CORE PROJECT III IMAGING OF PHYSIOLOGICAL FUNCTION: CARDIAC FUNCTION

通常，缺少较高的k空间值会导致模糊 重建图像中的伪影， 在附近区域产生空间振荡。 在某些情况下，例如 作为头皮中的脂质-这些不连续性会干扰 附近区域的低电平信号测量。 如果将军 这些明亮信号的位置是已知的，我们可以对这些信号进行后处理。 图像来“引导”不同方向的振荡。 我们开发了一种简单的方法来解决这个问题。 通过 指定明亮干涉的位置，相位响应 的滤波器进行了优化，以减少振荡的区域 兴趣 目前，该技术已在一维空间得到验证 模拟 它有希望大大减少 由附近明亮的物体引起的振动。 计算 复杂性--一旦滤波器被设计出来--也是同样的量级 作为来自k空间样本的原始重建。 我们目前正在 将该方法扩展到2-D和更一般的干涉图案。 我们研究了使用图像的局部方差模型， 表示原始数据中不连续性的先验知识。 的 模型分别代表水平方向的变化 和垂直方向。 我们假设一个精确的模型可以 可以从对象的标准MR侦察图像导出。 当地 方差模型被纳入重建通过惩罚 粗糙度在某种程度上与局部方差的 侦察图像。 这样，重建公式就变成了 移位变量，允许频谱外推的可能性 从观察到的k空间样本中。 我们的实验证实，这是一种非常有效的 图像模型的类型的先验信息，可从 解剖侦察图像。 而更高的分辨率的细节是 在某种程度上有限，重建是戏剧性的尖锐近 在图像中的边界，使模糊的边缘是最小的。

Ordinarily, the lack of higher k-space values causes a blurring artifact in the reconstructed images in which sharp discontinuities create spatial oscillations into nearby regions. In some cases - such as lipids in the scalp - these discontinuities con interfere with low-level signal measurements in nearby regions. If the general location of these bright signals is known, we can postprocess the image to "steer" the oscillations in a different direction. We have developed a simple approach to solving this problem. By specifying the location of the bright interference, the phase response of a filter is optimized to reduce the oscillations in the region of interest. At present, out technique has been verified in 1-D simulations. It holds promise for significantly reducing the oscillations caused by nearby bright objects. The computational complexity - once the filter has been designed - is on the same order as the original reconstruction from k-space samples. We are currently extending the method to 2-D and more general interference patterns. We investigated the use of a local variance model of the image to indicate a priori knowledge of discontinuities in the original. The model separately represented variations in the horizontal direction and the vertical direction. We assumed that an accurate model could be derived from a standard MR scout image of the object. The local variance model was incorporated into the reconstruction by penalizing roughness in a way inversely related to the local variance of the scout image. In this way, the reconstruction formula was made to be shift variant, allowing for the possibility of spectral extrapolation from the observed k-space samples. Our experiments confirmed that this is a very effective form of image model for the type of prior information that is available from an anatomic scout image. While higher resolution of fine details was somewhat limited, the reconstruction was dramatically sharper near boundaries in the image so that blurring of edges was minimal.