Center for Advanced Imaging Innovation and Research (CAI2R)

先进成像创新与研究中心 (CAI2R)

• 批准号: 10701713
• 负责人: Daniel K Sodickson
• 金额: $ 119.88万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-30 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10701713
• 关键词: Acceleration; Adopted; Area; Biology; Caring; Clinical; Collaborations; Color; Computer software; Country; Creativeness; Data; Data Set; Development; Diagnosis; Diagnostic; Disease; Doctor of Philosophy; Engineering; Feedback; Funding; Future; Goals; Health; Human; Image; Image Analysis; Imagination; Imaging Device; Imaging technology; Industrial Product; Industry; Institution; Intelligence; Knowledge; Legal patent; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Medical Imaging; Mission; Modeling; Modernization; Musculoskeletal Diseases; Patient Care; Patients; Performance; Persons; Philosophy; Physicians; Physics; Positron-Emission Tomography; Process; Productivity; Public Health; Publishing; Research; Research Personnel; Scanning; Scientist; Self Correction; Services; Software Tools; Stream; Structural Models; Structure; Technology; Technology Assessment; Testing; Therapeutic Intervention; Time; Tissues; Training; Translating; Validation; Visit; Work; artificial intelligence method; biomedical imaging; biophysical model; cancer imaging; clinical care; clinical practice; data acquisition; data streams; design; flexibility; image reconstruction; imaging approach; imaging modality; imaging scientist; improved; industry partner; innovation; interest; medical schools; multidisciplinary; musculoskeletal imaging; nervous system disorder; neuroimaging; novel imaging technique; open source tool; operation; radio frequency; reconstruction; sensor; technology development; technology research and development; tool; web site

Overall Project Summary The Center for Advanced Imaging Innovation and Research (CAI2R) pursues a mission of bringing people together to create new ways of seeing. The work of our Center has been focused on creating new paradigms for the acquisition, reconstruction, and interpretation of biomedical images, and on implementing new collaboration models in order to translate these developments rapidly into clinical practice. The world of biomedical imaging is changing, and CAI2R has been at the forefront of that change. Tasks that were once the sole domain of meticulously-engineered imaging hardware are now beginning to be accomplished in software, increasingly informed by diverse arrays of inexpensive auxiliary sensors. Information once pursued through the laborious acquisition of carefully separated image datasets is now being derived from newly integrated, and richly quantitative, data streams. In keeping with these themes, our Center will be organized around the following four Technology Research and Development (TR&D) projects going forward: 1. Reimagining the Future of Scanning: Intelligent image acquisition, reconstruction, and analysis. 2. Unshackling the Scanners of the Future: Flexible, self-correcting, multisensor machines. 3. Enriching the Data Stream: MRI and PET in concert. 4. Revealing Microstructure: Biophysical modeling and validation for discovery and clinical care. In each of these projects, we aim to push medical imaging technology to the next level, both in hardware and in software. Having made great strides in developing rapid, continuous imaging data streams, we will next aim to add key new information to those streams, both from physics-driven microstructural modeling and from data- driven machine learning. Having focused on the development of robust tools for image acquisition and reconstruction, we will extend the pipeline to image interpretation, using the results of human- or machine- derived evaluations of image content as feedback for the further improvement of acquisition strategies and sensor designs. We will also aim to close the loop between diagnostic sensing and therapeutic intervention, exploring new ways to guide therapy with continuously-acquired information about tissue bioeffects. Our Center has an explicit translational focus, which is reflected in the day-to-day operation of TR&D projects as well as in the topics of Collaborative Projects (CPs) and Service Projects (SPs), which are focused on three general areas of high public health impact: cancer, musculoskeletal disease, and neurologic disease. In keeping with this translational emphasis, CAI2R is also be driven by an embedded collaboration model in which basic scientists, clinicians, and industry developers sit down together regularly at the scanners for interactive technology development and assessment. With early involvement of clinical stakeholders and industry partners, we aim to make CAI2R technologies widely available, for the advancement of biomedical knowledge and for the benefit of patients and the physicians who care for them.

总体项目摘要 高级成像创新与研究中心（CAI2R）追求带人的使命 共同创建新的观察方式。我们中心的工作一直致力于创建新的范式 为了获得，重建和解释生物医学图像，并实施新的 为了将这些发展迅速转化为临床实践，协作模型。 生物医学成像的世界正在发生变化，CAI2R一直处于这种变化的最前沿。任务 曾经是精心设计成像硬件的唯一领域 在软件中，越来越多地由各种廉价辅助传感器阵列告知。一旦寻求信息 通过艰苦的获取精心分离的图像数据集，现在是从新的 集成且丰富的数据流。为了与这些主题保持一致，我们的中心将组织起来 围绕以下四个技术研发（TR＆D）项目的发展： 1。重新想象扫描的未来：智能图像获取，重建和分析。 2。解开未来的扫描仪：灵活，自我校正的多传感器机器。 3。丰富数据流：MRI和PET音乐会。 4。揭示微观结构：发现和临床护理的生物物理建模和验证。 在这些项目中，我们的目标是将医学成像技术推向一个新的水平，无论是在硬件还是 在软件中。在开发快速，连续成像数据流方面取得了长足的进步，我们将下一个目标 从物理驱动的微结构建模和数据 - 驱动的机器学习。专注于开发可靠的图像获取工具和 重建，我们将使用人类或机器的结果将管道扩展到图像解释 对图像内容的评估是进一步改善采集策略的反馈和 传感器设计。我们还将旨在关闭诊断感和治疗干预之间的循环， 探索有关组织生物效应的连续信息的新方法来指导治疗。 我们的中心具有明确的翻译重点，这反映在TR＆D项目的日常运营中 以及协作项目（CPS）和服务项目（SPS）的主题，该项目专注于三个 高公共卫生影响的一般领域：癌症，肌肉骨骼疾病和神经系统疾病。 为了保持这种翻译的重点，CAI2R也受到嵌入式协作模型的驱动 哪些基础科学家，临床医生和行业开发人员会定期在扫描仪上一起坐下 交互式技术开发和评估。随着临床利益相关者和 行业合作伙伴，我们旨在使CAI2R技术广泛使用，以促进生物医学的发展 知识和为照顾他们的医生的利益。

项目成果

Impact of 3D printed models on quantitative surgical outcomes for patients undergoing robotic-assisted radical prostatectomy: a cohort study.

• DOI: 10.1007/s00261-023-03815-2
• 发表时间: 2023-04
• 期刊: ABDOMINAL RADIOLOGY
• 影响因子: 2.4
• 作者: Wake, Nicole;Rosenkrantz, Andrew B.;Huang, Richard;Ginocchio, Luke A.;Wysock, James S.;Taneja, Samir S.;Huang, William C. C.;Chandarana, Hersh
• 通讯作者: Chandarana, Hersh

Consideration of the effects of intense tissue heating on the RF electromagnetic fields during MRI: simulations for MRgFUS in the hip.

考虑 MRI 期间强烈组织加热对射频电磁场的影响：髋部 MRgFUS 的模拟

• DOI: 10.1088/0031-9155/60/1/301
• 发表时间: 2015-01-07
• 期刊: Physics in medicine and biology
• 影响因子: 3.5
• 作者: Xin SX;Gu S;Carluccio G;Collins CM
• 通讯作者: Collins CM

Global brain volume and N-acetyl-aspartate decline over seven decades of normal aging.

• DOI: 10.1016/j.neurobiolaging.2020.10.024
• 发表时间: 2021-03
• 期刊: Neurobiology of aging
• 影响因子: 4.2
• 作者: Kirov II;Sollberger M;Davitz MS;Glodzik L;Soher BJ;Babb JS;Monsch AU;Gass A;Gonen O
• 通讯作者: Gonen O

Rapid compositional mapping of knee cartilage with compressed sensing MRI.

• DOI: 10.1002/jmri.26274
• 发表时间: 2018-11
• 期刊: Journal of magnetic resonance imaging : JMRI
• 作者: Zibetti MVW;Baboli R;Chang G;Otazo R;Regatte RR
• 通讯作者: Regatte RR

MARIE 2.0: A Perturbation Matrix Based Patient-Specific MRI Field Simulator.

• DOI: 10.1109/tbme.2022.3222748
• 发表时间: 2023-05
• 期刊: IEEE transactions on bio-medical engineering