Research on Imaging Diagnosis of Cardiovascular Disease

Dear Colleagues,

We are excited to announce a Special Issue titled "Research on Imaging Diagnosis of Cardiovascular Disease". This Special Issue seeks to highlight the latest research, advancements, and innovations in the realm of imaging techniques and their diagnostic applications for cardiovascular diseases.

Cardiovascular disease (CVD) remains a leading cause of mortality and morbidity worldwide. With the rapid advancement in medical technology, imaging diagnostics have emerged as pivotal tools in the early detection, management, and study of CVD. High-resolution imaging modalities, including MRI, CT scans, and echocardiography, have transformed our understanding of CVD, enabling clinicians to obtain detailed anatomical, functional, and molecular insights. However, while these technologies have significantly improved patient care, they also present challenges such as interpretation complexities and the integration of multifaceted data. Moreover, the rise of artificial intelligence and machine learning in image processing promises to revolutionize diagnostic accuracy but requires rigorous research and validation.

Meanwhile, the quest for improved diagnostic accuracy has ushered in a new era of research focusing on novel imaging biomarkers. These biomarkers, often subtle and intricate, hold the promise of enhancing our understanding of cardiovascular pathophysiology, offering early disease detection and paving the way for personalized therapeutic approaches. Validation of these novel imaging biomarkers is of paramount importance. It ensures not only their scientific robustness but also their applicability and relevance in real-world settings. A rigorous validation process is thus essential to ascertain their potential benefits and limitations. Furthermore, the utilization of these biomarkers in both pre-clinical and clinical settings presents a myriad of opportunities. In pre-clinical scenarios, they can guide research directions, aid in drug development, and offer insights into disease mechanisms. In clinical settings, their implementation can revolutionize patient care, from diagnosis to treatment monitoring.

In light of these developments and challenges, this Special Issue seeks to bring together innovative research focused on the imaging diagnosis of CVD with an emphasis on the discovery, validation, and application of innovative imaging biomarkers and technology. We invite submissions that address current gaps, present novel techniques, or provide comprehensive reviews on existing methodologies. This is an opportunity for researchers and clinicians to share their insights, promote collaboration, and contribute to the advancement of this crucial medical field.

The aim of this Special Issue is to assemble a comprehensive collection of research articles focused on the imaging diagnosis of cardiovascular disease (CVD), including, but not limited to, the following aspects:

• Studies on advanced imaging techniques in cardiovascular diagnosis, technological advancements in imaging equipment, and unique findings through imaging.
• Research on the potential of artificial intelligence and machine learning in revolutionizing diagnostic accuracy through image processing.
• Emphasis on the importance of discovering, validating, and implementing novel imaging biomarkers in understanding cardiovascular pathophysiology, offering early disease detection, and enabling personalized therapeutic approaches.
• Studies focusing on the discovery, validation, and practical application of novel imaging biomarkers for CVD. This includes understanding their potential benefits, limitations, and the rigorous validation processes they undergo.
• Research on the utilization of novel biomarkers in both pre-clinical and clinical settings. In the pre-clinical context, discussions on how they can shape research directions, facilitate drug development, and offer insights into disease mechanisms are encouraged. In the clinical context, the emphasis should be on how these biomarkers can redefine patient care, from accurate diagnostics to efficient treatment monitoring.
• Comprehensive reviews on existing methodologies, techniques, and tools used in the imaging diagnosis of CVD.
• Articles that address current gaps in the field, present novel diagnostic techniques, and promote interdisciplinary collaboration between researchers, clinicians, and technologists in the domain of CVD imaging.

Dr. Khaled Abd-Elmoniem
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• imaging diagnosis
• cardiovascular disease (CVD)
• artificial intelligence (AI)
• machine learning
• image processing
• imaging biomarkers
• early disease detection
• CVD imaging diagnostics