Purpose To compare the performance of improved nonenhanced whole-heart balanced steady-state free precession (bSSFP) coronary MR angiography (CMRA) with that of the modified Dixon (mDixon) water-fat separation method at 3-T imaging. Materials and Methods From September 2023 to December 2023, patients with suspected coronary artery disease who underwent bSSFP and mDixon CMRA after coronary CT angiography (CCTA) were consecutively recruited. The two sequences' acquisition success rates, subjective image quality scores, objective image quality measurements, and diagnostic performance for coronary stenosis with CCTA as the reference standard were analyzed. Results Sixty-two participants completed two CMRA sequences. Data from 49 participants (30 male and 19 female participants; mean age, 62 years ± 10 [SD]) were ultimately analyzed. The acquisition success rates, overall subjective image quality scores, apparent signal-to-noise ratios, and contrast-to-noise ratios of bSSFP and mDixon were significantly different: 93.5% versus 80.6% (P = .021), 5 versus 4 (P < .001), 33.4 ± 10.6 versus 20.7 ± 7.5 (P < .001), and 14.9 ± 6.2 versus 7.0 ± 3.1 (P < .001), respectively. The sensitivity and specificity of bSSFP in predicting stenosis greater than or equal to 50% were 94.7% (95% CI: 71.9, 99.7) and 96.7% (95% CI: 80.9, 99.8) per participant, 95.8% (95% CI: 76.9, 99.8) and 96.7% (95% CI: 91.3, 98.9) per vessel, and 96.6% (95% CI: 80.4, 99.8) and 99.0% (95% CI: 97.3, 99.7) per segment, respectively. Conclusion Compared with the mDixon water-fat separation method, the improved nonenhanced whole-heart bSSFP sequence performed excellently at 3-T imaging. Nonenhanced bSSFP CMRA sequences at 3-T imaging may be recommended for broader clinical applications. Keywords: Coronary Arteries, Imaging Sequences, Comparative Studies, Technology Assessment, Cardiac, MR Angiography, Coronary Angiography, MRI, Image Quality Enhancement Supplemental material is available for this article. © RSNA, 2025.

A four-dimensional phase-contrast magnetic resonance imaging sequence with respiratory-controlled adaptive k-space reordering (ReCAR-4DPC) offers potential benefits of improved scan efficiency and motion robustness. The purpose of this study was to evaluate the reproducibility of flow measurement using this technique and to compare hemodynamic metrics obtained to two-dimensional phase contrast MRI (2DPC)-derived metrics of the thoracic aorta. ReCAR-4DPC was performed with identical scan parameters in 15 healthy volunteers (6M,9F, mean [range] 37 [23-47] years) and 11 patients with thoracic aortic dissection (6M,5F, 56 [31-81] years) and acquisition time was recorded. Peak systolic velocity (PSV), average flow (AF) and net forward volume (NFV) were quantified by two readers for ReCAR-4DPC at ascending, descending and diaphragmatic aorta levels. Reference standard 2DPC measurements at the same levels were performed by a separate experienced cardiovascular radiologist. ReCAR-4DPC intra-reader agreement, inter-reader agreement, inter-scan repeatability and concordance with 2DPC-derived metrics (all segments combined) were evaluated with Lin's concordance correlation coefficient (LCCC) and reduced major axis regression. The overall average ± SD MRI acquisition time of all subjects was 11:59 ± 3:57 min, with shorter average times (9:37 ± 1:57 min) in healthy volunteers compared to patients (15:13 ± 3:44 min). There was near-perfect intra-reader, inter-reader and inter-scan concordance (LCCC for all metrics > 0.97, > 0.98 and > 0.92 respectively) for ReCAR-4DPC. Concordance with 2DPC was also high (LCCC all > 0.89), with overall minimally lower PSV, AF and NFV values derived from ReCAR-4DPC compared to reference 2DPC derived metrics. ReCAR-4DPC is a reproducible and relatively fast approach for comprehensive measurement of thoracic aortic flow metrics, with robust correlation to conventional 2DPC.

This study aims to identify optimal acceleration factors (AFs) for compressed sensing (CS) technology to enhance its clinical application for suspected coronary artery disease (CAD) in whole-heart non-contrast coronary magnetic resonance angiography (CMRA).

Two hundred and seventeen individuals with suspected CAD underwent whole-heart non-contrast CMRA on a 1.5T CMR scanner with CS AFs of 2, 4, and 6 (CS2, CS4, and CS6). Two radiologists independently and blindly scored the image quality. The overall image scores, coronary artery segment scores, signal-to-noise ratios (SNR), contrast-to-noise ratios (CNR), and scan times were compared. The scores for the left anterior descending coronary artery (LAD), left circumflex coronary artery (LCX), and right coronary artery (RCA) were assessed. Of the 217 patients, 37 (37/217, 17.1%) underwent x-ray coronary angiography (CAG). The images from CS2, CS4, and CS6 were evaluated by two radiologists blinded to CAG results to identify significant luminal narrowing. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated.

The CS2 group exhibited higher overall scores, coronary artery segment scores, SNR, and CNR, but longer scan times compared to the CS4 and CS6 groups (overall score: 24.5 vs 22.0 vs 21.0, p < 0.001; SNR: 127 vs 112 vs 99, p < 0.001; CNRcor-fat: 118 vs 101 vs 84, p < 0.001; CNRcor-myo: 69.7 vs 62.8 vs 53.5, p < 0.001; scan time: 884 ± 308 s vs 473 ± 163 s vs 331 ± 146 s, p < 0.001). Proximal and middle segments received higher scores compared to their corresponding distal segments, and the RCA exhibited higher image quality than LAD and LCX in all groups (p < 0.05). In the subgroup analysis, 19 (19/37, 51.3%) were diagnosed with CAD by CAG. The sensitivity, specificity, PPV, NPV, and accuracy were as follows: CS2 (94.7%, 88.9%, 90.0%, 94.1%, and 91.9%), CS4 (89.5%, 94.4%, 94.4%, 89.5%, and 91.9%), and CS6 (89.5%, 66.7%, 73.9%, 85.7%, and 78.4%), respectively, in patient-based analysis.

Image quality showed a decreasing trend with increasing CS AFs, while scan time decreased in non-contrast CMRA. A scanning protocol using CS4 provided high-quality images with relatively short scan times and showed potential for detecting significant coronary stenosis, making it an optimal protocol for coronary magnetic resonance imaging.

Numerous efforts have been invested in previous algorithms to expose and enhance blood vessel (BV) visibility derived from clinical coronary angiography (CAG) procedures, such as noise reduction, segmentation, and background subtraction. Yet, the visibility of the BVs and their luminal content, particularly the small ones, is still limited. We propose a novel visibility enhancement algorithm, whose main body is inspired by a line completion mechanism of the visual system, i.e., lateral interactions. It facilitates the enhancement of the BVs along with simultaneous noise reduction. In addition, we developed a specific algorithm component that allows better visibility of small BVs and the various CAG tools utilized during the procedure. It is accomplished by enhancing the BVs' fine resolutions, located in the coarse resolutions at the BV zone. The visibility of the most significant clinical features during the CAG procedure was evaluated and qualitatively compared by the consensus of two cardiologists (MM and JE) to the algorithm's results. These included the visibility of the whole frame, the coronary BVs as well as the small ones, the main obstructive lesions within the BVs, and the various angiography interventional tools utilized during the procedure. The algorithm succeeded in producing better visibility of all these features, even under low-contrast or low-radiation conditions. Despite its major advantages, the algorithm also caused the appearance of disturbing vertebral and bony artifacts, which could somewhat lower diagnostic accuracy. Yet, viewing the processed images from multiple angles and not just from a single one and evaluating the cine mode usually overcomes this drawback. Thus, our novel algorithm potentially leads to a better clinical diagnosis, improved procedural capabilities, and a successful outcome.

Clinical implementation of coronary magnetic resonance angiography (CMRA) is limited due to variability in image quality. A protocol utilizing an image navigator (iNAV) integrated with automated scan planning has been developed to facilitate consistent diagnostic image quality. The aim of this study was to evaluate the agreement of automated iNAV CMRA compared with coronary computed tomography angiography (CCTA) using Coronary Artery Disease-Reporting and Data System (CAD-RADS) to classify coronary artery disease (CAD).

Ninety-five individuals underwent automated iNAV CMRA at a resolution of 0.7 mm3 with a deep learning-assisted automated scan planning and trigger-delay detection protocol. CMRA and CCTA data sets were analysed using CAD-RADS to classify the per-patient severity of CAD. Additionally, the accuracy of both imaging modalities in predicting referral for invasive coronary angiography (ICA) and coronary revascularization was assessed. CMRA classification for CAD-RADS ≥ 1, ≥2, ≥3, and ≥4 agreed with CCTA for 80%, 73%, 63%, and 70% of cases, respectively. The area under the receiver operating characteristic curves with CAD-RADS ≥ 4 and ≥3 for CMRA and CCTA were comparable in predicting ICA referral (0.75 vs. 0.70, P = 0.687, and 0.70 vs. 0.70, P = 0.945) and revascularization (0.72 vs. 0.74, P = 0.811, and 0.68 vs. 0.76, P = 0.089).

A novel automated iNAV CMRA protocol was implemented, investigating individuals at risk of CAD. Using the CAD-RADS classification, there was moderate to good agreement between CMRA and CCTA. In patients with CAD-RADS ≥ 4 and ≥3, CMRA was as effective as CCTA in predicting ICA referral and revascularization.

Anomalous coronary arteries are associated with sudden cardiac death, although only in a relatively small percentage. Although this has been a field of study for years and there are many ongoing studies of larger patient cohorts and longer term follow-up, important questions remain at present in clinical decision-making, particularly whether or not to have surgery. Advanced imaging techniques including coronary computed tomography (CT) and cardiac magnetic resonance (MR) play an important role in the workup of patients with known or suspected anomalies including delineating origin and course as well as defining presence of high-risk imaging features. Multidisciplinary decision-making is important when considering whether patients might need to undergo surgery. This review will describe types of anomalies, imaging techniques, and features as well as types of surgeries and postoperative imaging with highlights of imaging and surgical literature and several case examples.

To develop a new MR coronary angiography (MRCA) technique by employing a zigzag fan-shaped centric ky-kz k-space trajectory combined with high-resolution deep learning reconstruction (HR-DLR).

All imaging data were acquired from 12 healthy subjects and 2 patients using two clinical 3-T MR imagers, with institutional review board approval. Ten healthy subjects underwent both standard 3D fast gradient echo (sFGE) and centric ky-kz k-space trajectory FGE (cFGE) acquisitions to compare the scan time and image quality. Quantitative measures were also performed for signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as sharpness of the vessel. Furthermore, the feasibility of the proposed cFGE sequence was assessed in two patients. For assessing the feasibility of the centric ky-kz trajectory, the navigator-echo window of a 30-mm threshold was applied in cFGE, whereas sFGE was applied using a standard 5-mm threshold. Image quality of MRCA using cFGE with HR-DLR and sFGE without HR-DLR was scored in a 5-point scale (non-diagnostic = 1, fair = 2, moderate = 3, good = 4, and excellent = 5). Image evaluation of cFGE, applying HR-DLR, was compared with sFGE without HR-DLR. Friedman test, Wilcoxon signed-rank test, or paired t tests were performed for the comparison of related variables.

The actual MRCA scan time of cFGE with a 30-mm threshold was acquired in less than 5 min, achieving nearly 100% efficiency, showcasing its expeditious and robustness. In contrast, sFGE was acquired with a 5-mm threshold and had an average scan time of approximately 15 min. Overall image quality for MRCA was scored 3.3 for sFGE and 2.7 for cFGE without HR-DLR but increased to 3.6 for cFGE with HR-DLR and (p < 0.05). The clinical result of patients obtained within 5 min showed good quality images in both patients, even with a stent, without artifacts. Quantitative measures of SNR, CNR, and sharpness of vessel presented higher in cFGE with HR-DLR.

Our findings demonstrate a robust, time-efficient solution for high-quality MRCA, enhancing patient comfort and increasing clinical throughput.

Coronary magnetic resonance angiography (CMRA) presents distinct advantages, but its reliance on manual image post-processing is labor-intensive and requires specialized knowledge. This study aims to design and test an efficient artificial intelligence (AI) model capable of automating coronary artery segmentation and reformation from CMRA images for coronary artery disease (CAD) diagnosis.

By leveraging transfer learning from a pre-existing coronary computed tomography angiography model, a three-dimensional attention-aware U-Net was established, trained, and validated on a dataset of 104 subjects' CMRA. Furthermore, an independent clinical evaluation was conducted on an additional cohort of 70 patients. The AI model's performance in segmenting coronary arteries was assessed using the Dice similarity coefficient (DSC) and recall. The comparison between the AI model and manual processing by experienced radiologists on vessel reformation was based on reformatted image quality (rIQ) scoring, post-processing time, and the number of necessary user interactions. The diagnostic performance of AI-segmented CMRA for significant stenosis (≥50% diameter reduction) was evaluated using conventional coronary angiography (CAG) as a reference in sub-set data.

The DSC of the AI model achieved on the training and validation sets were 0.952 and 0.944, with recalls of 0.936 and 0.923, respectively. In the clinical evaluation, the model outperformed manual processes by reducing vessel post-processing time, from 632.6±17.0 s to 77.4±8.9 s, and the number of user interactions from 221±59 to 8±2. The AI post-processed images maintained high rIQ scores comparable to those processed manually (2.7±0.8 vs 2.7±0.6; P = 0.4806). In subjects with CAG, the prevalence of CAD was 71%. The sensitivity, specificity, and accuracy at patient-based analysis were 94%, 71%, and 88%, respectively, by AI post-processed whole-heart CMRA.

The AI auto-segmentation system can effectively facilitate CMRA vessel reformation and reduce the time consumption for radiologists. It has the potential to become a standard component of daily workflows, optimizing the clinical application of CMRA in the future.

5D, free-running imaging resolves sets of 3D whole-heart images in both cardiac and respiratory dimensions. In an application such as coronary imaging when a single, static image is of interest, computationally expensive offline iterative reconstruction is still needed to compute the multiple 3D datasets.

Evaluate how the number of physiologic bins included in the reconstruction affects the computational cost and resulting image quality of a single, static volume reconstruction.

Retrospective.

15 pediatric patients following Ferumoxytol infusion (4 mg/kg).

1.5 T/Ungated 5D free-running GRE sequence.

The raw data of each subject were binned and reconstructed into a 5D (x-y-z-cardiac-respiratory) images. 1, 3, 5, 7, and 9 bins adjacent to both sides of the retrospectively determined cardiac resting phase and 1, 3 bins adjacent to the end-expiration phase are used for limited frame reconstructions. The static volume within each limited reconstruction was compared with the corresponding full 5D reconstruction using the structural similarity index measure (SSIM). A non-linear regression model was used to fit SSIM with the percentage of data used compared to full reconstruction (% data). A linear regression model was used to fit computation time with % raw data used. Coronary artery sharpness is measured on each limited reconstructed images to determine the minimal number of cardiac and respiratory bins needed to preserve image quality.

The coefficient of determination (R2) is computed for each regression model.

The % of data used in the reconstruction was linearly related to the computational time (R2 = 0.99). The SSIM of the static image from the limited reconstructions is non-linearly related with the % of data used (R2 = 0.80). Over the 15 patients, the model showed SSIM of 0.9 with 18% of data, and SSIM of 0.96 with 30% of data. The coronary artery sharpness of images reconstructed using no less than 5 cardiac and all respiratory phases is not significantly different from the full reconstructed images using all cardiac and respiratory bins.

Reconstruction using only a limited number of acquired physiological states can linearly reduce the computational cost while preserving similarity to the full reconstruction image. It is suggested to use no less than 5 cardiac and all respiratory phases in the limited reconstruction to best preserve the original quality seen on the full reconstructed images.

To investigate the feasibility of a deep learning-constrained compressed sensing (DL-CS) method in non-contrast-enhanced modified DIXON (mDIXON) coronary magnetic resonance angiography (MRA) and compare its diagnostic accuracy using coronary CT angiography (CCTA) as a reference standard.

Ninety-nine participants were prospectively recruited for this study. Thirty healthy subjects (age range: 20-65 years; 50% female) underwent three non-contrast mDIXON-based coronary MRA sequences including DL-CS, CS, and conventional sequences. The three groups were compared based on the scan time, subjective image quality score, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The remaining 69 patients suspected of coronary artery disease (CAD) (age range: 39-83 years; 51% female) underwent the DL-CS coronary MRA and its diagnostic performance was compared with that of CCTA.

The scan time for the DL-CS and CS sequences was notably shorter than that of the conventional sequence (9.6 ± 3.1 min vs 10.0 ± 3.4 min vs 13.0 ± 4.9 min; p < 0.001). The DL-CS sequence obtained the highest image quality score, mean SNR, and CNR compared to CS and conventional methods (all p < 0.001). Compared to CCTA, the accuracy, sensitivity, and specificity of DL-CS mDIXON coronary MRA per patient were 84.1%, 92.0%, and 79.5%; those per vessel were 90.3%, 82.6%, and 92.5%; and those per segment were 98.0%, 85.1%, and 98.0%, respectively.

The DL-CS mDIXON coronary MRA provided superior image quality and short scan time for visualizing coronary arteries in healthy individuals and demonstrated high diagnostic value compared to CCTA in CAD patients.

DL-CS resulted in improved image quality with an acceptable scan time, and demonstrated excellent diagnostic performance compared to CCTA, which could be an alternative to enhance the workflow of coronary MRA.

Current coronary MRA techniques are limited by scan time and the need for noise reduction. DL-CS reduced the scan time in coronary MR angiography. Deep learning achieved the highest image quality among the three methods. Deep learning-based coronary MR angiography demonstrated high performance compared to CT angiography.

Coronary computed tomography angiography (CCTA) is recommended as the first-line diagnostic imaging modality in low-to-intermediate-risk individuals suspected of stable coronary artery disease (CAD). However, CCTA exposes patients to ionizing radiation and potentially nephrotoxic contrast agents. Invasive coronary angiography is the gold-standard investigation to guide coronary revascularisation strategy; however, invasive procedures incur an inherent risk to the patient. Coronary magnetic resonance angiography (CMRA) avoids these issues. Nevertheless, clinical implementation is currently limited due to extended scanning durations, inconsistent image quality, and consequent lack of diagnostic accuracy. Several technical CMRA innovations, including advanced respiratory motion correction with 100% scan efficiency (no data rejection), fast image acquisition with motion-corrected undersampled image reconstruction, and deep-learning-based automated planning, have been implemented and now await clinical validation in multi-center trials.

The objective of the image navigator-based, automated CMRA prospective multi-center study is to evaluate the diagnostic accuracy of a newly developed, state-of-the-art, standardized, and automated CMRA framework compared to CCTA in 201 patients undergoing clinical investigation for CAD. The study protocol mandates the administration of oral beta-blockers to decrease heart rate to below 60 bpm and the use of sublingual nitroglycerine spray to induce vasodilation. Additionally, the study incorporates the utilization of standardized postprocessing with sliding-thin-slab multiplanar reformatting, in combination with evaluation of the source images, to optimize the visualization of coronary artery stenosis.

If proven effective, CMRA could provide a non-invasive, needle-free, yet also clinically viable, alternative to CCTA.

This study is registered at ClinicalTrials.gov (NCT05473117).

Despite significant goals achieved in diagnosis and treatment in recent decades, coronary artery disease (CAD) remains a high mortality entity and continues to pose substantial challenges to healthcare systems globally. After the latest guidelines, novel data have emerged and have not been yet considered for routine practice. The scope of this review is to go beyond the guidelines, providing insights into the most recent clinical updates in CAD, focusing on non-invasive diagnostic techniques, risk stratification, medical management and interventional therapies in the acute and stable scenarios. Highlighting and synthesizing the latest developments in these areas, this review aims to contribute to the understanding and management of CAD helping healthcare providers worldwide.

Using numerical indices and visual evaluation, we evaluated the dependence of coronary-artery depictability on the denoising parameter in compressed sensing magnetic resonance angiography (CS-MRA). This study was conducted to clarify the acceleration factor (AF) and denoising factor (DF) dependence of CS-MRA image quality. Vascular phantoms and clinical images were acquired using three-dimensional CS-MRA on a clinical 1.5 T system. For the phantom measurements, we compared the full width at half maximum (FWHM), sharpness, and contrast ratio of the vascular profile curves for various AFs and DFs. In the clinical cases, the FWHM, sharpness, contrast ratio, signal-to-noise ratio, noise level values, and visual evaluation results were compared for various DFs. Phantom image analyses demonstrated that the respective measurements of the FWHM, sharpness, and contrast ratios did not significantly change with an increase in AF. The FWHM and sharpness measurements slightly changed with the DF level. However, the contrast ratio tended to increase with an increase in the DF level. In the clinical cases, the FWHM and sharpness showed no significant differences, even when the DF level was changed. However, the contrast ratio tended to decrease as the DF level increased. When the DF levels of the clinical cases increased, the background signals of the myocardium, fat, and noise levels decreased. We investigated the dependence of the coronary-artery depictability on AF and DF using CS-MRA. Analysis of the coronary-artery profile curves indicated that a better image quality was achieved with a stronger DF on coronary CS-MRA.

To investigate the feasibility of non-enhanced and free-breathing whole-heart magnetic resonance coronary angiography (MRCA) using multishot gradient-echo planar imaging (MSG-EPI).

In total, 29 healthy volunteers were recruited for free-breathing whole-heart MRCA acquisition using the MSG-EPI sequence and fast gradient echo (GRE) sequence. After the examination, the actual scanning times, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the left main (LM) coronary artery, subjective quality scores for each segment, and evaluable length of the coronary artery were recorded and statistically analysed.

There was no significant difference between the SNRLM of the MSG-EPI sequence and fast GRE sequence (p=0.130), but the CNRLM of the MSG-EPI sequence was higher (p=0.001). The subjective quality score of the mid- and distal left anterior descending branch as well as the distal circumflex branch of the coronary artery in the MSG-EPI sequence was higher than that in the fast GRE sequence (p=0.003, 0.001, and 0.003, respectively). The evaluable length of the left anterior descending branch and the circumflex branch was better using the MSG-EPI sequence than that of the fast GRE sequence (p=0.015 and < 0.001, respectively). Moreover, the scanning time of the MSG-EPI sequence was 54.5% less than that of the fast GRE sequence (p<0.001).

The MSG-EPI sequence improves the subjective and objective image quality of MRCA as well as reduces the scanning time.

In adult patients with transposition of the great arteries (dTGA) after arterial switch operation (ASO), the coronary artery circulation after neonatal surgical transfer remains a major culprit for long-term sequelae, including myocardial ischaemia and sudden cardiac death. As coronary imaging in paediatric age is often incomplete and classification mainly relies on the surgeon's description in the operation report, we intended to develop a systematic, understandable pattern of the coronary status for each young patient, combining unambiguous coding with non-invasive imaging.

The monocentric prospective study evaluated 89 young adults (mean 23 years) after ASO for dTGA including cardiac magnetic resonance (CMR) coronary angiography. Following 'The Leiden Convention coronary coding system', we describe the systematic transformation process and provide a graphical illustration considering surgical and imaging views for the six main coronary types, followed by a comparison with adult CMR. Discordance between surgeon's and CMR classification is evaluated.In seven (7.9%) patients, a discordance between the surgeon's post-operative and the CMR classification was found; therefore, the initial classification had to be corrected according to adult CMR. Three cases (3.4%) with particularly challenging coronary variants (intramural and interarterial course, functional common ostium) are presented.

Considering the risks of a possible neonatal coronary misclassification and of increasing additional acquired coronary artery disease with age, reliable cooperation between surgeons, cardiologists, and imaging specialists must be ensured. Therefore, after completion of growth, a systematic pattern of the coronary artery status, combining unambiguous coding with CMR imaging, should be established for each patient.

During percutaneous coronary intervention, the guiding catheter plays an important role. Tracking the catheter tip placed at the coronary ostium in the X-ray fluoroscopy sequence can obtain image displacement information caused by the heart beating, which can help dynamic coronary roadmap overlap on X-ray fluoroscopy images. Due to a low exposure dose, the X-ray fluoroscopy is noisy and low contrast, which causes some difficulties in tracking. In this paper, we developed a new catheter tip tracking framework. First, a lightweight efficient catheter tip segmentation network is proposed and boosted by a self-distillation training mechanism. Then, the Bayesian filtering post-processing method is used to consider the sequence information to refine the single image segmentation results. By separating the segmentation results into several groups based on connectivity, our framework can track multiple catheter tips. The proposed tracking framework is validated on a clinical X-ray sequence dataset.

Stress perfusion cardiovascular magnetic resonance can be performed without rest perfusion for the quantification of ischemia burden. However, the optimal method of analysis is uncertain.

We identified 666 patients from CE-MARC (Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease) with complete stress perfusion, rest perfusion, late gadolinium enhancement (LGE), and quantitative coronary angiography data. For each segment of the 16-segment model, perfusion was visually graded during stress and rest imaging, with infarct transmurality assessed from LGE imaging. In the stress-LGE analysis, a segment was defined as ischemic if it had a subendocardial perfusion defect with no infarction. Rest perfusion was not used in this analysis. We compared the diagnostic accuracy of stress-LGE analysis against quantitative coronary angiography and the stress-rest method validated in the original CE-MARC analysis. The diagnostic accuracy of the stress-LGE method was evaluated with different thresholds of infarct transmurality used to define whether an infarcted segment had peri-infarct ischemia.

The optimal stress-LGE analysis classified all segments with a stress perfusion defect as ischemic unless they had >75% infarct transmurality (area under the curve, 0.843; sensitivity, 75.6%; specificity, 93.1%; P<0.001). This analysis method has superior diagnostic accuracy to the stress-rest method (area under the curve, 0.834; sensitivity, 73.6%; specificity, 93.1%; P<0.001, P value for difference=0.02). Patients were followed-up for median 6.5 years for major adverse cardiovascular events, with the presence of inducible ischemia by either the stress-LGE or stress-rest analysis being similar and strongly predictive (hazard ratio, 2.65; P<0.001, for both).

In this analysis of CE-MARC, the optimum definition of inducible ischemia was the presence of a stress-induced perfusion defect without transmural infarction. This definition improved the diagnostic accuracy compared with the stress-rest analysis validated in the original study. The absence of ischemia by either analysis strategy conferred a favorable long-term prognosis.

The clinical application of coronary MR angiography (MRA) combining diastole and systole imaging has never been described comprehensively in coronary artery disease (CAD) patients. We aimed to design an optimal non-contrast coronary MRA scan protocol combining diastolic and systolic imaging and to (1) evaluate its diagnostic performance for detecting significant coronary stenosis; (2) evaluate the feasibility of this protocol to noninvasively measure the coronary distensibility index (CDI).

From June 2021 to May 2022, 33 healthy volunteers and 91 suspected CAD patients scheduled for X-ray coronary angiography (CAG) were prospectively enrolled. 3T non-contrast water-fat coronary MRA was carried out twice at diastole and systole. Significant coronary stenosis was defined as a luminal diameter reduction of ≥ 50% using CAG as the reference and was evaluated as follows: (1) by coronary MRA in diastole alone; (2) by coronary MRA in systole alone; (3) by combined coronary MRA in diastole and systole. According to CAG, the patients were divided into significant CAD patients and non-significant CAD patients. The difference in CDI among participants was evaluated.

Combined coronary MRA was completed in 31 volunteers and 76 patients. The per-patient sensitivity, specificity, and accuracy of combined coronary MRA were 97.5%, 83.3%, and 90.8%, respectively. Compared with single diastolic mode, combined coronary MRA showed equally high sensitivity but improved specificity on a per-patient basis (83.3% vs. 63.9%, adjusted P = 0.013). The CDI tested by coronary MRA decreased incrementally from healthy volunteers to non-significant and significant CAD patients.

Compared with single-phase mode, 3 T non-contrast combined coronary MRA significantly improved specificity and may have potential to be a simple noninvasive method to measure CDI.

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.

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.

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.

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

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.

• 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.

The detection and characterization of coronary artery stenosis and atherosclerosis using imaging tools are key for clinical decision-making in patients with known or suspected coronary artery disease. In this regard, imaging-based quantification can be improved by choosing the most appropriate imaging modality for diagnosis, treatment and procedural planning. In this Consensus Statement, we provide clinical consensus recommendations on the optimal use of different imaging techniques in various patient populations and describe the advances in imaging technology. Clinical consensus recommendations on the appropriateness of each imaging technique for direct coronary artery visualization were derived through a three-step, real-time Delphi process that took place before, during and after the Second International Quantitative Cardiovascular Imaging Meeting in September 2022. According to the Delphi survey answers, CT is the method of choice to rule out obstructive stenosis in patients with an intermediate pre-test probability of coronary artery disease and enables quantitative assessment of coronary plaque with respect to dimensions, composition, location and related risk of future cardiovascular events, whereas MRI facilitates the visualization of coronary plaque and can be used in experienced centres as a radiation-free, second-line option for non-invasive coronary angiography. PET has the greatest potential for quantifying inflammation in coronary plaque but SPECT currently has a limited role in clinical coronary artery stenosis and atherosclerosis imaging. Invasive coronary angiography is the reference standard for stenosis assessment but cannot characterize coronary plaques. Finally, intravascular ultrasonography and optical coherence tomography are the most important invasive imaging modalities for the identification of plaques at high risk of rupture. The recommendations made in this Consensus Statement will help clinicians to choose the most appropriate imaging modality on the basis of the specific clinical scenario, individual patient characteristics and the availability of each imaging modality.

We applied a fully automated pixel-wise post-processing framework to evaluate fully quantitative cardiovascular magnetic resonance myocardial perfusion imaging (CMR-MPI). In addition, we aimed to evaluate the additive value of coronary magnetic resonance angiography (CMRA) to the diagnostic performance of fully automated pixel-wise quantitative CMR-MPI for detecting hemodynamically significant coronary artery disease (CAD).

A total of 109 patients with suspected CAD were prospectively enrolled and underwent stress and rest CMR-MPI, CMRA, invasive coronary angiography (ICA), and fractional flow reserve (FFR). CMRA was acquired between stress and rest CMR-MPI acquisition, without any additional contrast agent. Finally, CMR-MPI quantification was analyzed by a fully automated pixel-wise post-processing framework.

Of the 109 patients, 42 patients had hemodynamically significant CAD (FFR ≤ 0.80 or luminal stenosis ≥ 90% on ICA) and 67 patients had hemodynamically non-significant CAD (FFR ˃ 0.80 or luminal stenosis < 30% on ICA) were enrolled. On the per-territory analysis, patients with hemodynamically significant CAD had higher myocardial blood flow (MBF) at rest, lower MBF under stress, and lower myocardial perfusion reserve (MPR) than patients with hemodynamically non-significant CAD (p < 0.001). The area under the receiver operating characteristic curve of MPR (0.93) was significantly larger than those of stress and rest MBF, visual assessment of CMR-MPI, and CMRA (p < 0.05), but similar to that of the integration of CMR-MPI with CMRA (0.90).

Fully automated pixel-wise quantitative CMR-MPI can accurately detect hemodynamically significant CAD, but the integration of CMRA obtained between stress and rest CMR-MPI acquisition did not provide significantly additive value.

• Full quantification of stress and rest cardiovascular magnetic resonance myocardial perfusion imaging can be postprocessed fully automatically, generating pixel-wise myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) maps. • Fully quantitative MPR provided higher diagnostic performance for detecting hemodynamically significant coronary artery disease, compared with stress and rest MBF, qualitative assessment, and coronary magnetic resonance angiography (CMRA). • The integration of CMRA and MPR did not significantly improve the diagnostic performance of MPR alone.

This study was aimed to investigate 3.0 T unenhanced Dixon water-fat whole-heart CMRA (coronary magnetic resonance angiography) using compressed-sensing sensitivity encoding (CS-SENSE) and conventional sensitivity encoding (SENSE) in vitro and in vivo. The key parameters of CS-SENSE and conventional 1D/2D SENSE were compared in vitro phantom study. In vivo study, fifty patients with suspected coronary artery disease (CAD) completed unenhanced Dixon water-fat whole-heart CMRA at 3.0 T using both CS-SENSE and conventional 2D SENSE methods. We compared mean acquisition time, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and the diagnostic accuracy between two techniques. In vitro study, CS-SENSE achieved better effectiveness between higher SNR/CNR and shorter scan times using the appropriate acceleration factor compared with conventional 2D SENSE. In vivo study, CS-SENSE CMRA had better performance than 2D SENSE in terms of the mean acquisition time, SNR and CNR (7.4 ± 3.2 min vs. 8.3 ± 3.4 min, P = 0.001; SNR: 115.5 ± 35.4 vs. 103.3 ± 32.2; CNR: 101.1 ± 33.2 vs. 90.6 ± 30.1, P < 0.001 for both). The diagnostic accuracy between CS-SENSE and 2D SENSE had no significant difference on a patient-based analysis (sensitivity: 97.3% vs. 91.9%; specificity: 76.9% vs. 61.5%; accuracy: 92.0% vs. 84.0%; P > 0.05 for each). Unenhanced CS-SENSE Dixon water-fat separation whole-heart CMRA at 3.0 T can improve the SNR and CNR, shorten the acquisition time while providing equally satisfactory image quality and diagnostic accuracy compared with 2D SENSE CMRA.

Sufficient annotated data is critical to the success of deep learning methods. Annotating for vessel segmentation in X-ray coronary angiograms is extremely difficult because of the small and complex structures to be processed. Although unsupervised domain adaptation methods can be utilized to alleviate the annotation burden by using data in other domains, e.g., eye fundus images, these methods cannot perform well due to the characteristic of medical images. Data augmentation can help improve the similarity of source domain and target domain in unsupervised domain adaptation tasks. Existing data augmentation methods play a limited role in improving domain adaptation performance, especially for special medical image segmentation tasks. In this paper, we propose an effective perceptual data augmentation method to improve the similarity between eye fundus images and coronary angiograms by synthesizing virtual samples. Auto Foreground Augment method is designed to search for geometric transformations that improve the similarity between foreground vessels of eye fundus images and coronary angiograms. The Haar Wavelet-Based Perceptual Similarity Index is utilized to guide the synthesis of virtual samples in foreground and background mixup. Extensive experiments show that our data augmentation method can synthesize high-quality virtual samples and thus improve the domain adaptation performance. To our best knowledge, this is the first work to apply perceptual data augmentation to vessel segmentation in coronary angiograms.

Currently, noninvasive arteriography for the diagnosis of coronary artery disease is clinically limited to the computed tomography scanning, where patients have to be exposed to the radiation and risks associated with iodinated contrast. We aimed to investigate the diagnostic performance and safety of a novel ferumoxytol-enhanced coronary magnetic resonance angiography (CMRA) in patients with suspected coronary artery disease.

Thirty patients, 19 males, with a median age of 63 years old, and 17 with renal insufficiency, who were scheduled for invasive coronary angiography, were enrolled. Ferumoxytol was administered intravenously with a dose of 3 mg/kg during CMRA. Images were acquired with an ECG-triggered, navigator-gated, inversion recovery-prepared 3D fast low-angle shot sequence, and the image quality was assessed by a 4-point scale. Eighteen-segment coronary artery model was adopted to evaluate the visibility of the coronary arteries, and the image quality and stenosis were evaluated in nine segments. The diagnostic performance of CMRA is described as sensitivity, specificity, positive and negative predictive values, and accuracy with the invasive coronary angiography results as reference. The patients' vital signs were monitored during CMRA, and their hepatic and renal functions were followed up for 3 months to evaluate the safety of ferumoxytol.

Two hundred fifty-two of the 270 study segments were identified by CMRA, and their quality score reached 3.6±0.7. Referring to the invasive coronary angiography results, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of ferumoxytol-enhanced CMRA reached 100.0%, 66.7%, 92.3%, 100.0%, and 93.3% respectively in patient-based analysis; 91.4%, 90.9%, 86.5%, 94.3%, and 91.1%, respectively in vessel-based analysis; and 92.3%, 96.7%, 83.7%, 98.6%, and 96.0%, respectively in segment-based analysis. No ferumoxytol-related adverse event was observed during the 3-month follow-up.

Ferumoxytol-enhanced CMRA demonstrated good diagnostic performance and excellent safety in the diagnosis of significant coronary stenosis, providing an alternative to coronary computed tomography angiography for the diagnosis of coronary artery disease.

URL: https://www.

gov; Unique identifier: NCT05032937.

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.

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.

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.

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.

This study aims to compare the value of a gadolinium contrast-enhanced 1.5-T three-dimensional (3D) steady-state free precession (SSFP) sequence with that of a noncontrast 3D SSFP sequence for magnetic resonance coronary angiography in a pediatric population.

Seventy-nine patients from 1 month to 18 years old participated in this study. A 3D SSFP coronary MRA at 1.5-T was applied before and after gadolinium-diethylenetriaminepentaaceticacid (DTPA) injection. The detection rates of coronary arteries and side branches were assessed by McNemar's χ² test. The image quality, vessel length, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the coronary arteries were analyzed by the Wilcoxon signed-rank test. The intra- and interobserver agreements were evaluated with a weighted kappa test or an intraclass correlation efficient test.

A contrast-enhanced scan detected more coronary arteries than a noncontrast-enhanced scan in patients under 2 years old (P < 0.05). The SSFP sequence with contrast media detected more coronary artery side branches in patients younger than 5 years (P < 0.05). The image quality of all the coronary arteries was better after the injection of gadolinium-DTPA in children younger than 2 years (P < 0.05) but not significantly improved in children older than 2 years (P > 0.05). The contrast-enhanced 3D SSFP protocol detected longer lengths for the left anterior descending coronary artery in children younger than 2 years and the left circumflex coronary artery (LCX) in children younger than 5 years (P < 0.05). SNR and CNR of all the coronary arteries in children younger than 5 years and the LCX and right coronary artery in children older than 5 years enhanced after the injection of gadolinium-DTPA (P < 0.05). The intra- and interobserver agreements were high (0.803-0.998) for image quality, length, SNR, and CNR of the coronary arteries in both pre- and postcontrast groups.

The use of gadolinium contrast in combination with the 3D SSFP sequence is necessary for coronary imaging in children under 2 years of age and may be helpful in children between 2 and 5 years. Coronary artery visualization is not significantly improved in children older than 5 years.

To develop a respiratory-resolved motion-compensation method for free-breathing, high-resolution coronary magnetic resonance angiography (CMRA) using a 3D cones trajectory.

To achieve respiratory-resolved 0.98 mm resolution images in a clinically relevant scan time, we undersample the imaging data with a variable-density 3D cones trajectory. For retrospective motion compensation, translational estimates from 3D image-based navigators (3D iNAVs) are used to bin the imaging data into four phases from end-expiration to end-inspiration. To ensure pseudo-random undersampling within each respiratory phase, we devise a phyllotaxis readout ordering scheme mindful of eddy current artifacts in steady state free precession imaging. Following binning, residual 3D translational motion within each phase is computed using the 3D iNAVs and corrected for in the imaging data. The noise-like aliasing characteristic of the combined phyllotaxis and cones sampling pattern is leveraged in a compressed sensing reconstruction with spatial and temporal regularization to reduce aliasing in each of the respiratory phases.

In initial studies of six subjects, respiratory motion compensation using the proposed method yields improved image quality compared to non-respiratory-resolved approaches with no motion correction and with 3D translational correction. Qualitative assessment by two cardiologists and quantitative evaluation with the image edge profile acutance metric indicate the superior sharpness of coronary segments reconstructed with the proposed method (P < 0.01).

We have demonstrated a new method for free-breathing, high-resolution CMRA based on a variable-density 3D cones trajectory with modified phyllotaxis ordering and respiratory-resolved motion compensation with 3D iNAVs.

The relatively long scan time has hampered the clinical use of whole-heart noncontrast coronary magnetic resonance angiography (NCMRA). The compressed sensitivity encoding (SENSE) technique, also known as the CS technique, has been found to improve scan times. This study aimed to identify the optimal CS acceleration factor for NCMRA.

Thirty-six participants underwent four NCMRA sequences: three sequences using the CS technique with acceleration factors of 4, 5, and 6, and one sequence using the conventional SENSE technique with the acceleration factor of 2. Coronary computed tomography angiography (CCTA) was considered as a reference sequence. The acquisition times of the four NCMRA sequences were assessed. The correlation and agreement between the visible vessel lengths obtained via CCTA and NCMRA were also assessed. The image quality scores and contrast ratio (CR) of eight coronary artery segments from the four NCMRA sequences were quantitatively evaluated.

The mean acquisition time of the conventional SENSE was 343 s, while that of CS4, CS5, and CS6 was 269, 215, and 190 s, respectively. The visible vessel length from the CS4 sequence showed good correlation and agreement with CCTA. The image quality score and CR from the CS4 sequence were not statistically significantly different from those in the other groups (p > 0.05). Moreover, the image score and CR showed a decreasing trend with the increase in the CS factor.

The CS technique could significantly shorten the acquisition time of NCMRA. The CS sequence with an acceleration factor of 4 was generally acceptable for NCMRA in clinical settings to balance the image quality and acquisition time.

We investigated the feasibility of evaluating coronary arteries with a contrast-enhanced (CE) self-navigated sparse isotropic 3D whole heart T1-weighted magnetic resonance imaging (MRI) study sequence.

A total of 22 consecutive patients underwent coronary angiography and/or cardiac computed tomography (CT) including cardiac MRI. The image quality was evaluated on a 3-point Likert scale. Inter-reader variability for image quality was analyzed with Cohen's kappa for the main coronary segments (left circumflex [LCX], left anterior descending [LAD], right coronary artery [RCA]) and the left main trunk (LMT).

Inter-reader agreement for image quality of the coronary tree ranged from substantial to perfect, with a Cohen's kappa of 0.722 (RCA_mid) to 1 (LCX_prox). The LMT had the best image quality. Image quality of the proximal vessel segments differed significantly from the mid- and distal segments (RCA_prox vs. RCA_dist, p < 0.05). The LCX segments showed no significant difference in image quality along the vessel length (LCX_prox vs. LCX_dist, p = n.s.). The mean acquisition time for the study sequence was 553 s (±46 s).

Coronary imaging with a sparse 3D whole-heart sequence is feasible in a reasonable amount of time producing good-quality imaging. Image quality was poorer in distal coronary segments and along the entire course of the LCX.

Residual severe coronary artery (CA) lesion (SCL) in children after cardiac surgery involving the CA is a major concern.

To evaluate the value of exercise electrocardiogram (eECG) for risk-based screening of SCL.

We analysed 135 maximal eECG from 115 children (mean age 13.6±3.7 years) who underwent concomitant CA imaging. SCL was defined as a stenosis exceeding 50%.

Underlying congenital heart diseases were transposition of the great arteries (TGA) (n = 116), CA pathway anomaly (n = 13) and left CA from the pulmonary artery (n = 6). Eleven SCLs were identified in 10 patients, of which 3 had a known untreated non-severe lesion and 4 had no lesions on previous imaging. In multivariable analysis, risks markers for SCL were effort chest pain (OR: 4.72, 95% CI: 1.23-18.17; P=0.024), intramural pathway (OR: 4.37, 95% CI: 1.14-16.81; P=0.032). Yacoubs C-type CA was added as a risk marker for patients with TGA (P=0.0009). All patients with SCL had a positive eECG (sensitivity: 100%, 95% CI: 72-100). Specificity was 81% (95% CI: 73-87). In the low-risk group (0 risk markers), 3/95 patients had SCL (3%), and the post-test probability of SCL with positive eECG (PPr+) was 15% (95% CI: 8-21). In the high-risk group (≥1 risk marker) comprising 8/40 SCLs (20%), PPr+ was 53% (95% CI: 35-67).

Most SCL tended to develop gradually, years after surgery. Provided it is near maximal, a negative eECG appears sufficient to exclude SCL. In the high-risk group, PPr+ exceeded 50%.