Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: A Comparison With Intravascular Ultrasound.
OBJECTIVE
The purpose of this study was to determine whether use of iterative image reconstruction algorithms improves the accuracy of coronary CT angiography (CCTA) compared with intravascular ultrasound (IVUS) in semiautomated plaque burden assessment.


METHODS
CCTA and IVUS images of seven coronary arteries were acquired ex vivo. CT images were reconstructed with filtered back projection (FBP) and adaptive statistical (ASIR) and model-based (MBIR) iterative reconstruction algorithms. Cross-sectional images of the arteries were coregistered between CCTA and IVUS in 1-mm increments. In CCTA, fully automated (without manual corrections) and semiautomated (allowing manual corrections of vessel wall boundaries) plaque burden assessments were performed for each of the reconstruction algorithms with commercially available software. In IVUS, plaque burden was measured manually. Agreement between CCTA and IVUS was determined with Pearson correlation.


RESULTS
A total of 173 corresponding cross sections were included. The mean plaque burden measured with IVUS was 63.39% ± 10.63%. With CCTA and the fully automated technique, it was 54.90% ± 11.70% with FBP, 53.34% ± 13.11% with ASIR, and 55.35% ± 12.22% with MBIR. With CCTA and the semiautomated technique mean plaque burden was 54.90% ± 11.76%, 53.40% ± 12.85%, 57.09% ± 11.05%. Manual correction of the semiautomated assessments was performed in 39% of all cross sections and improved plaque burden correlation with the IVUS assessment independently of reconstruction algorithm (p < 0.0001). Furthermore, MBIR was superior to FBP and ASIR independently of assessment method (semiautomated, r = 0.59 for FBP, r = 0.52 for ASIR, r = 0.78 for MBIR, all p < 0.001; fully automated, r = 0.40 for FBP, r = 0.37 for ASIR, r = 0.53 for MBIR, all p < 0.001).


CONCLUSIONS
For the quantification of plaque burden with CCTA, MBIR led to better correlation with IVUS than did traditional reconstruction algorithms such as FBP, independently of the use of a fully automated or semiautomated assessment approach. The highest accuracy for quantifying plaque burden with CCTA can be achieved by using MBIR data with semiautomated assessment.
1 Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Boston, MA 02114.|2 Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
Stefan B
SB
Puchner
1 Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Boston, MA 02114.|3 Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Maros
M
Ferencik
4 Cardiovascular Research Foundation, Columbia University Medical Center, New York, NY.
Akiko
A
Maehara
1 Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Boston, MA 02114.|5 Institute of Diagnostic and Interventional Radiology, University Hospital, Zurich, Switzerland.
Paul
P
Stolzmann
4 Cardiovascular Research Foundation, Columbia University Medical Center, New York, NY.
Shixin
S
Ma
1 Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Boston, MA 02114.
Synho
S
Do
6 Department of Diagnostic and Interventional Radiology, Heidelberg University, Heidelberg, Germany.
Hans-Ulrich
HU
Kauczor
4 Cardiovascular Research Foundation, Columbia University Medical Center, New York, NY.
Gary S
GS
Mintz
1 Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Boston, MA 02114.
Udo
U
Hoffmann
1 Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Boston, MA 02114.|6 Department of Diagnostic and Interventional Radiology, Heidelberg University, Heidelberg, Germany.
Christopher L
CL
Schlett
D000328:Adult; D000368:Aged; D000465:Algorithms; D016893:Carotid Stenosis / Q000000981:diagnostic imaging*; D000072226:Computed Tomography Angiography / Q000379:methods*; D017023:Coronary Angiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D066298:In Vitro Techniques; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018084:Ultrasonography, Interventional / Q000379:methods*
accuracy; coronary CT angiography; coronary plaque burden; intravascular ultrasound; iterative image reconstruction algorithms