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，其中尖锐的不连续 在附近区域产生空间振荡。在某些情况下-例如 作为头皮中的脂类-这些不连续会干扰 附近地区的低电平信号测量。如果将军 这些明亮信号的位置是已知的，我们可以后处理 图像以将振荡“引导”到不同的方向。 我们已经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 对滤光片的性能进行了优化，以减少以下区域的振荡 利息。目前，OUT技术已在一维空间中得到验证 模拟。它有望显著减少 由附近明亮的物体引起的振荡。计算性的 复杂程度--一旦过滤器设计完成--也是如此 作为k-空间样本的原始重构。我们目前正在 将该方法扩展到二维和更一般的干涉图样。 我们研究了使用图像的局部方差模型来 表示对原稿中的不连续的先验知识。这个 单独表示水平方向变化的模型 和垂直方向。我们假设一个准确的模型可以 是从目标的标准MR侦察图像导出的。当地人 通过惩罚将方差模型引入到重构中 粗糙度以一种与局部方差成反比的方式 童子军形象。这样，重建公式就被制作成 移位变量，允许光谱外推的可能性 从观察到的k空间样本。 我们的实验证实，这是一种非常有效的 可从获取的先验信息类型的图像模型 一张解剖的童子军图像。而精细细节的更高分辨率是 在某种程度上，重建是显着尖锐的附近 图像中的边界，以使边缘模糊最小。

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.