A model for quantitative correction of coronary calcium scores on multidetector, dual source, and electron beam computed tomography for influences of linear motion, calcification density, and temporal resolution: a cardiac phantom study

Impact of Cardiac Motion on coronary artery calcium scoring using a virtual non-iodine algorithm on photon-counting detector CT: a dynamic phantom study

This study assessed the impact of cardiac motion and in-vessel attenuation on coronary artery calcium (CAC) scoring using virtual non-iodine (VNI) against virtual non-contrast (VNC) reconstructions on photon-counting detector CT. Two artificial vessels containing calcifications and different in-vessel attenuations (500, 800HU) were scanned without (static) and with cardiac motion (60, 80, 100 beats per minute [bpm]). Images were post-processed using a VNC and VNI algorithm at 70 keV and quantum iterative reconstruction (QIR) strength 2. Calcium mass, Agatston scores, cardiac motion susceptibility (CMS)-indices were compared to physical mass, static scores as well as between reconstructions, heart rates and in-vessel attenuations. VNI scores decreased with rising heart rate (p < 0.01) and showed less underestimation than VNC scores (p < 0.001). Only VNI scores were similar to the physical mass at static measurements, and to static scores at 60 bpm. Agatston scores using VNI were similar to static scores at 60 and 80 bpm. Standard deviation of CMS-indices was lower for VNI-based than for VNC-based CAC scoring. VNI scores were higher at 500 than 800HU (p < 0.001) and higher than VNC scores (p < 0.001) with VNI scores at 500 HU showing the lowest deviation from the physical reference. VNI-based CAC quantification is influenced by cardiac motion and in-vessel attenuation, but least when measuring Agatston scores, where it outperforms VNC-based CAC scoring.

Coronary Computed Tomography Angiography-Based Calcium Scoring: In Vitro and In Vivo Validation of a Novel Virtual Noniodine Reconstruction Algorithm on a Clinical, First-Generation Dual-Source Photon Counting-Detector System

PURPOSE

The aim of this study was to evaluate coronary computed tomography angiography (CCTA)-based in vitro and in vivo coronary artery calcium scoring (CACS) using a novel virtual noniodine reconstruction (PureCalcium) on a clinical first-generation photon-counting detector-computed tomography system compared with virtual noncontrast (VNC) reconstructions and true noncontrast (TNC) acquisitions.

MATERIALS AND METHODS

Although CACS and CCTA are well-established techniques for the assessment of coronary artery disease, they are complementary acquisitions, translating into increased scan time and patient radiation dose. Hence, accurate CACS derived from a single CCTA acquisition would be highly desirable. In this study, CACS based on PureCalcium, VNC, and TNC, reconstructions was evaluated in a CACS phantom and in 67 patients (70 [59/80] years, 58.2% male) undergoing CCTA on a first-generation photon counting detector-computed tomography system. Coronary artery calcium scores were quantified for the 3 reconstructions and compared using Wilcoxon test. Agreement was evaluated by Pearson and Spearman correlation and Bland-Altman analysis. Classification of coronary artery calcium score categories (0, 1-10, 11-100, 101-400, and >400) was compared using Cohen κ.

RESULTS

Phantom studies demonstrated strong agreement between CACSPureCalcium and CACSTNC (60.7 ± 90.6 vs 67.3 ± 88.3, P = 0.01, r = 0.98, intraclass correlation [ICC] = 0.98; mean bias, 6.6; limits of agreement [LoA], -39.8/26.6), whereas CACSVNC showed a significant underestimation (42.4 ± 75.3 vs 67.3 ± 88.3, P < 0.001, r = 0.94, ICC = 0.89; mean bias, 24.9; LoA, -87.1/37.2). In vivo comparison confirmed a high correlation but revealed an underestimation of CACSPureCalcium (169.3 [0.7/969.4] vs 232.2 [26.5/1112.2], P < 0.001, r = 0.97, ICC = 0.98; mean bias, -113.5; LoA, -470.2/243.2). In comparison, CACSVNC showed a similarly high correlation, but a substantially larger underestimation (24.3 [0/272.3] vs 232.2 [26.5/1112.2], P < 0.001, r = 0.97, ICC = 0.54; mean bias, -551.6; LoA, -2037.5/934.4). CACSPureCalcium showed superior agreement of CACS classification (κ = 0.88) than CACSVNC (κ = 0.60).

CONCLUSIONS

The accuracy of CACS quantification and classification based on PureCalcium reconstructions of CCTA outperforms CACS derived from VNC reconstructions.

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.

Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors

BACKGROUND

Motion artifacts affect the images of coronary calcified plaques. This study utilized convolutional neural networks (CNNs) to classify the motion-contaminated images of moving coronary calcified plaques and to determine the influential factors for the classification performance.

METHODS

Two artificial coronary arteries containing four artificial plaques of different densities were placed on a robotic arm in an anthropomorphic thorax phantom. Each artery moved linearly at velocities ranging from 0 to 60 mm/s. CT examinations were performed with four state-of-the-art CT systems. All images were reconstructed with filtered back projection and at least three levels of iterative reconstruction. Each examination was performed at 100%, 80% and 40% radiation dose. Three deep CNN architectures were used for training the classification models. A five-fold cross-validation procedure was applied to validate the models.

RESULTS

The accuracy of the CNN classification was 90.2 ± 3.1%, 90.6 ± 3.5%, and 90.1 ± 3.2% for the artificial plaques using Inception v3, ResNet101 and DenseNet201 CNN architectures, respectively. In the multivariate analysis, higher density and increasing velocity were significantly associated with higher classification accuracy (all P < 0.001). The classification accuracy in all three CNN architectures was not affected by CT system, radiation dose or image reconstruction method (all P > 0.05).

CONCLUSIONS

The CNN achieved a high accuracy of 90% when classifying the motion-contaminated images into the actual category, regardless of different vendors, velocities, radiation doses, and reconstruction algorithms, which indicates the potential value of using a CNN to correct calcium scores.

Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study

OBJECTIVE

To classify motion-induced blurred images of calcified coronary plaques so as to correct coronary calcium scores on nontriggered chest CT, using a deep convolutional neural network (CNN) trained by images of motion artifacts.

METHODS

Three artificial coronary arteries containing nine calcified plaques of different densities (high, medium, and low) and sizes (large, medium, and small) were attached to a moving robotic arm. The artificial arteries moving at 0-90 mm/s were scanned to generate nine categories (each from one calcified plaque) of images with motion artifacts. An inception v3 CNN was fine-tuned and validated. Agatston scores of the predicted classification by CNN were considered as corrected scores. Variation of Agatston scores on moving plaque and by CNN correction was calculated using the scores at rest as reference.

RESULTS

The overall accuracy of CNN classification was 79.2 ± 6.1% for nine categories. The accuracy was 88.3 ± 4.9%, 75.9 ± 6.4%, and 73.5 ± 5.0% for the high-, medium-, and low-density plaques, respectively. Compared with the Agatston score at rest, the overall median score variation was 37.8% (1st and 3rd quartile, 10.5% and 68.8%) in moving plaques. CNN correction largely decreased the variation to 3.7% (1.9%, 9.1%) (p < 0.001, Mann-Whitney U test) and improved the sensitivity (percentage of non-zero scores among all the scores) from 65 to 85% for detection of coronary calcifications.

CONCLUSIONS

In this experimental study, CNN showed the ability to classify motion-induced blurred images and correct calcium scores derived from nontriggered chest CT. CNN correction largely reduces the overall Agatston score variation and increases the sensitivity to detect calcifications.

KEY POINTS

• A deep CNN architecture trained by CT images of motion artifacts showed the ability to correct coronary calcium scores from blurred images. • A correction algorithm based on deep CNN can be used for a tenfold reduction in Agatston score variations from 38 to 3.7% of moving coronary calcified plaques and to improve the sensitivity from 65 to 85% for the detection of calcifications. • This experimental study provides a method to improve its accuracy for coronary calcium scores that is a fundamental step towards a real clinical scenario.

Influence of heart rate on coronary calcium scores: a multi-manufacturer phantom study

To evaluate the influence of heart rate on coronary calcium scores (CCS) using a dynamic phantom on four high-end computed tomography (CT) systems from different manufacturers. Artificial coronary arteries were moved in an anthropomorphic chest phantom at linear velocities, corresponding to < 60, 60-75 and > 75 beats per minute (bpm). Data was acquired with routinely used clinical protocols for CCS on four high-end CT systems (CT1-CT4). CCS, quantified as Agatston and mass scores were compared to reference scores at < 60 bpm. Influence of heart rate was assessed for each system with the cardiac motion susceptibility (CMS) Index. At increased heart rates (> 75 bpm), Agatston scores of the low mass calcification were similar to the reference score, while Agatston scores of the medium and high mass calcification increased significantly up to 50% for all CT systems. Threefold CMS increases at > 75 bpm in comparison with < 60 bpm were shown. For medium and high mass calcifications, significant differences in CMS between CT systems were found. Heart rate substantially influences CCS for high-end CT systems of four major manufacturers, but CT systems differ in motion susceptibility. Follow-up CCS CT scans should be acquired on the same CT system and protocol, and preferably with comparable heart rates.

Influence of dose reduction and iterative reconstruction on CT calcium scores: a multi-manufacturer dynamic phantom study

To evaluate the influence of dose reduction in combination with iterative reconstruction (IR) on coronary calcium scores (CCS) in a dynamic phantom on state-of-the-art CT systems from different manufacturers. Calcified inserts in an anthropomorphic chest phantom were translated at 20 mm/s corresponding to heart rates between 60 and 75 bpm. The inserts were scanned five times with routinely used CCS protocols at reference dose and 40 and 80% dose reduction on four high-end CT systems. Filtered back projection (FBP) and increasing levels of IR were applied. Noise levels were determined. CCS, quantified as Agatston and mass scores, were compared to physical mass and scores at FBP reference dose. For the reference dose in combination with FBP, noise level variation between CT systems was less than 18%. Decreasing dose almost always resulted in increased CCS, while at increased levels of IR, CCS decreased again. The influence of IR on CCS was smaller than the influence of dose reduction. At reference dose, physical mass was underestimated 3–30%. All CT systems showed similar CCS at 40% dose reduction in combinations with specific reconstructions. For some CT systems CCS was not affected at 80% dose reduction, in combination with IR. This multivendor study showed that radiation dose reductions of 40% did not influence CCS in a dynamic phantom using state-of-the-art CT systems in combination with specific reconstruction settings. Dose reduction resulted in increased noise and consequently increased CCS, whereas increased IR resulted in decreased CCS.

Role of computed tomography screening for detection of coronary artery disease

Coronary artery disease (CAD) is a leading cause of morbidity and mortality in Western populations, and the prediction and prevention of CAD is an inherent challenge facing current health care societies. Computed tomography (CT) has emerged as a noninvasive imaging tool in the field of cardiovascular disease. Notably, CT scanning for detection of coronary artery calcium (CAC) has proven useful in predicting adverse cardiovascular outcomes as well as early identification of CAD. In asymptomatic persons undergoing screening for CAD, CAC is well established as a surrogate of CAD risk and has demonstrated incremental benefit over and above traditional risk prediction tools. In addition, a zero CAC score has shown to reflect a substantially lower risk of CAD and may therefore be considered an important marker of CAD protection. Irrespective of screening in the asymptomatic population, CAC scanning has also displayed a beneficial role in the symptomatic population, specifically as gatekeeper in guiding further treatment decision making. Further still, the combination of alternative CT screening strategies such as CT screening for lung cancer with CAC scanning may hold particular promise as an effective screening approach by lowering overall health costs as well as limiting radiation exposure.

Can nontriggered thoracic CT be used for coronary artery calcium scoring? A phantom study

PURPOSE

Coronary artery calcium score, traditionally based on electrocardiography (ECG)-triggered computed tomography (CT), predicts cardiovascular risk. However, nontriggered CT is extensively utilized. The study-purpose is to evaluate the in vitro agreement in coronary calcium score between nontriggered thoracic CT and ECG-triggered cardiac CT.

METHODS

Three artificial coronary arteries containing calcifications of different densities (high, medium, and low), and sizes (large, medium, and small), were studied in a moving cardiac phantom. Two 64-detector CT systems were used. The phantom moved at 0-90 mm∕s in nontriggered low-dose CT as index test, and at 0-30 mm∕s in ECG-triggered CT as reference. Differences in calcium scores between nontriggered and ECG-triggered CT were analyzed by t-test and 95% confidence interval. The sensitivity to detect calcification was calculated as the percentage of positive calcium scores.

RESULTS

Overall, calcium scores in nontriggered CT were not significantly different to those in ECG-triggered CT (p>0.05). Calcium scores in nontriggered CT were within the 95% confidence interval of calcium scores in ECG-triggered CT, except predominantly at higher velocities (≥50 mm∕s) for the high-density and large-size calcifications. The sensitivity for a nonzero calcium score was 100% for large calcifications, but 46%±11% for small calcifications in nontriggered CT.

CONCLUSIONS

When performing multiple measurements, good agreement in positive calcium scores is found between nontriggered thoracic and ECG-triggered cardiac CT. Agreement decreases with increasing coronary velocity. From this phantom study, it can be concluded that a high calcium score can be detected by nontriggered CT, and thus, that nontriggered CT likely can identify individuals at high risk of cardiovascular disease. On the other hand, a zero calcium score in nontriggered CT does not reliably exclude coronary calcification.

Validation and Prognosis of Coronary Artery Calcium Scoring in Nontriggered Thoracic Computed Tomography

Background—

Coronary calcium score (CS), traditionally based on electrocardiography-triggered computed tomography (CT), predicts cardiovascular risk. Currently, nontriggered thoracic CT is extensively used, such as in lung cancer screening. The purpose of the study was to determine the correlation in CS between nontriggered and electrocardiography-triggered CT, and to evaluate the prognostic performance of the CS derived from nontriggered CT.

Methods and Results—

PubMed, Embase, and Web of Knowledge were searched until November 2012. Two reviewers independently screened 2120 records to identify studies reporting the CS in nontriggered CT and extracted information. Study quality was evaluated by standardized assessment tools. Cohen κ was extracted for agreement of CS categories between nontriggered and electrocardiography-triggered CT (validation). Hazard ratio (HR) was extracted for prognostic performance. Five studies about validation comprising 1316 individuals were included. Five studies about prognosis comprising 34 028 cardiac asymptomatic individuals, mainly from lung cancer screening trials, were included. All studies were of high quality. Meta-analysis could only be performed for validation studies because studies on prognostic performance were highly heterogeneous. Pooled Cohen κ for agreement between the 2 techniques was 0.89 (95% confidence interval, 0.83–0.95) for increasing CS categories. Increasing CS categories were associated with increasing risk of cardiovascular death or events. Nontriggered CT yielded false-negative CS in 8.8% of individuals and underestimated high CS in 19.1% of individuals.

Conclusions—

Our analysis shows the prognostic value and potential role of nontriggered assessment of coronary calcium, but it does not suggest that electrocardiography-triggered CT should be replaced by nontriggered examinations.

A phantom study of the effect of heart rate, coronary artery displacement and vessel trajectory on coronary artery calcium score: potential for risk misclassification

BACKGROUND

Accurate coronary artery calcium scoring improves risk stratification in some strata of the population.

OBJECTIVE

We evaluated individual and combined effects of reader experience, heart rate, vessel displacement, and trajectory on computed tomography (CT) Agatston score, calcium volume, and calcium mass in a cardiac phantom model.

METHODS

A cardiac motion phantom was scanned with a 64-slice CT scanner with artificial electrocardiogram gating with combinations of the following: heart rates 60, 80, and 100 beat/min; vessel displacement of 1.25 and 2.5 cm; and multiple vessel trajectories of craniocaudal, right-left, anteroposterior, right coronary artery (RCA), left anterior descending, and left circumflex (LCX). Calcium quantification was done by 2 different readers with the use of 3 methods: Agatston, calcium volume, and calcium mass.

RESULTS

Heart rate, coronary displacement, and trajectory had significant effects on all 3 techniques, with a general decrease in score as the heart rate increased. A vessel displacement of 2.5 cm decreased the Agatston score by 16% (P < 0.0001) and LCX motion decreased the score by 17% (P < 0.0001). Combined effects often resulted in larger differences; for example, a heart rate of 60 beat/min, vessel displacement of 1.25 cm, and RCA motion resulted in an Agatston score of 907, whereas with a heart rate of 100 beat/min, vessel displacement of 2.5 cm, and LCX motion the score was 604.

CONCLUSION

The calcium score is affected by heart rate, vessel displacement, and trajectory.

Comparison of calcium scoring with 4-multidetector computed tomography (4-MDCT) and 64-MDCT: a phantom study

OBJECTIVE

To determine differences in coronary artery calcium (CAC) measurement performed with the use of 2 generations of multidetector computed tomography (CT) scanners of the same manufacturer.

METHODS

Agatston Score (AS) and calcium mass (CM) were measured with a 4-row scanner (AS4 and CM4) and a 64-row scanner (AS64 and CM64) using a cardiac phantom with calcium inserts.

RESULTS

The results of the AS measurements (mean ± SD) varied significantly between the equipment: 880.6 ± 30.1 (AS4) vs 586.5 ± 24.0 (AS64; P < 0.0001). The AS interscanner variability was 31.6% for the phantom and from 25.5% to 110.1% for particular inserts. Mean ± SD CM values were different as well: 192.8 ± 5.0 mg (CM4) vs 152.4 ± 2.6 mg (CM64; P < 0.0001). Determination of CM with 64-row CT was more accurate than that with an older scanner; the mean relative error was -9.1% and 15.0%, respectively (P < 0.0001). The CM interscanner variability was 23.3% for the phantom and from 19.0% to 122.8% for particular inserts. The interexamination variability ranged from 1.7% (CM64) to 5.6% (AS4).

CONCLUSIONS

Coronary artery calcium scoring with the 64-row CT scanner is more accurate than with the 4-row device The difference between the results of AS and CM measurements carried out with both scanners is statistically significant.

Computerized method for evaluating diagnostic image quality of calcified plaque images in cardiac CT: validation on a physical dynamic cardiac phantom

PURPOSE

In cardiac computed tomography (CT), important clinical indices, such as the coronary calcium score and the percentage of coronary artery stenosis, are often adversely affected by motion artifacts. As a result, the expert observer must decide whether or not to use these indices during image interpretation. Computerized methods potentially can be used to assist in these decisions. In a previous study, an artificial neural network (ANN) regression model provided assessability (image quality) indices of calcified plaque images from the software NCAT phantom that were highly agreeable with those provided by expert observers. The method predicted assessability indices based on computer-extracted features of the plaque. In the current study, the ANN-predicted assessability indices were used to identify calcified plaque images with diagnostic calcium scores (based on mass) from a physical dynamic cardiac phantom. The basic assumption was that better quality images were associated with more accurate calcium scores.

METHODS

A 64-channel CT scanner was used to obtain 500 calcified plaque images from a physical dynamic cardiac phantom at different heart rates, cardiac phases, and plaque locations. Two expert observers independently provided separate sets of assessability indices for each of these images. Separate sets of ANN-predicted assessability indices tailored to each observer were then generated within the framework of a bootstrap resampling scheme. For each resampling iteration, the absolute calcium score error between the calcium scores of the motion-contaminated plaque image and its corresponding stationary image served as the ground truth in terms of indicating images with diagnostic calcium scores. The performances of the ANN-predicted and observer-assigned indices in identifying images with diagnostic calcium scores were then evaluated using ROC analysis.

RESULTS

Assessability indices provided by the first observer and the corresponding ANN performed similarly (AUC(OBS1) = 0.80 [0.73, 0.86] vs AUC(ANN1) = 0.88 [0.82, 0.92]) as that of the second observer and the corresponding ANN (AUC(OBS2) = 0.87 [0.83,0.91] vs. AUC(ANN2) = 0.90 [0.85, 0.94]). Moreover, the ANN-predicted indices were generated in a fraction of the time required to obtain the observer-assigned indices.

CONCLUSIONS

ANN-predicted assessability indices performed similar to observer-assigned assessability indices in identifying images with diagnostic calcium scores from the physical dynamic cardiac phantom. The results of this study demonstrate the potential of using computerized methods for identifying images with diagnostic clinical indices in cardiac CT images.