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预采样、算法因素和噪声:特别是CT以及一般医学成像的注意事项。

Presampling, algorithm factors, and noise: considerations for CT in particular and for medical imaging in general.

作者信息

Kachelriess Marc, Kalender Willi A

机构信息

Institute of Medical Physics, University of Erlangen-Nürnberg, Erlangen, Germany.

出版信息

Med Phys. 2005 May;32(5):1321-34. doi: 10.1118/1.1897083.

Abstract

CT scanners acquire noisy data at discrete sample positions. Typically, a convention of how to continue these data from discrete integer positions to the continuous domain must be applied during processing. We study the properties of three typical one-dimensional spatial domain interpolation algorithms in terms of a cost or quality factor Q. This figure of merit Q is a function of spatial resolution, data noise, and dose and is used to optimize detector design. Spatial resolution R is defined as either mean square width delta or as the full width at half maximum W of the point spread function (PSF). Our results show that a trapezoidal interpolation algorithm is optimal for the high resolution domain (relative to the detector aperture size g) and should be replaced by a triangular or Gaussian interpolation function for spatial resolutions of about 1.3g or larger; these result in bell-shaped PSFs. Assuming such a hybrid algorithm we find a 1.5-fold increase of Q2-this is equivalent to 50% improved dose usage-when smoothing the data to a spatial resolution of 3g or more compared to a highest resolution reconstruction. Therefore it is advisable to use detectors of one-third of the size of the desired spatial resolution W and to compensate for the 1.5-fold increase in Q2 by reducing dose by 33%. Under the presence of moderately sized septa (e.g., 10% of the spatial resolution element size) the benefit of optimizing still lies in the order of 30% improved dose usage; in that case the detector size g should be on the order of W/2 and a dose reduction of 23% can be achieved. Again, bell-shaped PSFs show a better tradeoff between noise and resolution for a given dose than rectangular-shaped PSFs. The general interpretation of our results is that the degree of freedom of choosing the weighting or interpolation function for a given resolution is large for small detectors and small for large detectors. Thus systems with small g have a higher potential of optimization compared to systems with large g. Similarly, detector binning, which corresponds to replacing g by 2g, should be avoided. Note that the figures reported correspond to a one-dimensional interpolation. Two-dimensional detectors typically separate and resulting quality factors can be easily obtained by multiplication. Then, Q2 is expected to improve by a factor of 1.52 without septa and by a factor of 1.32 with septa. This indicates that dose can be reduced by about 56% and about 41%, respectively. Our findings are general and not restricted to CT. They can be readily applied to medical or nonmedical imaging devices and digital detectors and they may also turn out to be useful in other fields.

摘要

CT扫描仪在离散的采样位置采集有噪声的数据。通常,在处理过程中必须应用一种将这些数据从离散整数位置延续到连续域的惯例。我们根据成本或质量因子Q研究了三种典型的一维空间域插值算法的特性。这个品质因数Q是空间分辨率、数据噪声和剂量的函数,用于优化探测器设计。空间分辨率R定义为均方宽度δ或点扩散函数(PSF)的半高全宽W。我们的结果表明,梯形插值算法在高分辨率域(相对于探测器孔径大小g)是最优的,对于约1.3g或更大的空间分辨率,应被三角形或高斯插值函数取代;这些会产生钟形的PSF。假设采用这种混合算法,我们发现当将数据平滑到3g或更高的空间分辨率时,与最高分辨率重建相比,Q2会增加1.5倍——这相当于剂量使用提高了50%。因此,建议使用所需空间分辨率W三分之一大小的探测器,并通过将剂量降低33%来补偿Q2增加的1.5倍。在存在中等大小的间隔物(例如,空间分辨率元素大小的10%)的情况下,优化的益处仍在于剂量使用提高约30%;在这种情况下,探测器大小g应约为W/2,并且可以实现23%的剂量降低。同样,对于给定剂量,钟形PSF在噪声和分辨率之间显示出比矩形PSF更好的权衡。我们结果的一般解释是,对于小探测器,在给定分辨率下选择加权或插值函数的自由度大,而对于大探测器则小。因此,与大g的系统相比,小g的系统具有更高的优化潜力。同样,应避免对应于将g替换为2g的探测器合并。请注意,报告的数字对应于一维插值。二维探测器通常是分开的,通过乘法可以很容易地得到相应的品质因数。那么,预计在没有间隔物的情况下Q2会提高1.52倍,在有间隔物的情况下会提高1.32倍。这表明剂量可以分别降低约56%和约41%。我们的发现是普遍的,不限于CT。它们可以很容易地应用于医学或非医学成像设备和数字探测器,并且它们在其他领域可能也会有用。

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