Half-dose Coronary Artery Calcium Scoring

Santis, Domenico De; Biondi, Tommaso; Panvini, Nicola; Zerunian, Marta; Rivosecchi, Flaminia; Montesano, Marta; Bellini, Davide; Rengo, Marco; Laghi, Andrea

doi:10.1097/rti.0000000000000340

Cited by 18 publications

(11 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, the mean BMI of our population was small. Increased image noise in obese patients influences the quantification of the Agatston score and leads to reclassification in the risk category (41). Finally, this is a single-center study and findings are limited to one CT vendor.…”

Section: Discussionmentioning

confidence: 99%

Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques

Otgonbaatar

Jeon

Ryu³

et al. 2023

Acta Radiol

View full text Add to dashboard Cite

Background The reference protocol for the quantification of coronary artery calcium (CAC) should be updated to meet the standards of modern imaging techniques. Purpose To assess the influence of filtered-back projection (FBP), hybrid iterative reconstruction (IR), and three levels of deep learning reconstruction (DLR) on CAC quantification on both in vitro and in vivo studies. Material and Methods In vitro study was performed with a multipurpose anthropomorphic chest phantom and small pieces of bones. The real volume of each piece was measured using the water displacement method. In the in vivo study, 100 patients (84 men; mean age = 71.2 ± 8.7 years) underwent CAC scoring with a tube voltage of 120 kVp and image thickness of 3 mm. The image reconstruction was done with FBP, hybrid IR, and three levels of DLR including mild (DLRmild), standard (DLRstd), and strong (DLRstr). Results In the in vitro study, the calcium volume was equivalent ( P = 0.949) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. In the in vivo study, the image noise was significantly lower in images that used DLRstr-based reconstruction, when compared images other reconstructions ( P < 0.001). There were no significant differences in the calcium volume ( P = 0.987) and Agatston score ( P = 0.991) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. The highest overall agreement of Agatston scores was found in the DLR groups (98%) and hybrid IR (95%) when compared to standard FBP reconstruction. Conclusion The DLRstr presented the lowest bias of agreement in the Agatston scores and is recommended for the accurate quantification of CAC.

show abstract

Section: Discussionmentioning

confidence: 99%

Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques

Otgonbaatar

Jeon

Ryu³

et al. 2023

Acta Radiol

View full text Add to dashboard Cite

show abstract

“…Caruso and colleagues 57 analyzed coronary artery calcium scoring protocols at SD (120 kVp, 170 mAs) reconstructed by FBP and at half dose (120 kVp, 85 mAs) reconstructed by ASiR at various strength levels. Results demonstrated that ASiR-70% showed higher SNR and CNR compared with FBP and that the half-dose protocol provided 52% less radiation dose without a significant impact on subjective analysis.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Iterative Reconstruction: State-of-the-Art and Future Perspectives

Guido

Polici²,

Nacci³

et al. 2022

J Comput Assist Tomogr

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nowadays, the IR algorithm has been introduced by all different vendors in CT scanners because it has the advantages of reducing artefacts and noise scanning at lower radiation dose [13,14]. These algorithms represent an extraordinary method to reduce radiation dose and preserving image quality, in particular for oncologic patients and cardiovascular imaging [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Influence of Adaptive Statistical Iterative Reconstructions on CT Radiomic Features in Oncologic Patients

et al. 2021

Self Cite

View full text Add to dashboard Cite

Iterative reconstructions (IR) might alter radiomic features extraction. We aim to evaluate the influence of Adaptive Statistical Iterative Reconstruction-V (ASIR-V) on CT radiomic features. Patients who underwent unenhanced abdominal CT (Revolution Evo, GE Healthcare, USA) were retrospectively enrolled. Raw data of filtered-back projection (FBP) were reconstructed with 10 levels of ASIR-V (10–100%). CT texture analysis (CTTA) of liver, kidney, spleen and paravertebral muscle for all datasets was performed. Six radiomic features (mean intensity, standard deviation (SD), entropy, mean of positive pixel (MPP), skewness, kurtosis) were extracted and compared between FBP and all ASIR-V levels, with and without altering the spatial scale filter (SSF). CTTA of all organs revealed significant differences between FBP and all ASIR-V reconstructions for mean intensity, SD, entropy and MPP (all p < 0.0001), while no significant differences were observed for skewness and kurtosis between FBP and all ASIR-V reconstructions (all p > 0.05). A per-filter analysis was also performed comparing FBP with all ASIR-V reconstructions for all six SSF separately (SSF0-SSF6). Results showed significant differences between FBP and all ASIR-V reconstruction levels for mean intensity, SD, and MPP (all filters p < 0.0315). Skewness and kurtosis showed no differences for all comparisons performed (all p > 0.05). The application of incremental ASIR-V levels affects CTTA across various filters. Skewness and kurtosis are not affected by IR and may be reliable quantitative parameters for radiomic analysis.

show abstract

Half-dose Coronary Artery Calcium Scoring

Cited by 18 publications

References 38 publications

Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques

Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques

Iterative Reconstruction: State-of-the-Art and Future Perspectives

Influence of Adaptive Statistical Iterative Reconstructions on CT Radiomic Features in Oncologic Patients

Contact Info

Product

Resources

About