Cardiac magnetic resonance imaging (CMR) has a unique role in the non-invasive characterization of the myocardial structure and viability. In current clinical practice, this relies primarily on gadolinium-based contrast agents (GBCA) to assess late gadolinium enhancement (LGE).
Despite the initial promise of parametric native T1 and T2 mapping to provide a quantitative pixel-wise myocardial tissue phenotyping, the lack of standardization of mapping sequences and thresholds for health and disease has limited its translation into routine clinical use. Instead of relying on specific myocardial thresholds, it is now possible to automatically identify myocardial disease patterns by artificial intelligence (AI) approaches that exploit the existing myocardial signal features associated with fibrosis and oedema in non-contrast steady-state free precession cine imaging and T1 and T2 mapping.
The research community has been optimistic regarding the impact that these AI-based solutions for automatic image analysis can have on preventing unnecessary gadolinium exposition, shortening scan time, and reducing operator dependency thereby facilitating the widespread use of CMR namely in low-income countries.
We are excited to provide you with an opportunity to contribute your valuable research in AI strategies toward the development of novel models for myocardial tissue phenotyping.
Keywords:
MRI, Cardiac AI, Tissue Characterization, Magnetic Resonance Imaging, Myocardial Tissue Characterization
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Cardiac magnetic resonance imaging (CMR) has a unique role in the non-invasive characterization of the myocardial structure and viability. In current clinical practice, this relies primarily on gadolinium-based contrast agents (GBCA) to assess late gadolinium enhancement (LGE).
Despite the initial promise of parametric native T1 and T2 mapping to provide a quantitative pixel-wise myocardial tissue phenotyping, the lack of standardization of mapping sequences and thresholds for health and disease has limited its translation into routine clinical use. Instead of relying on specific myocardial thresholds, it is now possible to automatically identify myocardial disease patterns by artificial intelligence (AI) approaches that exploit the existing myocardial signal features associated with fibrosis and oedema in non-contrast steady-state free precession cine imaging and T1 and T2 mapping.
The research community has been optimistic regarding the impact that these AI-based solutions for automatic image analysis can have on preventing unnecessary gadolinium exposition, shortening scan time, and reducing operator dependency thereby facilitating the widespread use of CMR namely in low-income countries.
We are excited to provide you with an opportunity to contribute your valuable research in AI strategies toward the development of novel models for myocardial tissue phenotyping.
Keywords:
MRI, Cardiac AI, Tissue Characterization, Magnetic Resonance Imaging, Myocardial Tissue Characterization
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.