Statistically adaptive filtering for low signal correction in x-ray computed tomography

Obaidullah Rahman; Ken D. Sauer; Charles A. Bouman; Roman Melnyk; Brian Nett

doi:10.1117/12.2581032

15 February 2021 Statistically adaptive filtering for low signal correction in x-ray computed tomography

Obaidullah Rahman, Ken D. Sauer, Charles A. Bouman, Roman Melnyk, Brian Nett

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 115952D (2021) https://doi.org/10.1117/12.2581032
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Low x-ray dose is desirable in x-ray computed tomographic (CT) imaging due to health concerns. But low dose comes with a cost of low signal artifacts such as streaks and low frequency bias in the reconstruction. As a result, low signal correction is needed to help reduce artifacts while retaining relevant anatomical structures. Low signal can be encountered in cases where sufficient number of photons do not reach the detector to have confidence in the recorded data. X-ray photons, assumed to have Poisson distribution, have signal to noise ratio proportional to the dose, with poorer SNR in low signal areas. Electronic noise added by the data acquisition system further reduces the signal quality. In this paper we will demonstrate a technique to combat low signal artifacts through adaptive filtration. It entails statistics-based filtering on the uncorrected data, correcting the lower signal areas more aggressively than the high signal ones. We look at local averages to decide how aggressive the filtering should be, and local standard deviation to decide how much detail preservation to apply. Implementation consists of a pre-correction step i.e. local linear minimum mean-squared error correction, followed by a variance stabilizing transform, and finally adaptive bilateral filtering. The coefficients of the bilateral filter are computed using local statistics. Results show that improvements were made in terms of low frequency bias, streaks, local average and standard deviation, modulation transfer function and noise power spectrum.

Conference Presentation

Citation Download Citation

Obaidullah Rahman, Ken D. Sauer, Charles A. Bouman, Roman Melnyk, and Brian Nett "Statistically adaptive filtering for low signal correction in x-ray computed tomography", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115952D (15 February 2021); https://doi.org/10.1117/12.2581032

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