Mr. Cheng Shi Profile

Cheng Shi

at Tsinghua Univ

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Proceedings Article | 24 February 2010 Paper

Optimization of detector thickness for single slice helical CT with ROC study

Cheng Shi, Yuxiang Xing

Proceedings Volume 7627, 76271D (2010) https://doi.org/10.1117/12.844611

KEYWORDS: Sensors, Signal to noise ratio, Computed tomography, X-ray computed tomography, Numerical simulations, 3D image processing, 3D displays, X-rays, Diagnostics, Performance modeling

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Helical CT scan has been acknowledged to be a very useful scanning mode. Normally, the speed of bed movement per rotation (pitch) for a helical CT is fixed to meet the requirement of CT scanning speed. To reduce system cost, single slice helical CT (SSHCT) is often chosen in many applications. It is interesting and useful in real life to answer the question that how to design the detector to obtain optimal performance of a SSHCT in a detection task. In this work, we applied ROC study for the optimization of detector thickness along the direction of rotation axis for our SSHCT. Numerical simulations followed by human observer studies are done in this investigation. Compound Gaussian noises are modeled in our numerical simulations for objects both with and without lesions. An analytical FBP reconstruction method with rebinning is used for noisy data reconstruction. It can be seen in the reconstructions that thin detectors lead to artifacts, and that thick detectors lead to lesion blurring and lower contrast. All these impact on lesion detection in the practical imaging applications. According to our ROC tests done on images from five choices of detector thickness, optimal performance is obtained when choosing detector thickness being around 1~1.25 times of the helical pitch. Moreover, we find that, under different noise level, the optimal point is about the same. Some other figures of merit including SNR and HTC are also calculated and examined in this work. The results relates well with the results of AUCs. It shows that, they could serve very well as the indicators for system optimization when few non-linear physical effect and reconstruction processing are involved.

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