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Radiation dose reduction with dictionary learning based processing for head CT

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Abstract

In CT, ionizing radiation exposure from the scan has attracted much concern from patients and doctors. This work is aimed at improving head CT images from low-dose scans by using a fast Dictionary learning (DL) based post-processing. Both Low-dose CT (LDCT) and Standard-dose CT (SDCT) nonenhanced head images were acquired in head examination from a multi-detector row Siemens Somatom Sensation 16 CT scanner. One hundred patients were involved in the experiments. Two groups of LDCT images were acquired with 50 % (LDCT50 %) and 25 % (LDCT25 %) tube current setting in SDCT. To give quantitative evaluation, Signal to noise ratio (SNR) and Contrast to noise ratio (CNR) were computed from the Hounsfield unit (HU) measurements of GM, WM and CSF tissues. A blinded qualitative analysis was also performed to assess the processed LDCT datasets. Fifty and seventy five percent dose reductions are obtained for the two LDCT groups (LDCT50 %, 1.15 ± 0.1 mSv; LDCT25 %, 0.58 ± 0.1 mSv; SDCT, 2.32 ± 0.1 mSv; P < 0.001). Significant SNR increase over the original LDCT images is observed in the processed LDCT images for all the GM, WM and CSF tissues. Significant GM–WM CNR enhancement is noted in the DL processed LDCT images. Higher SNR and CNR than the reference SDCT images can even be achieved in the processed LDCT50 % and LDCT25 % images. Blinded qualitative review validates the perceptual improvements brought by the proposed approach. Compared to the original LDCT images, the application of DL processing in head CT is associated with a significant improvement of image quality.

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Abbreviations

LDCT:

Low-dose CT

SDCT:

Standard-dose CT

DL:

Dictionary learning

K-SVD:

K-means singular value decomposition

SD:

Standard deviation

SNR:

Signal to noise ratio

CNR:

Contrast to noise ratio

CTDIvol :

Volume CT dose index

DLP:

Dose length product

ED:

Effective dose

FBP:

Filtered back-projection

mA:

Milliampere

mAs:

Milliampere second

HU:

Hounsfield unit

ASIR:

Adaptive statistical iterative reconstruction

PICCS:

Prior image constrained compressed sensing

ROI:

Region of interest

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Acknowledgments

This research was supported by National Basic Research Program of China (2010CB732503), National Natural Science Foundation (81370040, 31100713), and the Natural Science Foundation of Jiangsu Province (BK2011593), This work was also supported by the Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education.

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Authors and Affiliations

  1. Laboratory of Image Science and Technology, Southeast University, 210096, Nanjing, People’s Republic of China

    Yang Chen, Luyao Shi, Yining Hu & Limin Luo

  2. The Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, People’s Republic of China

    Yang Chen, Luyao Shi, Yining Hu & Limin Luo

  3. Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France

    Yang Chen, Luyao Shi, Yining Hu, Limin Luo & Jean-Louis Coatrieux

  4. Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, Beijing, People’s Republic of China

    Jiang Yang

  5. Department of Radiology, Nanjing Hospital Affiliated to Nanjing Medical University, 210096, Nanjing, People’s Republic of China

    Xindao Yin

  6. INSERM, U1099, Rennes, 35000, France

    Jean-Louis Coatrieux

  7. LTSI, Université de Rennes 1, Rennes, 35000, France

    Jean-Louis Coatrieux

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  1. Yang Chen
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  2. Luyao Shi
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  3. Jiang Yang
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  4. Yining Hu
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  5. Limin Luo
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  6. Xindao Yin
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  7. Jean-Louis Coatrieux
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Correspondence to Yang Chen.

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Chen, Y., Shi, L., Yang, J. et al. Radiation dose reduction with dictionary learning based processing for head CT. Australas Phys Eng Sci Med 37, 483–493 (2014). https://doi.org/10.1007/s13246-014-0276-7

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  • DOI: https://doi.org/10.1007/s13246-014-0276-7

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