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. 2025 May;35(5):2866-2876.
doi: 10.1007/s00330-024-11132-5. Epub 2024 Oct 18.

The pericoronary adipose tissue attenuation in CT strongly depends on kernels and iterative reconstructions

Costanza Lisi #  1   2   3 , Konstantin Klambauer #  1 , Lukas J Moser  1 , Victor Mergen  1 , Robert Manka  1 , Thomas Flohr  1 , Matthias Eberhard  1 , Hatem Alkadhi  4
Affiliations

The pericoronary adipose tissue attenuation in CT strongly depends on kernels and iterative reconstructions

Costanza Lisi et al. Eur Radiol. 2025 May.

Abstract

Objectives: To investigate the influence of kernels and iterative reconstructions on pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA).

Materials and methods: Twenty otherwise healthy subjects (16 females; median age 52 years) with atypical chest pain, low risk of coronary artery disease (CAD), and without CAD in photon-counting detector CCTA were included. Images were reconstructed with a quantitative smooth (Qr36) and three vascular kernels of increasing sharpness levels (Bv36, Bv44, Bv56). Quantum iterative reconstruction (QIR) was either switched-off (QIRoff) or was used with strength levels 2 and 4. The fat-attenuation-index (FAI) of the PCAT surrounding the right coronary artery was calculated in each dataset. Histograms of FAI measurements were created. Intra- and inter-reader agreements were determined. A CT edge phantom was used to determine the edge spread function (ESF) for the same datasets.

Results: Intra- and inter-reader agreement of FAI was excellent (intra-class correlation coefficient = 0.99 and 0.98, respectively). Significant differences in FAI were observed depending on the kernel and iterative reconstruction strength level (each, p < 0.001), with considerable inter-individual variation up to 34 HU and intra-individual variation up to 33 HU, depending on kernels and iterative reconstruction levels. The ESFs showed a reduced range of edge-smoothing with increasing kernel sharpness, causing an FAI decrease. Histogram analyses revealed a narrower peak of PCAT values with increasing iterative reconstruction levels, causing a FAI increase.

Conclusions: PCAT attenuation determined with CCTA heavily depends on kernels and iterative reconstruction levels both within and across subjects. Standardization of CT reconstruction parameters is mandatory for FAI studies to enable meaningful interpretations.

Key points: Question Do kernels and iterative reconstructions influence pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA)? Findings Significant differences in fat-attenuation-index (FAI) were observed depending on the kernel and iterative reconstruction strength level with considerable inter- and intra-individual variation. Clinical relevance PCAT attenuation heavily depends on kernels and iterative reconstructions requiring CT reconstruction parameter standardization to enable meaningful interpretations of fat-attenuation differences across subjects.

Keywords: Computed tomography angiography; Image reconstruction; Inflammation; Pericoronary adipose tissue.

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Conflict of interest statement

Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Professor Hatem Alkadhi. Conflict of interest: Diagnostic and Interventional Radiology Department of University Hospital Zurich reports a relationship with Bayer, Canon, Guerbet, Siemens that includes: funding grants. Hatem Alkadhi and Matthias Eberhard report a relationship with Siemens Healthineers AG that includes a speaking Honorarium. They are also members of the European Radiology Editorial Board. They have not taken part in the review or selection process of this article. Statistics and biometry: No complex statistical methods were necessary for this paper. Informed consent: Informed consent was obtained from all subjects (patients) in this study. Ethical approval: Institutional Review Board approval was obtained. Study subjects or cohorts overlap: No study subjects or cohorts have been previously reported. Methodology: Retrospective Observational Performed at one institution

Figures

Fig. 1
Fig. 1
Illustration of the semiautomated pericoronary adipose tissue analysis software approach in a 48-year-old male subject undergoing CCTA for atypical chest pain showing no evidence of CAD. Curved reformation of the proximal right coronary artery (RCA) (a), segmentation of proximal 40 mm RCA segment length by proximal (red) and distal markers (green) with vessel wall automatic contouring (light blue lines) (b). Pericoronary adipose tissue definition (c) in curved and short-axis reformats (insert in c) is the tissue within a radial distance from the inner vessel wall equal to the vessel diameter. Pericoronary adipose tissue identification (c) in the attenuation range from −190 HU to −30 HU (blu voxels) with corresponding histogram (d, left side). Quantitative pericoronary fat tissue analysis results (d, right side) indicate a FAI value of 93 HU in this subject and in this individual reconstruction (Bv36, QIRoff)
Fig. 2
Fig. 2
Boxplots demonstrating the FAI (median and standard deviation) by kernel and iterative reconstruction strength level. Note the considerable variability of FAI values both within and between subjects
Fig. 3
Fig. 3
Exemplary histograms of subject no. 10 from the segmented pericoronary adipose tissue volume-of-interest surrounding the proximal right coronary artery (RCA) for the different reconstructions. Gray-shaded areas represent the attenuation interval relevant for FAI from − 190 to −30 HU. Note the different steepness and shift of peaks depending on reconstruction kernel and iterative reconstruction, leading to a different amount of voxels and attenuations included in the FAI analysis
Fig. 4
Fig. 4
Normalized edge-spread function for the quantitative soft tissue kernel Qr36, the soft tissue kernels Bv36 (a), Bv44 (b), and the sharp tissue kernels Bv56 (c) without iterative reconstruction (QIR off, blue line), and with iterative reconstruction at two different strength levels (QIR 2, dotted red line; QIR 4, dashed green line). The phantom was scanned with identical CT parameters as the subjects. The edge-spread function shows how an edge is represented in the CT image (black function). The figure shows how soft kernels, namely Bv36 and Qr36 (a) and QIR off (blue line) smoothen the curve, while sharper kernels, namely Bv44 and Bv56 (b, c) at QIR4 (dashed green line) lead to a steeper curve
See this image and copyright information in PMC

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