Optimized Parallelization for Nonlocal Means Based Low Dose CT Image Processing

Low dose CT (LDCT) images are often significantly degraded by severely increased mottled noise/artifacts, which can lead to lowered diagnostic accuracy in clinic. The nonlocal means (NLM) filtering can effectively remove mottled noise/artifacts by utilizing large-scale patch similarity information in LDCT images. But the NLM filtering application in LDCT imaging also requires high computation cost because intensive patch similarity calculation within a large searching window is often required to be used to include enough structure-similarity information for noise/artifact suppression. To improve its clinical feasibility, in this study we further optimize the parallelization of NLM filtering by avoiding the repeated computation with the row-wise intensity calculation and the symmetry weight calculation. The shared memory with fastI/Ospeed is also used in row-wise intensity calculation for the proposed method. Quantitative experiment demonstrates that significant acceleration can be achieved with respect to the traditional straight pixel-wise parallelization.

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Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means

Physics in Medicine and Biology ◽

10.1088/0031-9155/57/9/2667 ◽

2012 ◽

Vol 57 (9) ◽

pp. 2667-2688 ◽

Cited By ~ 80

Author(s):

Yang Chen ◽

Zhou Yang ◽

Yining Hu ◽

Guanyu Yang ◽

Yongcheng Zhu ◽

...

Keyword(s):

Image Processing ◽

Large Scale ◽

Low Dose ◽

Ct Image ◽

Nonlocal Means ◽

Low Dose Ct

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De-noising of low-dose CT images using space-time nonlocal means over large-scale neighborhoods

The 2011 IEEE/ICME International Conference on Complex Medical Engineering ◽

10.1109/iccme.2011.5876783 ◽

2011 ◽

Cited By ~ 2

Author(s):

Weimin Yu ◽

Yang Chen ◽

Limin Luo

Keyword(s):

Large Scale ◽

Low Dose ◽

Ct Images ◽

Space Time ◽

Nonlocal Means ◽

Low Dose Ct

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Low dose CT reconstruction with nonlocal means-based prior predicted from normal-dose CT database

Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging ◽

10.1117/12.2294051 ◽

2018 ◽

Author(s):

Junyan Rong ◽

Yuanke Zhang ◽

Tianshuai Liu ◽

Peng Gao ◽

Yuxiang Xing ◽

...

Keyword(s):

Low Dose ◽

Ct Reconstruction ◽

Nonlocal Means ◽

Normal Dose ◽

Low Dose Ct

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Statistical image reconstruction for low-dose CT using nonlocal means-based regularization

Computerized Medical Imaging and Graphics ◽

10.1016/j.compmedimag.2014.05.002 ◽

2014 ◽

Vol 38 (6) ◽

pp. 423-435 ◽

Cited By ~ 51

Author(s):

Hao Zhang ◽

Jianhua Ma ◽

Jing Wang ◽

Yan Liu ◽

Hongbing Lu ◽

...

Keyword(s):

Image Reconstruction ◽

Low Dose ◽

Nonlocal Means ◽

Low Dose Ct ◽

Statistical Image Reconstruction

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Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach

Computerized Medical Imaging and Graphics ◽

10.1016/j.compmedimag.2015.02.008 ◽

2015 ◽

Vol 43 ◽

pp. 26-35 ◽

Cited By ~ 27

Author(s):

Hao Zhang ◽

Jianhua Ma ◽

Jing Wang ◽

Yan Liu ◽

Hao Han ◽

...

Keyword(s):

Image Reconstruction ◽

Low Dose ◽

Adaptive Approach ◽

Nonlocal Means ◽

Low Dose Ct ◽

Statistical Image Reconstruction

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Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT

The Lancet Digital Health ◽

10.1016/s2589-7500(20)30064-9 ◽

2020 ◽

Vol 2 (5) ◽

pp. e259-e267 ◽

Cited By ~ 3

Author(s):

Lisa Y W Tang ◽

Harvey O Coxson ◽

Stephen Lam ◽

Jonathon Leipsic ◽

Roger C Tam ◽

...

Keyword(s):

Chronic Obstructive Pulmonary Disease ◽

Pulmonary Disease ◽

Large Scale ◽

Low Dose ◽

Case Finding ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Low Dose Ct

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Low-Dose CT Image Restoration Based on Adaptive Prior Feature Matching and Nonlocal Means

International Journal of Image and Graphics ◽

10.1142/s0219467819500177 ◽

2019 ◽

Vol 19 (03) ◽

pp. 1950017 ◽

Cited By ~ 1

Author(s):

Lu Cheng ◽

Yuan-Ke Zhang ◽

Yun Song ◽

Chen Li ◽

Dao-Shun Guo

Keyword(s):

Image Quality ◽

Low Dose ◽

Noise Suppression ◽

Feature Matching ◽

Texture Feature ◽

Patient Specific ◽

Major Drawback ◽

Nonlocal Means ◽

Normal Dose ◽

Low Dose Ct

Although the low-dose CT (LDCT) technique can reduce the radiation damage to patients, it will be highly detrimental to the reconstructed image quality. The normal-dose scan assisted algorithms have shown their potential in improving LDCT image quality by using a registered previously scanned normal-dose CT (NDCT) reference to regularize the corresponding LDCT target. The major drawback of such methods is the requirement of a previous patient-specific NDCT scan, which limits their clinical application. To address these problems, this paper proposed adaptive prior feature matching method for better restoration of the LDCT image. The innovation lies in construction of offline texture feature database and online adaptive prior feature matching integrated with the NLM regularization. Specifically, the prior features were extracted by the gray level co-occurrence matrix (GLCM) from regions of interest (ROIs) in existing NDCT scans of population patients. For online adaptive prior feature matching, ROIs with their texture features being similar to those of the current noisy target ROI are selected from the database as the references for the NLM regularization. The effectiveness of the proposed algorithm is validated by clinical lung cancer studies, the gain over traditional methods is noticeable in terms of both noise suppression and textures preservation.

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