“…Bai et al showed that an automatic method based on the use of DL in the process of segmentation and measurement of quantitative parameters in CMR imaging presented a performance equal to that of human experience [ 55 ]. Similar results were shown by Penso et al, who demonstrated a good correlation between volume calculated with DL and that calculated with a manual approach [ 56 ]. Atrial segmentation could be useful for management of atrial fibrillation, in particular for the planning of atrial fibrillation ablation both in the preoperative period and in follow-up.…”
Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.
“…Bai et al showed that an automatic method based on the use of DL in the process of segmentation and measurement of quantitative parameters in CMR imaging presented a performance equal to that of human experience [ 55 ]. Similar results were shown by Penso et al, who demonstrated a good correlation between volume calculated with DL and that calculated with a manual approach [ 56 ]. Atrial segmentation could be useful for management of atrial fibrillation, in particular for the planning of atrial fibrillation ablation both in the preoperative period and in follow-up.…”
Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.
“…The usual MRI data sets of the trunk are acquired without ECG triggering and are, therefore, usually not evaluable with regard to cardiac size and function. However, modern MRI procedures have already been described that can reconstruct respiratory and cardiac phase-dependent MRI images even without external triggering [ 17 ], as well as automated procedures for determining cardiac contours [ 18 ]. Fig.…”
“…In Cardiovascular Magnetic Resonance (CMR) the application of AI is well established to be useful in image acquisition [ 26 , 27 ]], as well as post-processing in both ventricular function [ 28 ] and tissue characterization [ 14 ].…”
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