Multi-modal and Extreme PET/MRI Reconstruction Methods

多模态和极限 PET/MRI 重建方法

• 批准号: 10296658
• 负责人: Abhejit Rajagopal
• 金额: $ 7.21万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10296658
• 关键词: 3-Dimensional; Abdomen; Address; Affect; Algorithms; Attention; Chest; Childhood; Clinical; Clinical Research; Data; Data Set; Development; Diagnosis; Discipline of Nuclear Medicine; Disease; Dose; Engineering; Epilepsy; FOLH1 gene; Fellowship; Financial compensation; Goals; Grant; Head; Head and Neck Cancer; Heart Diseases; Human; Hybrids; Image; Imagery; Implant; Joint repair; Joints; Learning; Liver; Lung; Lung nodule; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Malignant Female Reproductive System Neoplasm; Malignant Neoplasms; Malignant neoplasm of prostate; Measures; Medical Imaging; Mentors; Metabolic; Metals; Metastatic Neoplasm to the Liver; Methods; Modality; Modeling; Morphologic artifacts; Motion; Neurodegenerative Disorders; Oncology; Organ; Output; Patients; Pediatrics; Pelvis; Performance; Physicians; Physics; Positron-Emission Tomography; Prognosis; Research; Research Personnel; Research Support; Resolution; Scanning; Signal Transduction; Standardization; Structure; System; Time; Tissues; Tracer; Training; Translating; Writing; attenuation; biomedical imaging; bone; cancer type; clinical application; clinical practice; combat; computing resources; data acquisition; deep learning model; heart imaging; high resolution imaging; imaging modality; imaging scientist; imaging system; improved; information model; lung imaging; machine learning model; method development; multimodality; nervous system disorder; neuro-oncology; novel; patient response; programs; radiological imaging; radiologist; reconstruction; respiratory; soft tissue; spatiotemporal; success; systems research; treatment response; uptake

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.

项目摘要 /摘要 混合宠物/MRI系统对于多种临床应用非常有利 MRI与PET的功能和代谢信息的组织对比。这些系统已找到成功 对于肿瘤学研究，特别是头部和腹部/骨盆，以及癫痫，神经系统疾病， 心脏病和降低剂量的儿科。但是，宠物分辨率和SNR通常比 MRI，并且由于运动而遭受特征和数据的损失。宠物/MRI系统提供潜力 为了创建更准确，更高分辨率的宠物重建，包括对伪影的校正，运动和进一步 通过执行利用简单数据采集的协同重构来实现的本地化。在 特别是，这项奖学金提案旨在开发新颖的物理受限的机器学习模型，以提供信息 PET和MRI之间共享空间定位，衰减和活动的估计以及 运动。我们建议对衰减和活动（MLAA）进行深度最大的象征估计（MLAA） 可以补偿工件并提高宠物重建精度。我们还提出了一个运动增强的 联合PET/MRI重建以捕获任意运动并减少胸部和腹部的剂量要求 研究。这些模型旨在改善PET时空分辨率，SNR和量化的量化 广泛的临床应用，将评估骨盆，肝脏和肺部的癌症评估。 该奖学金将在UCSF的放射学和生物医学成像系进行 Peder Larson教授的指导，他领导了一项有关高级成像方法开发的研究计划， 放射科医生和核医学医师托马斯·霍普（Thomas Hope）博士领导多个宠物/MRI项目。 部门是生物医学成像研究的主要中心之一，一直处于最前沿 将宠物/MRI系统转化为临床实践。 UCSF PET/MRI扫描仪有专门的研究时间， 它也可在其他MRI和PET/CT研究系统以及广泛的计算资源上使用 支持拟议的项目。申请人Abhejit Rajagopal博士具有计算成像背景 和机器学习，将由该工程师/医师团队共同修复。他将被训练成为 通过参加医学成像系统正式课程的生物医学成像科学家，培训 宠物/MRI系统，授予写作和进行临床研究，支持他的发展成为创意， 独立的生物医学研究人员。