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Increasing the Rate of Datasets Amenable to CT and Quantitative Plaque Analysis: Value of Software for Reducing Stair-step Artifacts Demonstrated in Photon-counting Detector CT

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Specialty Radiology
Date 2024 Jun 17
PMID 38882632
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Abstract

Purpose: To determine the value of an algorithm for reducing stair-step artifacts for advanced coronary analyses in sequential mode coronary CT angiography (CCTA).

Methods: Forty patients undergoing sequential mode photon-counting detector CCTA with at least one stair-step artifact were included. Twenty patients (14 males; mean age 57±17years) with 45 segments showing stair-step artifacts and without atherosclerosis were included for CT analysis. Twenty patients (20 males; mean age 74±13years) with 22 segments showing stair-step artifacts crossing an atherosclerotic plaque were included for quantitative plaque analysis. Artifacts were graded, and CT and quantitative coronary plaque analyses were performed in standard reconstructions and in those reconstructed with a software (entitled ) for artifact reduction.

Results: Stair-step artifacts were significantly reduced in compared to standard reconstructions (p<0.05). In standard reconstructions, CT was not feasible in 3/45 (7 %) segments but was feasible in all reconstructions. In 9/45 (20 %) segments without atherosclerosis, the algorithm led to a change of CT values from pathologic in standard to physiologic values in reconstructions. In one segment (1/22, 5 %), quantitative plaque analysis was not feasible in standard but only in reconstruction. The mean overall plaque volume (111±60 mm), the calcific (77±47 mm), fibrotic (31±28 mm), and lipidic (4±3 mm) plaque components were higher in standard than in reconstructions (overall 75±50 mm, p<0.001; calcific 51±42 mm, p<0.001; fibrotic 22±19 mm, p<0.05; lipidic 3±3 mm, p=0.055).

Conclusion: Despite the lack of reference standard modalities for CT and coronary plaque analysis, initial evidence indicates that an algorithm for reducing stair-step artifacts in sequential mode CCTA increases the rate and quality of datasets amenable to advanced coronary artery analysis, hereby potentially improving patient management.

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