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Channelized hotelling observers for signal detection in stack-mode reading of volumetric images on medical displays with slow response time

机译:通道化的热门观察者用于在医疗显示器上以堆叠模式读取体积图像的信号检测,响应时间慢

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摘要

Volumetric medical images are commonly read in stack-browsing mode. However, previous studies suggest that slow temporal response of medical liquid crystal displays may degrade the diagnostic accuracy (lesion detectability) at browsing rates as low as 10 frames per second (fps). Recently, a multi-slice channelized Hotelling observer (msCHO) model was proposed to estimate the detection performance in 3D images. This implementation of the msCHO restricted the analysis to the luminance of a display pixel at the end of the frame time (end-of-frame luminance) while ignoring the luminance transition within the frame time (intra-frame luminance). Such an approach fails to differentiate between, for example, the commonly found case of two displays with different temporal profiles of luminance as long as their end-of-frame luminance levels are the same. In order to overcome this limitation of the msCHO, we propose a new upsampled msCHO (umsCHO) which acts on images obtained using both the intra-frame and the end-of-frame luminance information. The two models are compared on a set of synthesized 3D images for a range of browsing rates (16.67, 25 and 50 fps). Our results demonstrate that, depending on the details of the luminance transition profiles, neglecting the intra-frame luminance information may lead to over- or underestimation of lesion detectability. Therefore, we argue that using the umsCHO rather than msCHO model is more appropriate for estimating the detection performance in the stack-browsing mode.
机译:体积医学图像通常以堆栈浏览模式读取。但是,先前的研究表明,医用液晶显示器的缓慢时间响应可能会以低至每秒10帧(fps)的浏览速率降低诊断准确性(病变可检测性)。最近,提出了一种多层通道化的Hotelling观察者(msCHO)模型来估计3D图像中的检测性能。 msCHO的此实现将分析限制在帧时间结束时显示像素的亮度(帧结束亮度),而忽略了帧时间内的亮度转换(帧内亮度)。这种方法无法区分例如两个常见的情况,即两个显示器具有不同的亮度时间分布,只要它们的帧结束亮度级别相同即可。为了克服msCHO的这种局限性,我们提出了一种新的上采样msCHO(umsCHO),该方法对使用帧内和帧末亮度信息获得的图像起作用。在一组合成的3D图像上比较这两种模型的浏览速率范围(16.67、25和50 fps)。我们的结果表明,根据亮度过渡曲线的细节,忽略帧内亮度信息可能会导致病变检测能力的高估或低估。因此,我们认为使用umsCHO而不是msCHO模型更适合于估计堆栈浏览模式下的检测性能。

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