Coronary Magnetic Resonance Angiography in Chronic Coronary Syndromes

Feasibility of accelerated non-contrast-enhanced whole-heart bSSFP coronary MR angiography by deep learning-constrained compressed sensing

OBJECTIVES

To examine a compressed sensing artificial intelligence (CSAI) framework to accelerate image acquisition in non-contrast-enhanced whole-heart bSSFP coronary magnetic resonance (MR) angiography.

METHODS

Thirty healthy volunteers and 20 patients with suspected coronary artery disease (CAD) scheduled for coronary computed tomography angiography (CCTA) were enrolled. Non-contrast-enhanced coronary MR angiography was performed with CSAI, compressed sensing (CS), and sensitivity encoding (SENSE) methods in healthy participants and with CSAI in patients. Acquisition time, subjective image quality score, and objective image quality measurement (blood pool homogeneity, signal-to-noise ratio [SNR], and contrast-to-noise ratio [CNR]) were compared among the three protocols. The diagnostic performance of CASI coronary MR angiography for predicting significant stenosis (≥ 50% diameter stenosis) on CCTA was evaluated. The Friedman test was performed to compare the three protocols.

RESULTS

Acquisition time was significantly shorter in the CSAI and CS groups than in the SENSE group (10.2 ± 3.2 min vs. 10.9 ± 2.9 min vs. 13.0 ± 4.1 min, p < 0.001). However, the CSAI approach had the highest image quality scores, blood pool homogeneity, mean SNR value, and mean CNR value (all p < 0.001) compared with the CS and SENSE approaches. The sensitivity, specificity, and accuracy of CSAI coronary MR angiography per patient were 87.5% (7/8), 91.7% (11/12), and 90.0% (18/20); those per vessel were 81.8% (9/11), 93.9% (46/49), and 91.7% (55/60); and those per segment were 84.6% (11/13), 98.0% (244/249), and 97.3% (255/262), respectively.

CONCLUSIONS

CSAI yielded superior image quality within a clinically feasible acquisition time in healthy participants and patients with suspected CAD.

CLINICAL RELEVANCE STATEMENT

The non-invasive and radiation-free CSAI framework could be a promising tool for rapid screening and comprehensive examination of the coronary vasculature in patients with suspected CAD.

KEY POINTS

• This prospective study showed that CSAI enables a reduction in acquisition time by 22% with superior diagnostic image quality compared with the SENSE protocol. • CSAI replaces the wavelet transform with a CNN as a sparsifying transform in the CS algorithm, achieving high coronary MR image quality with reduced noise. • CSAI achieved per-patient sensitivity of 87.5% (7/8) and specificity of 91.7% (11/12) respectively for detecting significant coronary stenosis.

Deep Learning-Based Acceleration of Compressed Sensing for Noncontrast-Enhanced Coronary Magnetic Resonance Angiography in Patients With Suspected Coronary Artery Disease

BACKGROUND

The clinical application of coronary MR angiography (MRA) remains limited due to its long acquisition time and often unsatisfactory image quality. A compressed sensing artificial intelligence (CSAI) framework was recently introduced to overcome these limitations, but its feasibility in coronary MRA is unknown.

PURPOSE

To evaluate the diagnostic performance of noncontrast-enhanced coronary MRA with CSAI in patients with suspected coronary artery disease (CAD).

STUDY TYPE

Prospective observational study.

POPULATION

A total of 64 consecutive patients (mean age ± standard deviation [SD]: 59 ± 10 years, 48.4% females) with suspected CAD.

FIELD STRENGTH/SEQUENCE

A 3.0-T, balanced steady-state free precession sequence.

ASSESSMENT

Three observers evaluated the image quality for 15 coronary segments of the right and left coronary arteries using a 5-point scoring system (1 = not visible; 5 = excellent). Image scores ≥3 were considered diagnostic. Furthermore, the detection of CAD with ≥50% stenosis was evaluated in comparison to reference standard coronary computed tomography angiography (CTA). Mean acquisition times for CSAI-based coronary MRA were measured.

STATISTICAL TESTS

For each patient, vessel and segment, sensitivity, specificity, and diagnostic accuracy of CSAI-based coronary MRA for detecting CAD with ≥50% stenosis according to coronary CTA were calculated. Intraclass correlation coefficients (ICCs) were used to assess the interobserver agreement.

RESULTS

The mean MR acquisition time ± SD was 8.1 ± 2.4 minutes. Twenty-five (39.1%) patients had CAD with ≥50% stenosis on coronary CTA and 29 (45.3%) patients on MRA. A total of 885 segments on the CTA images and 818/885 (92.4%) coronary MRA segments were diagnostic (image score ≥3). The sensitivity, specificity, and diagnostic accuracy were as follows: per patient (92.0%, 84.6%, and 87.5%), per vessel (82.9%, 93.4%, and 91.1%), and per segment (77.6%, 98.2%, and 96.6%), respectively. The ICCs for image quality and stenosis assessment were 0.76-0.99 and 0.66-1.00, respectively.

DATA CONCLUSION

The image quality and diagnostic performance of coronary MRA with CSAI may show good results in comparison to coronary CTA in patients with suspected CAD.

EVIDENCE LEVEL

1.

TECHNICAL EFFICACY

2.

Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography

BACKGROUND

Coronary magnetic resonance angiography (coronary MRA) is increasingly being considered as a clinically viable method to investigate coronary artery disease (CAD). Accurate determination of the trigger delay to place the acquisition window within the quiescent part of the cardiac cycle is critical for coronary MRA in order to reduce cardiac motion. This is currently reliant on operator-led decision making, which can negatively affect consistency of scan acquisition. Recently developed deep learning (DL) derived software may overcome these issues by automation of cardiac rest period detection.

METHODS

Thirty individuals (female, n = 10) were investigated using a 0.9 mm isotropic image-navigator (iNAV)-based motion-corrected coronary MRA sequence. Each individual was scanned three times utilising different strategies for determination of the optimal trigger delay: (1) the DL software, (2) an experienced operator decision, and (3) a previously utilised formula for determining the trigger delay. Methodologies were compared using custom-made analysis software to assess visible coronary vessel length and coronary vessel sharpness for the entire vessel length and the first 4 cm of each vessel.

RESULTS

There was no difference in image quality between any of the methodologies for determination of the optimal trigger delay, as assessed by visible coronary vessel length, coronary vessel sharpness for each entire vessel and vessel sharpness for the first 4 cm of the left mainstem, left anterior descending or right coronary arteries. However, vessel length of the left circumflex was slightly greater using the formula method. The time taken to calculate the trigger delay was significantly lower for the DL-method as compared to the operator-led approach (106 ± 38.0 s vs 168 ± 39.2 s, p < 0.01, 95% CI of difference 25.5-98.1 s).

CONCLUSIONS

Deep learning-derived automated software can effectively and efficiently determine the optimal trigger delay for acquisition of coronary MRA and thus may simplify workflow and improve reproducibility.

Diagnostic accuracy of whole heart coronary magnetic resonance angiography: a systematic review and meta-analysis

BACKGROUND

The purpose of this meta-analysis was to comprehensively investigate the diagnostic ability of 1.5 T and 3.0 T whole heart coronary angiography (WHCA) to detect significant coronary artery disease (CAD) on X-ray coronary angiography.

METHODS

A literature search of electronic databases, including PubMed, Web of Science Core Collection, Cochrane advanced search, and EMBASE, was performed to retrieve and integrate articles showing significant CAD detectability of 1.5 and 3.0 T WHCA.

RESULTS

Data from 1899 patients from 34 studies were included in the meta-analysis. 1.5 T WHCA had a summary area under ROC of 0.88 in the patient-based analysis, 0.90 in the vessel-based analysis, and 0.92 in the segment-based analysis. These values for 3.0 T WHCA were 0.94, 0.95, 0.96, respectively. Contrast-enhanced 3.0 T WHCA had significantly higher specificity than non-contrast-enhanced 1.5 T WHCA on a patient-based analysis (0.87, 95% CI 0.80-0.92 vs. 0.74, 95% CI 0.64-0.82, P = 0.02). There were no differences in diagnostic performance on a patient-based analysis by use of vasodilators, beta-blockers or between Asian and Western countries.

CONCLUSIONS

The diagnostic performance of WHCA was deemed satisfactory, with contrast-enhanced 3.0 T WHCA exhibiting higher specificity compared to non-contrast-enhanced 1.5 T WHCA in a patient-based analysis. There were no significant differences in diagnostic performance on a patient-based analysis in terms of vasodilator or beta-blocker use, nor between Asian and Western countries. However, further large-scale multicentre studies are crucial for the widespread global adoption of WHCA.

Atrial Fibrillation and Chronic Coronary Ischemia: A Challenging Vicious Circle

Atrial fibrillation, the most frequent arrhythmia in clinical practice and chronic coronary syndrome, is one of the forms of coronary ischemia to have a strong dual relationship. Atrial fibrillation may accelerate atherosclerosis and may increase oxygen consumption in the myocardium, creating a mismatch between supply and demand, thus promoting the development or worsening of coronary ischemia. Chronic coronary syndrome alters the structure and function of gap junction proteins, affecting the conduction of action potential and leading to ischemic necrosis of cardiomyocytes and their replacement with fibrous tissue, in this way sustaining the focal ectopic activity in atrial myocardium. They have many risk factors in common, such as hypertension, obesity, type 2 diabetes mellitus, and dyslipidemia. It is vital for the prognosis of patients to break this vicious circle by controlling risk factors, drug therapies, of which antithrombotic therapy may sometimes be challenging in terms of prothrombotic and bleeding risk, and interventional therapies (revascularization and catheter ablation).

Feedback attention network for cardiac magnetic resonance imaging super-resolution

BACKGROUND AND OBJECTIVE

Atrial fibrillation (AF) is a common clinical arrhythmia with a high disability and mortality rate. Improving the resolution of atrial structure and its changes in patients with AF is very important for understanding and treating AF.

METHODS

Aiming at the problems of previous deep learning-based image super-resolution (SR) reconstruction methods simply deepening the network, loss of upsampling information, and difficulty in the reconstruction of high-frequency information, we propose the Feedback Attention Network (FBAN) for cardiac magnetic resonance imaging (CMRI) super-resolution. The network comprises a preprocessing module, a multi-scale residual group module, an upsampling module, and a reconstruction module. The preprocessing module uses a convolutional layer to extract shallow features and dilate the number of channels of the feature map. The multi-scale residual group module adds a multi-channel network, a mixed attention mechanism, and a long and short skip connection to expand the receptive field of the feature map, improve the multiplexing of multi-scale features and strengthen the reconstruction of high-frequency information. The upsampling module adopts the sub-pixel method to upsample the feature map to the target image size. The reconstruction module consists of a convolutional layer, which is used to restore the number of channels of the feature map to the original number to obtain the reconstructed high-resolution (HR) image.

RESULTS

Furthermore, the test results on the public dataset of CMRI show that the HR images reconstructed by the FBAN method not only have a good improvement in reconstructed edge and texture information but also have a good improvement in the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) objective evaluation indicators.

CONCLUSION

Compared with the local magnified image, the edge information of the FBAN method reconstructed image has been enhanced, more high-frequency information of the CMRI is restored, the texture details are less lost, and the reconstructed image is less blurry. Overall, the reconstructed image has a lighter feeling of smearing, and the visual experience is more apparent and sharper.

Assessment of myocardial bridging and the pericoronary fat attenuation index on coronary computed tomography angiography: predicting coronary artery disease risk

BACKGROUND

The fat attenuation index (FAI) is a radiological parameter that represents pericoronary adipose tissue (PCAT) inflammation, along with myocardial bridging (MB), which leads to pathological shear stress in the coronary vessels; both are associated with coronary atherosclerosis. In the present study, we assessed the predictive value of FAI values and MB parameters through coronary computed tomography angiography (CCTA) for predicting the risk of coronary atherosclerosis and vulnerable plaque in patients with MB.

METHODS

We included 428 patients who underwent CCTA and were diagnosed with MB. FAI values, MB parameters, and high-risk coronary plaque (HRP) characteristics were recorded. The subjects were classified into two groups (A and B) according to the absence or presence of coronary plaque in the segment proximal to the MB. Group B was further divided into Groups B₁ (HRP-positive) and B₂ (HRP-negative) according to the HRP characteristic classification method. The differences among the groups were analysed. Multiple logistic regression analysis was performed to determine the independent correlation between FAI values and MB parameters and coronary atherosclerosis and vulnerable plaque risk.

RESULTS

Compared to the subjects in Group A, those in Group B presented greater MB lengths, MB depths and muscle index values, more severe MB systolic stenosis and higher FAI_lesion values (all P < 0.05). In multivariate logistic analysis, age (OR 1.076, P < 0.001), MB systolic stenosis (OR 1.102, P < 0.001) and FAI_lesion values (OR 1.502, P < 0.001) were independent risk factors for the occurrence of coronary atherosclerosis. Compared to subjects in Group B₂, those in Group B₁ presented greater MB lengths and higher FAI values (both P < 0.05). However, only the FAI_lesion value was an independent factor for predicting HRP (OR 1.641, P < 0.001).

CONCLUSION

In patients with MB, MB systolic stenosis was associated with coronary plaque occurrence in the segment proximal to the MB. The FAI value was not only closely related to coronary atherosclerosis occurrence but also associated with plaque vulnerability. FAI values may provide more significant value in the prediction of coronary atherosclerosis than MB parameters in CCTA.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.