Impact of Advanced Modeled Iterative Reconstruction on Coronary Artery Calcium Quantification

Implications of coronary calcification on the assessment of plaque pathology: a comparison of computed tomography and multimodality intravascular imaging

OBJECTIVES

This study aimed to investigate the impact of calcific (Ca) on the efficacy of coronary computed coronary angiography (CTA) in evaluating plaque burden (PB) and composition with near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) serving as the reference standard.

MATERIALS AND METHODS

Sixty-four patients (186 vessels) were recruited and underwent CTA and 3-vessel NIRS-IVUS imaging (NCT03556644). Expert analysts matched and annotated NIRS-IVUS and CTA frames, identifying lumen and vessel wall borders. Tissue distribution was estimated using NIRS chemograms and the arc of Ca on IVUS, while in CTA Hounsfield unit cut-offs were utilized to establish plaque composition. Plaque distribution plots were compared at segment-, lesion-, and cross-sectional-levels.

RESULTS

Segment- and lesion-level analysis showed no effect of Ca on the correlation of NIRS-IVUS and CTA estimations. However, at the cross-sectional level, Ca influenced the agreement between NIRS-IVUS and CTA for the lipid and Ca components (p-heterogeneity < 0.001). Proportional odds model analysis revealed that Ca had an impact on the per cent atheroma volume quantification on CTA compared to NIRS-IVUS at the segment level (p-interaction < 0.001). At lesion level, Ca affected differences between the modalities for maximum PB, remodelling index, and Ca burden (p-interaction < 0.001, 0.029, and 0.002, respectively). Cross-sectional-level modelling demonstrated Ca's effect on differences between modalities for all studied variables (p-interaction ≤ 0.002).

CONCLUSION

Ca burden influences agreement between NIRS-IVUS and CTA at the cross-sectional level and causes discrepancies between the predictions for per cent atheroma volume at the segment level and maximum PB, remodelling index, and Ca burden at lesion-level analysis.

CLINICAL RELEVANCE STATEMENT

Coronary calcification affects the quantification of lumen and plaque dimensions and the characterization of plaque composition coronary CTA. This should be considered in the analysis and interpretation of CTAs performed in patients with extensive Ca burden.

KEY POINTS

Coronary CT Angiography is limited in assessing coronary plaques by resolution and blooming artefacts. Agreement between dual-source CT angiography and NIRS-IVUS is affected by a Ca burden for the per cent atheroma volume. Advanced CT imaging systems that eliminate blooming artefacts enable more accurate quantification of coronary artery disease and characterisation of plaque morphology.

Influence of deep learning-based super-resolution reconstruction on Agatston score

OBJECTIVE

To evaluate the impact of deep learning-based super-resolution reconstruction (DLSRR) on image quality and Agatston score.

METHODS

Consecutive patients who underwent cardiac CT, including unenhanced CT for Agatston scoring, were enrolled. Four types of non-contrast CT images were reconstructed using filtered back projection (FBP) and three strengths of DLSRR. Image quality was assessed by measuring image noise, signal-to-noise ratio (SNR) of the aorta, contrast-to-noise ratio (CNR), and edge rise slope (ERS) of coronary artery calcium (CAC). Agatston score and CAC volume were also measured. These results were compared among the four CT datasets. Patients were categorized into four risk levels based on the Coronary Artery Calcium Data and Reporting System (CAC-DRS), and the concordance rate between FBP and DLSRR classifications was evaluated.

RESULTS

For the 111 patients enrolled, DLSRR significantly reduced image noise (p < 0.001) and improved SNR and CNR (p < 0.001), with stronger effects at higher DLSRR strengths (p < 0.01). ERS was significantly enhanced using DLSRR compared with FBP (p < 0.001), whereas there was no significant difference among the three strengths of DLSRR (p = 0.90-0.98). Agatston score and CAC volume were not significantly affected by DLSRR (p = 0.952 and 0.901, respectively). The concordance rate of CAC-DRS classification between FBP and DLSRR was 93%.

CONCLUSION

DLSRR significantly improves image quality by reducing noise and enhancing sharpness without significantly altering Agatston scores or CAC volumes. The concordance rate of CAC-DRS classification with FBP was high, although some reclassifications were observed.

KEY POINTS

Question The utility of deep learning-based super-resolution reconstruction (DLSRR) in coronary CT angiography is well known, but its impact on the Agatston score remains unclear. Findings DLSRR significantly improved image quality without altering the Agatston scores, but some reclassifications of Coronary Artery Calcium Data and Reporting System (CAC-DRS) were observed. Clinical relevance DLSRR should be cautiously used in clinical settings owing to the occurrence of some cases of CAC-DRS reclassification.

Investigating the effectiveness of MAR algorithm on magnitude of artifacts in CBCT images: a systematic review

BACKGROUND

There has been an increasing interest in the use of implants to treat edentulous patients. In this regard, the use of cone beam computed tomography (CBCT) offers a variety of advantages compared with other imaging methods. However, the creation of beam-hardening artifacts adversely affects the quality of images. To our knowledge, little is known about the actual effectiveness of the Metal Artifact Reduction (MAR) algorithm on image quality improvement.

OBJECTIVES

The objective of this study is to conduct a systematic review to investigate the effectiveness of the MAR algorithm on the magnitude of artifacts generated in CBCT images.

MATERIALS AND METHODS

An electronic search was performed in electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar. For each database, the search strategy was defined specifically. Studies that had the inclusion criteria for this review were imported into Endnote version 20. The risk of bias in the studies included in this systematic review was assessed by two independent reviewers based on the Joanna Briggs Institute (JBI)'s Critical Appraisal checklist. The selected final articles were scored based on the specified checklist. After reviewing selected articles, it was not possible to perform a meta-analysis due to the heterogeneity and multiplicity of the variables, and the studies were included in the systematic review.

RESULTS

A total of 4738 studies were identified. After eliminating duplicate and unrelated articles, 10 articles met the inclusion criteria. Results showed that the use of the MAR algorithm in the preparation of CBCT scans reduces the standard deviation (SD) of gray values. However, no definite result was achieved in relation to the contrast-to-noise ratio (CNR). In fact, it cannot be definitively concluded whether the use of the MAR algorithm will increase the CNR.

CONCLUSION

The results of this systematic review demonstrated that we cannot provide a definite answer regarding the effect of the MAR algorithm on reducing the artifacts around dental implants. The explanation is that this factor is affected by many variables, whose change can have a significant effect on the magnitude of artifacts generated in the image.

Effect of temporal resolution on calcium scoring: insights from photon-counting detector CT

To intra-individually investigate the variation of coronary artery calcium (CAC), aortic valve calcium (AVC), and mitral annular calcium (MAC) scores and the presence of blur artifacts as a function of temporal resolution in patients undergoing non-contrast cardiac CT on a dual-source photon counting detector (PCD) CT. This retrospective, IRB-approved study included 70 patients (30 women, 40 men, mean age 78 ± 9 years) who underwent ECG-gated cardiac non-contrast CT with PCD-CT (gantry rotation time 0.25 s) prior to transcatheter aortic valve replacement. Each scan was reconstructed at a temporal resolution of 66 ms using the dual-source information and at 125 ms using the single-source information. Average heart rate and heart rate variability were calculated from the recorded ECG. CAC, AVC, and MAC were quantified according to the Agatston method on images with both temporal resolutions. Two readers assessed blur artifacts using a 4-point visual grading scale. The influence of average heart rate and heart rate variability on calcium quantification and blur artifacts of the respective structures were analyzed by linear regression analysis. Mean heart rate and heart rate variability during data acquisition were 76 ± 17 beats per minute (bpm) and 4 ± 6 bpm, respectively. CAC scores were smaller on 66 ms (median, 511; interquartile range, 220-978) than on 125 ms reconstructions (538; 203-1050, p < 0.001). Median AVC scores [2809 (2009-3952) versus 3177 (2158-4273)] and median MAC scores [226 (0-1284) versus 251 (0-1574)] were also significantly smaller on 66ms than on 125ms reconstructions (p < 0.001). Reclassification of CAC and AVC risk categories occurred in 4% and 11% of cases, respectively, whereby the risk category was always overestimated on 125ms reconstructions. Image blur artifacts were significantly less on 66ms as opposed to 125 ms reconstructions (p < 0.001). Intra-individual analyses indicate that temporal resolution significantly impacts on calcium scoring with cardiac CT, with CAC, MAC, and AVC being overestimated at lower temporal resolution because of increased motion artifacts eventually leading to an overestimation of patient risk.

Influence of Deep Learning Based Image Reconstruction on Quantitative Results of Coronary Artery Calcium Scoring

RATIONALE AND OBJECTIVES

To assess the impact of deep learning-based imaging reconstruction (DLIR) on quantitative results of coronary artery calcium scoring (CACS) and to evaluate the potential of DLIR for radiation dose reduction in CACS.

METHODS

For a retrospective cohort of 100 consecutive patients (mean age 62 ±10 years, 40% female), CACS scans were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASiR-V in 30%, 60% and 90% strength) and DLIR in low, medium and high strength. CACS was quantified semi-automatically and compared between image reconstructions. In a phantom study, a cardiac calcification insert was scanned inside an anthropomorphic thorax phantom at standard dose, 50% dose and 25% dose. FBP reconstructions at standard dose served as the reference standard.

RESULTS

In the patient study, DLIR led to a mean underestimation of Agatston score by 3.5, 6.4 and 11.6 points at low, medium and high strength, respectively. This underestimation of Agatston score was less pronounced for DLIR than for ASiR-V. In the phantom study, quantitative CACS results increased with reduced radiation dose and decreased with increasing strength of DLIR. Medium strength DLIR reconstruction at 50% dose reduction and high strength DLIR reconstruction at 75% dose reduction resulted in quantitative CACS results that were comparable to FBP reconstructions at standard dose.

CONCLUSION

Compared to FBP as the historical reference standard, DLIR leads to an underestimation of CACS but this underestimation is more moderate than with ASiR-V. DLIR can offset the increase in image noise and calcium score at reduced dose and may thus allow for substantial radiation dose reductions in CACS studies.

Advanced Imaging of Total Knee Arthroplasty

The prevalence of total knee arthroplasty (TKA) is increasing with the aging population. Although long-term results are satisfactory, suspected postoperative complications often require imaging with the implant in place. Advancements in computed tomography (CT), such as tin prefiltration, metal artifact reduction algorithms, dual-energy CT with virtual monoenergetic imaging postprocessing, and the application of cone-beam CT and photon-counting detector CT, allow a better depiction of the tissues adjacent to the metal. For magnetic resonance imaging (MRI), high bandwidth (BW) optimization, the combination of view angle tilting and high BW, as well as multispectral imaging techniques with multiacquisition variable-resonance image combination or slice encoding metal artifact correction, have significantly improved imaging around metal implants, turning MRI into a useful clinical tool for patients with suspected TKA complications.

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.

Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging

BACKGROUND

To assess the accuracy of fully automated deep learning (DL) based coronary artery calcium scoring (CACS) from non-contrast computed tomography (CT) as acquired for attenuation correction (AC) of cardiac single-photon-emission computed tomography myocardial perfusion imaging (SPECT-MPI).

METHODS AND RESULTS

Patients were enrolled in this study as part of a larger prospective study (NCT03637231). In this study, 56 Patients who underwent cardiac SPECT-MPI due to suspected coronary artery disease (CAD) were prospectively enrolled. All patients underwent non-contrast CT for AC of SPECT-MPI twice. CACS was manually assessed (serving as standard of reference) on both CT datasets (n = 112) and by a cloud-based DL tool. The agreement in CAC scores and CAC score risk categories was quantified. For the 112 scans included in the analysis, interscore agreement between the CAC scores of the standard of reference and the DL tool was 0.986. The agreement in risk categories was 0.977 with a reclassification rate of 3.6%. Heart rate, image noise, body mass index (BMI), and scan did not significantly impact (p=0.09 - p=0.76) absolute percentage difference in CAC scores.

CONCLUSION

A DL tool enables a fully automated and accurate estimation of CAC scores in patients undergoing non-contrast CT for AC of SPECT-MPI.

Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification

Background

Deep learning image reconstructions (DLIR) have been recently introduced as an alternative to filtered back projection (FBP) and iterative reconstruction (IR) algorithms for computed tomography (CT) image reconstruction. The aim of this study was to evaluate the effect of DLIR on image quality and quantification of coronary artery calcium (CAC) in comparison to FBP.

Methods

One hundred patients were consecutively enrolled. Image quality–associated variables (noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) as well as CAC-derived parameters (Agatston score, mass, and volume) were calculated from images reconstructed by using FBP and three different strengths of DLIR (low (DLIR_L), medium (DLIR_M), and high (DLIR_H)). Patients were stratified into 4 risk categories according to the Coronary Artery Calcium - Data and Reporting System (CAC-DRS) classification: 0 Agatston score (very low risk), 1–99 Agatston score (mildly increased risk), Agatston 100–299 (moderately increased risk), and ≥ 300 Agatston score (moderately-to-severely increased risk).

Results

In comparison to standard FBP, increasing strength of DLIR was associated with a significant and progressive decrease of image noise (p < 0.001) alongside a significant and progressive increase of both SNR and CNR (p < 0.001). The use of incremental levels of DLIR was associated with a significant decrease of Agatston CAC score and CAC volume (p < 0.001), while mass score remained unchanged when compared to FBP (p = 0.232). The underestimation of Agatston CAC led to a CAC-DRS misclassification rate of 8%.

Conclusion

DLIR systematically underestimates Agatston CAC score. Therefore, DLIR should be used cautiously for cardiovascular risk assessment.

Key Points

• In coronary artery calcium imaging, the implementation of deep learning image reconstructions improves image quality, by decreasing the level of image noise.

• Deep learning image reconstructions systematically underestimate Agatston coronary artery calcium score.

• Deep learning image reconstructions should be used cautiously in clinical routine to measure Agatston coronary artery calcium score for cardiovascular risk assessment.

Metal artifact reduction in ultra-high-resolution cone-beam CT imaging with a twin robotic X-ray system

Cone-beam computed tomography (CBCT) has been shown to be a powerful tool for 3D imaging of the appendicular skeleton, allowing for detailed visualization of bone microarchitecture. This study was designed to compare artifacts in the presence of osteosynthetic implants between CBCT and multidetector computed tomography (MDCT) in cadaveric wrist scans. A total of 32 scan protocols with varying tube potential and current were employed: both conventional CBCT and MDCT studies were included with tube voltage ranging from 60 to 140 kVp as well as additional MDCT protocols with dedicated spectral shaping via tin prefiltration. Irrespective of scanner type, all examinations were conducted in ultra-high-resolution (UHR) scan mode. For reconstruction of UHR-CBCT scans an additional iterative metal artifact reduction algorithm was employed, an image correction tool which cannot be used in combination with UHR-MDCT. To compare applied radiation doses between both scanners, the volume computed tomography dose index for a 16 cm phantom (CTDIvol) was evaluated. Images were assessed regarding subjective and objective image quality. Without automatic tube current modulation or tube potential control, radiation doses ranged between 1.3 mGy (with 70 kVp and 50.0 effective mAs) and 75.2 mGy (with 140 kVp and 383.0 effective mAs) in UHR-MDCT. Using the pulsed image acquisition method of the CBCT scanner, CTDIvol ranged between 2.3 mGy (with 60 kVp and 0.6 mean mAs per pulse) and 61.0 mGy (with 133 kVp and 2.5 mean mAs per pulse). In essence, all UHR-CBCT protocols employing a tube potential of 80 kVp or more were found to provide superior overall image quality and artifact reduction compared to UHR-MDCT (all p < .050). Interrater reliability of seven radiologists regarding image quality was substantial for tissue assessment and moderate for artifact assessment with Fleiss kappa of 0.652 (95% confidence interval 0.618-0.686; p < 0.001) and 0.570 (95% confidence interval 0.535-0.606; p < 0.001), respectively. Our results demonstrate that the UHR-CBCT scan mode of a twin robotic X-ray system facilitates excellent visualization of the appendicular skeleton in the presence of metal implants. Achievable image quality and artifact reduction are superior to dose-comparable UHR-MDCT and even MDCT protocols employing spectral shaping with tin prefiltration do not achieve the same level of artifact reduction in adjacent soft tissue.

Combining gantry-free cone-beam computed tomography with iterative metal artefact reduction for surgical follow-up imaging of the appendicular skeleton

PURPOSE

Post-surgical evaluation of osteosynthesis material and adjacent tissue can be challenging in both radiography and cross-sectional imaging. This study investigates the performance of a multi-purpose X-ray scanner with cone-beam CT (CBCT) function and iterative metal artefact reduction capabilities in patients after osteoplasty of the appendicular skeleton.

METHOD

Eighty individuals who underwent both conventional X-ray imaging and CBCT after osteoplasty of the hand/wrist (48), elbow (14), or ankle/foot (18) with the gantry-free twin robotic system were retrospectively enrolled. Radiological reports from clinical routine for both imaging modalities were retrospectively analyzed and compared with consensus expert reading by two musculoskeletal specialists serving as the standard of reference. Findings of screw dislocation or implant loosening, fragment displacement, and delayed healing were compared between X-ray and CBCT reports using the McNemar test.

RESULTS

The median dose-area-product of CBCT and X-ray scans amounted to 27.98 and 0.2 dGy*cm², respectively. Diagnostic accuracy for screw dislocation was superior in CBCT compared to standard radiograms (98.8 % vs 83.8 %; p = 0.002). Implant loosening (98.8 % vs 86.3 %; p = 0.006), fragment displacement (98.8 % vs 85.0 %; p < 0.001), and delayed healing (97.5 % vs 88.8 %; p = 0.016) were also more reliably detected in CBCT. Employing CBCT, postoperative complications were detected with a sensitivity and specificity of at least 95.8 % and 98.1 %, compared to 33.3 % and 92.86 % in radiography.

CONCLUSIONS

With superior accuracy for various osteoplasty-related complications, the CBCT scan mode of a gantry-free twin robotic X-ray system with iterative metal artefact reduction aids post-surgical assessment in the appendicular skeleton.

Diagnostic Value of Fully Automated Artificial Intelligence Powered Coronary Artery Calcium Scoring from 18F-FDG PET/CT

Objectives: The objective of this study was to assess the feasibility and accuracy of a fully automated artificial intelligence (AI) powered coronary artery calcium scoring (CACS) method on ungated CT in oncologic patients undergoing 18F-FDG PET/CT. Methods: A total of 100 oncologic patients examined between 2007 and 2015 were retrospectively included. All patients underwent 18F-FDG PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on non-contrast ECG-gated CT scans obtained from SPECT-MPI (i.e., reference standard). Additionally, CACS was performed using a cloud-based, user-independent tool (AI-CACS) on ungated CT scans from 18F-FDG-PET/CT examinations. Agatston scores from the manual CACS and AI-CACS were compared. Results: On a per-patient basis, the AI-CACS tool achieved a sensitivity and specificity of 85% and 90% for the detection of CAC. Interscore agreement of CACS between manual CACS and AI-CACS was 0.88 (95% CI: 0.827, 0.918). Interclass agreement of risk categories was 0.8 in weighted Kappa analysis, with a reclassification rate of 44% and an underestimation of one risk category by AI-CACS in 39% of cases. On a per-vessel basis, interscore agreement of CAC scores ranged from 0.716 for the circumflex artery to 0.863 for the left anterior descending artery. Conclusions: Fully automated AI-CACS as performed on non-contrast free-breathing, ungated CT scans from 18F-FDG-PET/CT examinations is feasible and provides an acceptable to good estimation of CAC burden. CAC load on ungated CT is, however, generally underestimated by AI-CACS, which should be taken into account when interpreting imaging findings.

Impact of Adaptive Statistical Iterative Reconstruction-V on Coronary Artery Calcium Scores Obtained From Low-Tube-Voltage Computed Tomography - A Patient Study

OBJECTIVE

To evaluate the impact of adaptive statistical iterative reconstruction-V (ASIR-V) on the accuracy of ultra-low-dose coronary artery calcium (CAC) scoring.

MATERIALS AND METHOD

One-hundred-and-three patients who underwent computed tomography (CT) for CAC scoring were prospectively included. All underwent standard scanning with 120-kilovolt-peak (kVp) and with 80- and 70-kVp tube voltage. ASiR-V was applied to the 80- and 70-kVp scans at different levels. The 120-kVp scans reconstructed with filtered back projection served as the standard of reference. Recently published novel kVp-adapted thresholds were used for calculation of CAC scores from 80- and 70-kVp scans and the resulting CAC scores were compared against the standard of reference. Patients were stratified into six CAC score risk categories: 0, 1-10, 11-100, 101-400, 401-1000, and >1000.

RESULTS

Increasing levels of ASIR-V led to an increasing underestimation of CAC scores with bias ranging from -128 to -118 and from -205 to -198 for the 80- and 70-kVp scans, respectively, when compared with the standard of reference. Reconstruction with 20% and 40% ASIR-V for the 80- and 70-kVp scans, respectively, yielded noise levels comparable to the standard of reference. Nevertheless, a change in risk-class was observed in 29 (28.6%) and 46 (44.7%) patients, exclusively to a lower risk-class, when CAC scores were derived from these reconstructions.

CONCLUSION

ASIR-V leads to noise reduction in CT scans acquired with low tube-voltages. However, ASIR-V introduces substantial inaccuracies and marked underestimation of ultra-low-dose CAC scoring as compared with standard-dose CAC scoring despite normalization of noise.

Tube voltage-independent coronary calcium scoring on a first-generation dual-source photon-counting CT-a proof-of-principle phantom study

To evaluate the accuracy of coronary artery calcium (CAC) scoring at various tube voltages and different monoenergetic image reconstructions on a first-generation dual-source photon-counting detector CT (PCD-CT). A commercially available anthropomorphic chest phantom with calcium inserts was scanned at different tube voltages (90 kV, Sn100kV, 120 kV, and Sn140kV) on a first-generation dual-source PCD-CT system with quantum technology using automatic exposure control with an image quality (IQ) level of 20. The same phantom was also scanned on a conventional energy-integrating detector CT (120 kV; weighted filtered back projection) for reference. Extension rings were used to emulate different patient sizes. Virtual monoenergetic images at 65 keV and 70 keV applying different levels of quantum iterative reconstruction (QIR) were reconstructed from the PCD-CT data sets. CAC scores were determined and compared to the reference. Radiation doses were noted. At an IQ level of 20, radiation doses ranged between 1.18 mGy and 4.64 mGy, depending on the tube voltage and phantom size. Imaging at 90 kV or Sn100kV was associated with a size-dependent radiation dose reduction between 23% and 48% compared to 120 kV. Tube voltage adapted image reconstructions with 65 keV and QIR 3 at 90 kV and with 70 keV and QIR 1 at Sn100kV allowed to calculate CAC scores comparable to conventional EID-CT scans with a percentage deviation of ≤ 5% for all phantom sizes. Our phantom study indicates that CAC scoring with dual-source PCD-CT is accurate at various tube voltages, offering the possibility of substantial radiation dose reduction.

Extraction of Coronary Atherosclerotic Plaques From Computed Tomography Imaging: A Review of Recent Methods

Background: Atherosclerotic plaques are the major cause of coronary artery disease (CAD). Currently, computed tomography (CT) is the most commonly applied imaging technique in the diagnosis of CAD. However, the accurate extraction of coronary plaque geometry from CT images is still challenging.

Summary of Review: In this review, we focused on the methods in recent studies on the CT-based coronary plaque extraction. According to the dimension of plaque extraction method, the studies were categorized into two-dimensional (2D) and three-dimensional (3D) ones. In each category, the studies were analyzed in terms of data, methods, and evaluation. We summarized the merits and limitations of current methods, as well as the future directions for efficient and accurate extraction of coronary plaques using CT imaging.

Conclusion: The methodological innovations are important for more accurate CT-based assessment of coronary plaques in clinical applications. The large-scale studies, de-blooming algorithms, more standardized datasets, and more detailed classification of non-calcified plaques could improve the accuracy of coronary plaque extraction from CT images. More multidimensional geometric parameters can be derived from the 3D geometry of coronary plaques. Additionally, machine learning and automatic 3D reconstruction could improve the efficiency of coronary plaque extraction in future studies.

Aortic valve calcification scoring with computed tomography: impact of iterative reconstruction techniques

To investigate whether image reconstruction with iterative reconstruction (IR) affects aortic valve calcification (AVC) scoring and likelihood categorization of severe aortic stenosis (AS). In this IRB-approved retrospective study, we included 100 consecutive patients with AS (40 females; mean age 77 ± 10 years; age range: 36-99 years) undergoing CT prior to transcatheter aortic valve replacement. Non-enhanced, electrocardiography-gated CT of the heart was reconstructed with filtered back projection (FBP) and with advanced modeled IR at strength levels 1-5. AVC Agatston scores were calculated and gender-specific cut-off values for AS likelihood categorization were applied according to current European Society of Cardiology recommendations (from unlikely to very likely). Friedman test with post-hoc Bonferroni correction was applied to analyze interval- and ordinal-scaled data. Compared to FBP, each IR strength level produced significantly different AVC Agatston scores (p < 0.001-0.002). Median AVC Agatston score for image reconstruction with FBP was 2527 (IQR: 1711-3663) and decreased with increasing IR strength levels up to 2281 (IQR: 1471-3357) at strength level 5. Likelihood categorization of severe AS was significantly different among image reconstruction algorithms (p < 0.001). Image reconstruction with IR strength level 5 led to a downward shift of likelihood categorization in 28 patients (28%) compared to images reconstructed with FBP. IR significantly impacts AVC scoring with significantly decreasing AVC scores with increasing IR strength levels. This leads to relevant changes in likelihood categorization of patients with severe AS., leading to underestimation of severe AS.

Metallic artifact reduction by evaluation of the additional value of iterative reconstruction algorithms in hip prosthesis computed tomography imaging

To evaluate iterative metal artifact reduction (iMAR) technique in images data of hip prosthesis on computed tomography (CT) and the added value of advanced modeled iterative reconstruction (ADMIRE) compared with standard filtered back projection (FBP).Twenty-eight patients addressed to CT examinations for hip prosthesis were included prospectively. Images were reconstructed with iMAR algorithm in addition to FBP and ADMIRE techniques. Measuring image noise assessed objective image quality and attenuation values with standardized region of interest (ROI) in 4 predefined anatomical structures (gluteus medius and rectus femoris muscles, inferior and anterior abdominal fat, and femoral vessels when contrast media was present). Subjective image quality was graded on a 5-point Likert scale, taking into account the size of artifacts, the metal-bone interface and the conspicuity of pelvic organs, and the diagnostic confidence.Improvement in overall image quality was statistically significant using iMAR (P<.001) compared with ADMIRE and FBP. ADMIRE did not show any impact in image noise, attenuation value, or global quality image. iMAR showed a significant decrease in image noise in all ROIs (Hounsfield Unit) as compared with FBP and ADMIRE. Interobserver agreement was high in all reconstructions (FBP, FBP+iMAR, ADMIRE, and ADMIRE + iMAR) more than 0.8. iMAR reconstructions showed emergence of new artifacts in bone-metal interface.iMAR algorithm allows a significant reduction of metal artifacts on CT images with unilateral or bilateral prostheses without additional value of ADMIRE. It improves the analysis of surrounding tissue but potentially generates new artifacts in bone-metal interface.

Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review

Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.

Diagnostic accuracy of chest X-ray dose-equivalent CT for assessing calcified atherosclerotic burden of the thoracic aorta

OBJECTIVE

To determine the value of ultralow-dose chest CT for estimating the calcified atherosclerotic burden of the thoracic aorta using tin-filter CT and compare its diagnostic accuracy with chest direct radiography.

METHODS

A total of 106 patients from a prospective, IRB-approved single-centre study were included and underwent standard dose chest CT (1.7 ± 0.7 mSv) by clinical indication followed by ultralow-dose CT with 100 kV and spectral shaping by a tin filter (0.13 ± 0.01 mSv) to achieve chest X-ray equivalent dose in the same session. Two independent radiologists reviewed the CT images, rated image quality and estimated presence and extent of calcification of aortic valve, ascending aorta and aortic arch. Conventional radiographs were also reviewed for presence of aortic calcifications.

RESULTS

The sensitivity of ultralow-dose CT for the detection of calcifications of the aortic valve, ascending aorta and aortic arch was 93.5, 96.2 and 96.2%, respectively, compared with standard dose CT. The sensitivity for the detection of thoracic aortic calcification was significantly lower on chest X-ray (52.3%) compared with ultralow-dose CT (p < 0.001).

CONCLUSION

A reliable estimation of calcified atherosclerotic burden of the thoracic aorta can be achieved with modern tin-filter CT at dose values comparable to chest direct radiography. Advances in knowledge: Our findings suggest that ultralow-dose CT is an excellent tool for assessing the calcified atherosclerotic burden of the thoracic aorta with higher diagnostic accuracy than conventional chest radiography and importantly without the additional cost of increased radiation dose.

Emphysema quantification and lung volumetry in chest X-ray equivalent ultralow dose CT - Intra-individual comparison with standard dose CT

OBJECTIVES

To determine whether ultralow dose chest CT with tin filtration can be used for emphysema quantification and lung volumetry and to assess differences in emphysema measurements and lung volume between standard dose and ultralow dose CT scans using advanced modeled iterative reconstruction (ADMIRE).

METHODS

84 consecutive patients from a prospective, IRB-approved single-center study were included and underwent clinically indicated standard dose chest CT (1.7±0.6mSv) and additional single-energy ultralow dose CT (0.14±0.01mSv) at 100kV and fixed tube current at 70mAs with tin filtration in the same session. Forty of the 84 patients (48%) had no emphysema, 44 (52%) had emphysema. One radiologist performed fully automated software-based pulmonary emphysema quantification and lung volumetry of standard and ultralow dose CT with different levels of ADMIRE. Friedman test and Wilcoxon rank sum test were used for multiple comparison of emphysema and lung volume. Lung volumes were compared using the concordance correlation coefficient.

RESULTS

The median low-attenuation areas (LAA) using filtered back projection (FBP) in standard dose was 4.4% and decreased to 2.6%, 2.1% and 1.8% using ADMIRE 3, 4, and 5, respectively. The median values of LAA in ultralow dose CT were 5.7%, 4.1% and 2.4% for ADMIRE 3, 4, and 5, respectively. There was no statistically significant difference between LAA in standard dose CT using FBP and ultralow dose using ADMIRE 4 (p=0.358) as well as in standard dose CT using ADMIRE 3 and ultralow dose using ADMIRE 5 (p=0.966). In comparison with standard dose FBP the concordance correlation coefficients of lung volumetry were 1.000, 0.999, and 0.999 for ADMIRE 3, 4, and 5 in standard dose, and 0.972 for ADMIRE 3, 4 and 5 in ultralow dose CT.

CONCLUSIONS

Ultralow dose CT at chest X-ray equivalent dose levels allows for lung volumetry as well as detection and quantification of emphysema. However, longitudinal emphysema analyses should be performed with the same scan protocol and reconstruction algorithms for reproducibility.

Clinical indications for coronary artery calcium scoring in asymptomatic patients: Expert consensus statement from the Society of Cardiovascular Computed Tomography

This expert consensus statement summarizes the available data regarding the prognostic value of CAC in the asymptomatic population and its ability to refine individual risk prediction, addresses the limitations identified in the current traditional risk factor-based treatment strategies recommended by the 2013 ACC/AHA Prevention guidelines including use of the Pooled Cohort Equations (PCE), and the US Preventive Services Task Force (USPSTF) Recommendation Statement for Statin Use for the Primary Prevention of Cardiovascular Disease in Adults. It provides CAC based treatment recommendations both within the context of the shared decision making model espoused by the 2013 ACC/AHA Prevention guidelines and independent of these guidelines.