title: Iterative image reconstruction algorithms in coronary CT angiography improve the detection of lipid-core plaque--a comparison with histology.
abstract: OBJECTIVE
  To evaluate whether iterative reconstruction algorithms improve the diagnostic accuracy of coronary CT angiography (CCTA) for detection of lipid-core plaque (LCP) compared to histology.
  
  
  METHODS
  CCTA and histological data were acquired from three ex vivo hearts. CCTA images were reconstructed using filtered back projection (FBP), adaptive-statistical (ASIR) and model-based (MBIR) iterative algorithms. Vessel cross-sections were co-registered between FBP/ASIR/MBIR and histology. Plaque area <60 HU was semiautomatically quantified in CCTA. LCP was defined by histology as fibroatheroma with a large lipid/necrotic core. Area under the curve (AUC) was derived from logistic regression analysis as a measure of diagnostic accuracy.
  
  
  RESULTS
  Overall, 173 CCTA triplets (FBP/ASIR/MBIR) were co-registered with histology. LCP was present in 26 cross-sections. Average measured plaque area <60 HU was significantly larger in LCP compared to non-LCP cross-sections (mm(2): 5.78 ± 2.29 vs. 3.39 ± 1.68 FBP; 5.92 ± 1.87 vs. 3.43 ± 1.62 ASIR; 6.40 ± 1.55 vs. 3.49 ± 1.50 MBIR; all p < 0.0001). AUC for detecting LCP was 0.803/0.850/0.903 for FBP/ASIR/MBIR and was significantly higher for MBIR compared to FBP (p = 0.01). MBIR increased sensitivity for detection of LCP by CCTA.
  
  
  CONCLUSIONS
  Plaque area <60 HU in CCTA was associated with LCP in histology regardless of the reconstruction algorithm. However, MBIR demonstrated higher accuracy for detecting LCP, which may improve vulnerable plaque detection by CCTA.
  
  
  CONCLUSIONS
  • A low attenuation plaque area is associated with the presence of lipid-core plaque • MBIR leads to higher diagnostic accuracy for detecting lipid-core plaque • The benefit of MBIR is mainly due to increased sensitivity at high specificities • Semiautomated CCTA assessment can detect vulnerable plaques non-invasively.
authors.affiliation: Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
authors.forename: Stefan B
authors.initials: SB
authors.lastname: Puchner
authors.forename: Maros
authors.initials: M
authors.lastname: Ferencik
authors.forename: Pal
authors.initials: P
authors.lastname: Maurovich-Horvat
authors.forename: Masataka
authors.initials: M
authors.lastname: Nakano
authors.forename: Fumiyuki
authors.initials: F
authors.lastname: Otsuka
authors.forename: Hans-Ulrich
authors.initials: HU
authors.lastname: Kauczor
authors.forename: Renu
authors.initials: R
authors.lastname: Virmani
authors.forename: Udo
authors.initials: U
authors.lastname: Hoffmann
authors.forename: Christopher L
authors.initials: CL
authors.lastname: Schlett
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000175:diagnosis*; D003331:Coronary Vessels / Q000473:pathology*; D006801:Humans; D008055:Lipids; D008875:Middle Aged; D058226:Plaque, Atherosclerotic / Q000175:diagnosis*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*
chemical_list: D008055:Lipids