Half-dose Coronary Artery Calcium Scoring: Impact of Iterative Reconstruction

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

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.

Intravenous Contrast Material for Cardiac Computed Tomography: Results From the Open-label Multicenter, Multivendor Italian Registry of Contrast Material Use in Cardiac Computed Tomography

PURPOSE

The Italian Registry of Contrast Material use in Cardiac Computed Tomography (iRCM-CCT) is a multicenter, multivendor, observational study on the use of contrast media (CM) in patients undergoing cardiac computed tomography (CCT). The aim of iRCM-CCT is to assess image quality and safety profile of intravenous CM compounds.

MATERIALS AND METHODS

iRCM-CCT enrolled 1842 consecutive patients undergoing CCT (≥50 per site) at 20 cluster sites with the indication of suspected coronary artery disease. Demographic characteristics, CCT, and CM protocols, clinical indications, safety markers, radiation dose reports, qualitative (ie, poor vascular enhancement) and quantitative (ie, HU attenuation values) image parameters were recorded. A centralized coordinating center collected and assessed all image parameters.

RESULTS

The cohort included 891 men and 951 women (age: 63±14 y, body mass index: 26±4 kg/m2) studied with ≥64 detector rows computed tomography scanners and different iodinated intravenous CM protocols and compounds (iodixanol, iopamidol, iohexol, iobitridol, iopromide, and iomeprol). The following vascular attenuation was reported: 504±147 HU in the aorta, 451±146 HU in the right coronary artery, 474±146 HU in the left main, 451±146 HU in the left anterior descending artery, and 441±149 HU in the circumflex artery. In 4% of cases the image quality was not satisfactory due to poor enhancement. The following adverse reactions to CM were recorded: 6 (0.3%) extravasations and 17 (0.9%) reactions (11 mild, 4 moderate, 2 severe).

CONCLUSIONS

In a multicenter registry on CM use during CCT the prevalence of CM-related adverse reactions was very low. The appropriate use of CM is a major determinant of image quality.

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

ABSTRACT

Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.

Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography

PURPOSE

To perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V).

MATERIAL AND METHODS

Fifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient.

RESULTS

DLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021). DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P ≥ 0.281), while achieved the highest subjective image quality (4, IQR: 4-4; P ≤ 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001).

CONCLUSION

DLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD.

Coronary calcium scoring using virtual non-contrast reconstructions on a dual-layer spectral CT system: Feasibility in the clinical practice

PURPOSE

To evaluate the clinical applicability of a prototype virtual non-contrast (VNC) reconstruction algorithm based on coronary CT angiography (cCTA) to assess calcified coronary plaques by calcium scoring (CACS).

METHODS

Eighty consecutive patients suspected of coronary artery disease were retrospectively included. All patients underwent a cardiac CT using a dual-layer spectral-detector CT system. The standardized acquisition protocol included unenhanced CACS and cCTA. Datasets were acquired using 120 keV. VNC-reconstructions were calculated from the cCTA images at 2.5 mm (VNC group 1), 2.5 of 0.9 mm (group 2), and 0.9 mm (group 3) slice thickness. We compared the Agatston score and Coronary Artery Calcium Data and Reporting System (CAC-DRS) of all VNC reconstructions with the true non-contrast (TNC)-dataset as the gold standard.

RESULTS

In total, 73 patients were evaluated. Fifty patients (68.5 %) had a CACS > 0 based on TNC. We found a significant difference in the Agatston score comparing all VNC-reconstructions (1: 1.35, 2: 3.7, 3: 10.4) with the TNC dataset (3.8) (p < 0.001). Correlation analysis of the datasets showed an excellent correlation of the TNC results with the different VNC-reconstructions (r = 0.904-0.974, p < 0.001) with a slope of 1.89-2.53. Mean differences and limits of agreement by Bland-Altman analysis between TNC and group 1 were 83 and -196 to 362, respectively. By using the VNC-reconstructions, in group 1 23 patients (31.5 %), in group 2 10 (13.7 %), and in group 3 23 (31.5 %) were reclassified according to CAC-DRS compared to TNC. Classification according to CAC-DRS revealed a significant difference between TNC and group 1 (p = 0.024) and no significance compared to groups 2 and 3 (p = 0.670 and 0.273).

CONCLUSION

The investigated VNC reconstruction algorithm of routine cCTA allows the detection and evaluation of coronary calcium burden without the requirement for an additional acquisition of an unenhanced CT scan for CACS and, therefore, a reduction of radiation exposure.

Pitfalls and pearls in the imaging of cardiac ischemia

Coronary artery disease is the most common cause of cardiac ischemia and a leading cause of death globally. There are multiple imaging modalities which can assess cardiac ischemia, in particular coronary CT calcium score, coronary CT angiography, and cardiac MRI. Each of these modalities offers insight into the overall patient picture. However, coronary CT and cardiac MRI are not free from limitations. This article will review the roles of CT and MRI in cardiac imaging, mimics, technical limitations, and potential pitfalls that may be encountered.

Coronary Artery Calcium Scoring: Toward a New Standard

OBJECTIVES

Although the Agatston score is a commonly used quantification method, rescan reproducibility is suboptimal, and different CT scanners result in different scores. In 2007, McCollough et al (Radiology 2007;243:527-538) proposed a standard for coronary artery calcium quantification. Advancements in CT technology over the last decade, however, allow for improved acquisition and reconstruction methods. This study aims to investigate the feasibility of a reproducible reduced dose alternative of the standardized approach for coronary artery calcium quantification on state-of-the-art CT systems from 4 major vendors.

MATERIALS AND METHODS

An anthropomorphic phantom containing 9 calcifications and 2 extension rings were used. Images were acquired with 4 state-of-the-art CT systems using routine protocols and a variety of tube voltages (80-120 kV), tube currents (100% to 25% dose levels), slice thicknesses (3/2.5 and 1/1.25 mm), and reconstruction techniques (filtered back projection and iterative reconstruction). Every protocol was scanned 5 times after repositioning the phantom to assess reproducibility. Calcifications were quantified as Agatston scores.

RESULTS

Reducing tube voltage to 100 kV, dose to 75%, and slice thickness to 1 or 1.25 mm combined with higher iterative reconstruction levels resulted in an on average 36% lower intrascanner variability (interquartile range) compared with the standard 120 kV protocol. Interscanner variability per phantom size decreased by 34% on average. With the standard protocol, on average, 6.2 ± 0.4 calcifications were detected, whereas 7.0 ± 0.4 were detected with the proposed protocol. Pairwise comparisons of Agatston scores between scanners within the same phantom size demonstrated 3 significantly different comparisons at the standard protocol (P < 0.05), whereas no significantly different comparisons arose at the proposed protocol (P > 0.05).

CONCLUSIONS

On state-of-the-art CT systems of 4 different vendors, a 25% reduced dose, thin-slice calcium scoring protocol led to improved intrascanner and interscanner reproducibility and increased detectability of small and low-density calcifications in this phantom. The protocol should be extensively validated before clinical use, but it could potentially improve clinical interscanner/interinstitutional reproducibility and enable more consistent risk assessment and treatment strategies.

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

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.

Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography

PURPOSE

The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data.

MATERIALS AND METHODS

Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated.

RESULTS

CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined.

CONCLUSION

The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA.

Tube Voltage, DNA Double-Strand Breaks, and Image Quality in Coronary CT Angiography

OBJECTIVE

To evaluate the effects of tube voltage on image quality in coronary CT angiography (CCTA), the estimated radiation dose, and DNA double-strand breaks (DSBs) in peripheral blood lymphocytes to optimize the use of CCTA in the era of low radiation doses.

MATERIALS AND METHODS

This study included 240 patients who were divided into 2 groups according to the DNA DSB analysis methods, i.e., immunofluorescence microscopy and flow cytometry. Each group was subdivided into 4 subgroups: those receiving CCTA only with different tube voltages of 120, 100, 80, or 70 kVp. Objective and subjective image quality was evaluated by analysis of variance. Radiation dosages were also recorded and compared.

RESULTS

There was no significant difference in demographic characteristics between the 2 groups and 4 subgroups in each group (all p > 0.05). As tube voltage decreased, both image quality and radiation dose decreased gradually and significantly. After CCTA, γ-H2AX foci and mean fluorescence intensity in the 120-, 100-, 80-, and 70-kVp groups increased by 0.14, 0.09, 0.07, and 0.06 foci per cell and 21.26, 9.13, 8.10, and 7.13 (all p < 0.05), respectively. The increase in the DNA DSB level in the 120-kVp group was higher than those in the other 3 groups (all p < 0.05), while there was no significant difference in the DSBs levels among these latter groups (all p > 0.05).

CONCLUSION

The 100-kVp tube voltage may be optimal for CCTA when weighing DNA DSBs against the estimated radiation dose and image quality, with further reductions in tube voltage being unnecessary for CCTA.

Evaluation of a Tube Voltage-Tailored Contrast Medium Injection Protocol for Coronary CT Angiography: Results From the Prospective VOLCANIC Study

OBJECTIVE. The purpose of this study was to prospectively evaluate, using software support, the feasibility and the quantitative and qualitative image quality parameters of a tube voltage-tailored contrast medium (CM) application protocol for patient-specific injection during coronary CT angiography (CCTA). SUBJECTS AND METHODS. In the Voltage-Based Contrast Media Adaptation in Coronary Computed Tomography Angiography (VOLCANIC-CTA) study, a single-center trial, 120 patients referred for CCTA were prospectively assigned to a tube voltage-tailored CM injection protocol. Automated tube voltage levels were selected in 10-kV intervals and ranged from 70 to 130 kV, and the iodine delivery rate (IDR) was adapted to the tube voltage level using dedicated software. The administered CM volume (370 mg I/mL) ranged from 33 mL at 70 kV (IDR, 0.7 g I/s) to 65 mL at 130 kV (IDR, 1.7 g I/s). Attenuation was measured in the aorta and coronary arteries to calculate quantitative signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and 5-point scales were used to evaluate overall image quality. Radiation metrics were also assessed and compared among the protocols. RESULTS. The mean age of the study patients was 62.5 ± 11.9 (SD) years. Image quality was rated as diagnostic in all patients. Contrast attenuation peaked at 70 kV (p < 0.001), whereas SNR and CNR parameters showed no significant differences between tube voltage levels (p ≥ 0.085). Additionally, no significant differences in subjective image quality parameters were found among the different protocols (p ≥ 0.139). The lowest radiation dose values were observed in the group assigned to the 70-kV protocol, which had a median radiation effective dose of 2.0 mSv (p < 0.001). CONCLUSION. The proposed tube voltage-tailored injection protocol allows individualized scanning of patients undergoing CCTA and significantly reduces CM and radiation dose while maintaining a high diagnostic image quality.

Sinogram-Affirmed Iterative Reconstruction Negatively Impacts the Risk Category Based on Agatston Score: A Study Combining Coronary Calcium Score Measurement and Coronary CT Angiography

Purpose

To assess the impact of sinogram-affirmed iterative reconstruction (SAFIRE) on risk category for coronary artery disease by combining coronary calcium score measurement and coronary CT angiography (CCTA).

Materials and Methods

Eighty-nine patients (64.0% male) older than 18 years (64.4 ± 10.3 years) underwent coronary artery calcium scanning and prospectively ECG-triggered sequential CCTA examination. All raw data acquired in coronary artery calcium scanning were reconstructed by both filtered back projection (FBP) and SAFIRE algorithms with 5 different levels. Objective image quality and calcium quantification were evaluated and compared between FBP and all SAFIRE levels by the Sphericity Assumed test or Greenhouse-Geisser ε correction coefficient. Coronary artery stenosis was assessed in CCTA. Risk categories of all patients and of the patients with coronary artery stenosis in CCTA were compared between FBP and all SAFIRE levels by the Friedman test.

Results

The reconstruction protocol from traditional FBP to SAFIRE 5 was associated with a gradual reduction in CT value and image noise (P < 0.001) but associated with a gradual improvement in the signal-to-noise ratio (P < 0.001). There was a gradual reduction in coronary calcification quantification (Agatston score: from 73.5 in FBP to 38.1 in SAFIRE 5, P < 0.001) from traditional FBP to SAFIRE 5. There was a significant difference for the risk category between FBP and all levels of SAFIRE in all patients (from 3.5 in FBP to 3.2 in SAFIRE 5, P < 0.001) and in the patients with coronary artery stenosis in CCTA (from 4.0 in FBP to 3.6 in SAFIRE 5, P < 0.001).

Conclusions

SAFIRE significantly reduces coronary calcification quantification compared to FBP, resulting in the reduction of risk categories based on the Agatston score. The risk categories of the patients with coronary artery stenosis in CCTA may also decline. Thus, SAFIRE may lead risk categories to underestimate the existence of significant coronary artery stenosis.

Machine Learning and Coronary Artery Calcium Scoring

PURPOSE OF REVIEW

To summarize current artificial intelligence (AI)-based applications for coronary artery calcium scoring (CACS) and their potential clinical impact.

RECENT FINDINGS

Recent evolution of AI-based technologies in medical imaging has accelerated progress in CACS performed in diverse types of CT examinations, providing promising results for future clinical application in this field. CACS plays a key role in risk stratification of coronary artery disease (CAD) and patient management. Recent emergence of AI algorithms, particularly deep learning (DL)-based applications, have provided considerable progress in CACS. Many investigations have focused on the clinical role of DL models in CACS and showed excellent agreement between those algorithms and manual scoring, not only in dedicated coronary calcium CT but also in coronary CT angiography (CCTA), low-dose chest CT, and standard chest CT. Therefore, the potential of AI-based CACS may become more influential in the future.

Individualized coronary calcium scoring at any tube voltage using a kV-independent reconstruction algorithm

PURPOSE

We prospectively investigate the feasibility of a patient specific automated tube voltage selection (ATVS)-based coronary artery calcium scoring (CACS) protocol, using a kV-independent reconstruction algorithm, to achieve significant dose reductions while maintaining the overall cardiac risk classification.

METHODS

Forty-three patients (mean age, 61.8 ± 9.0 years; 40% male) underwent a clinically indicated CACS scan at 120kVp, as well as an additional CACS acquisition using an individualized tube voltage between 70 and 130kVp based on the ATVS selection (CARE-kV). Datasets of the additional CACS scans were reconstructed using a kV-independent algorithm that allows for calcium scoring without changing the weighting threshold of 130HU, regardless of the tube voltage chosen for image acquisition. Agatston scores and radiation dose derived from the different ATVS-based CACS studies were compared to the standard acquisition at 120kVp.

RESULTS

Thirteen patients displayed a score of 0 and were correctly identified with the ATVS protocol. Agatston scores derived from the standard 120kVp (median, 33.4; IQR, 0-289.7) and the patient-tailored kV-independent protocol (median, 47.5; IQR, 0-287.5) showed no significant differences (p = 0.094). The intra-class correlation for Agatston scores derived from the two different protocols was excellent (ICC = 0.99). The mean dose-length-product was 29.8 ± 11.9 mGy × cm using the ATVS protocol and 31.7 ± 11.4 mGy × cm using the standard 120kVp protocol (p < 0.001). Additionally, 95% of patients were classified into the same risk category (0, 1-10, 11-100, 101-400, or > 400) using the patient-tailored protocol.

CONCLUSIONS

ATVS-based CACS, using a kV-independent algorithm, allows for high accuracy compared to the standard 120kVp scanning, while significantly reducing radiation dose parameters.

KEY POINTS

• ATVS allows for CT scanning with reduced radiation dose values. • KV-independent CACS is feasible at any tube voltage between 70 and 130 kVp. • ATVS applied to kV-independent CACS can significantly reduce the radiation dose.