Christian Dold, Younis Waheed, Winter Jeff, Sakas Georgios, Firle Evelyn, Stergiopoulos Stergios
Fraunhofer Institute for Computer Graphics, Darmstadt, Germany.
Stud Health Technol Inform. 2004;98:75-81.
We aim to provide a next generation Magnetic Resonance Imaging (MRI) technology with an integrated solution for reducing motion artifacts in brain imaging applications. New developments in the field of MRI are revolutionizing the diagnostic capabilities e.g. of functional (fMRI) of the technique. Unfortunately, motion artifacts are eminent problems in cerebral MRI images, especially in difficult patient populations (e.g. chronic pain, children, neonates). Patient motion artifacts are present in 2D sequences, but are extremely detrimental in multi-slice 3D sequences often employed in fMRI. The problem of motion compensation in MRI technology deals with: Identification of the source as well as pattern of motion. Obtaining a mathematical model of motion that can be used to identify and then compensate the motion effects. Optimizing the image acquisition sequence in order to minimize, or even eliminate, the effect of motion. We propose a method to obtain a quantitative measure of the movement of the head between different data acquisition points in both MRI, and functional MRI examination.
我们旨在提供一种下一代磁共振成像（MRI）技术，该技术具有集成解决方案，可减少脑成像应用中的运动伪影。MRI领域的新发展正在彻底改变该技术的诊断能力，例如功能磁共振成像（fMRI）。不幸的是，运动伪影是脑MRI图像中的突出问题，尤其是在困难的患者群体中（例如慢性疼痛患者、儿童、新生儿）。患者运动伪影在二维序列中存在，但在功能磁共振成像中常用的多层三维序列中极其有害。MRI技术中的运动补偿问题涉及：识别运动的来源和模式。获得可用于识别并随后补偿运动影响的运动数学模型。优化图像采集序列，以最小化甚至消除运动的影响。我们提出了一种方法，用于在MRI和功能磁共振成像检查中获得不同数据采集点之间头部运动的定量测量。
