Coronary artery disease is the leading cause of death globally. One of the most common tools used to diagnose and monitor heart disease, myocardial perfusion imaging (MPI) by single photon emission computed tomography (SPECT), uses a radioactive tracer and special camera to provide detailed images of blood flow to the heart, helping doctors detect coronary artery disease and other cardiovascular abnormalities.
Coronary artery disease is the leading cause of death globally. One of the most common tools used to diagnose and monitor heart disease, myocardial perfusion imaging (MPI) by single photon emission computed tomography (SPECT), uses a radioactive tracer and special camera to provide detailed images of blood flow to the heart, helping doctors detect coronary artery disease and other cardiovascular abnormalities. However, traditional SPECT imaging requires an additional CT scan to ensure accurate results, exposing patients to more radiation and increasing costs.
A new deep learning technique developed by researchers at Washington University in St. Louis with collaborators from Cleveland Clinic and University of California Santa Barbara could transform the way heart health is monitored, making it safer and more accessible. The method, known as CTLESS, leverages deep learning to remove the CT requirement without compromising diagnostic accuracy. The project, led by Abhinav Jha, associate professor of biomedical engineering in the McKelvey School of Engineering and of radiology at WashU Medicine Mallinckrodt institute of Radiology, was published online Nov. 25, 2024 in IEEE Transactions in Medical Imaging.
SPECT imaging requires an additional CT scan for attenuation compensation (AC), which corrects for how the emitted signal weakens, or attenuates, as it moves through body tissue, potentially obscuring heart images and leading to diagnostic inaccuracies. Such CT scans are typically acquired on a SPECT/CT scanner, but many facilities do not have this CT component.
Read more at Washington University in St. Louis
