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Article type: Research Article
Authors: Getzin, Matthewa | Garfield, Josephine J.b | Rundle, David S.c | Kruger, Uwea | Butler, Anthony P.H.c | Gkikas, Manosb; * | Wang, Gea; *
Affiliations: [a] Biomedical Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, USA | [b] Chemistry Department, University of Massachusetts Lowell, Lowell, MA, USA | [c] MARS Bioimaging Ltd., Christchurch, New Zealand
Correspondence: [*] Corresponding authors: Manos Gkikas, Chemistry Department, University of Massachusetts Lowell, Lowell, MA, USA. Tel.: +1 978 934 3245; E-mail: [email protected] and Ge Wang, Biomedical Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, USA. Tel.: +1 518 276 3726; E-mail: [email protected].
Abstract: BACKGROUND:X-ray CT/micro-CT methods with photon-counting detectors (PCDs) and high Z materials are a hot research topic. One method using PCDs allows for spectral imaging in 5 energy windows while conventional X-ray detectors only collect energy-integrating data. OBJECTIVE:To demonstrate the enhanced separation of contrast materials by using PCDs, multivariate analysis, and linear discriminant methods. METHODS:Phantoms containing iodine and aqueous nanomaterials were scanned on a MARS spectral micro-CT. Image volumes were segmented into separate material-specific populations. Contrast comparisons were made by calculating T2 test statistics in the univariate, pseudo-conventional and multivariate, spectral CT data sets. Separability after Fisher discriminant analysis (FDA) was also assessed. RESULTS:The T2 values calculated for material comparisons increased as a result of the spectral expansion. The majority of the tested contrast agents showed increased T2 values by a factor of ∼2 -3. The total significant T2 statistics in the pure and mixed lanthanide image sets increased in the spectral data set. CONCLUSION:This work consolidates the groundwork for photon-counting-based material decomposition with micro-CT, facilitating future development of novel nanomaterials and their preclinical applications.
Keywords: Material-discriminative performance, spectral CT, K-edge imaging, multivariate statistics, dimensionality reduction
DOI: 10.3233/XST-18382
Journal: Journal of X-Ray Science and Technology, vol. 26, no. 5, pp. 707-726, 2018
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