Detectability of Brain Metastases by Using Frequency-Selective Nonlinear Blending in Contrast-Enhanced Computed Tomography

Purpose The aim of this study to evaluate the role of frequency-selective nonlinear blending (FS-NLB) for the detectability of brain metastases with contrast-enhanced computed tomography (CECT) using magnetic resonance imaging (MRI) as standard of reference. Materials and Methods A retrospective patient data search at our institution yielded 91 patients who underwent both brain CECT and MRI for screening of brain metastases (n = 173) between 2014 and 2016 (mean time interval, 29 ± 37 [malignant: 15 ± 16/benign: 42 ± 47] days). A recently introduced FS-NLB postprocessing technique was applied to CECT images. Two readers interpreted all CT images in an independent fashion. The conventional, linear blending (LB) CT images were evaluated first. After a washout period, the same readers evaluated the FS-NLB CT images. The standard of reference was established by a consensus interpretation of the brain MRI studies. Outcome variables included determination of best performing FS-NLB settings, region of interest (ROI)–based calculation of contrast-to-noise ratios (CNRs), size, and number of brain metastases. Based on the number of metastases, we classified patients in 5 therapeutically relevant categories (0, no metastasis; 1, singular metastasis; 2, less than 4 metastases; 3, >4 and 10 metastases). Statistical comparison and diagnostic performance tests were applied. Results A center of 47 Hounsfield units (HU), delta of 5 HU, and slope of 5 resulted in the best delineation of hyperdense brain metastases, whereas for hypodense brain metastases, a center of 32 HU, delta of 5 HU, and slope of 5 showed best delineation. Frequency-selective nonlinear blending significantly increased CNR in hyperdense cerebral metastases (CECT: 9.11 [6.9–10.9], FS-NLB: 18.1 [11.9–22.8]; P

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