Clinical Application of Dynamic Contrast Enhanced Perfusion Imaging by Cardiovascular Magnetic Resonance

Review and critical appraisal of the indications for cardiac magnetic resonance imaging in the ESC guidelines

Cardiac MRI has made significant advances in the past decade, becoming an important technique for the evaluation of various cardiac pathologies. The aim of this document is to review the current indications for performing cardiac MRI based on the current ESC guidelines for STEMI, NSTEMI, chronic coronary artery disease, heart failure, arrhythmias, sudden cardiac death, valvular heart disease, pericardial disease and congenital heart disease. The review discusses the diagnostic and prognostic value of cMR for numerous cardiac diseases, and its important value in assessing structural heart disease and predicting arrhythmia risk. Additionally, it reflects upon the appropriateness of the guidelines and points out areas where the indications should be revised in future editions, based on the author's personal opinion. It is suggested that guideline criteria for the use of cMR should be more explicit to promote its use and lead to more specific reimbursements. However, further studies are needed to even better document the value of cMR in the future.

Fully automated pixel-wise quantitative CMR-myocardial perfusion with CMR-coronary angiography to detect hemodynamically significant coronary artery disease

OBJECTIVES

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).

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

KEY POINTS

• 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.

Cardiovascular Disease and Cardiac Imaging in Inflammatory Arthritis

Cardiovascular morbidity and mortality are more prevalent in inflammatory arthritis (IA) compared to the general population. Recognizing the importance of addressing this issue, the European League Against Rheumatism (EULAR) published guidelines on cardiovascular disease (CVD) risk management in IA in 2016, with plans to update going forward based on the latest emerging evidence. Herein we review the latest evidence on cardiovascular disease in IA, taking a focus on rheumatoid arthritis, psoriatic arthritis, and axial spondylarthritis, reflecting on the scale of the problem and imaging modalities to identify disease. Evidence demonstrates that both traditional CVD factors and inflammation contribute to the higher CVD burden. Whereas CVD has decreased with the newer anti-rheumatic treatments currently available, CVD continues to remain an important comorbidity in IA patients calling for prompt screening and management of CVD and related risk factors. Non-invasive cardiovascular imaging has been attracting much attention in view of the possibility of detecting cardiovascular lesions in IA accurately and promptly, even at the pre-clinical stage. We reflect on imaging modalities to screen for CVD in IA and on the important role of rheumatologists and cardiologists working closely together.

Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images

BACKGROUND

The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels.

METHODS

In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes.

RESULTS

The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve.

DISCUSSION

The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.

Current Use and Complementary Value of Combining in Vivo Imaging Modalities to Understand the Renoprotective Effects of Sodium-Glucose Cotransporter-2 Inhibitors at a Tissue Level

Sodium-glucose cotransporter-2 inhibitors (SGLT2i) were initially developed to treat diabetes and have been shown to improve renal and cardiovascular outcomes in patients with- but also without diabetes. The mechanisms underlying these beneficial effects are incompletely understood, as is the response variability between- and within patients. Imaging modalities allow in vivo quantitative assessment of physiological, pathophysiological, and pharmacological processes at kidney tissue level and are therefore increasingly being used in nephrology. They provide unique insights into the renoprotective effects of SGLT2i and the variability in response and may thus contribute to improved treatment of the individual patient. In this mini-review, we highlight current work and opportunities of renal imaging modalities to assess renal oxygenation and hypoxia, fibrosis as well as interaction between SGLT2i and their transporters. Although every modality allows quantitative assessment of particular parameters of interest, we conclude that especially the complementary value of combining imaging modalities in a single clinical trial aids in an integrated understanding of the pharmacology of SGLT2i and their response variability.