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

Malte N. Bongers, Georg Bier, Christoph Schabel, Jan Fritz, Marius Horger

Research output: Contribution to journalArticle

Abstract

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; 4, >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 < 0.0001) and hypodense cerebral metastases (CECT: 6.3 [5.2-8], FS-NLB: 17.8 [14.5-19.7]; P < 0.0001). Sensitivity, specificity, negative predictive values, positive predictive values, and accuracy for LB, and FS-NLB were 40%, 98%, 99%, 31%, and 52%, and 62%, 94%, 97%, 40%, and 69%, respectively. Magnetic resonance imaging, LB, and FS-NLB classification of metastatic patients were group 0 (47, 47, 46), group 1 (14, 8, 11), group 2 (16, 12, 15), group 3 (8, 7, 8), and group 4 (6, 4, 6). CONCLUSIONS: Frequency-selective nonlinear blending postprocessing of CECT significantly increases the detection of brain metastases over conventional CECT; however, the sensitivity remains lower than MRI. Frequency-selective nonlinear blending is slightly inferior in the categorization of patients into therapeutically relevant groups, when compared with MRI.

Original languageEnglish (US)
Pages (from-to)98-102
Number of pages5
JournalInvestigative Radiology
Volume54
Issue number2
DOIs
StatePublished - Feb 1 2019

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Tomography
Neoplasm Metastasis
Brain
Magnetic Resonance Imaging
Noise
Routine Diagnostic Tests
Sensitivity and Specificity

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Detectability of Brain Metastases by Using Frequency-Selective Nonlinear Blending in Contrast-Enhanced Computed Tomography. / Bongers, Malte N.; Bier, Georg; Schabel, Christoph; Fritz, Jan; Horger, Marius.

In: Investigative Radiology, Vol. 54, No. 2, 01.02.2019, p. 98-102.

Research output: Contribution to journalArticle

Bongers, Malte N. ; Bier, Georg ; Schabel, Christoph ; Fritz, Jan ; Horger, Marius. / Detectability of Brain Metastases by Using Frequency-Selective Nonlinear Blending in Contrast-Enhanced Computed Tomography. In: Investigative Radiology. 2019 ; Vol. 54, No. 2. pp. 98-102.
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N2 - 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; 4, >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 < 0.0001) and hypodense cerebral metastases (CECT: 6.3 [5.2-8], FS-NLB: 17.8 [14.5-19.7]; P < 0.0001). Sensitivity, specificity, negative predictive values, positive predictive values, and accuracy for LB, and FS-NLB were 40%, 98%, 99%, 31%, and 52%, and 62%, 94%, 97%, 40%, and 69%, respectively. Magnetic resonance imaging, LB, and FS-NLB classification of metastatic patients were group 0 (47, 47, 46), group 1 (14, 8, 11), group 2 (16, 12, 15), group 3 (8, 7, 8), and group 4 (6, 4, 6). CONCLUSIONS: Frequency-selective nonlinear blending postprocessing of CECT significantly increases the detection of brain metastases over conventional CECT; however, the sensitivity remains lower than MRI. Frequency-selective nonlinear blending is slightly inferior in the categorization of patients into therapeutically relevant groups, when compared with MRI.

AB - 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; 4, >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 < 0.0001) and hypodense cerebral metastases (CECT: 6.3 [5.2-8], FS-NLB: 17.8 [14.5-19.7]; P < 0.0001). Sensitivity, specificity, negative predictive values, positive predictive values, and accuracy for LB, and FS-NLB were 40%, 98%, 99%, 31%, and 52%, and 62%, 94%, 97%, 40%, and 69%, respectively. Magnetic resonance imaging, LB, and FS-NLB classification of metastatic patients were group 0 (47, 47, 46), group 1 (14, 8, 11), group 2 (16, 12, 15), group 3 (8, 7, 8), and group 4 (6, 4, 6). CONCLUSIONS: Frequency-selective nonlinear blending postprocessing of CECT significantly increases the detection of brain metastases over conventional CECT; however, the sensitivity remains lower than MRI. Frequency-selective nonlinear blending is slightly inferior in the categorization of patients into therapeutically relevant groups, when compared with MRI.

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