I-Corps: Coordinates and Volumetrics in MRI Imaging

I-Corps：MRI 成像中的坐标和体积测量

基本信息

批准号：
1811323
负责人：
Nidhal Bouaynaya
金额：
$ 5万
依托单位：
Rowan University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-01-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1811323&HistoricalAwards=false
关键词：
Corps Coordinates Volumetrics MRI Imaging

项目摘要

The broader impact/commercial potential of this I-Corps project is to fill a critical need in neuro-radiology, neuro-oncology, and radiation therapy, by developing a clinic-ready software suite that computes physician-approved volumes of key three-dimensional (3D) structures in the brain from Magnetic Resonance Imaging (MRI). Currently, physicians make a decision on tumor status by visual inspection and by measuring the largest perpendicular diameters from a single two-dimensional (2D) axial image. Radiation oncologists use manual segmentation to delineate the boundaries of the tumor regions; these contours are used for treatment planning. Current practice is associated with significant limitations that impede the quality of patient care. This software suite will potentially change the status-quo by generating 3D volumetric structures of tumors and other key regions in the brain. The physician can readily apply this information to better understand the disease and optimize care leading to early treatment, less morbidity, and longer survival times.This I-Corps project produces a practical, clinic-ready suite of Magnetic Resonance (MR) analyses and display tools to solve the number one impediment to reliable use of MRIs for guiding radiation treatment of brain cancer: accurate and timely multimodal 3D segmentation of key structures in the brain. The main engine of the MR suite is a mathematical optimization framework that combines calculus of variation with deep learning techniques; thus amenable to pixel-level-accurate 3D segmentation in almost real-time. The proposed technology was developed from an algorithm for multimodal brain segmentation, which consists of (a) an automated, accurate and robust algorithm for 3D image segmentation, combined with (b) semi-automated and interactive multimodal labeling that requires physician approval.

该I-Corps项目的更广泛的影响/商业潜力是通过开发一个诊所就绪的软件套件来填补神经 - 放射学，神经肿瘤学和放射疗法的关键需求，该软件套件计算出医生批准的大量关键的三维（3D）结构的大脑中磁共振成像（MRI）的关键三维（3D）结构。目前，医生通过视觉检查和测量单个二维（2D）轴向图像的最大垂直直径来决定肿瘤状态。辐射肿瘤学家使用手动分割来描述肿瘤区域的边界；这些轮廓用于治疗计划。当前的实践与阻碍患者护理质量的重大局限性有关。该软件套件将通过产生肿瘤和大脑其他关键区域的3D体积结构来改变状态。医师可以轻松地应用此信息以更好地了解疾病并优化护理，从而导致早期治疗，较少的生存时间和更长的生存时间。该I-Corps项目产生了一个实用的，可用于诊所的磁共振共鸣（MR）分析和展示工具，以求解MRIS对脑癌的可靠性进行可靠的跨度构建型和及时的跨度分层的障碍。 MR套件的主要引擎是一个数学优化框架，将变异的计算与深度学习技术结合在一起。因此，几乎实时地不适合像素级级别的3D分割。提出的技术是从用于多模式脑分割的算法开发的，该算法由（a）用于3D图像分割的自动化，准确且可靠的算法，并结合了（b）半自动化和互动性多模态标记，需要进行医生批准。