How fat imaging helps in combating obesity, lifestyle disorders

How you wondered how to assess your body fat composition and its distribution? Fat imaging is the answer to the question where a bunch of techniques are used to assess and obtain more information about it.

 

Dr Sonal Krishan, Associate Director Body and GI Imaging, Medanta, Gurugram, said that measures such as BMI (Body Mass Index) were ancient as they do not capture vital fat distribution nuances.

 

Purpose of Fat Imaging

 

Obesity and associated metabolic disorders are a chronic disease . The body’s capacity to store fat once had evolutionary advantages but has now become a liability in modern societies with abundant food and sedentary lifestyles. To combat obesity-related health risks, advanced imaging technologies are now critical for mapping fat distribution, assessing metabolic dysfunction, and guiding interventions.

 

Fat Imaging helps us understand how the stress of having excess fat (adiposity) is driving inflammation, insulin resistance, and eventually death risk. It helps clinicians to guide lifestyle modifications for at-risk patients; track responses to medication and make early interventions.

 

The most prominent fat imaging technique is Magnetic Resonance Imaging (MRI) including Magnetic Resonance Elastography (MRE). These yield high-resolution images of fat depots and assess disease processes associated with obesity, liver disease, complications related to diabetes and cardiovascular well-being.

 

Fat distribution, liver fibrosis may be systematically assessed to include changes that have occurred over time. MRE is an innovative non-invasive method that assesses both liver stiffness (fibrosis) and fat content at the same time. It monitors real-time liver health alterations and also delineates where fat is stored in the abdomen. There is no exposure to radiation and thus it can be used repeatedly.

 

AI driving factor

 

Artificial intelligence improves imaging by automating report generation, risk prediction, and fat quantification. AI algorithms can classify fat content types, identify liver steatosis (abnormal accumulation of fat), and forecast cardiovascular risks, simplifying clinical workflow and lowering diagnostic variability.

 

The risk of obesity and its association with diabetes, cardiovascular disease, and fatty liver disease is looming large. With advanced imaging there can be early identification of risks and individualised therapies planned. This can revolutionise preventive medicine.

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