When too few photons reach detector elements, strong streaks appear through paths of high X-ray attenuation and an image becomes completely useless. This photon starvation artifact phenomenon occurs frequently when a pelvis or shoulder is scanned with thin slices. The common understanding regarding photon starvation streaks is that they are a manifestation of irregularities caused by noise in the raw data profile. Therefore, the common countermeasure is local raw-data filtering, which detects and smoothes out the highly noisy part of the raw data. However, the photon starvation artifact can be solved only partly with such a method and a more effective solution is necessary. Here, we examined the mean level shift of raw data attributable to the nonlinear nature of logarithmic conversion, which is the process required for generating raw data from detected X-ray data. We judge that the real culprit of the photon starvation artifact is this mean level shift. When the noise level is very high or the photon level is very low, this mean level shift can become prominent and can become manifest as thick streaks against which the conventional local raw data filtering has no power. To solve this problem, we propose a new scheme of local raw data filtering, which consists of reverting log-converted raw data to a form that is equivalent to pre-log detector data. With this method, not only fine streaks, but also thick streaks are removed effectively. A better image quality with lower X-ray doses is possible with this method. © 2012 Japanese Society of Radiological Technology and Japan Society of Medical Physics.