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

磁共振成像(MRI)是一种基石的非侵入性成像技术，可以诊断多种疾病并回答相关的研究问题。在加拿大，每年进行近200万次核磁共振检查，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。在磁共振成像采集过程中，扫描仪收集被称为k空间的原始数据，这是图像在傅立叶域中的表示。重建包括将k空间转换为空间域中可解释的图像。传统上，k-空间的采样遵循奈奎斯特定理。在这种情况下，简单的傅里叶逆变换运算就足以重建空间域图像。然而，这些完全抽样的采集会导致较长的MRI检查时间(一次检查45分钟)，与计算机断层扫描等其他成像方式相比，这会造成较长的等待时间(&gt；9周)。MRI扫描仪在扫描过程中收集多个序列，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(即多次就诊)，以监测疾病进展。压缩传感(CS)和并行成像(PI)重建方法从次奈奎斯特采集重建MRI，从而使MRI检查速度更快。传统的PI和CS方法忽略了多序列和多访问信息。我的研究计划专注于开发有效地整合不同类型和不同来源的信息的方法，以提高知识并更好地支持成像应用程序的决策。在这项发现拨款中，我将研究多序列和多访问信息的整合，以改进MRI重建。多次访问信息还将用于检测图像之间的时间变化，这将加快放射科医生(即解释MRI的专业人员)对MRI的分析。Discovery Grant提案的具体目标是：1)从欠采样的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，以进一步改进从目标1重建MRI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。我的研究计划将在加拿大的战略部门培训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提高10倍，我的试验结果表明，对多次访问的受试者(即先前扫描的受试者)的扫描速度最高可提高20倍。更快的核磁共振检查将降低价格，减少核磁共振检查的等待时间。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很大的商业化潜力。将我的模型嵌入到核磁共振扫描仪中的成本将不到扫描仪安装成本的2%。

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.