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學術報告:Advances in diffusion MRI acquisition and reconstructio

2017-04-27 09:50:55

電子科技大學核磁共振中心
中古聯合實驗室特邀英國牛津大學吳文川博士,與我院師生分享他在擴散磁共振成像技術領域的研究及進展。具體安排如下,歡迎感興趣的教師和博士生參加。 一、 時間:2017年5月2日星期二上午10:00-11:30 二、 地點:信息醫學研究中心131會議室 三、 主題:Advances in diffusion MRI acquisition and reconstruction 四、 主持人:Pedro Antonio Valdes Sosa 五、 承辦單位:生命科學與技術學院 六、 交流內容: Advances in diffusion MRI acquisition and reconstruction Diffusion MRI is a standard imaging tool in clinical neurology, and is becoming increasingly important for neuroscience studies due to its ability to depict complex neuro-anatomy (e.g., white matter connectivity). High spatial resolution is desired in diffusion MRI as it can provide the ability to resolve small brain structures, enabling investigations of detailed white matter architecture. A major challenge for in vivo high-resolution diffusion MRI is the low signal-to-noise ratio (SNR). In the first part of my talk, I will describe a new approach we proposed to improve the SNR of diffusion MRI data, which combines two highly compatible methods, ultra-high field and three-dimensional multi-slab acquisition. In vivo results demonstrate that using the new method, high-quality diffusion MRI data with ~1mm isotropic resolution can be achieved. Another challenge faced by diffusion MRI is the long scan time, as typically a large number of diffusion volumes (directions) are acquired. In the second part of my talk, I’ll introduce a new method we proposed to improve the reconstruction of diffusion MRI data. As diffusion volumes contain an abundance of common features (e.g. structural boundaries, mean signal attenuation), yet each volume is usually reconstructed independently. We proposed to integrate Gaussian processes into image reconstruction to leverage shared information between the k and q domains to reduce image artifacts associated with parallel imaging. I’ll show some preliminary results with 12 folds acceleration (MB=4, R=3, 7T), in which the proposed method clearly outperforms conventional reconstruction methods. 主講人簡介:英國牛津大學磁共振物理博士。主要從事擴散磁共振成像在神經科學研究;主要包括高分辨率,高信噪比擴散磁共振成像技術;超高場(7T)磁共振成像技術;磁共振采集和重建方法;等。目前其團隊提出使用高斯過程對擴散磁共振信號進行建模,并基于模型進行圖像重建,利用圖像數據中的冗余信息提高重建算法的性能。在Magnetic Resonance Imaging等國際期刊發表論文10篇;專利2項。
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