists (i.e., professionals that interpret MRI). The specific objectives of this Discovery Grant proposal are: 1) To develop multi-sequence MRI reconstruction models from undersampled k-spaces; 2) To develop models that incorporate multi-visit MRI information to further improve the MRI reconstruction from objective one and detect temporal image changes; 3) To embed the methods developed into an MRI scanner and deploy them in real-time. My research program will train a large and diverse group of highly qualified personnel in Canada's strategic sectors. Multi-sequence MRI reconstruction will enable 10-fold faster scanning of new subjects and my pilot results indicate up to 20-fold faster scanning of multi-visit subjects (i.e., subjects with a previous scan). Faster MRI exams will decrease the price and reduce wait times of MRI exams. The detection of image changes across time will facilitate image interpretation. The outcomes of my research program have great potential for commercialization. The cost of embedding my models into an MRI scanner will be less than 2% of the scanner installation cost.

磁共振成像（MRI）是一种基石非侵入性成像技术，可以诊断多种疾病并回答相关研究问题。加拿大每年进行近200万次MRI检查，平均每次检查的费用为700美元，使其成为一个价值10亿美元的行业。在MRI采集期间，扫描仪收集原始数据，称为k空间，这是傅立叶域中图像的表示。重建包括将k空间转换为空间域中的可解释图像。传统上，k空间是按照奈奎斯特定理采样的。在这种情况下，简单的逆傅立叶变换操作足以重构空间域图像。然而，这些完全采样的采集导致较长的MRI检查时间（> 45分钟/检查），与其他成像模式（如计算机断层扫描）相比，这会导致较长的等待时间（> 9周）。 MRI扫描仪在扫描会话期间收集多个序列，从而产生具有互补信息的相同位置的图像。受试者还经常被扫描多次（即，多次访视）以监测疾病进展。压缩感知（CS）和并行成像（PI）重建方法从亚奈奎斯特采集重建MRI，从而加快MRI检查速度。传统的PI和CS方法忽略了多序列和多访问信息。 我的研究计划侧重于开发方法，以有效地整合不同类型和不同来源的信息，以促进知识和更好地支持成像应用程序的决策。在本次发现资助中，我将研究多序列和多访视信息的整合，以改善MRI重建。多次访问信息还将用于检测图像上的时间变化，这将加快放射科医师对MRI的分析（即，专业人士解释MRI）。这项发现补助金提案的具体目标是：1）根据欠采样k空间开发多序列MRI重建模型; 2）开发包含多次访问MRI信息的模型，以进一步改进目标MRI重建并检测时间图像变化; 3）将开发的方法嵌入MRI扫描仪并实时部署它们。 我的研究计划将在加拿大的战略部门培养一大批高素质的人才。多序列MRI重建将使新受试者的扫描速度加快10倍，我的试验结果表明，多次访视受试者的扫描速度加快20倍（即，有先前扫描的受试者）。更快的MRI检查将降低价格并减少MRI检查的等待时间。检测图像随时间的变化将有助于图像解释。我的研究项目的成果具有巨大的商业化潜力。将我的模型嵌入MRI扫描仪的成本将不到扫描仪安装成本的2%。