A new deep learning technique could eliminate the need for additional CT scans in myocardial perfusion imaging, reducing costs and radiation exposure while improving access to heart disease diagnosis.

Summary:

Researchers at Washington University in St Louis, in collaboration with Cleveland Clinic and the University of California Santa Barbara, have developed a deep learning technique called CTLESS that eliminates the need for an additional CT scan in myocardial perfusion imaging (MPI) by single photon emission computed tomography (SPECT). The method estimates attenuation directly from emission scan data, maintaining diagnostic accuracy while reducing radiation exposure and costs. This advancement could improve access to heart disease diagnosis, particularly in rural and resource-limited settings, where many facilities lack the necessary CT component.

Key Takeaways:

  • AI-Powered Heart Imaging – Researchers developed CTLESS, a deep learning technique that removes the need for an additional CT scan in MPI SPECT without compromising diagnostic accuracy.
  • Improved Accessibility and Cost Savings – The method could expand access to accurate heart disease diagnosis in rural and resource-limited hospitals by reducing equipment requirements.
  • Comparable Performance Across Populations – CTLESS demonstrated consistent accuracy across different scanner models, degrees of heart damage, and patient demographics, supporting its potential for widespread clinical adoption.

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 change how 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, PhD, 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 in IEEE Transactions in Medical Imaging.

The next stage of research is for them to validate this method while working to make this tech more available to rural community hospitals. Their cost-saving technique is particularly significant for cases where access to such scans may be limited, such as in rural or otherwise resource-limited communities, says Jha.

How CTLESS Works and Its Potential for Clinical Adoption

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.

“Due to cost, complexity, equipment availability, regulatory concerns, and other local factors at hospitals and remote care centers, approximately 75% of all SPECT MPI scans are performed without AC, potentially compromising the diagnostic accuracy of these scans,” Jha says in a release. “By integrating concepts in physics and deep learning, the proposed CTLESS method estimates a synthetic attenuation map that is then used for AC. Thus, CTLESS may enable a mechanism where an additional scan may not be required.”

CTLESS uses photons from the emission scan to estimate attenuation, which can then be used to enhance image quality and improve diagnostic interpretation. Jha and his collaborators evaluated the performance of CTLESS using real-world clinical data and found that their method showed comparable results to traditional attenuation compensation.

Notably, CTLESS demonstrated robust performance across different scanner models, degrees of heart damage, and patient demographics. Jha noted that anatomical differences between men and women result in varying levels of attenuation in these groups and confirmed that the CTLESS method yields similar performance as traditional AC for both sexes. The performance of CTLESS was also relatively stable even as the size of the training data was reduced. All these observations make CTLESS a promising option for widespread clinical adoption following additional validation.

“Our results provide promise that in the future, a separate CT scan may not be required for performing attenuation correction in MPI SPECT. This is particularly significant for cases where access to such scans may be limited, such as in rural or otherwise resource-limited communities,” Jha says in a release. “By providing the ability to perform AC without requiring a CT, the proposed CTLESS method may help boost technological health equality across the US and worldwide.”

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