Complication detection in MRI guided cardiac ablation: Atrial wall damage and hepatic oedema.

Magnetic resonance imaging is a novel imaging technique for guiding electrophysiology based ablation operations for atrial flutter and typical atrial fibrillation. When compared to standard electrophysiology ablation, this innovative method allows for better outcomes. Intra-procedural imaging is important for following the catheter in real time throughout the ablation operation while also seeing cardiac architecture and determining whether the ablation is being completed appropriately utilizing oedema sequences. At the same time, intra-procedural imaging allows immediate visualization of any complications of the procedure. We describe a case of a 67 year old male underwent an isthmus-cavo-tricuspid magnetic resonance-guided thermoablation procedure for atrial flutter episodes. During the procedure we noted an atypical focal thinning of the right atrial wall at the isthmus cava-tricuspidal zone. The post-procedural Black Blood T2 STIR showed an area of hyperintensity at the hepatic dome and glissonian capsule, which was consistent with intraparenchymal hepatic oedema, in close proximity to the atrial finding. Given the opportunity to direct monitoring of adjacent tissues, we aim to highlight with our case the ability of magnetic resonance-guided cardiac ablation to immediately detect peri-procedural complications in the ablative treatment of atrial fibrillation.

Advancements and Challenges in Cardiac Amyloidosis Imaging: A Comprehensive Review of Novel Techniques and Clinical Applications.

Cardiac amyloidosis, characterized by amyloid fibril deposition in the myocardium, leads to restrictive cardiomyopathy and heart failure. This review explores recent advancements in imaging techniques for diagnosing and managing cardiac amyloidosis, highlighting their clinical applications, strengths, and limitations. Echocardiography remains a primary, non-invasive imaging modality but lacks specificity. Cardiac MRI (CMR), with Late Gadolinium Enhancement (LGE) and T1 mapping, offers superior tissue characterization, though at higher costs and limited availability. Scintigraphy with Tc-99m-PYP reliably diagnoses transthyretin (TTR) amyloidosis but is less effective for light chain (AL) amyloidosis, necessitating complementary diagnostics. Amyloid-specific PET tracers, such as florbetapir and flutemetamol, provide precise imaging and quantitative assessment for both TTR and AL amyloidosis. Challenges include differentiating between TTR and AL amyloidosis, early disease detection, and standardizing imaging protocols. Future research should focus on developing novel tracers, integrating multimodality imaging, and leveraging AI to enhance diagnostic accuracy and personalized treatment. Advancements in imaging have improved cardiac amyloidosis management. A multimodal approach, incorporating echocardiography, CMR, scintigraphy, and PET tracers, offers comprehensive assessment. Continued innovation in tracers and AI applications promises further enhancements in diagnosis, early detection, and patient outcomes.

Deep learning-based segmentation of left ventricular myocardium on dynamic contrast-enhanced MRI: a comprehensive evaluation across temporal frames.

PURPOSE: Cardiac perfusion MRI is vital for disease diagnosis, treatment planning, and risk stratification, with anomalies serving as markers of underlying ischemic pathologies. AI-assisted methods and tools enable accurate and efficient left ventricular (LV) myocardium segmentation on all DCE-MRI timeframes, offering a solution to the challenges posed by the multidimensional nature of the data. This study aims to develop and assess an automated method for LV myocardial segmentation on DCE-MRI data of a local hospital. METHODS: The study consists of retrospective DCE-MRI data from 55 subjects acquired at the local hospital using a 1.5 T MRI scanner. The dataset included subjects with and without cardiac abnormalities. The timepoint for the reference frame (post-contrast LV myocardium) was identified using standard deviation across the temporal sequences. Iterative image registration of other temporal images with respect to this reference image was performed using Maxwell's demons algorithm. The registered stack was fed to the model built using the U-Net framework for predicting the LV myocardium at all timeframes of DCE-MRI. RESULTS: The mean and standard deviation of the dice similarity coefficient (DSC) for myocardial segmentation using pre-trained network Net_cine is 0.78 ± 0.04, and for the fine-tuned network Net_dyn which predicts mask on all timeframes individually, it is 0.78 ± 0.03. The DSC for Net_dyn ranged from 0.71 to 0.93. The average DSC achieved for the reference frame is 0.82 ± 0.06. CONCLUSION: The study proposed a fast and fully automated AI-assisted method to segment LV myocardium on all timeframes of DCE-MRI data. The method is robust, and its performance is independent of the intra-temporal sequence registration and can easily accommodate timeframes with potential registration errors.

Physical enhancement of older adults using hyperbaric oxygen: a randomized controlled trial.

INTRODUCTION: Aging is associated with a progressive decline in the capacity for physical activity. The objective of the current study was to evaluate the effect of an intermittent hyperbaric oxygen therapy (HBOT) protocol on maximal physical performance and cardiac perfusion in sedentary older adults. METHODS: A randomized controlled clinical trial randomized 63 adults (> 64yrs) either to HBOT (n = 30) or control arms (n = 33) for three months. Primary endpoint included the maximal oxygen consumption (VO2Max) and VO2Max/Kg, on an E100 cycle ergometer. Secondary endpoints included cardiac perfusion, evaluated by magnetic resonance imaging and pulmonary function. The HBOT protocol comprised of 60 sessions administered on a daily basis, for 12 consecutive weeks, breathing 100% oxygen at 2 absolute atmospheres (ATA) for 90 min with 5-minute air breaks every 20 min. RESULTS: Following HBOT, improvements were observed in VO2Max/kg, with a significant increase of 1.91 ± 3.29 ml/kg/min indicated by a net effect size of 0.455 (p = 0.0034). Additionally, oxygen consumption measured at the first ventilatory threshold (VO2VT1) showed a significant increase by 160.03 ± 155.35 ml/min (p < 0.001) with a net effect size of 0.617. Furthermore, both cardiac blood flow (MBF) and cardiac blood volume (MBV) exhibited significant increases when compared to the control group. The net effect size for MBF was large at 0.797 (p = 0.008), while the net effect size for MBV was even larger at 0.896 (p = 0.009). CONCLUSION: The findings of the study indicate that HBOT has the potential to improve physical performance in aging adults. The enhancements observed encompass improvements in key factors including VO2Max, and VO2VT1. An important mechanism contributing to these improvements is the heightened cardiac perfusion induced by HBOT. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT02790541 (registration date 06/06/2016).

Recurrence of an undifferentiated pleomorphic pulmonary artery sarcoma 8 years after initial presentation: a case report.

Background: Primary cardiac tumors remain exceptionally rare, characterized by a poor prognosis. Among them, sarcomas originating in the pulmonary arteries constitute the most infrequent subgroup within primary cardiac sarcomas. Case summary: This report presents the case of a 76-year-old female experiencing a recurrence of an undifferentiated pleomorphic intracardiac pulmonary artery sarcoma located in the right ventricular outflow tract, manifesting 8 years after initial remission. Successful outcomes were attained through a combination of surgical resection, state-of-the-art radiotherapy, and chemotherapy. This comprehensive approach proved essential for optimizing both survival and quality of life. Discussion: The unexpectedly prolonged recurrence-free survival observed in this case underscores the effectiveness of the comprehensive multimodal treatment approach outlined in the existing literature. This highlights the pivotal role of a multidisciplinary strategy in addressing primary cardiac sarcomas, particularly those arising in the pulmonary arteries.

Mechanisms of left ventricular systolic dysfunction in light chain amyloidosis: a multiparametric cardiac MRI study.

Background: Cardiac systolic dysfunction is a poor prognostic marker in light-chain (AL) cardiomyopathy, a primary interstitial disorder; however, its pathogenesis is poorly understood. Purpose: This study aims to analyze the effects of extracellular volume (ECV) expansion, a surrogate marker of amyloid burden on myocardial blood flow (MBF), myocardial work efficiency (MWE), and left ventricular (LV) systolic dysfunction in AL amyloidosis. Methods: Subjects with biopsy-proven AL amyloidosis were prospectively enrolled (April 2016-June 2021; Clinicaltrials.gov ID NCT02641145) and underwent cardiac magnetic resonance imaging (MRI) to quantify rest MBF by perfusion imaging, LV ejection fraction (LVEF) by cine MRI, and ECV by pre- and post-contrast T1 mapping. The MWE was estimated as external cardiac work from the stroke volume and mean arterial pressure normalized to the LV myocardial mass. Results: Rest MBF in 92 subjects (62 ± 8 years, 52 men) with AL amyloidosis averaged 0.87 ± 0.21 ml/min/g and correlated with MWE (r = 0.42; p < 0.001). Rest MBF was similarly low in subjects with sustained hematologic remission after successful AL amyloidosis therapy (n = 21), as in those with recently diagnosed AL amyloidosis. Both MBF and MWE decreased by ECV tertile (p < 0.01 for linear trends). The association of ECV with MWE comprised a direct effect (84% of the total effect; p < 0.001) on MWE from adverse interstitial remodeling assessed by ECV and an indirect effect (16% of the total effect; p < 0.001) mediated by MBF. There was a significant base-to-apex gradient of rest MBF in subjects with higher amyloid burden. Conclusions: In AL amyloidosis, both MBF and MWE decrease as cardiac amyloid burden and ECV expansion increase. Both structural and vascular changes from ECV expansion and myocardial amyloid burden appear to contribute to lower MWE.

Free-breathing 3D whole-heart joint T1/T2 mapping and water/fat imaging at 0.55 T.

PURPOSE: To develop and validate a highly efficient motion compensated free-breathing isotropic resolution 3D whole-heart joint T1/T2 mapping sequence with anatomical water/fat imaging at 0.55 T. METHODS: The proposed sequence takes advantage of shorter T1 at 0.55 T to acquire three interleaved water/fat volumes with inversion-recovery preparation, no preparation, and T2 preparation, respectively. Image navigators were used to facilitate nonrigid motion-compensated image reconstruction. T1 and T2 maps were jointly calculated by a dictionary matching method. Validations were performed with simulation, phantom, and in vivo experiments on 10 healthy volunteers and 1 patient. The performance of the proposed sequence was compared with conventional 2D mapping sequences including modified Look-Locker inversion recovery and T2-prepared balanced steady-SSFP sequence. RESULTS: The proposed sequence has a good T1 and T2 encoding sensitivity in simulation, and excellent agreement with spin-echo reference T1 and T2 values was observed in a standardized T1/T2 phantom (R2 = 0.99). In vivo experiments provided good-quality co-registered 3D whole-heart T1 and T2 maps with 2-mm isotropic resolution in a short scan time of about 7 min. For healthy volunteers, left-ventricle T1 mean and SD measured by the proposed sequence were both comparable with those of modified Look-Locker inversion recovery (640 ± 35 vs. 630 ± 25 ms [p = 0.44] and 49.9 ± 9.3 vs. 54.4 ± 20.5 ms [p = 0.42]), whereas left-ventricle T2 mean and SD measured by the proposed sequence were both slightly lower than those of T2-prepared balanced SSFP (53.8 ± 5.5 vs. 58.6 ± 3.3 ms [p < 0.01] and 5.2 ± 0.9 vs. 6.1 ± 0.8 ms [p = 0.03]). Myocardial T1 and T2 in the patient measured by the proposed sequence were in good agreement with conventional 2D sequences and late gadolinium enhancement. CONCLUSION: The proposed sequence simultaneously acquires 3D whole-heart T1 and T2 mapping with anatomical water/fat imaging at 0.55 T in a fast and efficient 7-min scan. Further investigation in patients with cardiovascular disease is now warranted.

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology.

Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.

Effects of Enzyme Replacement Therapy on Cardiac MRI Findings in Fabry Disease: A Systematic Review and Meta-Analysis.

Purpose To perform a systematic review and meta-analysis to assess the effect of enzyme replacement therapy on cardiac MRI parameters in patients with Fabry disease. Materials and Methods A systematic literature search was conducted from January 1, 2000, through January 1, 2024, in PubMed, ClinicalTrials.gov, Embase, and Cochrane Library databases. Study outcomes were changes in the following parameters: (a) left ventricular wall mass (LVM), measured in grams; (b) LVM indexed to body mass index, measured in grams per meters squared; (c) maximum left ventricular wall thickness (MLVWT), measured in millimeters; (d) late gadolinium enhancement (LGE) extent, measured in percentage of LVM; and (e) native T1 mapping, measured in milliseconds. A random-effects meta-analysis of the pooled mean differences between baseline and follow-up parameters was conducted. The study protocol was registered in PROSPERO (CRD42022336223). Results The final analysis included 11 studies of a total of 445 patients with Fabry disease (mean age ± SD, 41 years ± 11; 277 male, 168 female). Between baseline and follow-up cardiac MRI, the following did not change: T1 mapping (mean difference, 6 msec [95% CI: -2, 15]; two studies, 70 patients, I2 = 88%) and LVM indexed (mean difference, -1 g/m2 [95% CI: -6, 3]; four studies, 290 patients, I2 = 81%). The following measures minimally decreased: LVM (mean difference, -18 g [95% CI: -33, -3]; seven studies, 107 patients, I2 = 96%) and MLVWT (mean difference, -1 mm [95% CI: -2, -0.02]; six studies, 151 patients, I2 = 90%). LGE extent increased (mean difference, 1% [95% CI: 1, 1]; three studies, 114 patients, I2 = 85%). Conclusion In patients with Fabry disease, enzyme replacement therapy was associated with stabilization of LVM, MLVWT, and T1 mapping values, whereas LGE extent mildly increased. Keywords: Fabry Disease, Enzyme Replacement Therapy (ERT), Cardiac MRI, Late Gadolinium Enhancement (LGE) Supplemental material is available for this article. © RSNA, 2024.

Correlation of Noninvasive Cardiac MRI Measures of Left Ventricular Myocardial Function and Invasive Pressure-Volume Parameters in a Porcine Ischemia-Reperfusion Model.

Purpose To assess the correlation between noninvasive cardiac MRI-derived parameters with pressure-volume (PV) loop data and evaluate changes in left ventricular function after myocardial infarction (MI). Materials and Methods Sixteen adult female swine were induced with MI, with six swine used as controls and 10 receiving platelet-derived growth factor-AB (PDGF-AB). Load-independent measures of cardiac function, including slopes of end-systolic pressure-volume relationship (ESPVR) and preload recruitable stroke work (PRSW), were obtained on day 28 after MI. Cardiac MRI was performed on day 2 and day 28 after infarct. Global longitudinal strain (GLS) and global circumferential strain (GCS) were measured. Ventriculo-arterial coupling (VAC) was derived from PV loop and cardiac MRI data. Pearson correlation analysis was performed. Results GCS (r = 0.60, P = .01), left ventricular ejection fraction (LVEF) (r = 0.60, P = .01), and cardiac MRI-derived VAC (r = 0.61, P = .01) had a significant linear relationship with ESPVR. GCS (r = 0.75, P < .001) had the strongest significant linear relationship with PRSW, followed by LVEF (r = 0.67, P = .005) and cardiac MRI-derived VAC (r = 0.60, P = .01). GLS was not significantly correlated with ESPVR or PRSW. There was a linear correlation (r = 0.82, P < .001) between VAC derived from cardiac MRI and from PV loop data. GCS (-3.5% ± 2.3 vs 0.5% ± 1.4, P = .007) and cardiac MRI-derived VAC (-0.6 ± 0.6 vs 0.3 ± 0.3, P = .001) significantly improved in the animals treated with PDGF-AB 28 days after MI compared with controls. Conclusion Cardiac MRI-derived parameters of MI correlated with invasive PV measures, with GCS showing the strongest correlation. Cardiac MRI-derived measures also demonstrated utility in assessing therapeutic benefit using PDGF-AB. Keywords: Cardiac MRI, Myocardial Infarction, Pressure Volume Loop, Strain Imaging, Ventriculo-arterial Coupling Supplemental material is available for this article. © RSNA, 2024.

Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.

Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant mitral regurgitation or left ventricular (LV) dysfunction undergoing late gadolinium enhancement (LGE) cardiac MRI between October 2007 and June 2020 in 15 European tertiary centers. The study end point was a composite of sustained ventricular tachycardia, (aborted) sudden cardiac death, or unexplained syncope. Unsupervised data-driven hierarchical k-mean algorithm was utilized to identify phenotypic clusters. The association between clusters and the study end point was assessed by Cox proportional hazards model. Results A total of 474 patients (mean age, 47 years ± 16 [SD]; 244 female, 230 male) with two phenotypic clusters were identified. Patients in cluster 2 (199 of 474, 42%) had more severe mitral valve degeneration (ie, bileaflet MVP and leaflet displacement), left and right heart chamber remodeling, and myocardial fibrosis as assessed with LGE cardiac MRI than those in cluster 1. Demographic and clinical features (ie, symptoms, arrhythmias at Holter monitoring) had negligible contribution in differentiating the two clusters. Compared with cluster 1, the risk of developing the study end point over a median follow-up of 39 months was significantly higher in cluster 2 patients (hazard ratio: 3.79 [95% CI: 1.19, 12.12], P = .02) after adjustment for LGE extent. Conclusion Among patients with MVP without significant mitral regurgitation or LV dysfunction, unsupervised machine learning enabled the identification of two phenotypic clusters with distinct arrhythmic outcomes based primarily on cardiac MRI features. These results encourage the use of in-depth imaging-based phenotyping for implementing arrhythmic risk prediction in MVP. Keywords: MR Imaging, Cardiac, Cardiac MRI, Mitral Valve Prolapse, Cluster Analysis, Ventricular Arrhythmia, Sudden Cardiac Death, Unsupervised Machine Learning Supplemental material is available for this article. © RSNA, 2024.

Technical Note: Swing golden angle - A navigator-interleaved golden angle trajectory with eddy current suppression - Application in free-running cardiac MRI.

BACKGROUND: Golden angle (GA) radial trajectory is advantageous for dynamic magnetic resonance imaging (MRI). Recently, several advanced algorithms have been developed based on navigator-interleaved GA trajectory to realize free-running cardiac MRI. However, navigator-interleaved GA trajectory suffers from the eddy-current effect, which reduces the image quality. PURPOSE: This work aims to integrate the navigator-interleaved GA trajectory with clinical cardiac MRI acquisition, with the minimum eddy-current artifacts. The ultimate goal is to realize a high-quality free-running cardiac imaging technique. METHODS: In this paper, we propose a new "swing golden angle" (swingGA) radial profile order. SwingGA samples the k-space by rotating back and forth at the generalized golden ratio interval, with smoothly interleaved navigator readouts. The sampling efficiency and angle increment distributions were investigated by numerical simulations. Static phantom imaging experiments were conducted to evaluate the eddy current effect, compared with cartesian, golden angle radial (GA), and tiny golden angle (tGA) trajectories. Furthermore, 12 heart-healthy subjects (aged 21-25 years) were recruited for free-running cardiac imaging with different sampling trajectories. Dynamic images were reconstructed by a low-rank subspace-constrained algorithm. The image quality was evaluated by signal-to-noise-ratio and spectrum analysis in the heart region, and compared with traditional clinical cardiac MRI images. RESULTS: SwingGA pattern achieves the highest sampling efficiency (mSE > 0.925) and the minimum azimuthal angle increment (mAD < 1.05). SwingGA can effectively suppress eddy currents in static phantom images, with the lowest normalized root mean square error (nRMSE) values among radial trajectories. For the in-vivo cardiac images, swingGA enjoys the highest SNR both in the blood pool and myocardium, and contains the minimum level of high-frequency artifacts. The free-running cardiac images have good consistency with traditional clinical cardiac MRI, and the swingGA sampling pattern achieves the best image quality among all sampling patterns. CONCLUSIONS: The proposed swingGA sampling pattern can effectively improve the sampling efficiency and reduce the eddy currents for the navigator-interleaved GA sequence. SwingGA is a promising sampling pattern for free-running cardiac MRI.

Wall motion assessment by feature tracking in pediatric patients with coronary anomalies undergoing dobutamine stress CMR.

Background: Left ventricular (LV) wall motion assessment is an important adjunct in addition to perfusion defects in assessing ischemic changes. This study aims to investigate the feasibility and utility of performing feature tracking (FT) in pediatric patients with coronary anomalies undergoing dobutamine stress CMR to assess wall motion abnormalities (WMA) and perfusion defects. Method: This is a retrospective study where 10 patients with an inducible first-pass perfusion (FPP) defect and 10 without were selected. Global LV circumferential strain/strain rate (GCS/GCSR) was measured at rest and at peak stress (systole and diastole) using a commercially available feature tracking software. Peak GCS and GCSR were compared to indexed wall motion score (WMSI) between groups with and without FPP defect and in subjects with and without WMA. Results: The median age of patients was 13.5 years (Q1, 11 years; Q3, 15 years). Five subjects had qualitatively WMA at peak stress. A moderate correlation of GCS with WMSI at peak stress (0.48, p = 0.026) and a significant difference between GCS at rest and stress in patients with no inducible WMA (p = 0.007) were seen. No significant difference was noted in GCS between rest and stress in patients with WMA (p = 0.13). There was a larger absolute GCS/GCSR at peak stress in subjects with no inducible FPP defect or WMA. Conclusion: Smaller absolute GCS and a lack of significant change in GCS at peak stress in those with inducible WMA or perfusion defect are suggestive of compromised LV deformation in subjects with inducible WMA. Given these findings, GCS derived from CMR-FT may be used to objectively assess WMA in pediatric patients undergoing stress CMR.

Exploring the Unknown: Appreciating the Challenges of Non-compaction Cardiomyopathy.

Left ventricular non-compaction cardiomyopathy (LVNC), or non-compaction cardiomyopathy (NCCM), is defined by pronounced left ventricular trabeculations and deep intertrabecular recesses connecting with the ventricular cavity. Patients with NCCM can be asymptomatic or have severe complications, including heart failure, arrhythmias, thromboembolism, and sudden cardiac death. Our case discusses a patient with shortness of breath who was found to have a newly decreased ejection fraction. The workup revealed non-ischemic cardiomyopathy and cardiac MRI showed hyper-trabeculations consistent with NCCM. The patient was started on oral anticoagulation and guideline-directed medical therapy (GDMT) and discharged with an event monitor. NCCM stands as a relatively rare and enigmatic condition, often veiled in ambiguity. The absence of standardized diagnostic and management protocols further complicates its clinical landscape. While echocardiography is the primary diagnostic tool, its tendency for under-diagnosis poses a significant challenge. Conversely, advanced imaging modalities like cardiac MRI may lead to instances of overdiagnosis. Treatment approaches are non-specific, incorporating GDMT, anticoagulation, implantable cardioverter-defibrillator placement, and genetic testing paired with counseling. Prioritizing genetic research is crucial to uncover tailored therapeutic interventions. Establishing consensus guidelines and refining diagnostic accuracy are pivotal steps toward mitigating the risks associated with under and over-diagnosis.

Prevalence and Risk Factors of Cardiovascular Disease in Chronic Kidney Disease Patients at King Abdulaziz University Hospital (KAUH).

BACKGROUND: Chronic kidney disease (CKD) has emerged as a significant global health concern, with its incidence doubling over the past three decades. Cardiovascular diseases (CVD) pose a major threat to CKD patients, surpassing the risk of progressing to end-stage renal disease. While previous studies worldwide have shed light on this association, limited research has been conducted in Saudi Arabia regarding this burden. This study aims to fill this gap by identifying the prevalence and risk factors of CVD in CKD patients at King Abdulaziz University Hospital (KAUH), Jeddah, Saudi Arabia, between 2017 and 2022. METHODS: A six-year retrospective review of medical records at KAUH was conducted, including 465 non-end-stage CKD patients aged 30 to 79. Data, including demographics, clinical information, and laboratory results, were collected and statistically analyzed to investigate the association between variables. RESULTS: Out of 465 CKD patients, 262 (56.3%) were diagnosed with CVD, with congestive heart failure and ischemic heart disease being the most common types. The majority were male 259 (55.7%), non-Saudi 278 (59.8%), and aged 60 years and older 291 (62.6%). Hypertension 394 (84.7%) and diabetes mellitus 336 (72.3%) were prevalent comorbidities. Severely reduced left ventricular ejection fraction, proteinuria, diabetes mellitus, and higher BMI were identified as significant risk factors for CVD in CKD patients. CONCLUSION: This research contributes valuable insights into the prevalence and risk factors of CVD in CKD patients in Saudi Arabia, emphasizing the need for early detection and intervention. The identified risk factors provide a basis for developing targeted preventive strategies to mitigate this population's CVD burden.

Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.

OBJECT: To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability. MATERIALS AND METHODS: While PG-DL has emerged as a powerful image reconstruction method, its application to large-scale 3D non-Cartesian MRI is hindered by hardware limitations and limited availability of training data. We combine several recent advances in deep learning and MRI reconstruction to tackle the former challenge, and we further propose a 2.5D reconstruction using 2D convolutional neural networks, which treat 3D volumes as batches of 2D images to train the network with a limited amount of training data. Both 3D and 2.5D variants of the PG-DL networks were compared to conventional methods for high-resolution 3D kooshball coronary MRI. RESULTS: Proposed PG-DL reconstructions of 3D non-Cartesian coronary MRI with 3D and 2.5D processing outperformed all conventional methods both quantitatively and qualitatively in terms of image assessment by an experienced cardiologist. The 2.5D variant further improved vessel sharpness compared to 3D processing, and scored higher in terms of qualitative image quality. DISCUSSION: PG-DL reconstruction of large-scale 3D non-Cartesian MRI without compromising image size or network complexity is achieved, and the proposed 2.5D processing enables high-quality reconstruction with limited training data.