Impact of a New Adaptive Statistical Iterative Reconstruction (ASIR)-V Algorithm on Image Quality in Coronary Computed Tomography Angiography

Beyond Stress Ischemia: Unveiling the Multifaceted Nature of Coronary Vulnerable Plaques Using Cardiac Computed Tomography

Historically, cardiovascular prevention has been predominantly focused on stress-induced ischemia, but recent trials have challenged this paradigm, highlighting the emerging role of vulnerable, non-flow-limiting coronary plaques, leading to a shift towards integrating plaque morphology with functional data into risk prediction models. Coronary computed tomography angiography (CCTA) represents a high-resolution, low-risk, and largely available non-invasive modality for the precise delineation of plaque composition, morphology, and inflammatory activity, further enhancing our ability to stratify high-risk plaque and predict adverse cardiovascular outcomes. Coronary artery calcium (CAC) scoring, derived from CCTA, has emerged as a promising tool for predicting future cardiovascular events in asymptomatic individuals, demonstrating incremental prognostic value beyond traditional cardiovascular risk factors in terms of myocardial infarction, stroke, and all-cause mortality. Additionally, CCTA-derived information on adverse plaque characteristics, geometric characteristics, and hemodynamic forces provides valuable insights into plaque vulnerability and seems promising in guiding revascularization strategies. Additionally, non-invasive assessments of epicardial and pericoronary adipose tissue (PCAT) further refine risk stratification, adding prognostic significance to coronary artery disease (CAD), correlating with plaque development, vulnerability, and rupture. Moreover, CT imaging not only aids in risk stratification but is now emerging as a screening tool able to monitor CAD progression and treatment efficacy over time. Thus, the integration of CAC scoring and PCAT evaluation into risk stratification algorithms, as well as the identification of high-risk plaque morphology and adverse geometric and hemodynamic characteristics, holds promising results for guiding personalized preventive interventions, helping physicians in identifying high-risk individuals earlier, tailoring lifestyle and pharmacological interventions, and improving clinical outcomes in their patients.

CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: A clinically-inspired deep learning pipeline

BACKGROUND AND OBJECTIVE

The standard non-invasive imaging technique used to assess the severity and extent of Coronary Artery Disease (CAD) is Coronary Computed Tomography Angiography (CCTA). However, manual grading of each patient's CCTA according to the CAD-Reporting and Data System (CAD-RADS) scoring is time-consuming and operator-dependent, especially in borderline cases. This work proposes a fully automated, and visually explainable, deep learning pipeline to be used as a decision support system for the CAD screening procedure. The pipeline performs two classification tasks: firstly, identifying patients who require further clinical investigations and secondly, classifying patients into subgroups based on the degree of stenosis, according to commonly used CAD-RADS thresholds.

METHODS

The pipeline pre-processes multiplanar projections of the coronary arteries, extracted from the original CCTAs, and classifies them using a fine-tuned Multi-Axis Vision Transformer architecture. With the aim of emulating the current clinical practice, the model is trained to assign a per-patient score by stacking the bi-dimensional longitudinal cross-sections of the three main coronary arteries along channel dimension. Furthermore, it generates visually interpretable maps to assess the reliability of the predictions.

RESULTS

When run on a database of 1873 three-channel images of 253 patients collected at the Monzino Cardiology Center in Milan, the pipeline obtained an AUC of 0.87 and 0.93 for the two classification tasks, respectively.

CONCLUSION

According to our knowledge, this is the first model trained to assign CAD-RADS scores learning solely from patient scores and not requiring finer imaging annotation steps that are not part of the clinical routine.

Computed tomography and nuclear medicine for the assessment of coronary inflammation: clinical applications and perspectives

There is increasing evidence that in patients with atherosclerotic cardiovascular disease (ASCVD) under optimal medical therapy, a persisting dysregulation of the lipid and glucose metabolism, associated with adipose tissue dysfunction and inflammation, predicts a substantial residual risk of disease progression and cardiovascular events. Despite the inflammatory nature of ASCVD, circulating biomarkers such as high-sensitivity C-reactive protein and interleukins may lack specificity for vascular inflammation. As known, dysfunctional epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) produce pro-inflammatory mediators and promote cellular tissue infiltration triggering further pro-inflammatory mechanisms. The consequent tissue modifications determine the attenuation of PCAT as assessed and measured by coronary computed tomography angiography (CCTA). Recently, relevant studies have demonstrated a correlation between EAT and PCAT and obstructive coronary artery disease, inflammatory plaque status and coronary flow reserve (CFR). In parallel, CFR is well recognized as a marker of coronary vasomotor function that incorporates the haemodynamic effects of epicardial, diffuse and small-vessel disease on myocardial tissue perfusion. An inverse relationship between EAT volume and coronary vascular function and the association of PCAT attenuation and impaired CFR have already been reported. Moreover, many studies demonstrated that 18F-FDG PET is able to detect PCAT inflammation in patients with coronary atherosclerosis. Importantly, the perivascular FAI (fat attenuation index) showed incremental value for the prediction of adverse clinical events beyond traditional risk factors and CCTA indices by providing a quantitative measure of coronary inflammation. As an indicator of increased cardiac mortality, it could guide early targeted primary prevention in a wide spectrum of patients. In this review, we summarize the current evidence regarding the clinical applications and perspectives of EAT and PCAT assessment performed by CCTA and the prognostic information derived by nuclear medicine.

Multimodality imaging in acute myocarditis

The diagnosis of acute myocarditis often involves several noninvasive techniques that can provide information regarding volumes, ejection fraction, and tissue characterization. In particular, echocardiography is extremely helpful for the evaluation of biventricular volumes, strain and ejection fraction. Cardiac magnetic resonance, beyond biventricular volumes, strain, and ejection fraction allows to characterize myocardial tissue providing information regarding edema, hyperemia, and fibrosis. Contemporary cardiac computed tomography angiography (CCTA) can not only be extremely important for the assessment of coronary arteries, pulmonary arteries and aorta but also tissue characterization using CCTA can be an additional tool that can explain chest pain with a diagnosis of myocarditis.

Reference ranges of tricuspid annulus geometry in healthy adults using a dedicated three-dimensional echocardiography software package

Background

Tricuspid annulus (TA) sizing is essential for planning percutaneous or surgical tricuspid procedures. According to current guidelines, TA linear dimension should be assessed using two-dimensional echocardiography (2DE). However, TA is a complex three-dimensional (3D) structure.

Aim

Identify the reference values for TA geometry and dynamics and its physiological determinants using a commercially available three-dimensional echocardiography (3DE) software package dedicated to the tricuspid valve (4D AutoTVQ, GE).

Methods

A total of 254 healthy volunteers (113 men, 47 ± 11 years) were evaluated using 2DE and 3DE. TA 3D area, perimeter, diameters, and sphericity index were assessed at mid-systole, early- and end-diastole. Right atrial (RA) and ventricular (RV) end-diastolic and end-systolic volumes were also measured by 3DE.

Results

The feasibility of the 3DE analysis of TA was 90%. TA 3D area, perimeter, and diameters were largest at end-diastole and smallest at mid-systole. Reference values of TA at end-diastole were 9.6 ± 2.1 cm2 for the area, 11.2 ± 1.2 cm for perimeter, and 38 ± 4 mm, 31 ± 4 mm, 33 ± 4 mm, and 34 ± 5 mm for major, minor, 4-chamber and 2-chamber diameters, respectively. TA end-diastolic sphericity index was 81 ± 11%. All TA parameters were correlated with body surface area (BSA) (r from 0.42 to 0.58, p < 0.001). TA 3D area and 4-chamber diameter were significantly larger in men than in women, independent of BSA (p < 0.0001). There was no significant relationship between TA metrics with age, except for the TA minor diameter (r = −0.17, p < 0.05). When measured by 2DE in 4-chamber (29 ± 5 mm) and RV-focused (30 ± 5 mm) views, both TA diameters resulted significantly smaller than the 4-chamber (33 ± 4 mm; p < 0.0001), and the major TA diameters (38 ± 4 mm; p < 0.0001) measured by 3DE. At multivariable linear regression analysis, RA maximal volume was independently associated with both TA 3D area at mid-systole (R2 = 0.511, p < 0.0001) and end-diastole (R2 = 0.506, p < 0.0001), whereas BSA (R2 = 0.526, p < 0.0001) was associated only to mid-systolic TA 3D area.

Conclusions

Reference values for TA metrics should be sex-specific and indexed to BSA. 2DE underestimates actual 3DE TA dimensions. RA maximum volume was the only independent echocardiographic parameter associated with TA 3D area in healthy subjects.

Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion

PURPOSE

To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation.

METHODS

One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DL_rest) and stress dataset (CTP-DL_stress) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DL_rest, and CCTA + CTP-DL_stress was measured and compared. The time of analysis for CTP stress, CTP-DL_rest, and CTP-DL_Stress was recorded.

RESULTS

Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTP_Stress were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DL_rest and CCTA + DL_stress were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DL_Rest, and CCTA + CTP-DL_Stress significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001).

CONCLUSION

Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress_..

The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance-A Comprehensive Review

Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.

Advances in Multimodality Cardiovascular Imaging in the Diagnosis of Heart Failure With Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) is a syndrome defined by the presence of heart failure symptoms and increased levels of circulating natriuretic peptide (NP) in patients with preserved left ventricular ejection fraction and various degrees of diastolic dysfunction (DD). HFpEF is a complex condition that encompasses a wide range of different etiologies. Cardiovascular imaging plays a pivotal role in diagnosing HFpEF, in identifying specific underlying etiologies, in prognostic stratification, and in therapeutic individualization. Echocardiography is the first line imaging modality with its wide availability; it has high spatial and temporal resolution and can reliably assess systolic and diastolic function. Cardiovascular magnetic resonance (CMR) is the gold standard for cardiac morphology and function assessment, and has superior contrast resolution to look in depth into tissue changes and help to identify specific HFpEF etiologies. Differently, the most important role of nuclear imaging [i.e., planar scintigraphy and/or single photon emission CT (SPECT)] consists in the screening and diagnosis of cardiac transthyretin amyloidosis (ATTR) in patients with HFpEF. Cardiac CT can accurately evaluate coronary artery disease both from an anatomical and functional point of view, but tissue characterization methods have also been developed. The aim of this review is to critically summarize the current uses and future perspectives of echocardiography, nuclear imaging, CT, and CMR in patients with HFpEF.

Comparison of image quality and radiation dose using different pre-ASiR-V and post-ASiR-V levels in coronary computed tomography angiography

OBJECTIVE

To determine the optimal pre-adaptive and post-adaptive level statistical iterative reconstruction V (ASiR-V) for improving image quality and reducing radiation dose in coronary computed tomography angiography (CCTA).

METHODS

The study was divided into two parts. In part I, 150 patients for CCTA were prospectively enrolled and randomly divided into 5 groups (A, B, C, D, and E) with progressive scanning from 40% to 80% pre-ASiR-V with 10% intervals and reconstructing with 70% post-ASiR-V. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective image quality was assessed using a 5-point scale. The CT dose index volume (CTDIvol) and dose-length product (DLP) of each patient were recorded and the effective radiation dose (ED) was calculated after statistical analysis by optimizing for the best pre-ASiR-V value with the lowest radiation dose while maintaining overall image quality. In part II, the images were reconstructed with the recommended optimal pre-ASiR-V values in part I (D group) and 40%-90% of post-ASiR-V. The reconstruction group (D group) was divided into 6 subgroups (interval 10%, D0:40% post-ASiR-V, D1:50% post - ASiR-V, D2:60% post-ASiR-V, D3:70% post-ASiR-V, D4:80% post-ASiR-V, and D5:90% post-ASiR-V).The SNR and CNR of D0-D5 subgroups were calculated and analyzed using one-way analysis of variance, and the consistency of the subjective scores used the k test.

RESULTS

There was no significant difference in the SNRs, CNRs, and image quality scores among A, B, C, and D groups (P > 0.05). The SNR, CNR, and image quality scores of the E group were lower than those of the A, B, C, and D groups (P < 0.05). The mean EDs in the B, C, and D groups were reduced by 7.01%, 13.37%, and 18.87%, respectively, when compared with that of the A group. The SNR and CNR of the D4-D5 subgroups were higher than the D0-D3 subgroups, and the image quality scores of the D4 subgroups were higher than the other subgroups (P < 0.05).

CONCLUSION

The wide-detector combined with 70% pre-ASiR-V and 80% post-ASiR-V significantly reduces the radiation dose of CCTA while maintaining overall image quality as compared with the manufacture's recommendation of 40% pre-ASiR-V.

Impact of Deep Learning-based Optimization Algorithm on Image Quality of Low-dose Coronary CT Angiography with Noise Reduction: A Prospective Study

RATIONALE AND OBJECTIVES

To evaluate deep learning (DL)-based optimization algorithm for low-dose coronary CT angiography (CCTA) image noise reduction and image quality (IQ) improvement.

MATERIALS AND METHODS

A postprocessing platform for the CCTA image was built using a DL-based algorithm. Seventy subjects referred for CCTA were randomly divided into two groups (study group A with 80 kVp and control group B with 100 kVp). Group C was obtained by DL optimization of group A. Subjective IQ was blindly graded by two experienced radiologists on a four-point scale (4-excellent,1-poor). The image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated to evaluate IQ objectively. The difference between the time consumed of iterative reconstruction and DL algorithm was also recorded.

RESULTS

The subjective IQ score of group C using the DL algorithm was significantly better than that of group A (p = 0.005). The noise of group C was significantly decreased, while SNR and CNR were significantly increased compared to group A (p < 0.001). The subjective IQ scores were lower in group A compared to group B (p = 0.037), whereas subjective IQ scores in group C were not significantly different (p = 0.874). For objective IQ, the noise of group A was significantly higher, while SNR and CNR were significantly lower than that of group B (p < 0.05). There was no significant difference in noise and SNR between group C and group B (p > 0.05), but CNR in group C was significantly higher than that in group B (p < 0.05). The DL algorithm processes the image twice as fast as the iterative reconstruction speed.

CONCLUSION

The DL-based optimization algorithm could effectively improve the IQ of low-dose CCTA by noise reduction.

Image Quality and Radiation Dose in CT Venography Using Model-Based Iterative Reconstruction at 80 kVp versus Adaptive Statistical Iterative Reconstruction-V at 70 kVp

Objective

To compare the objective and subjective image quality indicators and radiation doses of computed tomography (CT) venography performed using model-based iterative reconstruction (MBIR) at 80 kVp and adaptive statistical iterative reconstruction (ASIR)-V at 70 kVp.

Materials and Methods

Eighty-three patients who had undergone CT venography of the lower extremities with MBIR at 80 kVp (Group A; 21 men and 20 women; mean age, 55.5 years) or ASIR-V at 70 kVp (Group B; 18 men and 24 women; mean age, 57.3 years) were enrolled. Two radiologists retrospectively evaluated the objective (vascular enhancement, image noise, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR]) and subjective (quantum mottle, delineation of contour, venous enhancement) image quality indicators at the inferior vena cava and femoral and popliteal veins. Clinical information, radiation dose, reconstruction time, and objective and subjective image quality indicators were compared between groups A and B.

Results

Vascular enhancement, SNR, and CNR were significantly greater in Group B than in Group A (p ≤ 0.015). Image noise was significantly lower in Group B (p ≤ 0.021), and all subjective image quality indicators, except for delineation of vein contours, were significantly better in Group B (p ≤ 0.021). Mean reconstruction time was significantly shorter in Group B than in Group A (1 min 43 s vs. 131 min 1 s; p < 0.001). Clinical information and radiation dose were not significantly different between the two groups.

Conclusion

CT venography using ASIR-V at 70 kVp was better than MBIR at 80 kVp in terms of image quality and reconstruction time at similar radiation doses.

Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA

BACKGROUND AND AIMS

Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary computed tomography angiography (CCTA) in the correct Coronary Artery Disease Reporting and Data System (CAD-RADS) category.

METHODS

Two hundred eighty eight patients who underwent clinically indicated CCTA were included in this single-center retrospective study. The CCTAs were stratified by CAD-RADS scores by expert readers and considered as reference standard. A deep CNN was designed and tested on the CCTA dataset and compared to on-site reading. The deep CNN analyzed the diagnostic accuracy of the following three Models based on CAD-RADS classification: Model A (CAD-RADS 0 vs CAD-RADS 1-2 vs CAD-RADS 3,4,5), Model 1 (CAD-RADS 0 vs CAD-RADS>0), Model 2 (CAD-RADS 0-2 vs CAD-RADS 3-5). Time of analysis for both physicians and CNN were recorded.

RESULTS

Model A showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 47%, 74%, 77%, 46% and 60%, respectively. Model 1 showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 66%, 91%, 92%, 63%, 86%, respectively. Conversely, Model 2 demonstrated the following sensitivity, specificity, negative predictive value, positive predictive value and accuracy: 82%, 58%, 74%, 69%, 71%, respectively. Time of analysis was significantly lower using CNN as compared to on-site reading (530.5 ± 179.1 vs 104.3 ± 1.4 sec, p=0.01) CONCLUSIONS: Deep CNN yielded accurate automated classification of patients with CAD-RADS.

Application of Adaptive Statistical Iterative Reconstruction-V With Combination of 80 kV for Reducing Radiation Dose and Improving Image Quality in Renal Computed Tomography Angiography for Slim Patients

OBJECTIVES

To explore the application of adaptive statistical iterative reconstruction-V (ASIR-V) with combination of 80 kV for reducing radiation dose and improving image quality in renal computed tomography angiography (CTA) for slim patients compared with traditional filtered back projection (FBP) reconstruction using 120 kV.

METHODS

Eighty patients for renal CTA were prospectively enrolled and randomly divided into group A and group B. Group A used 120 kV and 600 mgI/kg contrast agent and FBP reconstruction, while group B used 80 kV and 350 mgI/kg contrast agent and both FBP and ASIR-V reconstruction from 10%ASIR-V to 100%ASIR-V with 10%ASIR-V interval. The CT values and SD values of the right renal artery and left renal artery were measured to calculate the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The image quality was subjectively scored by two experienced radiologists blindly using a five-point criterion. The contrast agent, volumetric CT dose index (CTDI_vol), and dose length product in both groups were recorded and the effective radiation dose was calculated.

RESULTS

There were no significant difference in patient characteristics between two groups (p > 0.05). The CTDI_vol, dose length product and effective radiation dose in group B were 59.0%, 65.0%, and 65.1% lower than those in group A, respectively (all p < 0.05), and the contrast agent in group B was 42.2% lower than that in group A (p < 0.05). In group B, with the increase of ASIR-V percentage, CT values showed no significant difference, SD values decreased gradually, SNR values and CNR values increased gradually. The CT values showed no statistically significant difference (p > 0.05) between two groups with different reconstructions. The SD values with 40%ASIR-V to 100%ASIR-V reconstruction in group B was significantly lower(p < 0.5), while the SNR values with 50% ASIR-V to 100% ASIR-V reconstruction and CNR values with 70%ASIR-V to 100%ASIR-V were significantly higher than those of group A with FBP reconstruction (p < 0.5). Two radiologists had excellent consistency in subjective scores of image quality for renal CTA (kappa >0.75, p < 0.05). The subjective scores with 60% ASIR-V to 90% ASIR-V in group B were significantly higher than those of FBP in group A (p < 0.5), of which 70%ASIR-V reconstruction obtained the highest subjective score for renal CTA.

CONCLUSION

ASIR-V with combination of 80 kV can significantly reduce effective radiation dose (about 65.1%) and contrast agent (about 42.2%) and improve image quality in renal CTA for slim patients compared with traditional FBP reconstruction using 120 kV, and the 70% ASIR-V was the best reconstruction algorithm in 80 kV renal CTA.

ADVANCES IN KNOWLEDGE

Using 80 kV with combination of ASIR-V can significantly reduce radiation dose and contrast agent dose as well as improve image quality in renal CTA for thin patients when compared with FBP using 120 kV.

Role of Iterative Reconstruction Algorithm for the Assessment of Myocardial Infarction with Dual Energy Computed Tomography

RATIONALE AND OBJECTIVES

Low monochromatic energy levels (40 keV) derived from delayed enhancement dual energy cardiac computed tomography (DE-DECT) allow the evaluation of myocardial infarcts (MI) among stable patients, although at the expense of high image noise. We explored whether the application of adaptive statistical iterative reconstruction (ASIR) to 40-keV DE-DECT (unavailable with previous software versions) might improve image quality and detection of MI in stable patients.

MATERIALS AND METHODS

We prospectively enrolled patients with a history of previous MI, and performed delayed-enhancement cardiac magnetic resonance (DE-CMR) and DE-DECT within the same week. DE-DECT images were reconstructed with 0% and 60% ASIR.

RESULTS

MI was identified in 18 (80%) patients with both DE-CMR and DE-DECT. On a per segment basis, we did not identify significant differences regarding the diagnostic performance of DE-DECT with and without ASIR [area under receiver operating characteristic curve 0.86 vs. 0.83, p = 0.10]. The application of ASIR improved the signal-to-noise ratio of DE-DECT with 0% ASIR compared to DE-DECT with 60% ASIR (6.07 ± 2.1 vs. 11.1 ± 4.5, p < 0.0001). However, qualitative assessment of MI image quality (3.35 ± 1.2, vs. 3.55 ± 1.1, p = 0.10) and diagnostic confidence (4.40 ± 0.9 vs. 4.60 ± 0.8, p = 0.10) were not significantly improved. Using DE-DECT with 60% ASIR, a threshold over 199 HU showed a sensitivity of 67% and a specificity of 92% for the detection of segments with MI.

CONCLUSION

In this study, DE-DECT allowed accurate detection of MI among stable patients compared with DE-CMR, and the application of ASIR improved signal-to-noise ratio of DE-DECT, although the diagnostic performance showed only non-significant improvements.

Ultra-low dose chest computed tomography: Effect of iterative reconstruction levels on image quality

PURPOSE

To optimize image quality and radiation dose of chest CT with respect to various iterative reconstruction levels, detector collimations and body sizes.

METHOD

A Kyoto Kagaku Lungman with and without extensions was scanned using fixed ultra-low doses of 0.25, 0.49 and 0.74 mGy CTDI_vol, and collimations of 40 and 80 mm. Images were reconstructed with the lung kernel, filtered back projection (FBP) and different ASIR-V levels (10-100%). Contrast-to-noise ratios (CNR) were calculated for 12 mm simulated lesions of different densities in the lung. Image noise, signal-to-noise ratios (SNR), variations in Hounsfield units (HU), noise power spectrum (NPS) and noise texture deviations (NTD) were evaluated for all reconstructions. NTD was calculated as percentage of pixels outside 3 standard deviations to evaluate IR-specific artefacts.

RESULTS

Compared to the FBP, image noise reduced (5-55%) with ASIR-V levels irrespective of dose or collimation. SNR correlated positively (r ≥ 0.925, p ≤ 0.001) with ASIR-V levels at all doses, collimations, and phantom sizes. ASIR-V enhanced the CNR of the lesion with the lowest contrast from 12.7-42.1 (0-100% ASIR-V) at 0.74 mGy with 40 mm collimation. As expected, higher SNR and CNR were measured in the smaller phantom than the bigger phantom. Uniform HU were observed between FBP and ASIR-V levels at all doses, collimations, and phantom sizes. NPS curves left-shifted towards lower frequencies at increasing levels of ASIR-V irrespective of collimation. A positive correlation (r ≥ 0.946, p ≥ 0.001) was observed between NTD and ASIR-V levels. NTD of the FBP was not significantly (p ≤ 0.087) different from NTD of ASIR-V ≤ 20%. The data from the NPS and NTD indicates a blotchier and coarser noise texture at higher levels of ASIR-V, especially at 100% ASIR-V.

CONCLUSION

In comparison with the FBP technique, ASIR-V enhanced quantitative image quality parameters at all ultra-low doses tested. Moreover, the use of ASIR-V showed consistency with body size and collimation. Hence, ASIR-V may be useful for improving image quality of chest CT at ultra-low doses.

Fractional Flow Reserve Derived from Coronary Computed Tomography Angiography Datasets: The Next Frontier in Noninvasive Assessment of Coronary Artery Disease

Fractional flow reserve (FFR) derived from coronary CTA datasets (FFR_CT) is a major advance in cardiovascular imaging that provides critical information to the Heart Team without exposing the patient to excessive risk. Previously, invasive FFR measurements obtained during a cardiac catheterization have been demonstrated to reduce contrast use, number of stents, and cost of care and improve outcomes. However, there are barriers to routine use of FFR in the cardiac catheterization suite. FFR_CT values are obtained using resting 3D coronary CTA images using computational fluid dynamics. Several multicenter clinical trials have demonstrated the diagnostic superiority of FFR_CT over traditional coronary CTA for the diagnosis of functionally significant coronary artery disease. This review provides a background of FFR, technical aspects of FFR_CT, clinical applications and interpretation of FFR_CT values, clinical trial data, and future directions of the technology.