Cardiac magnetic resonance (CMR) imaging is a valuable noninvasive tool for evaluating tissue response following catheter ablation of atrial tissue. This review provides an overview of the contemporary CMR strategies to visualize atrial ablation lesions in both the acute and chronic postablation stages, focusing on their strengths and limitations. Moreover, the accuracy of CMR imaging in comparison to atrial lesion histology is discussed. T2-weighted CMR imaging is sensitive to edema and tends to overestimate lesion size in the acute stage after ablation. Noncontrast agent-enhanced T1-weighted CMR imaging has the potential to provide more accurate assessment of lesions in the acute stage but may not be as effective in the chronic stage. Late gadolinium enhancement imaging can be used to detect chronic atrial scarring, which may inform repeat ablation strategies. Moreover, novel imaging strategies are being developed, but their efficacy in characterizing atrial lesions is yet to be determined. Overall, CMR imaging has the potential to provide virtual histology that aids in evaluating the efficacy and safety of catheter ablation and monitoring of postprocedural myocardial changes. However, technical factors, scanning during arrhythmia, and transmurality assessment pose challenges. Therefore, further research is needed to develop CMR strategies to visualize the ablation lesion maturation process more effectively.
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http://dx.doi.org/10.1016/j.jacep.2023.08.013 | DOI Listing |
Neth Heart J
January 2025
Department of Cardiology, Thorax Centre, Cardiovascular Institute, Erasmus Medical Centre, Rotterdam, The Netherlands.
Background: Cardiac sarcoidosis (CS) is associated with poor prognosis, making early diagnosis and treatment important. This study evaluated the results of a diagnostic approach in patients with known sarcoidosis and suspected cardiac involvement in a tertiary centre and their long-term outcomes.
Methods: We included 180 patients with sarcoidosis and a clinical suspicion of CS.
Alzheimers Dement
December 2024
Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Background: Cardiometabolic disorders are emerging risk factors for Alzheimer's disease (AD) and AD-related dementia (ADRD). There is currently insufficient understanding of how different cardiometabolic profiles and blood biomarkers impact different AD-related brain pathology regionally. This project uses data-driven approaches and explainable artificial intelligence methods to determine the cardiometabolic and fluid contributions toward AD-related pathophysiologic patterns in the brain.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Background: Cardiometabolic disorders are emerging risk factors for Alzheimer's disease (AD) and AD-related dementia (ADRD). There is currently insufficient understanding of how different cardiometabolic profiles and blood biomarkers impact different AD-related brain pathology regionally. This project uses data-driven approaches and explainable artificial intelligence methods to determine the cardiometabolic and fluid contributions toward AD-related pathophysiologic patterns in the brain.
View Article and Find Full Text PDFJACC Cardiovasc Imaging
January 2025
School of Biomedical Engineering and Imaging Sciences, King's College London, Guy's and St Thomas' Hospital, London, United Kingdom; Cardio-Oncology Centre of Excellence, Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom.
Eur Heart J Imaging Methods Pract
October 2024
Cardiologia 1-Emodinamica, Dipartimento Cardiotoracovascolare 'A. De Gasperis', ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy.
Artificial intelligence (AI) is transforming cardiovascular imaging by offering advancements across multiple modalities, including echocardiography, cardiac computed tomography (CCT), cardiovascular magnetic resonance (CMR), interventional cardiology, nuclear medicine, and electrophysiology. This review explores the clinical applications of AI within each of these areas, highlighting its ability to improve patient selection, reduce image acquisition time, enhance image optimization, facilitate the integration of data from different imaging modality and clinical sources, improve diagnosis and risk stratification. Moreover, we illustrate both the advantages and the limitations of AI across these modalities, acknowledging that while AI can significantly aid in diagnosis, risk stratification, and workflow efficiency, it cannot replace the expertise of cardiologists.
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