Multi-modal and Extreme PET/MRI Reconstruction Methods

项目摘要/摘要 混合的PET/MRI系统通过结合软件 MRI的组织对比与PET的功能和代谢信息。这些系统已经取得了成功 用于肿瘤学研究，特别是头部和腹部/骨盆研究，以及癫痫、神经疾病、 心脏病，以及减少剂量的儿科。然而，PET分辨率和SNR通常比 磁共振成像，并遭受由于运动造成的特征和数据的丢失。PET/MRI系统提供了潜在的 为了创建更准确、更高分辨率的PET重建，包括校正伪影、运动和图像- 通过执行利用同步数据采集的协同重建，证明了本地化。在……里面 具体地说，该研究会建议开发新的物理约束的机器学习模型，用于信息- 在正电子发射计算机断层扫描和磁共振成像之间共享增强的空间定位，估计衰减和活动，以及 动议。我们建议开发衰减和活动的深度最大似然估计(MLAA) 可以补偿伪影，提高PET重建精度。我们还提出了一项强化的动议 PET/MRI联合重建捕捉任意运动并降低胸腹部剂量需求 学习。总之，这些模型旨在提高正电子发射计算机断层成像的时空分辨率、信噪比和fi定量 广泛的临床应用，并将评估骨盆，肝脏和肺癌的癌症评估。 这项奖学金将在加州大学旧金山分校放射和生物医学成像系根据 在Peder Larson教授的指导下，他领导了一个先进成像方法开发的研究项目， 以及托马斯·霍普博士，他是一名放射学家和核医学内科医生，领导着多个PET/MRI项目。这个 科室是国内领先的生物医学影像研究中心之一，在 将PET/MRI系统转化为临床实践。加州大学旧金山分校的PET/MRI扫描仪有专门的研究时间， 它也可用于其他MRI和PET/CT研究系统，以及丰富的计算资源，以 支持拟议的项目。申请者Abhejit Rajagopal博士具有计算机成像背景 和机器学习，将由这个工程师/医生团队共同指导。他将被训练成为 生物医学成像科学家，通过参加关于医学成像系统的正式课程，培训 PET/MRI系统，资助撰写和进行临床研究，支持他发展成为一名有创造力的 独立的生物医学研究员。

Project Summary / Abstract Hybrid PET/MRI systems are very advantageous for a variety of clinical applications by combining the soft tissue contrast of MRI with the functional and metabolic information of PET. These systems have found success for oncology studies, particularly in head and abdomen/pelvis, as well as for epilepsy, neurological diseases, heart disease, and pediatrics for dose reduction. However, the PET resolution and SNR is typically worse than MRI, and suffers from the loss of feature and data due to motion as well. PET/MRI systems offer the potential to create more accurate, higher resolution PET reconstructions, including correction of artifacts, motion, and im- proved localization, by performing synergistic reconstructions that leverage the simultaneous data acquisition. In particular, this fellowship proposes to develop novel physics-constrained machine learning models for informa- tion sharing between PET and MRI for enhanced spatial localization, estimation of attenuation and activity, and motion. We propose to develop a deep maximum-likelihood estimation of attenuation and activity (MLAA) that can compensate for artifacts and improve PET reconstruction accuracy. We also propose a motion-enhanced joint PET/MRI reconstruction to capture arbitrary motions and reduce dose requirements for chest and abdomen studies. Together, these models aim to improve the PET spatio-temporal resolution, SNR, and quantification for a broad range of clinical applications, and will be evaluated for cancer assessment in the pelvis, liver, and lung. This fellowship will be performed in the Department of Radiology and Biomedical Imaging at UCSF under the guidance of Prof. Peder Larson, who leads a research program on advanced imaging methods development, and Dr. Thomas Hope, a radiologist and nuclear medicine physician who leads multiple PET/MRI projects. The Department is one of the leading centers in biomedical imaging research, and has been at the forefront on translating PET/MRI systems into clinical practice. The UCSF PET/MRI scanner has dedicated research time, which is also available on other MRI and PET/CT research systems, and extensive computational resources to support the proposed project. The applicant, Dr. Abhejit Rajagopal, has a background in computational imaging and machine learning, will be jointly mentored by this engineer/physician team. He will be trained to become a biomedical imaging scientist by participating in formal coursework on medical imaging systems, training on the PET/MRI system, grant writing, and performing clinical research, supporting his development into a creative, independent biomedical researcher.