ABSTRACT
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.
ABSTRACT
Nonalcoholic fatty liver disease (NAFLD) is a major cause of chronic liver disease globally and in India. The already high burden of NAFLD in India is expected to further increase in the future in parallel with the ongoing epidemics of obesity and type 2 diabetes mellitus. Given the high prevalence of NAFLD in the community, it is crucial to identify those at risk of progressive liver disease to streamline referral and guide proper management. Existing guidelines on NAFLD by various international societies fail to capture the entire landscape of NAFLD in India and are often difficult to incorporate in clinical practice due to fundamental differences in sociocultural aspects and health infrastructure available in India. A lot of progress has been made in the field of NAFLD in the 7 years since the initial position paper by the Indian National Association for the Study of Liver on NAFLD in 2015. Further, the ongoing debate on the nomenclature of NAFLD is creating undue confusion among clinical practitioners. The ensuing comprehensive review provides consensus-based, guidance statements on the nomenclature, diagnosis, and treatment of NAFLD that are practically implementable in the Indian setting.
