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.

项目总结/摘要 混合PET/MRI系统通过将软X射线成像与磁共振成像相结合，对于各种临床应用是非常有利的。 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，以及癫痫，神经系统疾病， 心脏病和儿科减少剂量。然而，PET分辨率和SNR通常比 磁共振成像，并遭受损失的功能和数据，由于运动以及。PET/MRI系统提供了 创建更准确、更高分辨率的PET重建，包括伪影、运动和不连续的校正。 证明本地化，通过执行协同重建，利用同步数据采集。在 特别是，该奖学金计划为informa开发新的物理约束机器学习模型， PET和MRI之间的信息共享，用于增强空间定位、估计衰减和活动，以及 议案我们建议开发一种深度最大似然估计衰减和活动（MLAA）， 可以补偿伪影并提高PET重建精度。我们还提出了一个运动增强 联合PET/MRI重建，以捕获任意运动并降低胸部和腹部的剂量要求 问题研究总之，这些模型旨在提高PET时空分辨率、SNR和量化， 广泛的临床应用，并将评估骨盆，肝脏和肺部的癌症评估。 该奖学金将在加州大学旧金山分校的放射学和生物医学成像系进行， Peder Larson教授领导了一项先进成像方法开发的研究计划， 以及托马斯霍普博士，他是一位放射学家和核医学医生，领导着多个PET/MRI项目。的 部门是在生物医学成像研究的领先中心之一，并一直处于前列， 将PET/MRI系统转化为临床实践。UCSF PET/MRI扫描仪有专门的研究时间， 它也可用于其他MRI和PET/CT研究系统，以及广泛的计算资源， 支持提议的项目。申请人Abhejit Rajagopal博士具有计算成像的背景 和机器学习，将由这个工程师/医生团队共同指导。他会被训练成 生物医学成像科学家通过参加正式的课程，对医学成像系统，培训 PET/MRI系统，拨款写作，并进行临床研究，支持他发展成为一个创造性的， 独立的生物医学研究员