Improving quantitative CT perfusion parameter measurements using principal component analysis.
OBJECTIVE
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.


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.
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.
Timothy Pok Chi
TP
Yeung
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.
Mark
M
Dekaban
London Regional Cancer Program, London Health Sciences Centre, London, ON, Canada.
Nathan
N
De Haan
Lawson Imaging, Lawson Health Research Institute, London, ON, Canada.
Laura
L
Morrison
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.
Lisa
L
Hoffman
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.
Yves
Y
Bureau
Robarts Research Institute, Western University, 1151 Richmond St. N., London, Ontario, N6A 5B7, Canada.
Xiaogang
X
Chen
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.
Slav
S
Yartsev
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.
Glenn
G
Bauman
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.
Ting-Yim
TY
Lee
D000465:Algorithms; D000818:Animals; D001932:Brain Neoplasms / Q000098:blood supply / Q000000981:diagnostic imaging*; D045744:Cell Line, Tumor; D002533:Cerebral Angiography / Q000379:methods*; D003627:Data Interpretation, Statistical; D005910:Glioma / Q000098:blood supply / Q000000981:diagnostic imaging*; D009389:Neovascularization, Pathologic / Q000000981:diagnostic imaging*; D055420:Perfusion Imaging / Q000379:methods; D025341:Principal Component Analysis; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D051381:Rats; D017208:Rats, Wistar; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
CT perfusion; blood flow; brain tumor; image noise; principal component analysis