Local noise weighted filtering for emphysema scoring of low-dose CT images
- PMID: 16608060
- DOI: 10.1109/TMI.2006.871545
Local noise weighted filtering for emphysema scoring of low-dose CT images
Abstract
Computed tomography (CT) has become the new reference standard for quantification of emphysema. The most popular measure of emphysema derived from CT is the pixel index (PI), which expresses the fraction of the lung volume with abnormally low intensity values. As PI is calculated from a single, fixed threshold on intensity, this measure is strongly influenced by noise. This effect shows up clearly when comparing the PI score of a high-dose scan to the PI score of a low-dose (i.e., noisy) scan of the same subject. In this paper, the noise variance (NOVA) filter is presented: a general framework for (iterative) nonlinear filtering, which uses an estimate of the spatially dependent noise variance in an image. The NOVA filter iteratively estimates the local image noise and filters the image. For the specific purpose of emphysema quantification of low-dose CT images, a dedicated, noniterative NOVA filter is constructed by using prior knowledge of the data to obtain a good estimate of the spatially dependent noise in an image. The performance of the NOVA filter is assessed by comparing characteristics of pairs of high-dose and low-dose scans. The compared characteristics are the PI scores for different thresholds and the size distributions of emphysema bullae. After filtering, the PI scores of high-dose and low-dose images agree to within 2%-3% points. The reproducibility of the high-dose bullae size distribution is also strongly improved. NOVA filtering of a CT image of typically 400 x 512 x 512 voxels takes only a couple of minutes which makes it suitable for routine use in clinical practice.
Similar articles
-
The Role of Computed Tomography in the Diagnosis and Treatment of Emphysema.Radiol Technol. 2016 Jan-Feb;87(3):340-3. Radiol Technol. 2016. PMID: 26721847 Review. No abstract available.
-
Efficient low-dose CT artifact mitigation using an artifact-matched prior scan.Med Phys. 2012 Aug;39(8):4748-60. doi: 10.1118/1.4736528. Med Phys. 2012. PMID: 22894400
-
Imaging studies in emphysema.Proc Am Thorac Soc. 2008 May 1;5(4):494-500. doi: 10.1513/pats.200708-128ET. Proc Am Thorac Soc. 2008. PMID: 18453361 Free PMC article. Review.
-
[Quantification of pulmonary emphysema in multislice-CT using different software tools].Rofo. 2006 Oct;178(10):987-98. doi: 10.1055/s-2006-926823. Rofo. 2006. PMID: 17021978 German.
-
MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies.IEEE Trans Med Imaging. 2006 Apr;25(4):464-75. doi: 10.1109/TMI.2006.870889. IEEE Trans Med Imaging. 2006. PMID: 16608061 Clinical Trial.
Cited by
-
AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation.J Digit Imaging. 2022 Jun;35(3):538-550. doi: 10.1007/s10278-022-00599-7. Epub 2022 Feb 18. J Digit Imaging. 2022. PMID: 35182291 Free PMC article.
-
Unpaired Low-Dose CT Denoising Network Based on Cycle-Consistent Generative Adversarial Network with Prior Image Information.Comput Math Methods Med. 2019 Dec 7;2019:8639825. doi: 10.1155/2019/8639825. eCollection 2019. Comput Math Methods Med. 2019. PMID: 31885686 Free PMC article.
-
Emphysema quantification using chest CT: influence of radiation dose reduction and reconstruction technique.Eur Radiol Exp. 2018 Nov 7;2(1):30. doi: 10.1186/s41747-018-0064-3. Eur Radiol Exp. 2018. PMID: 30402740 Free PMC article.
-
Convolutional auto-encoder for image denoising of ultra-low-dose CT.Heliyon. 2017 Aug 30;3(8):e00393. doi: 10.1016/j.heliyon.2017.e00393. eCollection 2017 Aug. Heliyon. 2017. PMID: 28920094 Free PMC article.
-
Emphysema Quantification on Cardiac CT Scans Using Hidden Markov Measure Field Model: The MESA Lung Study.Med Image Comput Comput Assist Interv. 2016 Oct;9901:624-631. doi: 10.1007/978-3-319-46723-8_72. Epub 2016 Oct 2. Med Image Comput Comput Assist Interv. 2016. PMID: 28845485 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical
Research Materials
Miscellaneous