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Nikou Lei

at Univ of Wisconsin School of Medicine and Public Health

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Proceedings Article | 7 April 2023 Presentation + Paper

Addressing the challenge of CT number bias in low-dose photon counting CT without access to raw detector count data

Dalton Griner, Nikou Lei, Guang-Hong Chen, Ke Li

Proceedings Volume 12463, 124631N (2023) https://doi.org/10.1117/12.2654372

KEYWORDS: Bias correction, Computed tomography, Photon counting, Image restoration, Tomography, Medical image reconstruction, Statistical analysis, Histograms, Digital recording, Detector development

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In recent years there has been increased focus on further reducing radiation dose in CT with photon counting CT using solid-state direct-conversion photon counting detectors (PCDs) to reduce the effective dose from routine CT exams to less than 1 mSv. However, despite its noise-reducing capabilities, PCD-CT faces challenges of inaccurate CT numbers at low-dose levels: with smaller pixel areas and multiple energy channels, the number of digital counts recorded in each bin of each PCD pixel can be as low as single-digit integers leading to statistical biases in CT sinograms due to the nonlinear log transformation operation. After tomographic reconstruction, those biases lead to inaccurate CT numbers in PCD-CT images. Previous correction methods require access to the original raw PCD counts. However, in almost all commercial CT systems, raw detector counts are hidden from the end users. Additionally, some CT systems perform the logarithmic transformation of raw counts as a part of the analog-to-digital conversion process for data compression reasons. For those systems, access to the PCD counts is irretrievably lost. Even for the post-log sinogram data, they are usually not archived for each patient. These practical considerations present challenges to the offline application of CT number bias corrections. The purpose of this work was to develop a method to address the statistical bias problem in low-dose PCD-CT without requiring any access to the raw detector counts. Innovations were made in this work to enable bias correction using the post-log sinogram data or using the reconstructed, bias-contaminated PCD-CT images.

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