PET-MR Motion Correction Based Purely on Routine Clinical Scans

纯粹基于常规临床扫描的 PET-MR 运动校正

• 批准号: EP/M009319/1
• 负责人: Andrew King
• 金额: $ 75.21万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM009319%2F1
• 关键词: PET; MR; Motion; Correction; Based

Globally, cancer is one of the most dangerous and prevalent diseases affecting mankind. Each year, around 14 million people are diagnosed with cancer and 8 million people die from the disease. One of the primary means of diagnosing cancer and monitoring/planning its treatment is through the use of PET imaging. However, the problem of patient motion, such as that caused by breathing, seriously hinders the effectiveness of current PET scanners.One way of estimating breathing motion, so that its effects can be corrected for, is through the use of a second imaging modality. Traditionally, PET imaging has been combined with CT scanning because of its usefulness in reconstructing PET images. However, CT scanning delivers a significant radiation dose to the patient and is too slow to be able to estimate breathing motion effectively. Recently, a new generation of PET scanners have been introduced to the market, which combine PET with MR imaging. MR offers a highly promising source of information for estimating breathing motion. However, techniques to fully exploit the rich information provided by MR scanning are currently lacking.The aim of this proposal is to devise techniques for estimating breathing motion from MR and PET imaging data, for the purpose of motion correcting both the PET and MR data. If successful, the outcome of this research will be to improve the quality of PET-MR imaging. This will enable cancer to be diagnosed earlier, allowing potentially more effective treatment. In addition, it will be possible to monitor the progress of cancer treatment more reliably, and even make the treatment more targeted and effective.A key benefit of our proposed approach is that it will not require clinical workflows to be altered. Normally, MR and PET scans are performed for clinical purposes: the PET images are used to diagnose/assess tumours, whereas MR images provide the anatomical context. The two modalities are therefore complementary, and both are clinically useful. All previous approaches to motion correcting PET using MR data have, as well as being limited in their effectiveness, required extensive use of MR scanner resources. Effectively, the MR scanner would be used as an expensive motion correction device. As a result none of these techniques are currently in widespread clinical use. Our proposed approach makes use only of images that are routinely acquired for clinical purposes, and would therefore be much more likely to be used clinically.

在全球范围内，癌症是影响人类的最危险和最流行的疾病之一。每年约有1400万人被诊断出患有癌症，800万人死于这种疾病。诊断癌症和监测/计划其治疗的主要手段之一是使用正电子发射计算机断层扫描成像。然而，患者运动的问题，如呼吸引起的问题，严重阻碍了当前PET扫描仪的有效性。估计呼吸运动的一种方法是通过使用第二种成像模式来校正其影响。传统上，PET成像是与CT扫描相结合的，因为它在重建PET图像方面是有用的。然而，CT扫描会给患者带来很大的辐射剂量，而且速度太慢，无法有效地估计呼吸运动。最近，市场上推出了新一代PET扫描仪，它将PET与磁共振成像结合在一起。MR为估计呼吸运动提供了一个非常有希望的信息来源。然而，目前还缺乏充分利用MR扫描所提供的丰富信息的技术，本方案的目的是设计从MR和PET成像数据中估计呼吸运动的技术，以便对PET和MR数据进行运动校正。如果成功，这项研究的结果将是提高PET-MR成像的质量。这将使癌症能够更早地被诊断出来，从而有可能获得更有效的治疗。此外，它还可以更可靠地监测癌症治疗的进展，甚至使治疗更有针对性和有效性。我们建议的方法的一个关键好处是它不需要改变临床工作流程。通常，磁共振和正电子发射计算机断层扫描是为了临床目的而进行的：正电子发射计算机断层扫描图像用于诊断/评估肿瘤，而磁共振图像提供解剖背景。因此，这两种模式是互补的，在临床上都是有用的。所有以前使用MR数据进行运动校正的PET方法都需要大量使用MR扫描仪资源，同时其有效性也受到限制。实际上，磁共振扫描仪将被用作一种昂贵的运动校正设备。因此，这些技术目前都没有在临床上广泛使用。我们建议的方法只使用常规获取的用于临床目的的图像，因此更有可能用于临床。

项目成果

PET-MR respiratory signal estimation using semi-supervised manifold alignment

使用半监督流形对齐进行 PET-MR 呼吸信号估计

• 发表时间: 2018
• 作者: D R Balfour

Respiratory motion correction of PET using MR-constrained PET-PET registration.

使用MR受限的PET-PET登记对PET进行呼吸运动校正。

• DOI: 10.1186/s12938-015-0078-5
• 发表时间: 2015-09-18
• 期刊: Biomedical engineering online
• 影响因子: 3.9
• 作者: Balfour DR;Marsden PK;Polycarpou I;Kolbitsch C;King AP
• 通讯作者: King AP

Motion-free Abdominal MRI using Manifold Alignment

使用歧管对齐进行自由运动腹部 MRI

• 发表时间: 2015
• 作者: Chen X

Coupled motion and activity estimation from PET and MR data with motion model-based parameter reduction

通过基于运动模型的参数缩减，根据 PET 和 MR 数据进行耦合运动和活动估计

• DOI: 10.1109/nssmic.2015.7582075
• 发表时间: 2015
• 作者: Balfour D

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part III

医学图像计算和计算机辅助干预 - MICCAI 2016 - 第 19 届国际会议，希腊雅典，2016 年 10 月 17-21 日，会议记录，第三部分

• DOI: 10.1007/978-3-319-46726-9_57
• 发表时间: 2016
• 作者: Chen X