title: 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.
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands. arnold@isi.uu.nl
authors.forename: Arnold M R
authors.initials: AM
authors.lastname: Schilham
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
authors.forename: Hester
authors.initials: H
authors.lastname: Gietema
authors.forename: Mathias
authors.initials: M
authors.lastname: Prokop
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D011656:Pulmonary Emphysema / Q000145:classification* / Q000000981:diagnostic imaging*; D011829:Radiation Dosage; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D014057:Tomography, X-Ray Computed / Q000379:methods*