Investigation into the optimal linear time-invariant lag correction for radar artifact removal.
Investigation into the optimal linear time-invariant lag correction for radar artifact removal.
复制标题
研究雷达伪影消除的最佳线性时不变滞后校正。
DOI：
10.1118/1.3574873
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发表时间：
2011
期刊：
影响因子：
3.8
通讯作者：
Fahrig,Rebecca
中科院分区：
文献类型：
--
作者：
Starman,Jared;Star-Lack,Josh;Virshup,Gary;Shapiro,Edward;Fahrig,Rebecca
目的：在非晶硅（a - Si）平板（FP）探测器中，检测器滞后或残留信号会在锥形束计算机断层扫描（CBCT）重建中引起明显的阴影伪影。迄今为止，大多数校正模型都假定为线性时不变（LTI）模型，并且通过脉冲响应函数（IRF）的反卷积来校正滞后。然而，有许多方法可以确定IRF。这项工作的目的是更好地理解瓦里安4030CB FP中的检测器滞后，并确定最能消除CBCT阴影伪影的IRF测量技术。方法：我们研究了在15帧/秒的动态增益模式下工作的瓦里安4030CB a - Si FP的滞后线性，方法是在10次入射曝光（0.5%-84%的a - Si FP饱和曝光）时，通过检测上升阶跃响应函数（RSRF）和下降阶跃响应函数（FSRF）。我们实施了一个多指数（N= 4） LTI模型进行滞后校正，并研究了各种确定IRF的技术（如RSRF与FSRF、曝光强度、曝光长度和空间位置）的影响。所得到的irf应用于(1)阶跃响应投影数据和(2)CBCT获取大骨盆幻影和丙烯酸头部幻影。对于投影数据，在校正前后分别测量第1帧和第50帧滞后。对于CBCT重建，定义了四对roi，并计算了不同曝光和阶跃响应边缘技术下每对roi的最大和平均误差。结果FSRF数据存在大于50%的非线性。用RSRF数据标定的模型导致了FSRF数据的过校正。相反，将FSRF数据校准的模型应用于RSRF数据会导致RSRF校正不足。当LTI模型应用于不同事件暴露下收集的数据时，可以看到类似的效果。在阶跃响应数据中观察到滞后的一些空间变化。对于CBCT重建，使用不同技术的irf时，平均误差范围为3-21 HU。对于我们的幻影和FP，基于FSRF的技术在1.6或3.4% a - Si FP饱和度下的平均误差最低，具体取决于所使用的幻影。结论步进响应边缘（RSRF vs FSRF）和曝光强度的选择可能会在步进响应数据中留下较大的残留滞后。对于CBCT重建，低曝光强度（1.6和3.4%）下的FSRF数据衍生的irf可以最好地去除CBCT阴影伪影。可以根据物体大小选择使用哪个IRF进行滞后校正。
PurposeDetector lag, or residual signal, in amorphous silicon (a‐Si) flat‐panel (FP) detectors can cause significant shading artifacts in cone‐beam computed tomography (CBCT) reconstructions. To date, most correction models have assumed a linear, time‐invariant (LTI) model and lag is corrected by deconvolution with an impulse response function (IRF). However, there are many ways to determine the IRF. The purpose of this work is to better understand detector lag in the Varian 4030CB FP and to identify the IRF measurement technique that best removes the CBCT shading artifact.MethodsWe investigated the linearity of lag in a Varian 4030CB a‐Si FP operating in dynamic gain mode at 15 frames per second by examining the rising step‐response function (RSRF) followed by the falling step‐response function (FSRF) at ten incident exposures (0.5%–84% of a‐Si FP saturation exposure). We implemented a multiexponential (N= 4) LTI model for lag correction and investigated the effects of various techniques for determining the IRF such as RSRF versus FSRF, exposure intensity, length of exposure, and spatial position. The resulting IRFs were applied to (1) the step‐response projection data and (2) CBCT acquisitions of a large pelvic phantom and acrylic head phantom. For projection data, 1st and 50th frame lags were measured pre‐ and postcorrection. For the CBCT reconstructions, four pairs of ROIs were defined and the maximum and mean errors within each pair were calculated for the different exposures and step‐response edge techniques.ResultsA nonlinearity greater than 50% was observed in the FSRF data. A model calibrated with RSRF data resulted in overcorrection of FSRF data. Conversely, models calibrated with FSRF data applied to RSRF data resulted in undercorrection of the RSRF. Similar effects were seen when LTI models were applied to data collected at different incident exposures. Some spatial variation in lag was observed in the step‐response data. For CBCT reconstructions, an average error range of 3–21 HU was observed when using IRFs from different techniques. For our phantoms and FP, the lowest average error occurred for the FSRF‐based techniques at exposures of 1.6 or 3.4% a‐Si FP saturation, depending on the phantom used.ConclusionsThe choice of step‐response edge (RSRF versus FSRF) and exposure intensity for IRF calibration could leave large residual lag in the step‐response data. For the CBCT reconstructions, IRFs derived from FSRF data at low exposure intensities (1.6 and 3.4%) best removed the CBCT shading artifact. Which IRF to use for lag correction could be selected based on the object size.