Automated selection of the optimal cardiac phase for single-beat coronary CT angiography reconstruction

Eigenbin compression for reducing photon-counting CT data size

BACKGROUND

Photon-counting CT (PCCT) systems acquire multiple spectral measurements at high spatial resolution, providing numerous image quality benefits while also increasing the amount of data that must be transferred through the gantry slip ring.

PURPOSE

This study proposes a lossy method to compress photon-counting CT data using eigenvector analysis, with the goal of providing image quality sufficient for applications that require a rapid initial reconstruction, such as to confirm anatomical coverage, scan quality, and to support automated advanced applications. The eigenbin compression method was experimentally evaluated on a clinical silicon PCCT prototype system.

METHODS

The proposed eigenbin method performs principal component analysis (PCA) on a set of PCCT calibration measurements. PCA finds the orthogonal axes or eigenvectors, which capture the maximum variance in the N dimensional photon-count data space, where N is the number of acquired energy bins. To reduce the dimensionality of the PCCT data, the data are linearly transformed into a lower dimensional space spanned by the M < N eigenvectors with highest eigenvalues (i.e., the vectors that account for most of the information in the data). Only M coefficients are then transferred per measurement, which we term eigenbin values. After transmission, the original N energy-bin measurements are estimated as a linear combination of the M eigenvectors. Two versions of the eigenbin method were investigated: pixel-specific and pixel-general. The pixel-specific eigenbin method determines eigenvectors for each individual detector pixel, while the more practically realizable pixel-general eigenbin method finds one set of eigenvectors for the entire detector array. The eigenbin method was experimentally evaluated by scanning a 20 cm diameter Gammex Multienergy phantom with different material inserts on a clinical silicon-based PCCT prototype. The method was evaluated with the number of eigenbins varied between two and four. In each case, the eigenbins were used to estimate the original 8-bin data, after which material decomposition was performed. The mean, standard deviation, and contrast-to-noise ratio (CNR) of values in the reconstructed basis and virtual monoenergetic images (VMI) were compared for the original 8-bin data and for the eigenbin data.

RESULTS

The pixel-specific eigenbin method reduced photon-counting CT data size by a factor of four with <5% change in mean values and a small noise penalty (mean change in noise of <12%, maximum change in noise of 20% for basis images). The pixel-general eigenbin compression method reduced data size by a factor of 2.67 with <5% change in mean values and a less than 10% noise penalty in the basis images (average noise penalty ≤5%). The noise penalty and errors were less for the VMIs than for the basis images, resulting in <5% change in CNR in the VMIs.

CONCLUSION

The eigenbin compression method reduced photon-counting CT data size by a factor of two to four with less than 5% change in mean values, noise penalty of less than 10%-20%, and change in CNR ranging from 15% decrease to 24% increase. Eigenbin compression reduces the data transfer time and storage space of photon-counting CT data for applications that require rapid initial reconstructions.

Usefulness of second-generation motion correction algorithm in improving delineation and reducing motion artifact of coronary computed tomography angiography

BACKGROUND

The purpose of this study was to investigate the usefulness of second-generation intra-cycle motion correction algorithm (SnapShot Freeze 2, GE Healthcare, MC2) in improving the delineation and interpretability of coronary arteries in coronary computed tomography angiography (CCTA) compared to first-generation intra-cycle motion correction algorithm (SnapShot Freeze, GE Healthcare, MC1).

METHODS

Fifty consecutive patients with known or suspected coronary artery disease who underwent CCTA on a 256-slice CT scanner were retrospectively studied. CCTA were reconstructed with three different algorithms: no motion correction (NMC), MC1, and MC2. The delineation of coronary arteries on CCTA was qualitatively rated on a 5-point scale from 1 (nondiagnostic) to 5 (excellent) by two radiologists blinded to the reconstruction method and the patient information.

RESULTS

On a per-vessel basis, the delineation scores of coronary arteries were significantly higher on MC2 images compared to MC1 images (median [interquartile range], right coronary artery, 5.0 [4.5-5.0] vs 4.5 [4.0-5.0]; left anterior descending artery, 5.0 [4.5-5.0] vs 4.5 [3.5-5.0]; left circumflex artery, 5.0 [4.5-5.0] vs 4.5 [3.9-5.0]; all p ​< ​0.05). On a per-segment basis, for both 2 observers, the delineation scores on segment 1, 2, 8, 9, 10, 12 and 13 on MC2 images were significantly better than those on MC1 images (p ​< ​0.05). The percentage of interpretable segments (rated score 3 or greater) on NMC, MC1, and MC2 images was 90.5-91.9%, 97.4-97.9%, and 100.0%, respectively.

CONCLUSION

Second-generation intra-cycle motion correction algorithm improves the delineation and interpretability of coronary arteries in CCTA compared to first-generation algorithm.

Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation

PURPOSE

To investigate the performance of a deep learning-based motion correction algorithm (MCA) at various cardiac phases of coronary computed tomography angiography (CCTA), and determine the extent to which it may allow for reliable morphological and functional evaluation.

MATERIALS AND METHODS

The acquired image data of 53 CCTA cases, where the patient heart rate (HR) was ≥75 bpm, were reconstructed at 0, ±2, ±4, ±6, and ±8% deviations from each optimal systolic phase, with and without the MCA, yielding a total of 954 images (53 cases × 9 phases × 2 reconstructions). The overall image quality and diagnostic confidence were graded by two radiologists using a 5-point scale, with scores ≥3 being deemed clinically interpretable. Signal-to-noise ratio, contrast-to-noise ratio, vessel sharpness, and circularity were measured. The CCTA-derived fractional flow reserve (CT-FFR) was calculated in 38 vessels on 24 patients to identify functionally significant stenosis, using the invasive fractional flow reserve (FFR) as reference. All metrics were compared between two reconstructions at various phases.

RESULTS

Inferior image quality was observed as the phase deviation was enlarged. However, MCA significantly improved the image quality at nonoptimal phases and the optimal phase. Coronary artery evaluation was feasible within 4% phase deviation using MCA, with interpretable overall image quality and high diagnostic confidence. With MCA, the performance of identifying functionally significant stenosis via CT-FFR was increased for images at various phase deviations. However, obvious decrease in accuracy, as compared to the image at the optimal phase, was found on those with deviations >4%.

CONCLUSION

The deep learning-based MCA allows up to 4% phase deviation in acquiring CCTA for reliable morphological and functional evaluation on patients with high HRs.

Image quality of automatic coronary CT angiography reconstruction for patients with HR ≥ 75 bpm using an AI-assisted 16-cm z-coverage CT scanner

Background

Coronary CT angiography (CCTA) is a complicated CT exam in comparison to other CT protocols. Exam success highly depends on image assessment of experienced radiologist and the procedure is often time-consuming. This study aims to evaluate feasibility of automatic CCTA reconstruction in 0.25 s rotation time, 16 cm coverage CT scanner with best phase selection and AI-assisted motion correction.

Methods

CCTA exams of 90 patients with heart rates higher than 75 bpm were included in this study. Two image series were reconstructed—one at automatically selected phase and another with additional motion correction. All reconstructions were performed without manual interaction of radiologist. A four-point Likert scale rating system was used to evaluate the image quality of coronary artery segment by two experienced radiologists, according to the 18-segment model. Analysis was done on per-segment basis.

Results

Total 1194 out of the 1620 segments were identified for quality evaluation in 90 patients. After automatic best phase selection, 1172 segments (98.3%) were rated as having diagnostic image quality (scores 2–4) and the average score is 3.64 ± 0.55. When motion corrections were applied, diagnostic segment number increases to 1192 (99.8%) and the average score is 3.85 ± 0.37.

Conclusions

With the help of 0.25 s rotation speed, 16-cm z-coverage and AI-assisted motion correction algorithm, CCTA exam reconstruction could be performed with minimum radiologist involvement and still meet image quality requirement.

Automated quantification and evaluation of motion artifact on coronary CT angiography images

PURPOSE

This study developed and validated a Motion Artifact Quantification algorithm to automatically quantify the severity of motion artifacts on coronary computed tomography angiography (CCTA) images. The algorithm was then used to develop a Motion IQ Decision method to automatically identify whether a CCTA dataset is of sufficient diagnostic image quality or requires further correction.

METHOD

The developed Motion Artifact Quantification algorithm includes steps to identify the right coronary artery (RCA) regions of interest (ROIs), segment vessel and shading artifacts, and to calculate the motion artifact score (MAS) metric. The segmentation algorithms were verified against ground-truth manual segmentations. The segmentation algorithms were also verified by comparing and analyzing the MAS calculated from ground-truth segmentations and the algorithm-generated segmentations. The Motion IQ Decision algorithm first identifies slices with unsatisfactory image quality using a MAS threshold. The algorithm then uses an artifact-length threshold to determine whether the degraded vessel segment is large enough to cause the dataset to be nondiagnostic. An observer study on 30 clinical CCTA datasets was performed to obtain the ground-truth decisions of whether the datasets were of sufficient image quality. A five-fold cross-validation was used to identify the thresholds and to evaluate the Motion IQ Decision algorithm.

RESULTS

The automated segmentation algorithms in the Motion Artifact Quantification algorithm resulted in Dice coefficients of 0.84 for the segmented vessel regions and 0.75 for the segmented shading artifact regions. The MAS calculated using the automated algorithm was within 10% of the values obtained using ground-truth segmentations. The MAS threshold and artifact-length thresholds were determined by the ROC analysis to be 0.6 and 6.25 mm by all folds. The Motion IQ Decision algorithm demonstrated 100% sensitivity, 66.7% ± 27.9% specificity, and a total accuracy of 86.7% ± 12.5% for identifying datasets in which the RCA required correction. The Motion IQ Decision algorithm demonstrated 91.3% sensitivity, 71.4% specificity, and a total accuracy of 86.7% for identifying CCTA datasets that need correction for any of the three main vessels.

CONCLUSION

The Motion Artifact Quantification algorithm calculated accurate (<10% error) motion artifact scores using the automated segmentation methods. The developed algorithms demonstrated high sensitivity (91.3%) and specificity (71.4%) in identifying datasets of insufficient image quality. The developed algorithms for automatically quantifying motion artifact severity may be useful for comparing acquisition techniques, improving best-phase selection algorithms, and evaluating motion compensation techniques.

The feasibility of 1-stop examination of coronary CT angiography and abdominal enhanced CT

This study aims to evaluate the feasibility of performing coronary computed tomography angiography (CCTA) and abdominal enhanced computed tomography (CT) with 1-time injection of the agent.CCTA images (right coronary artery, left anterior descending coronary artery, and left circumflex coronary artery) were collected from 20 patients who completed a 1-stop combined examination of CCTA and abdominal enhanced CT (group A), 20 patients who only underwent abdominal enhanced CT (group B1), and 20 patients who only underwent CCTA (group B2). These images were interpreted using the 5-point Likert scale system by 2 experienced radiologists, and abdominal images were observed for breathing artifact. CT value, signal-to-noise ratio (SNR), and CTDI were recorded and compare among the 3 groups.The difference in image quality of the coronary and total volume of the contrast agent between group A and group B1 was not statistical significant (P > .05). The CT value and SNR in group B1 (CCTA) (CT: 394.65 ± 59.23, SNR: 17.38 ± 4.13) increased, compare with Group A (CT: 360.35 ± 34.16, SNR: 13.76 ± 1.84, P = .03, .01), while CTDI was undifferentiated between group A (17.14 ± 6.20) and group B1 (18.38 ± 9.79) (P = .64). The difference in CT value and SNR at the arterial phase and CT value at the venous phase between group A (abdomen) and group B2 were statistically significant, the CTDI in group A (9.09 ± 1.05) increased, compared with group B2 (8.23 ± 1.33) (P = .03), and SNR at the venous phase in group B2 (12.50 ± 2.43) increased, compared with group A (10.89 ± 2.03) (P = .03).Revolution CT can capture full images and very rapidly switch to the scan mode, enabling a 1-stop axial CCTA and enhanced helical abdominal scan. The 1-stop combined scan resulted in a satisfactory image quality, which reduced the contrast agent dose and simplified the workflow.The 1-stop combined scan allows for the high success rate of the examination, reduces the number of examinations, and decreases the dose and risk of injection of the contrast agent. This would be helpful for patients to obtain diagnostic images in time.

A Preliminary Study of Computed Tomography Coronary Angiography Within a Single Cardiac Cycle in Patients With Atrial Fibrillation Using 256-Row Detector Computed Tomography

OBJECTIVE

The purpose of this study was to evaluate the image quality and radiation dose of computed tomography (CT) coronary angiography using a 256-row detector CT scanner in a single cardiac cycle in patients with atrial fibrillation (AF).

METHODS

Seventy consecutive patients (41 men and 29 women; age range was from 37 to 84 years, mean age was 61.7 ± 10.2 years; body mass index range was from 15.08 to 36.45 kg/m, mean body mass index was 25.9 ± 3.5 kg/m) with persistent or paroxysmal AF during acquisition, who were not receiving any medications for heart rate (HR) regulation, were imaged with a 256-row detector CT scanner (Revolution CT, GE healthcare). According to the HR or HR variability (HRV) the patients were divided into 4 groups: group A (HR, ≥75 bpm; n = 36), group B (HR, <75 bpm; n = 34), group C (HRV, ≥50 bpm; n = 26), and group D (HRV, <50 bpm; n = 44). The snapshot freeze algorithm reconstruction was used to reduce motion artifacts whenever necessary. Two experienced radiologists, who were blinded to the electrocardiograph and reconstruction information, independently graded the CT images in terms of visibility and artifacts with a 4-grade rating scale (1, excellent; 2, good; 3, poor; 4, insufficient) using the 18-segment model. Subjective image quality scores and effective dose (ED) were calculated and compared between these groups.

RESULTS

The HR during acquisition ranged from 47 to 222 bpm (88.24 ± 36.80 bpm). A total of 917 in 936 coronary artery segments were rated as diagnostically evaluable (98.2 ± 0.04%). There was no significant linear correlation between mean image quality and HR or HRV (P > 0.05). Snapshot freeze reconstruction technique was applied in 28 patients to reduce motion artifacts and thus showed image quality was improved from 93.2% to 98.4%. The ED was 3.05 ± 2.23 mSv (0.49-11.86 mSv) for all patients, and 3.76 ± 2.22 mSv (0.92-11.17 mSv), 2.30 ± 2.02 mSv (0.49-11.86 mSv), 3.89 ± 2.35 mSv (1.18-11.86 mSv), and 2.56 ± 2.03 mSv (0.49-11.17 mSv) for groups A, B, C, and D, respectively. There were significant differences in mean ED between groups A and B, as well as C and D (P <0.05).

CONCLUSIONS

This study shows that CT coronary angiography with use of a new 256-row detector CT in single cardiac cycle achieves diagnostic image quality but with lower radiation dose in patients with AF. Heart rate or HRV has no significant effect on image quality.

Evaluation of motion artifact metrics for coronary CT angiography

PURPOSE

This study quantified the performance of coronary artery motion artifact metrics relative to human observer ratings. Motion artifact metrics have been used as part of motion correction and best-phase selection algorithms for Coronary Computed Tomography Angiography (CCTA). However, the lack of ground truth makes it difficult to validate how well the metrics quantify the level of motion artifact. This study investigated five motion artifact metrics, including two novel metrics, using a dynamic phantom, clinical CCTA images, and an observer study that provided ground-truth motion artifact scores from a series of pairwise comparisons.

METHOD

Five motion artifact metrics were calculated for the coronary artery regions on both phantom and clinical CCTA images: positivity, entropy, normalized circularity, Fold Overlap Ratio (FOR), and Low-Intensity Region Score (LIRS). CT images were acquired of a dynamic cardiac phantom that simulated cardiac motion and contained six iodine-filled vessels of varying diameter and with regions of soft plaque and calcifications. Scans were repeated with different gantry start angles. Images were reconstructed at five phases of the motion cycle. Clinical images were acquired from 14 CCTA exams with patient heart rates ranging from 52 to 82 bpm. The vessel and shading artifacts were manually segmented by three readers and combined to create ground-truth artifact regions. Motion artifact levels were also assessed by readers using a pairwise comparison method to establish a ground-truth reader score. The Kendall's Tau coefficients were calculated to evaluate the statistical agreement in ranking between the motion artifacts metrics and reader scores. Linear regression between the reader scores and the metrics was also performed.

RESULTS

On phantom images, the Kendall's Tau coefficients of the five motion artifact metrics were 0.50 (normalized circularity), 0.35 (entropy), 0.82 (positivity), 0.77 (FOR), 0.77(LIRS), where higher Kendall's Tau signifies higher agreement. The FOR, LIRS, and transformed positivity (the fourth root of the positivity) were further evaluated in the study of clinical images. The Kendall's Tau coefficients of the selected metrics were 0.59 (FOR), 0.53 (LIRS), and 0.21 (Transformed positivity). In the study of clinical data, a Motion Artifact Score, defined as the product of FOR and LIRS metrics, further improved agreement with reader scores, with a Kendall's Tau coefficient of 0.65.

CONCLUSION

The metrics of FOR, LIRS, and the product of the two metrics provided the highest agreement in motion artifact ranking when compared to the readers, and the highest linear correlation to the reader scores. The validated motion artifact metrics may be useful for developing and evaluating methods to reduce motion in Coronary Computed Tomography Angiography (CCTA) images.

Clinical evaluation of new automatic coronary-specific best cardiac phase selection algorithm for single-beat coronary CT angiography

The aim of this study was to evaluate the workflow efficiency of a new automatic coronary-specific reconstruction technique (Smart Phase, GE Healthcare—SP) for selection of the best cardiac phase with least coronary motion when compared with expert manual selection (MS) of best phase in patients with high heart rate. A total of 46 patients with heart rates above 75 bpm who underwent single beat coronary computed tomography angiography (CCTA) were enrolled in this study. CCTA of all subjects were performed on a 256-detector row CT scanner (Revolution CT, GE Healthcare, Waukesha, Wisconsin, US). With the SP technique, the acquired phase range was automatically searched in 2% phase intervals during the reconstruction process to determine the optimal phase for coronary assessment, while for routine expert MS, reconstructions were performed at 5% intervals and a best phase was manually determined. The reconstruction and review times were recorded to measure the workflow efficiency for each method. Two reviewers subjectively assessed image quality for each coronary artery in the MS and SP reconstruction volumes using a 4-point grading scale. The average HR of the enrolled patients was 91.1±19.0bpm. A total of 204 vessels were assessed. The subjective image quality using SP was comparable to that of the MS, 1.45±0.85 vs 1.43±0.81 respectively (p = 0.88). The average time was 246 seconds for the manual best phase selection, and 98 seconds for the SP selection, resulting in average time saving of 148 seconds (60%) with use of the SP algorithm. The coronary specific automatic cardiac best phase selection technique (Smart Phase) improves clinical workflow in high heart rate patients and provides image quality comparable with manual cardiac best phase selection. Reconstruction of single-beat CCTA exams with SP can benefit the users with less experienced in CCTA image interpretation.

Artifacts at Cardiac CT: Physics and Solutions

Computed tomography is vulnerable to a wide variety of artifacts, including patient- and technique-specific artifacts, some of which are unique to imaging of the heart. Motion is the most common source of artifacts and can be caused by patient, cardiac, or respiratory motion. Cardiac motion artifacts can be reduced by decreasing the heart rate and variability and the duration of data acquisition; adjusting the placement of the data window within a cardiac cycle; performing single-heartbeat scanning; and using multisegment reconstruction, motion-correction algorithms, and electrocardiographic editing. Respiratory motion artifacts can be minimized with proper breath holding and shortened scan duration. Partial volume averaging is caused by the averaging of attenuation values from all tissue contained within a voxel and can be reduced by improving the spatial resolution, using a higher x-ray energy, or displaying images with a wider window width. Beam-hardening artifacts are caused by the polyenergetic nature of the x-ray beam and can be reduced by using x-ray filtration, applying higher-energy x-rays, altering patient position, modifying contrast material protocols, and applying certain reconstruction algorithms. Metal artifacts are complex and have multiple causes, including x-ray scatter, underpenetration, motion, and attenuation values that exceed the typical dynamic range of Hounsfield units. Quantum mottle or noise is caused by insufficient penetration of tissue and can be improved by increasing the tube current or peak tube potential, reconstructing thicker sections, increasing the rotation time, using appropriate patient positioning, and applying iterative reconstruction algorithms. ^©RSNA, 2016.