Self Super-resolution for Magnetic Resonance Images.
Self Super-resolution for Magnetic Resonance Images.
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DOI：
10.1007/978-3-319-46726-9_64
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发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Prince JL
中科院分区：
文献类型：
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作者：
Jog A;Carass A;Prince JL
获得平面内分辨率高于平面分辨率的磁共振图像（MRI）更快，因此要便宜。使用称为超分辨率（SR）算法的后处理技术可以增加此类采集的低分辨率。众所周知，SR是一个不适的问题。大多数最先进的SR算法都依赖于外部/训练数据的存在来学习将低分辨率输入转换为更高分辨率输出的转换。在本文中，提出了一种不取决于任何外部培训数据的SR方法，仅依赖于获得的图像。从获得的图像中提取的斑块用于估计一组新图像，其中每个图像都会沿特定方向提高分辨率。最终的SR图像是通过通过傅立叶爆发积累技术组合图像来估计图像的。我们的方法在模拟的低分辨率MRI图像上得到了验证，并且与竞争SR方法相比，图像质量和分割精度显示出显着提高。还证明了带有病变的流体衰减反转恢复（FLAIR）图像的SR。
It is faster and therefore cheaper to acquire magnetic resonance images (MRI) with higher in-plane resolution than through-plane resolution. The low resolution of such acquisitions can be increased using post-processing techniques referred to as super-resolution (SR) algorithms. SR is known to be an ill-posed problem. Most state-of-the-art SR algorithms rely on the presence of external/training data to learn a transform that converts low resolution input to a higher resolution output. In this paper an SR approach is presented that is not dependent on any external training data and is only reliant on the acquired image. Patches extracted from the acquired image are used to estimate a set of new images, where each image has increased resolution along a particular direction. The final SR image is estimated by combining images in this set via the technique of Fourier Burst Accumulation. Our approach was validated on simulated low resolution MRI images, and showed significant improvement in image quality and segmentation accuracy when compared to competing SR methods. SR of FLuid Attenuated Inversion Recovery (FLAIR) images with lesions is also demonstrated.