Learning effects in visual grading assessment of model-based reconstruction algorithms in abdominal Computed Tomography

Objectives

Images reconstructed with higher strengths of iterative reconstruction algorithms may impair radiologists' subjective perception and diagnostic performance due to changes in the amplitude of different spatial frequencies of noise. The aim of the present study was to ascertain if radiologists can learn to adapt to the unusual appearance of images produced by higher strengths of Advanced modeled iterative reconstruction algorithm (ADMIRE).

Methods

Two previously published studies evaluated the performance of ADMIRE in non-contrast and contrast-enhanced abdominal CT. Images from 25 (first material) and 50 (second material) patients, were reconstructed with ADMIRE strengths 3, 5 (AD3, AD5) and filtered back projection (FBP). Radiologists assessed the images using image criteria from the European guidelines for quality criteria in CT. To ascertain if there was a learning effect, new analyses of data from the two studies was performed by introducing a time variable in the mixed-effects ordinal logistic regression model.

Results

In both materials, a significant negative attitude to ADMIRE 5 at the beginning of the viewing was strengthened during the progress of the reviews for both liver parenchyma (first material: -0.70, p < 0.01, second material: -0.96, p < 0.001) and overall image quality (first material:-0.59, p < 0.05, second material::-1.26, p < 0.001). For ADMIRE 3, an early positive attitude for the algorithm was noted, with no significant change over time for all criteria except one (overall image quality), where a significant negative trend over time (-1.08, p < 0.001) was seen in the second material.

Conclusions

With progression of reviews in both materials, an increasing dislike for ADMIRE 5 images was apparent for two image criteria. In this time perspective (weeks or months), no learning effect towards accepting the algorithm could be demonstrated.

Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study

BACKGROUND

Post-processing and interpretation of coronary CT angiography (CCTA) imaging are time-consuming and dependent on the reader's experience. An automated deep learning (DL)-based imaging reconstruction and diagnosis system was developed to improve diagnostic accuracy and efficiency.

METHODS

Our study including 374 cases from five sites, inviting 12 radiologists, assessed the DL-based system in diagnosing obstructive coronary disease with regard to diagnostic performance, imaging post-processing and reporting time of radiologists, with invasive coronary angiography as a standard reference. The diagnostic performance of DL system and DL-assisted human readers was compared with the traditional method of human readers without DL system.

RESULTS

Comparing the diagnostic performance of human readers without DL system versus with DL system, the AUC was improved from 0.81 to 0.82 (p < 0.05) at patient level and from 0.79 to 0.81 (p < 0.05) at vessel level. An increase in AUC was observed in inexperienced radiologists (p < 0.05), but was absent in experienced radiologists. Regarding diagnostic efficiency, comparing the DL system versus human reader, the average post-processing and reporting time was decreased from 798.60 s to 189.12 s (p < 0.05). The sensitivity and specificity of using DL system alone were 93.55% and 59.57% at patient level and 83.23% and 79.97% at vessel level, respectively.

CONCLUSIONS

With the DL system serving as a concurrent reader, the overall post-processing and reading time was substantially reduced. The diagnostic accuracy of human readers, especially for inexperienced readers, was improved. DL-assisted human reader had the potential of being the reading mode of choice in clinical routine.

The influence of artificial intelligence assistance on the diagnostic performance of CCTA for coronary stenosis for radiologists with different levels of experience

BACKGROUND

The interpretation of coronary computed tomography angiography (CCTA) stenosis may be difficult among radiologists of different experience levels. Artificial intelligence (AI) may improve the diagnostic performance.

PURPOSE

To investigate whether the diagnostic performance and time efficiency of radiologists with different levels of experience in interpreting CCTA images could be improved by using CCTA with AI assistance (CCTA-AI).

MATERIAL AND METHODS

This analysis included 200 patients with complete CCTA and invasive coronary angiography (ICA) data, using ICA results as the reference. Eighteen radiologists were divided into three levels based on experience (Levels I, II, and III), and the three levels were divided into groups without (Groups 1, 2, and 3) and with (Groups 4, 5, and 6) AI assistance, totaling six groups (to avoid reader recall bias). The average sensitivity, specificity, NPV, PPV, and AUC were reported for the six groups and CCTA-AI at the patient, vessel, and segment levels. The interpretation time in the groups with and without CCTA-AI was recorded.

RESULTS

Compared to the corresponding group without CCTA-AI, the Level I group with CCTA-AI had improved sensitivity (75.0% vs. 83.0% on patient-based; P = 0.003). At Level III, the specificity was better with CCTA-AI. The median interpretation times for the groups with and without CCTA-AI were 413 and 615 s, respectively (P < 0.001).

CONCLUSION

CCTA-AI could assist with and improve the diagnostic performance of radiologists with different experience levels, with Level I radiologists exhibiting improved sensitivity and Level III radiologists exhibiting improved specificity. The use of CCTA-AI could shorten the training time for radiologists.

A highly predictive cardiac positron emission tomography (PET) risk score for 90-day and one-year major adverse cardiac events and revascularization

BACKGROUND

With the increase in cardiac PET/CT availability and utilization, the development of a PET/CT-based major adverse cardiovascular events, including death, myocardial infarction (MI), and revascularization (MACE-Revasc) risk assessment score is needed. Here we develop a highly predictive PET/CT-based risk score for 90-day and one-year MACE-Revasc.

METHODS AND RESULTS

11,552 patients had a PET/CT from 2015 to 2017 and were studied for the training and development set. PET/CT from 2018 was used to validate the derived scores (n = 5049). Patients were on average 65 years old, half were male, and a quarter had a prior MI or revascularization. Baseline characteristics and PET/CT results were used to derive the MACE-Revasc risk models, resulting in models with 5 and 8 weighted factors. The PET/CT 90-day MACE-Revasc risk score trended toward outperforming ischemic burden alone [P = .07 with an area under the curve (AUC) 0.85 vs 0.83]. The PET/CT one-year MACE-Revasc score was better than the use of ischemic burden alone (P < .0001, AUC 0.80 vs 0.76). Both PET/CT MACE-Revasc risk scores outperformed risk prediction by cardiologists.

CONCLUSION

The derived PET/CT 90-day and one-year MACE-Revasc risk scores were highly predictive and outperformed ischemic burden and cardiologist assessment. These scores are easy to calculate, lending to straightforward clinical implementation and should be further tested for clinical usefulness.

Automated Identification of Coronary Arteries in Assisting Inexperienced Readers: Comparison between Two Commercial Vendors

Background: to assess the performance and speed of two commercially available advanced cardiac software packages in the automated identification of coronary vessels as an aiding tool for inexperienced readers. Methods: Hundred and sixty patients undergoing coronary CT angiography (CCTA) were prospectively enrolled from February until September 2021 and randomized in two groups, each one composed by 80 patients. Patients in group 1 were scanned on Revolution EVO CT Scanner (GE Healthcare), while patients in group 2 had the CCTA performed on Brilliance iCT (Philips Healthcare); each examination was evaluated on the respective vendor proprietary advanced cardiac software (software 1 and 2, respectively). Two inexperienced readers in cardiac imaging verified the software performance in the automated identification of the three major coronary vessels: (RCA, LCx, and LAD) and in the number of identified coronary segments. Time of analysis was also recorded. Results: software 1 correctly and automatically nominated 202/240 (84.2%) of the three main coronary vessels, while software 2 correctly identified 191/240 (79.6%) (p = 0.191). Software 1 achieved greater performances in recognizing the LCx (81.2% versus 67.5%; p = 0.048), while no differences have been reported in detecting the RCA (p = 0.679), and the LAD (p = 0.618). On a per-segment analysis, software 1 outperformed software 2, automatically detecting 942/1062 (88.7%) coronary segments, while software 2 detected 797/1078 (73.9%) (p < 0.001). Average reconstruction and detection time was of 13.8 s for software 1 and 21.9 s for software 2 (p < 0.001). Conclusions: automated cardiac software packages are a reliable and time-saving tool for inexperienced reader. Software 1 outperforms software 2 and might therefore better assist inexperienced CCTA readers in automated identification of the three main vessels and coronaries segments, with a consistent time saving of the reading session.

Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects

Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.

Diagnostic Accuracy of Subtraction Coronary CT Angiography in Severely Calcified Segments: Comparison Between Readers With Different Levels of Experience

Purpose

Subtraction coronary CT angiography (CCTA) may reduce blooming and beam-hardening artifacts. This study aimed to assess its value in improving the diagnostic accuracy of readers with different experience levels.

Method

We prospectively enrolled patients with target segment who underwent CCTA and invasive coronary angiography (ICA). Target segment images were independently evaluated by three groups of radiologists with different experience levels with CCTA using ICA as the standard reference. Diagnostic accuracy was measured by the area under the curve (AUC), using ≥50% stenosis as the cut-off value.

Results

In total, 134 target segments with severe calcification from 47 patients were analyzed. The mean specificity of conventional CCTA for each group ranged from 22.4 to 42.2%, which significantly improved with subtraction CCTA, ranging from 81.3 to 85.7% (all p &lt; 0.001). The mean sensitivity of conventional CCTA for each group ranged from 83.3 to 88.0%. Following calcification subtraction, the mean sensitivity decreased for the novice (p &lt; 0.001) and junior (p = 0.017) radiologists but was unchanged for the senior radiologists (p = 0.690). With subtraction CCTA, the mean AUCs of CCTA significantly increased: values ranged from 0.53, 0.54, and 0.61 to 0.70, 0.74, and 0.85 for the novice, junior, and senior groups (all p &lt; 0.001).

Conclusion

Subtraction CCTA could improve the diagnostic accuracy of radiologists at all experience levels of CCTA interpretation.

Quality and safety of coronary computed tomography angiography at academic and non-academic sites: insights from a large European registry (ESCR MR/CT Registry)

OBJECTIVES

To compare the use of coronary computed tomography angiography (CCTA) between academic and non-academic sites across Europe over the last decade.

METHODS

We analyzed a large multicenter registry (ESCR MR/CT Registry) of stable symptomatic patients who received CCTA 01/2010-01/2020 at 47 (22%) academic and 165 (78%) non-academic sites across 19 European countries. We compared image quality, radiation dose, contrast-media-related adverse events, patient characteristics, CCTA findings, and downstream testing between academic and non-academic sites.

RESULTS

Among 64,317 included patients (41% female; 60 ± 13 years), academic sites accounted for most cases in 2010-2014 (52%), while non-academic sites dominated in 2015-2020 (71%). Despite less contemporary technology, non-academic sites maintained low radiation doses (4.76 [2.46-6.85] mSv) with a 30% decline of high-dose scans ( > 7 mSv) over time. Academic and non-academic sites both reported diagnostic image quality in 98% of cases and low rate of scan-related adverse events (0.4%). Academic and non-academic sites examined similar patient populations (41% females both; age: 61 ± 14 vs. 60 ± 12 years; pretest probability for obstructive CAD: low 21% vs. 23%, intermediate 73% vs. 72%, high 6% both, CAD prevalence on CCTA: 40% vs. 41%). Nevertheless, non-academic sites referred more patients to non-invasive ischemia testing (6.5% vs. 4.2%) and invasive coronary angiography/surgery (8.5% vs. 5.6%).

CONCLUSIONS

Non-academic and academic sites provide safe, high-quality CCTA across Europe, essential to successfully implement the recently updated guidelines for the diagnosis and management of chronic coronary syndromes. However, despite examining similar populations with comparable CAD prevalence, non-academic sites tend to refer more patients to downstream testing.

KEY POINTS

• Smaller non-academic providers increasingly use CCTA to rule out obstructive coronary artery disease. • Non-academic and academic sites provide comparably safe, high-quality CCTA across Europe. • Compared to academic sites, non-academic sites tend to refer more patients to downstream testing.

Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience

Background

To investigate the influence of artificial intelligence (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.

Methods

We enrolled 196 patents who had undergone both coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1–Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥ 50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with AI system assistance, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of the six readers were calculated at the patient and vessel levels. Additionally, we evaluated the interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader 4, and Reader 5 and Reader 6.

Results

The AI system had 94% and 78% sensitivity at the patient and vessel levels, respectively, which were higher than that of Reader 5 and Reader 6. AI-assisted Reader 3 and Reader 4 had higher sensitivity (range + 7.2–+ 16.6% and + 5.9–+ 16.1%, respectively) and NPVs (range + 3.7–+ 13.4% and + 2.7–+ 4.2%, respectively) than Reader 1 and Reader 2 without AI. Good interobserver consistency was found between Reader 3 and Reader 4 in interpreting ≥ 50% stenosis (Kappa value = 0.75 and 0.80 at the patient and vessel levels, respectively). Only Reader 1 and Reader 2 showed poor interobserver consistency (Kappa value = 0.25 and 0.37). Reader 5 and Reader 6 showed moderate agreement (Kappa value = 0.55 and 0.61).

Conclusions

Our study showed that using AI could effectively increase the sensitivity of inexperienced readers and significantly improve the consistency of coronary stenosis diagnosis via CCTA.

Trial registration Clinical trial registration number: ChiCTR1900021867. Name of registry: Diagnostic performance of artificial intelligence-assisted coronary computed tomography angiography for the assessment of coronary atherosclerotic stenosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00756-y.

Learning Curve for CT-Guided Percutaneous Transthoracic Core Needle Biopsy: Retrospective Evaluation Among 17 Thoracic Imaging Fellows at a Tertiary Referral Hospital

To listen to the podcast associated with this article, please select one of the following: iTunes, Google Play, or direct download. Background: CT-guided percutaneous transthoracic needle biopsy (PTNB) is widely used for evaluation of indeterminate pulmonary lesions, though guidelines are lacking regarding the experience needed to gain sufficient skill. Objective: To investigate the learning curve among a large number of operators in a tertiary referral hospital and to determine the number of procedures required to obtain acceptable performance. Methods: This retrospective study included CT-guided PTNBs with coaxial technique performed by 17 thoracic imaging fellows from March 2011 to August 2017 who were novices in the procedure. A maximum of 200 consecutive procedures per operator were included. Cumulative summation method was used to assess learning curves for diagnostic accuracy, false negative rate, pneumothorax rate, and hemoptysis rate. Operators were assessed individually and in a pooled analysis. Pneumothorax risk was also assessed in a model adjusting for risk factors. Acceptable failure rates were defined as 0.1 for diagnostic accuracy and false negative rate; 0.45 for pneumothorax rate; and 0.05 for hemoptysis rate. Results: The study included 3261 procedures in 3134 patients (mean age, 67.7±12.1 years; 1876 men, 1258 women). Overall diagnostic accuracy was 94.2% (2960/3141). All 17 operators achieved acceptable diagnostic accuracy [37 procedures required in pooled analysis; median of 33 procedures (range 19-67) required]. Overall false negative rate was 7.6% (179/2370). All 17 operators achieved acceptable false negative rate [52 procedures required in pooled analysis; median of 33 procedures (range 19-95) required]. Pneumothorax occurred in 32.6% (1063/3261), and hemoptysis in 2.7% (89/3261). All 17 operators achieved acceptable pneumothorax rate [20 procedures required in pooled analysis; median of 19 procedures (range 7-63) required]. In the risk-adjusted model, 15 operators achieved acceptable pneumothorax rate [54 procedures required in pooled analysis; median of 36 procedures (range 10-192) required]. Sixteen operators achieved acceptable hemoptysis rate [67 procedures required in pooled analysis; median of 55 procedures (range 41-152) required]. Conclusion: For CT-guided PTNB, at least 37 and 52 procedures are required to achieve acceptable diagnostic accuracy and false negative rate, respectively. Not all operators achieved acceptable complication rates. Clinical Impact: The findings may help set standards for training, supervision, and ongoing assessment of proficiency for this procedure.

A Computationally Efficient Approach to Segmentation of the Aorta and Coronary Arteries Using Deep Learning

Early detection and diagnosis of coronary artery disease could reduce the risk of developing a heart attack. The coronary arteries are optimally visualised using computed tomography coronary angiography (CTCA) imaging. These images are reviewed by specialist radiologists who evaluate the coronary arteries for potential narrowing. A lack of radiologists in the UK is a constraint to timely diagnosis of coronary artery disease, particularly in the acute accident and emergency department setting. The development of automated methods by which coronary artery narrowing can be identified rapidly and accurately are therefore timely. Such complex computer based tools also need to be sufficiently computationally efficient that they can run on servers typically found in hospital settings, where graphical processing units for example are unavailable. We propose a fully automatic two-dimensional Unet model to segment the aorta and coronary arteries on CTCA images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Importantly, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed without requiring graphical processing units, and therefore can be used in a hospital setting.

Are Training Programs Ready for the Rapid Adoption of CCTA?: CBME in CCTA

OBJECTIVES

This study sought to assess training volumes and its relationship to learning and identify potential new thresholds for determining expertise.

BACKGROUND

Competency-based medical education (CBME) is being rapidly adopted and therefore training programs will need to adapt and identify new and novel methods of defining, measuring, and assessing clinical skills.

METHODS

Consecutive cardiac computed tomography (CT) studies were interpreted independently by trainees and expert readers, and their interpretations (Agatston score, coronary artery disease severity, and Coronary Artery Disease Reporting and Data System) were collected. Kappa agreements were measured between trainees and experts for every 50 consecutive cases. Agreements between trainees and experts were tracked and compared with the agreement between expert readers.

RESULTS

A total of 36 trainees interpreted 14,432 cardiac CT studies. Agreement between trainees and experts increased with CT case volumes, but trainees learned at different rates. Using a threshold for expertise, skill of measuring coronary calcification was achieved within 50 cases, but expertise for coronary CT angiography appeared to require a mean case volume of 750, comprising 400 abnormal cases.

CONCLUSIONS

Current volume-based training guidelines may be insufficient and higher case volumes may be required. We demonstrate that tracking cardiac CT learners is feasible and that CBME could be incorporated into CT training programs.

Pilot study of the multicentre DISCHARGE Trial: image quality and protocol adherence results of computed tomography and invasive coronary angiography

OBJECTIVE

To implement detailed EU cardiac computed tomography angiography (CCTA) quality criteria in the multicentre DISCHARGE trial (FP72007-2013, EC-GA 603266), we reviewed image quality and adherence to CCTA protocol and to the recommendations of invasive coronary angiography (ICA) in a pilot study.

MATERIALS AND METHODS

From every clinical centre, imaging datasets of three patients per arm were assessed for adherence to the inclusion/exclusion criteria of the pilot study, predefined standards for the CCTA protocol and ICA recommendations, image quality and non-diagnostic (NDX) rate. These parameters were compared via multinomial regression and ANOVA. If a site did not reach the minimum quality level, additional datasets had to be sent before entering into the final accepted database (FADB).

RESULTS

We analysed 226 cases (150 CCTA/76 ICA). The inclusion/exclusion criteria were not met by 6 of the 226 (2.7%) datasets. The predefined standard was not met by 13 of 76 ICA datasets (17.1%). This percentage decreased between the initial CCTA database and the FADB (multinomial regression, 53 of 70 vs 17 of 75 [76%] vs [23%]). The signal-to-noise ratio and contrast-to-noise ratio of the FADB did not improve significantly (ANOVA, p = 0.20; p = 0.09). The CTA NDX rate was reduced, but not significantly (initial CCTA database 15 of 70 [21.4%]) and FADB 9 of 75 [12%]; p = 0.13).

CONCLUSION

We were able to increase conformity to the inclusion/exclusion criteria and CCTA protocol, improve image quality and decrease the CCTA NDX rate by implementing EU CCTA quality criteria and ICA recommendations.

KEY POINTS

• Failure to meet protocol adherence in cardiac CTA was high in the pilot study (77.6%). • Image quality varies between sites and can be improved by feedback given by the core lab. • Conformance with new EU cardiac CT quality criteria might render cardiac CTA findings more consistent and comparable.

Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey

Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.

Texture analysis of myocardial infarction in CT: Comparison with visual analysis and impact of iterative reconstruction

OBJECTIVES

To compare texture analysis (TA) with subjective visual diagnosis of myocardial infarction (MI) in cardiac computed tomography (CT) and to evaluate the impact of iterative reconstruction (IR).

METHODS

Ten patients (4 women, mean age 68 ± 11 years) with confirmed chronic MI and 20 controls (8 women, mean age 52 ± 11 years) with no cardiac abnormality underwent contrast-enhanced cardiac CT with the same protocol. Images were reconstructed with filtered back projection (FBP) and with advanced modeled IR at strength levels 3-5. Subjective diagnosis of MI was made by three independent, blinded readers with different experience levels. Classification of MI was performed using machine learning-based decision tree models for the entire data set and after splitting into training and test data to avoid overfitting.

RESULTS

Subjective visual analysis for diagnosis of MI showed excellent intrareader (kappa: 0.93) but poor interreader agreement (kappa: 0.3), with variable performance at different image reconstructions. TA showed high performance for all image reconstructions (correct classifications: 94%-97%, areas under the curve: 0.94-0.99). After splitting into training and test data, overall lower performances were observed, with best results for IR at level 5 (correct classifications: 73%, area under the curve: 0.65).

CONCLUSIONS

As compared with subjective, nonreliable visual analysis of inexperienced readers, TA enables objective and reproducible diagnosis of chronic MI in cardiac CT with higher accuracy. IR has a considerable impact on both subjective and objective image analysis.

Decision Support Tools, Systems, and Artificial Intelligence in Cardiac Imaging

Noninvasive cardiac imaging is widely used for the diagnosis and management of cardiac patients. The increasing demand for cardiac imaging begins to exceed the number of available interpreting physicians, leaving less time to interpret studies. In addition, the busy clinician is facing the increasingly daunting task of keeping abreast of current medical advancements and the ongoing changes in disease diagnosis and therapy. Committing to memory and recalling such large volumes of information is challenging and is responsible for difficulties in adopting the rapid changes in imaging practice, and is likely partially responsible for errors in patient diagnosis and management. Diagnostic errors rank high in the cause of death in the United States, and are more common than any other medical error and are responsible for most malpractice claims. Most of these errors are related to cognitive errors. The use of artificial intelligence systems that can serve as complementary methods to assist humans with decision making can potentially prevent these errors. The past decades witnessed the development and integration of these tools, which can assist physicians with image interpretation. These tools work to optimize image quality for better visualization and accompany all imaging modalities, starting from patient selection for the appropriate test, patient preparation, image acquisition, processing, and finally interpretation. Current and future directions for technologies that support cardiac imaging physicians are discussed in this review.

Inter-observer agreement of the Coronary Artery Disease Reporting and Data System (CAD-RADSTM) in patients with stable chest pain

Purpose

To assess inter-observer variability of the Coronary Artery Disease - Reporting and Data System (CAD-RADS) for classifying the degree of coronary artery stenosis in patients with stable chest pain.

Material and methods

A prospective study was conducted upon 96 patients with coronary artery disease, who underwent coronary computed tomography angiography (CTA). The images were classified using the CAD-RAD system according to the degree of stenosis, the presence of a modifier: graft (G), stent (S), vulnerable plaque (V), or non-diagnostic (n) and the associated coronary anomalies, and non-coronary cardiac and extra-cardiac findings. Image analysis was performed by two reviewers. Inter-observer agreement was assessed.

Results

There was excellent inter-observer agreement for CAD-RADS (k = 0.862), at 88.5%. There was excellent agreement for CAD-RADS 0 (k = 1.0), CAD-RADS 1 (k = 0.92), CAD-RADS 3 (k = 0.808), CAD-RADS 4 (k = 0.826), and CAD-RADS 5 (k = 0.833) and good agreement for CAD-RADS 2 (k = 0.76). There was excellent agreement for modifier G (k = 1.0) and modifier S (k = 1.0), good agreement for modifier N (k = 0.79), and moderate agreement for modifier V (k = 0.59). There was excellent agreement for associated coronary artery anomalies (k = 0.845), non-coronary cardiac findings (k = 0.857), and extra-cardiac findings (k = 0.81).

Conclusions

There is inter-observer agreement of CAD-RADS in categorising the degree of coronary arteries stenosis, and the modifier of the system and associated cardiac and extra-cardiac findings.

Learning curve and period of experience required for the competent diagnosis of acute appendicitis using abdominal computed tomography: a prospective observational study

Objective

To assess the learning curve of novice residents in diagnosing acute appendicitis using abdominal computed tomography (CT) scans.

Methods

This prospective observational study was conducted within a 4-month period from March 1 to June 30, 2015. After CT scans for right lower quadrant pain or similar acute abdomen were evaluated, postgraduate year 1 (PGY-1) residents completed an interpretation checklist. The primary outcome was evaluation of the learning curve for competent CT scan interpretation under suspicion of acute appendicitis. Secondary outcomes were cumulative numbers of accurate abdominal CT interpretations regardless of initial clinical impression and training period.

Results

PGY-1 residents recorded a total of 230 interpretation checklists. There were 53, 51, 46, 44, and 36 checklists recorded by individual residents and 92, 92, 91, 91, and 61 respective training days in the emergency department, excluding rotation periods in other departments. After 16 to 20 interpretations of abdominal CT scans performed under suspicion of acute appendicitis, the residents could diagnose acute appendicitis with more than 95% accuracy. Overall, the sensitivity and specificity for diagnosing acute appendicitis were 97% (95% confidence interval, 94 to 100) and 83% (95% confidence interval, 80 to 87), respectively. After 61 to 80 abdominal CT interpretations regardless of suspicion of acute appendicitis and after 41 to 50 days in training, PGY-1 emergency department residents could diagnose acute appendicitis with more than 95% accuracy.

Conclusion

PGY-1 residents require 16 to 20 checklist interpretations to acquire acceptable abdominal CT interpretation. After performing 61 to 80 CT scans regardless of suspicion of acute appendicitis, they could diagnose acute appendicitis with acceptable accuracy.

Central Core Laboratory versus Site Interpretation of Coronary CT Angiography: Agreement and Association with Cardiovascular Events in the PROMISE Trial

Purpose To assess concordance and relative prognostic utility between central core laboratory and local site interpretation for significant coronary artery disease (CAD) and cardiovascular events. Materials and Methods In the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial, readers at 193 North American sites interpreted coronary computed tomographic (CT) angiography as part of the clinical evaluation of stable chest pain. Readers at a central core laboratory also interpreted CT angiography blinded to clinical data, site interpretation, and outcomes. Significant CAD was defined as stenosis greater than or equal to 50%; cardiovascular events were defined as a composite of cardiovascular death or myocardial infarction. Results In 4347 patients (51.8% women; mean age ± standard deviation, 60.4 years ± 8.2), core laboratory and site interpretations were discordant in 16% (683 of 4347), most commonly because of a finding of significant CAD by site but not by core laboratory interpretation (80%, 544 of 683). Overall, core laboratory interpretation resulted in 41% fewer patients being reported as having significant CAD (14%, 595 of 4347 vs 23%, 1000 of 4347; P < .001). Over a median follow-up period of 25 months, 1.3% (57 of 4347) sustained myocardial infarction or cardiovascular death. The C statistic for future myocardial infarction or cardiovascular death was 0.61 (95% confidence interval [CI]: 0.54, 0.68) for the core laboratory and 0.63 (95% CI: 0.56, 0.70) for the sites. Conclusion Compared with interpretation by readers at 193 North American sites, standardized core laboratory interpretation classified 41% fewer patients as having significant CAD. ^© RSNA, 2017 Online supplemental material is available for this article. Clinical trial registration no. NCT01174550.

Outcomes of anatomical vs. functional testing for coronary artery disease : Lessons from the PROMISE trial

The development of coronary artery disease (CAD) is a major, final common pathway in heart disease worldwide. With a rise in stress testing and increased scrutiny on cost-effectiveness and radiation exposure in medical imaging, a focus on the relative merits of anatomic versus functional characterization of CAD has emerged. In this context, coronary computed tomography angiography (CCTA) is a noninvasive alternative to functional testing as a first-line test for CAD detection but is complimentary in its nature. Here, we discuss the design, results, and implications of the PROMISE trial, a randomized comparative effectiveness study of 10,003 patients across 193 sites in the United States and Canada comparing the prognostic and diagnostic power of CCTA and standard stress testing. Specifically, we discuss the safety (e. g., contrast, radiation exposure) of CCTA versus functional testing in CAD, the need for improved selection for noninvasive testing, the frequency of downstream testing after anatomic or functional imaging, the use of imaging results in clinical management, and novel modalities of CAD risk determination using CCTA. PROMISE demonstrated that in a real-world, low-to-intermediate risk patient population referred to noninvasive testing for CAD, both CCTA and functional testing approaches have similar clinical, economic, and safety-based outcomes. We conclude with open questions in CAD imaging, specifically as they pertain to the utilization of CCTA.

Color-coded visualization of magnetic resonance imaging multiparametric maps

Multiparametric magnetic resonance imaging (mpMRI) data are emergingly used in the clinic e.g. for the diagnosis of prostate cancer. In contrast to conventional MR imaging data, multiparametric data typically include functional measurements such as diffusion and perfusion imaging sequences. Conventionally, these measurements are visualized with a one-dimensional color scale, allowing only for one-dimensional information to be encoded. Yet, human perception places visual information in a three-dimensional color space. In theory, each dimension of this space can be utilized to encode visual information. We addressed this issue and developed a new method for tri-variate color-coded visualization of mpMRI data sets. We showed the usefulness of our method in a preclinical and in a clinical setting: In imaging data of a rat model of acute kidney injury, the method yielded characteristic visual patterns. In a clinical data set of N = 13 prostate cancer mpMRI data, we assessed diagnostic performance in a blinded study with N = 5 observers. Compared to conventional radiological evaluation, color-coded visualization was comparable in terms of positive and negative predictive values. Thus, we showed that human observers can successfully make use of the novel method. This method can be broadly applied to visualize different types of multivariate MRI data.

Evaluation of computed tomography in patients with atypical angina or chest pain clinically referred for invasive coronary angiography: randomised controlled trial

OBJECTIVE

 To evaluate whether invasive coronary angiography or computed tomography (CT) should be performed in patients clinically referred for coronary angiography with an intermediate probability of coronary artery disease.

DESIGN

 Prospective randomised single centre trial.

SETTING

 University hospital in Germany.

PARTICIPANTS

 340 patients with suspected coronary artery disease and a clinical indication for coronary angiography on the basis of atypical angina or chest pain.

INTERVENTIONS

 168 patients were randomised to CT and 172 to coronary angiography. After randomisation one patient declined CT and 10 patients declined coronary angiography, leaving 167 patients (88 women) and 162 patients (78 women) for analysis. Allocation could not be blinded, but blinded independent investigators assessed outcomes.

MAIN OUTCOME MEASURE

 The primary outcome measure was major procedural complications within 48 hours of the last procedure related to CT or angiography.

RESULTS

 Cardiac CT reduced the need for coronary angiography from 100% to 14% (95% confidence interval 9% to 20%, P<0.001) and was associated with a significantly greater diagnostic yield from coronary angiography: 75% (53% to 90%) v 15% (10% to 22%), P<0.001. Major procedural complications were uncommon (0.3%) and similar across groups. Minor procedural complications were less common in the CT group than in the coronary angiography group: 3.6% (1% to 8%) v 10.5% (6% to 16%), P=0.014. CT shortened the median length of stay in the angiography group from 52.9 hours (interquartile range 49.5-76.4 hours) to 30.0 hours (3.5-77.3 hours, P<0.001). Overall median exposure to radiation was similar between the CT and angiography groups: 5.0 mSv (interquartile range 4.2-8.7 mSv) v 6.4 mSv (3.4-10.7 mSv), P=0.45. After a median follow-up of 3.3 years, major adverse cardiovascular events had occurred in seven of 167 patients in the CT group (4.2%) and six of 162 (3.7%) in the coronary angiography group (adjusted hazard ratio 0.90, 95% confidence interval 0.30 to 2.69, P=0.86). 79% of patients stated that they would prefer CT for subsequent testing. The study was conducted at a University hospital in Germany and thus the performance of CT may be different in routine clinical practice. The prevalence was lower than expected, resulting in an underpowered study for the predefined primary outcome.

CONCLUSIONS

 CT increased the diagnostic yield and was a safe gatekeeper for coronary angiography with no increase in long term events. The length of stay was shortened by 22.9 hours with CT, and patients preferred non-invasive testing.Trial registration ClinicalTrials.gov NCT00844220.

Diagnostic performance of smartphone reading of the coronary CT angiography in patients with acute chest pain at ED

PURPOSE

The aims of this study were to simulate mobile consultation for the coronary computed tomography angiography (CCTA) at the emergency department (ED) and to measure the diagnostic performance of the mobile reading.

MATERIALS AND METHODS

A total of 107 patients with acute chest pain who underwent CCTA and coronary angiography (CAG) were included. The CCTA images were reviewed by a cardiac radiologist using a smartphone. The degree of stenosis at each coronary segment was scored with 4-point scale (score 1, <50%; score 2, 51%-70%; score 3, 71%-90%; score 4, >90%). The degree of stenosis at each coronary segments were also scored with preliminary CCTA report by on-call residents, final CCTA reports by in-house attending cardiac radiologists, and CAG. Interobserver agreement was measured using κ statistics. The areas under the receiver operating characteristic curves (AUCs) for diagnosing segments with obstructive stenosis were compared between each reader and CAG.

RESULTS

The smartphone reader's reading was more similar to the CAG results and in-house radiologists' reports than reading of on-call residents. The diagnostic performance of smartphone reading for detection of obstructive stenosis was significantly greater than that of on-call residents (AUC, 0.89 vs 0.75; P<.001) and did not significantly differ from that of the in-house radiologists (AUC, 0.89 vs 0.90; P=.05).

CONCLUSION

Smartphone reading by the cardiac radiologist was superior to the on-call residents' reading. Further study with real-time mobile consultation needs to be investigated to evaluate whether improvement in diagnostic competency can make a difference in the outcome of patients.

Initial Performance of Radiologists and Radiology Residents in Interpreting Low-Dose (2-mSv) Appendiceal CT

OBJECTIVE

The objective of our study was to prospectively evaluate the initial diagnostic performance and learning curve of a community of radiologists and residents in interpreting 2-mSv appendiceal CT.

SUBJECTS AND METHODS

We included 46 attending radiologists and 153 radiology residents from 22 hospitals who completed an online training course of 30 2-mSv CT cases. Appendicitis was confirmed in 14 cases. Most of the readers had limited (≤ 10 cases, n = 32) or no (n = 118) prior experience with low-dose appendiceal CT. The order of cases was randomized for each reader. A multireader multicase ROC analysis was performed. Generalized estimating equations were used to model the learning curves in diagnostic performance.

RESULTS

Diagnostic performance gradually improved with years of training. The average AUC was 0.94 (95% CI, 0.90-0.98), 0.92 (0.88-0.96), 0.90 (0.85-0.96), and 0.86 (0.80-0.92) for the attending radiologists, senior residents, 2nd-year residents, and 1st-year residents, respectively. We did not observe any notable intrareader learning curves over the training course of the 30 cases except a decrease in reading time. Diagnostic accuracy and sensitivity were significantly affected by the reader training level and prior overall experience with appendiceal CT but not by the prior specific experience with low-dose appendiceal CT.

CONCLUSION

The learning curve is likely prolonged and forms gradually over years by overall radiology training and clinical experience in general rather than by experience with low-dose appendiceal CT specifically.

Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis [Formula: see text]. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis [Formula: see text]. Visual identification of lesions with stenosis [Formula: see text] by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.

Residents' performance in the interpretation of on-call "triple-rule-out" CT studies in patients with acute chest pain

RATIONALE AND OBJECTIVES

To evaluate the performance of radiology residents in the interpretation of on-call, emergency "triple-rule-out" (TRO) computed tomographic (CT) studies in patients with acute chest pain.

MATERIALS AND METHODS

The study was institutional review board-approved and Health Insurance Portability and Accountability Act compliant. Data from 617 on-call TRO studies were analyzed. Dedicated software enables subspecialty attendings to grade discrepancies in interpretation between preliminary trainee reports and their final interpretation as "unlikely to be significant" (minor discrepancies) or "likely to be significant" for patient management (major discrepancies). The frequency of minor, major and all discrepancies in resident's TRO interpretations was compared to 609 emergent non-electrocardiography (ECG)-synchronized chest CT studies using Pearson χ(2) test.

RESULTS

Minor discrepancies occurred more often in the TRO group (9.1% vs. 3.9%, P < .001), but there was no difference in the frequency of major discrepancies (2.1% vs. 2.8%, P = .55). Minor discrepancies in the TRO group most commonly resulted from missed extrathoracic findings with missed liver lesions being the most frequent. Major discrepancies mostly encompassed cardiac and extracardiac vascular findings but did not result in unnecessary interventions, significant immediate changes in management, or adverse patient outcomes.

CONCLUSIONS

On-call resident interpretation of TRO CT studies in patients with acute chest pain is congruent with final subspecialty attending interpretation in the overwhelming majority of cases. The rate of discrepancies likely to affect patient management in this domain is not different from emergent non-ECG-synchronized chest CT.

Evaluation of novice reader diagnostic performance in coronary CT angiography using an advanced cardiac software package

Purpose

The purpose of this research was to evaluate whether a commercially available advanced cardiac software package for coronary CT angiography (CTA) interpretation may reliably assist inexperienced readers to screen for significant coronary artery stenoses.

Methods

Coronary CTA data sets of 61 consecutive patients with suspected coronary artery disease were evaluated by three novice readers with no experience in cardiac CT interpretation. In the first 15 patients, the novice readers were trained to use the advanced cardiac software package (includes automatic detection of coronary vessels, curved MPR and VRT reconstructions and a measurement too) knowing the results of an expert read. In the next 46 patients, the novice readers had to state whether there is a significant coronary artery stenosis (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>$$\end{document}>50 %) and if they are confident with their diagnosis. The results of the novice readers were compared to the expert read.

Results

The 46 coronary CTA data sets contained 184 vessels with 15 stenoses in 9 patients. On a per-vessel analysis, novice reader 1/2/3 demonstrated 60 %/100 %/ 93% sensitivity, and 98 %/90 %/86 % specificity. Per patient, the readers diagnosed 36/28/29 cases correctly as free of stenoses, 6/9/8 correctly as having at least one stenosis, missed 3/0/1 cases with a stenosis and overdiagnosed 1/9/8 patients. Cohen’s kappa values for the three readers versus the expert were 0.60, 0.61 and 0.54. The three novice readers felt confident in the diagnosis of 36/33/30 patients. In these patients, they missed one significant stenosis, showed a sensitivity of 100 %/100 %/75 % and a specificity of 100 %/92 %/88 %.

Conclusions

The evaluated advanced cardiac software package successfully assists novice readers in interpreting coronary CTA data sets especially in ruling out significant coronary artery stenosis.

Automated knowledge-based detection of nonobstructive and obstructive arterial lesions from coronary CT angiography

PURPOSE

Visual analysis of three-dimensional (3D) coronary computed tomography angiography (CCTA) remains challenging due to large number of image slices and tortuous character of the vessels. The authors aimed to develop a robust, automated algorithm for unsupervised computer detection of coronary artery lesions.

METHODS

The authors' knowledge-based algorithm consists of centerline extraction, vessel classification, vessel linearization, lumen segmentation with scan-specific lumen attenuation ranges, and lesion location detection. Presence and location of lesions are identified using a multi-pass algorithm which considers expected or "normal" vessel tapering and luminal stenosis from the segmented vessel. Expected luminal diameter is derived from the scan by automated piecewise least squares line fitting over proximal and mid segments (67%) of the coronary artery considering the locations of the small branches attached to the main coronary arteries.

RESULTS

The authors applied this algorithm to 42 CCTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥ 25%. The reference standard was provided by visual and quantitative identification of lesions with any stenosis ≥ 25% by three expert readers using consensus reading. The authors algorithm identified 42 lesions (93%) confirmed by the expert readers. There were 46 additional lesions detected; 23 out of 39 (59%) of these were less-stenosed lesions. When the artery was divided into 15 coronary segments according to standard cardiology reporting guidelines, per-segment basis, sensitivity was 93% and per-segment specificity was 81% using 10-fold cross-validation.

CONCLUSIONS

The authors' algorithm shows promising results in the detection of both obstructive and nonobstructive CCTA lesions.

Magnetic resonance imaging frequently changes classification of acute traumatic thoracolumbar spine injuries

OBJECTIVE

To evaluate the influence of additional (MRI) compared with computed tomography (CT) alone for the classification of traumatic spinal injuries using the Arbeitsgemeinshaft für Osteosynthesefragen (AO) system and the Thoraco-Lumbar Injury Classification and Severity (TLICS) scale.

MATERIALS AND METHODS

Images from 100 consecutive patients with at least one fracture on CT were evaluated retrospectively by three radiologists with regard to the AO and TLICS classification systems in 2 steps. First, all images from the initial CT examination were analyzed. Second, 6 weeks later, CT and MR images were analyzed together. Descriptive statistics and Wilcoxon tests were performed to identify changes in the number of fractures and ligamentous lesions detected and their corresponding classification.

RESULTS

CT and MRI together revealed a total of 196 fractures (CT alone 162 fractures). The AO classification changed in 31 %, the TLICS classification changed in 33 % of the patients compared with CT alone. Based on CT and MRI together, the TLICS value changed from values <5 (indication for conservative therapy) to values ≥ 5 (indication for surgical therapy) in 24 %.

CONCLUSION

MRI of patients with thoracolumbar spinal trauma considerably improved the detection of fractures and soft tissue injuries compared with CT alone and significantly changed the overall trauma classification.

Learning curve in multidetector CT coronary angiography (MDCT-CA)

PURPOSE

Coronary angiography using multidetector computed tomography (MDCT-CA) is a recent technique for the nonivasive study of coronary arteries. This study assessed the diagnostic accuracy of coronary artery stenosis evaluation obtained by three readers at different levels of training or at different points of the learning curve proposed by the international guidelines.

MATERIALS AND METHODS

Three radiologists in training with different levels of experience in MDCT-CA scored 50 cases at various time points of the learning curve: baseline, 4 weeks, 8 weeks and 6 months. The trainee radiologists evaluated the degree of stenosis on each coronary segment, and overall accuracy was calculated on a per-segment, pervessel and per-patient basis.

RESULTS

All readers improved analysis accuracy per segment (range, 73-90%); sensitivity reached 45% per segment, 84% per vessel and 93% per patient; specificity was 99% per segment and vessel and 98% per patient. Positive and negative predictive values increased to 94% and 92%, respectively.

CONCLUSIONS

Although all readers improved in diagnostic performance with growing experience with MDCT-CA, a longer training period may be necessary to achieve adequate levels of expertise in MDCT-CA to be able to perform as independent readers.

US FDA draft Guidance Standard for Clinical Trial Imaging Endpoints: more than just imaging?

The current cost of developing a successful drug is typically over a billion dollars, with the registration trial(s) determining the success or failure of the entire development program. Often the primary endpoint of these trials is a subjective assessment. For registration trials with subjective endpoints, a regulatory agency may require a blinded independent central review (BICR) of the trial data. The BICR is a mechanism to reduce bias in open-labeled trials and to potentially increase accuracy and precision. A decision tree algorithm has been developed that can be used to determine when and what type of a BICR is needed. The US FDA draft Guidance Standard for Clinical Trial Imaging Endpoints can be used as an effective process map in exploring the value and use of BICRs in imaging, and in any hard to interpret variable subjective assessment in general.

Effect of dose reduction on image quality and diagnostic performance in coronary computed tomography angiography

To evaluate the effect of radiation dose reduction on image quality and diagnostic accuracy of coronary computed tomography (CT) angiography. Coronary CT angiography studies of 40 patients with (n = 20) and without (n = 20) significant (≥50 %) stenosis were included (26 male, 14 female, 57 ± 11 years). In addition to the original clinical reconstruction (100 % dose), simulated images were created that correspond to 50, 25 and 12.5 % of the original dose. Image quality and diagnostic performance in identifying significant stenosis were determined. Receiver–operator-characteristics analysis was used to assess diagnostic accuracy at different dose levels. The identification of patients with significant stenosis decreased consistently at doses of 50, 25 and 12.5 of the regular clinical acquisition (100 %). The effect was relatively weak at 50 % dose, and was strong at dose levels of 25 and 12.5 %. At lower doses a steady increase was observed for false negative findings. The number of coronary artery segments that were rated as diagnostic decreased gradually with dose, this was most prominent for smaller segments. The area-under-the-curve (AUC) was 0.90 (p = 0.4) at 50 % dose; accuracy decreased significantly with 25 % (AUC 0.70) and 12.5 % dose (AUC 0.60) (p < 0.0001), with underestimation of patients having significant stenosis. The clinical acquisition protocol for evaluation of coronary artery stenosis with CT angiography represents a good balance between image quality and patient dose. A potential for a modest (<50 %) reduction of tube current might exist. However, more substantial reduction of tube current will reduce diagnostic performance of coronary CT angiography substantially.

Low-dose coronary-CT angiography using step and shoot at any heart rate: comparison of image quality at systole for high heart rate and diastole for low heart rate with a 128-slice dual-source machine

To compare image quality of coronary CT angiography in step-and-shoot mode at the diastolic phase at low heart rates (<70 bpm) and systolic phase at high heart rates (≥70 bpm). We prospectively included 96 consecutive patients then excluded 5 patients with arrhythmia. Coronary CT-angiography was performed using a dual-source 128-slice CT machine, at the diastolic phase in the 55 patients with heart rates <70 bpm (group D) and at the systolic phase in the 36 patients with heart rates ≥70 (group S). Image quality was scored on a 5 point-scale (1, not interpretable; 2, insufficient for diagnosis; 3, fair, sufficient for diagnosis; 4, good; 5, excellent). In addition, we compared the number of stair-step artifacts in the two groups. Mean image quality score was 4 (0.78) in group D and 4.1 (0.34) in group S (NS), with an unequal distribution (p = 0.01). Step artifacts were seen in 44 % of group D and 18 % of group S patients (p = 0.02). In 3 group D patients and no group S patients, the image score was <3 due to artifacts, requiring repeat CT-angiography. When performing dual-source 128-slice CT-angiography, step-and-shoot acquisition provides comparable mean image quality in systole, with less variability and fewer stair-step artifacts, compared to diastole. This method may be feasible at any heart rate in most patients in sinus rhythm, allowing low-dose prospective acquisition without beta-blocker premedication.

Cost-effectiveness analysis of 64-slice computed tomography vs. cardiac catheterization to rule out coronary artery disease before non-coronary cardiovascular surgery

AIMS

To explore the cost-effectiveness of two alternative strategies to rule out significant coronary artery disease (CAD) in the pre-operative evaluation of non-coronary cardiovascular surgery: initial pre-operative coronary 64-slice computed tomography angiography (CCTA) vs. invasive coronary angiography (ICA).

METHODS AND RESULTS

These diagnostic strategies are compared from the clinical and payee's perspective, on the basis of the results of four European studies including 490 patients, by an analytic model of a decision tree in terms of the cost-effectiveness as the percentage of catheterizations, complications, and deaths avoided. These studies show that 71.2% of the ICA and 3.56% of the post-ICA complications could have been avoided by an initial pre-operative CCTA with a saving of €411/patient. The sensitivity analysis did not find relevant differences in terms of the cost-effectiveness when we established the indication of ICA vs. CCTA in relation to the amount of coronary calcium and when ICA was always performed by radial access. However, the lack of team experience in CCTA increased the economical and biological cost due to involving an ICA and the exposure to double ionizing radiation sources.

CONCLUSION

In experienced groups, the diagnostic strategy with initial pre-operative CCTA is better than the strategy with initial ICA because it is capable of ruling out significant CAD avoiding ICA and post-ICA morbidity-mortality, with an important saving in the cost of the diagnostic process.

Automated analysis of coronary artery disease by computed tomography

Computer-assisted detection systems are widely used in many areas of radiology. Coronary computed tomography angiography is a growing area of clinical cardiology and computer-assisted detection systems play an integral part in analysis. Truly automated systems are still in clinical-trial stages, but manually assisted programs are in clinical use today for calcium scoring as well as plaque burden, composition, and stenosis analysis. They are being used as a tool for confirmation more than for diagnosis. Accurate plaque-composition analysis would be a critical tool for better understanding the mechanisms and effectiveness of novel therapies for coronary atherosclerosis. A need for a complete quick, safe, noninvasive plaque analysis is the goal of automated coronary stenosis detection systems; however, their potential clinical benefit remains unknown.