Pericoronary adipose tissue attenuation measured by fat-attenuation-index varied significantly depending on CT reconstruction kernels and iterative reconstruction strength levels (p < 0.001).
Observational (n=20)
No
Do kernels and iterative reconstructions influence pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography?
Pericoronary adipose tissue attenuation measured by CCTA is highly dependent on reconstruction kernels and iterative reconstruction levels, necessitating standardization for meaningful interpretation.
p-value: p=<0.001
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.
Lisi et al. (Fri,) conducted a observational in Low risk of coronary artery disease (n=20). CT reconstruction kernels (Qr36, Bv36, Bv44, Bv56) and quantum iterative reconstruction (QIR) levels vs. Intra-individual comparison across different reconstructions was evaluated on Fat-attenuation-index (FAI) of the pericoronary adipose tissue (p=<0.001). Pericoronary adipose tissue attenuation measured by fat-attenuation-index varied significantly depending on CT reconstruction kernels and iterative reconstruction strength levels (p < 0.001).