Timothy Pok Chi Yeung1, Mark Dekaban2, Nathan De Haan3, Laura Morrison4, Lisa Hoffman5, Yves Bureau6, Xiaogang Chen7, Slav Yartsev8, Glenn Bauman8, Ting-Yim Lee9. 1. London Regional Cancer Program, London Health Sciences Centre, London, ON, Canada; Lawson Imaging, Lawson Health Research Institute, London, ON, Canada; Robarts Research Institute, Western University, 1151 Richmond St. N., London, Ontario, N6A 5B7, Canada; Department of Medical Biophysics, Western University, London, ON, Canada. 2. Lawson Imaging, Lawson Health Research Institute, London, ON, Canada; Robarts Research Institute, Western University, 1151 Richmond St. N., London, Ontario, N6A 5B7, Canada; Department of Medical Biophysics, Western University, London, ON, Canada. 3. London Regional Cancer Program, London Health Sciences Centre, London, ON, Canada. 4. Lawson Imaging, Lawson Health Research Institute, London, ON, Canada. 5. Lawson Imaging, Lawson Health Research Institute, London, ON, Canada; Department of Medical Biophysics, Western University, London, ON, Canada; Department of Anatomy and Cell Biology, Western University, London, ON, Canada. 6. Lawson Imaging, Lawson Health Research Institute, London, ON, Canada; Department of Medical Biophysics, Western University, London, ON, Canada; Department of Medical Imaging, Western University, London, ON, Canada. 7. Robarts Research Institute, Western University, 1151 Richmond St. N., London, Ontario, N6A 5B7, Canada. 8. London Regional Cancer Program, London Health Sciences Centre, London, ON, Canada; Department of Medical Biophysics, Western University, London, ON, Canada; Department of Oncology, Western University, London, ON, Canada. 9. Lawson Imaging, Lawson Health Research Institute, London, ON, Canada; Robarts Research Institute, Western University, 1151 Richmond St. N., London, Ontario, N6A 5B7, Canada; Department of Medical Biophysics, Western University, London, ON, Canada; Department of Medical Imaging, Western University, London, ON, Canada; Department of Oncology, Western University, London, ON, Canada. Electronic address: tlee@robarts.ca.
Abstract
RATIONALE AND OBJECTIVES: To evaluate the improvements in measurements of blood flow (BF), blood volume (BV), and permeability-surface area product (PS) after principal component analysis (PCA) filtering of computed tomography (CT) perfusion images. To evaluate the improvement in CT perfusion image quality with poor contrast-to-noise ratio (CNR) in vivo. MATERIALS AND METHODS: A digital phantom with CT perfusion images reflecting known values of BF, BV, and PS was created and was filtered using PCA. Intraclass correlation coefficients and Bland-Altman analysis were used to assess reliability of measurements and reduction in measurement errors, respectively. Rats with C6 gliomas were imaged using CT perfusion, and the raw CT perfusion images were filtered using PCA. Differences in CNR, BF, BV, and PS before and after PCA filtering were assessed using repeated measures analysis of variance. RESULTS: From simulation, mean errors decreased from 12.8 (95% confidence interval [CI] = -19.5 to 45.0) to 1.4 mL/min/100 g (CI = -27.6 to 30.4), 0.2 (CI = -1.1 to 1.4) to -0.1 mL/100 g (CI = -1.1 to 0.8), and 2.9 (CI = -2.4 to 8.1) to 0.2 mL/min/100 g (CI = -3.5 to 3.9) for BF, BV, and PS, respectively. Map noise in BF, BV, and PS were decreased from 51.0 (CI = -3.5 to 105.5) to 11.6 mL/min/100 g (CI = -7.9 to 31.2), 2.0 (CI = 0.7 to 3.3) to 0.5 mL/100 g (CI = 0.1 to 1.0), and 8.3 (CI = -0.8 to 17.5) to 1.4 mL/min/100 g (CI = -0.4 to 3.1), respectively. For experiments, CNR significantly improved with PCA filtering in normal brain (P < .05) and tumor (P < .05). Tumor and brain BFs were significantly different from each other after PCA filtering with four principal components (P < .05). CONCLUSIONS: PCA improved image CNR in vivo and reduced the measurement errors of BF, BV, and PS from simulation. A minimum of four principal components is recommended.
RATIONALE AND OBJECTIVES: To evaluate the improvements in measurements of blood flow (BF), blood volume (BV), and permeability-surface area product (PS) after principal component analysis (PCA) filtering of computed tomography (CT) perfusion images. To evaluate the improvement in CT perfusion image quality with poor contrast-to-noise ratio (CNR) in vivo. MATERIALS AND METHODS: A digital phantom with CT perfusion images reflecting known values of BF, BV, and PS was created and was filtered using PCA. Intraclass correlation coefficients and Bland-Altman analysis were used to assess reliability of measurements and reduction in measurement errors, respectively. Rats with C6 gliomas were imaged using CT perfusion, and the raw CT perfusion images were filtered using PCA. Differences in CNR, BF, BV, and PS before and after PCA filtering were assessed using repeated measures analysis of variance. RESULTS: From simulation, mean errors decreased from 12.8 (95% confidence interval [CI] = -19.5 to 45.0) to 1.4 mL/min/100 g (CI = -27.6 to 30.4), 0.2 (CI = -1.1 to 1.4) to -0.1 mL/100 g (CI = -1.1 to 0.8), and 2.9 (CI = -2.4 to 8.1) to 0.2 mL/min/100 g (CI = -3.5 to 3.9) for BF, BV, and PS, respectively. Map noise in BF, BV, and PS were decreased from 51.0 (CI = -3.5 to 105.5) to 11.6 mL/min/100 g (CI = -7.9 to 31.2), 2.0 (CI = 0.7 to 3.3) to 0.5 mL/100 g (CI = 0.1 to 1.0), and 8.3 (CI = -0.8 to 17.5) to 1.4 mL/min/100 g (CI = -0.4 to 3.1), respectively. For experiments, CNR significantly improved with PCA filtering in normal brain (P < .05) and tumor (P < .05). Tumor and brain BFs were significantly different from each other after PCA filtering with four principal components (P < .05). CONCLUSIONS: PCA improved image CNR in vivo and reduced the measurement errors of BF, BV, and PS from simulation. A minimum of four principal components is recommended.
Authors: S Ramkumar; S Ranjbar; S Ning; D Lal; C M Zwart; C P Wood; S M Weindling; T Wu; J R Mitchell; J Li; J M Hoxworth Journal: AJNR Am J Neuroradiol Date: 2017-03-02 Impact factor: 3.825
Authors: Leland S Hu; Shuluo Ning; Jennifer M Eschbacher; Nathan Gaw; Amylou C Dueck; Kris A Smith; Peter Nakaji; Jonathan Plasencia; Sara Ranjbar; Stephen J Price; Nhan Tran; Joseph Loftus; Robert Jenkins; Brian P O'Neill; William Elmquist; Leslie C Baxter; Fei Gao; David Frakes; John P Karis; Christine Zwart; Kristin R Swanson; Jann Sarkaria; Teresa Wu; J Ross Mitchell; Jing Li Journal: PLoS One Date: 2015-11-24 Impact factor: 3.240