SNMMI Mars Shot: Cardiac Disease

Thirty million Americans have heart disease, and 660,000 die each year, accounting for 1 in 4 of all deaths annually. That means a patient dies every 36 seconds. Early prevention and diagnosis of coronary artery disease (CAD) and other cardiac issues are vitally important in taking appropriate, individually applicable action. Without data learning to fill in the gaps, the efficacy of early detection and treatment is reduced. Sometimes heart disease may be “silent” and not diagnosed until a person experiences signs or symptoms of a heart attack, heart failure, or an arrhythmia¹. In everyday clinical practice, predicting a heart attack is challenging. The predicted likelihood of a heart attack is based on cardiovascular risk factor and scores, especially in patients with suspected CAD. However, even in patients with confirmed CAD, cardiovascular risk factors and scores don’t always show the full picture.


Currently, heart disease is treated when small or invisible symptoms have gone unnoticed and resulted in a major medical event for a patient. By combining advanced imaging techniques with clinical data, physicians can improve their prediction of heart attacks. Machine learning calculates heart attack risk by studying and incorporating key variables from clinical assessment, radiopharmaceutical application, and quantitative CT variables. Advanced imaging techniques have shown considerable promise in determining which CAD patients are most at risk for a heart attack. These techniques include radiopharmaceuticals such as 18F-sodium fluoride PET, which assesses disease activity in the coronary arteries, and CT angiography, which provides a quantitative plaque analysis. By adjusting the approach of cardiologic treatment to involve nuclear medicine through molecular imaging, physicians can more accurately image early-onset symptoms and indications, achieve precision imaging of diseased or abnormal tissues and cellular functions, and assess the effectiveness of interventions before the condition of the patient requires more invasive treatment.


Diagnostic imaging uses technologies such as x-rays, CT, MRI, ultrasound, PET, and SPECT to provide physicians with a way to look inside the body without surgery. Combining molecular imaging (such as PET scanning) and artificial intelligence has the potential to enable precision medicine by guiding the use of advanced therapeutic interventions².

In patients who have had a heart attack or have chronic heart failure, molecular imaging can assess the potential for sudden cardiac death and other cardiac events and help select individuals who would benefit from an automatic internal cardiac defibrillator. PET imaging with the radiotracer 18F-FDG is able to detect the chronic inflammation associated with early stages of cardiac sarcoidosis, offering a distinct advantage over MRI, which identifies late-stage scarring. 

More research into radiopharmaceuticals and data science integration in nuclear medicine is needed to help physicians diagnose and treat patients with CAD and other cardiac issues at all stages of disease to provide better outcomes and greater clinical confidence in the applied methods of intervention.


Molecular imaging is likely to play a pivotal role in the evaluation, risk stratification, and management of patients with CAD. Targeted molecular imaging in combination with more conventional physiological imaging will provide a personalized approach to the management of cardiac disease. The use of molecular imaging to evaluate early molecular and cellular events associated with CAD will allow early detection of disease and potentially improve outcomes³. Paired with other aspects of nuclear medicine data research, treatment and diagnosis modalities are set to vastly improve as
research continues and more professionals take charge of precision medicine.