Iterative image reconstruction improves the accuracy of automated plaque burden assessment in coronary CT angiography

A comparison with intravascular ultrasound

Stefan B. Puchner, Maros Ferencik, Akiko Maehara, Paul Stolzmann, Shixin Ma, Synho Do, Hans Ulrich Kauczor, Gary S. Mintz, Udo Hoffmann, Christopher L. Schlett

Research output: Contribution to journalArticle

5 Citations (Scopus)

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 I VUS images of seven coronary a rteries 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.

Original languageEnglish (US)
Pages (from-to)777-784
Number of pages8
JournalAmerican Journal of Roentgenology
Volume208
Issue number4
DOIs
StatePublished - Apr 1 2017
Externally publishedYes

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Computer-Assisted Image Processing
Coronary Angiography
Computed Tomography Angiography
Statistical Models
Software
Arteries

Keywords

  • Accuracy
  • Coronary CT angiography
  • Coronary plaque burden
  • Intravascular ultrasound
  • Iterative image reconstruction algorithms

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Iterative image reconstruction improves the accuracy of automated plaque burden assessment in coronary CT angiography : A comparison with intravascular ultrasound. / Puchner, Stefan B.; Ferencik, Maros; Maehara, Akiko; Stolzmann, Paul; Ma, Shixin; Do, Synho; Kauczor, Hans Ulrich; Mintz, Gary S.; Hoffmann, Udo; Schlett, Christopher L.

In: American Journal of Roentgenology, Vol. 208, No. 4, 01.04.2017, p. 777-784.

Research output: Contribution to journalArticle

Puchner, Stefan B. ; Ferencik, Maros ; Maehara, Akiko ; Stolzmann, Paul ; Ma, Shixin ; Do, Synho ; Kauczor, Hans Ulrich ; Mintz, Gary S. ; Hoffmann, Udo ; Schlett, Christopher L. / Iterative image reconstruction improves the accuracy of automated plaque burden assessment in coronary CT angiography : A comparison with intravascular ultrasound. In: American Journal of Roentgenology. 2017 ; Vol. 208, No. 4. pp. 777-784.
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title = "Iterative image reconstruction improves the accuracy of automated plaque burden assessment in coronary CT angiography: A comparison with intravascular ultrasound",
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 I VUS images of seven coronary a rteries 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.",
keywords = "Accuracy, Coronary CT angiography, Coronary plaque burden, Intravascular ultrasound, Iterative image reconstruction algorithms",
author = "Puchner, {Stefan B.} and Maros Ferencik and Akiko Maehara and Paul Stolzmann and Shixin Ma and Synho Do and Kauczor, {Hans Ulrich} and Mintz, {Gary S.} and Udo Hoffmann and Schlett, {Christopher L.}",
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TY - JOUR

T1 - Iterative image reconstruction improves the accuracy of automated plaque burden assessment in coronary CT angiography

T2 - 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.

PY - 2017/4/1

Y1 - 2017/4/1

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 I VUS images of seven coronary a rteries 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.

AB - 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 I VUS images of seven coronary a rteries 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.

KW - Accuracy

KW - Coronary CT angiography

KW - Coronary plaque burden

KW - Intravascular ultrasound

KW - Iterative image reconstruction algorithms

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