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Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: A Comparison With Intravascular Ultrasound.
AJR Am J Roentgenol. 2017 Apr; 208(4):777-784.AA

Abstract

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.

MATERIALS AND 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).

CONCLUSION

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.

Authors+Show Affiliations

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.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.4 Cardiovascular Research Foundation, Columbia University Medical Center, New York, NY.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.4 Cardiovascular Research Foundation, Columbia University Medical Center, New York, NY.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.4 Cardiovascular Research Foundation, Columbia University Medical Center, New York, NY.1 Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Boston, MA 02114.1 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.

Pub Type(s)

Comparative Study
Evaluation Study
Journal Article

Language

eng

PubMed ID

28177655

Citation

Puchner, Stefan B., et al. "Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: a Comparison With Intravascular Ultrasound." AJR. American Journal of Roentgenology, vol. 208, no. 4, 2017, pp. 777-784.
Puchner SB, Ferencik M, Maehara A, et al. Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: A Comparison With Intravascular Ultrasound. AJR Am J Roentgenol. 2017;208(4):777-784.
Puchner, S. B., Ferencik, M., Maehara, A., Stolzmann, P., Ma, S., Do, S., Kauczor, H. U., Mintz, G. S., Hoffmann, U., & Schlett, C. L. (2017). Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: A Comparison With Intravascular Ultrasound. AJR. American Journal of Roentgenology, 208(4), 777-784. https://doi.org/10.2214/AJR.16.17187
Puchner SB, et al. Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: a Comparison With Intravascular Ultrasound. AJR Am J Roentgenol. 2017;208(4):777-784. PubMed PMID: 28177655.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: A Comparison With Intravascular Ultrasound. AU - Puchner,Stefan B, AU - Ferencik,Maros, AU - Maehara,Akiko, AU - Stolzmann,Paul, AU - Ma,Shixin, AU - Do,Synho, AU - Kauczor,Hans-Ulrich, AU - Mintz,Gary S, AU - Hoffmann,Udo, AU - Schlett,Christopher L, Y1 - 2017/02/08/ PY - 2017/2/9/pubmed PY - 2017/4/11/medline PY - 2017/2/9/entrez KW - accuracy KW - coronary CT angiography KW - coronary plaque burden KW - intravascular ultrasound KW - iterative image reconstruction algorithms SP - 777 EP - 784 JF - AJR. American journal of roentgenology JO - AJR Am J Roentgenol VL - 208 IS - 4 N2 - 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. MATERIALS AND 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). CONCLUSION: 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. SN - 1546-3141 UR - https://www.unboundmedicine.com/medline/citation/28177655/Iterative_Image_Reconstruction_Improves_the_Accuracy_of_Automated_Plaque_Burden_Assessment_in_Coronary_CT_Angiography:_A_Comparison_With_Intravascular_Ultrasound_ L2 - https://www.ajronline.org/doi/10.2214/AJR.16.17187 DB - PRIME DP - Unbound Medicine ER -