Deep Learning to Increase Accessibility, Ease of Heart Imaging

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 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, said Jha.

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 said. “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 said. “By providing the ability to perform AC without requiring a CT, the proposed CTLESS method may help boost technological health equality across the U.S. and worldwide.”

Yu Z, Rahman MA, Abbey CK, Laforest R, Siegel BA, Jha A.
CTLESS: A scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.
IEEE Transactions in Medical Imaging, Nov. 25, 2024, doi: 10.1109/TMI.2024.3496870

Most Popular Now

Researchers Find Telemedicine may Help R…

Low-value care - medical tests and procedures that provide little to no benefit to patients - contributes to excess medical spending and both direct and cascading harms to patients. A...

AI Revolutionizes Glaucoma Care

Imagine walking into a supermarket, train station, or shopping mall and having your eyes screened for glaucoma within seconds - no appointment needed. With the AI-based Glaucoma Screening (AI-GS) network...

AI may Help Clinicians Personalize Treat…

Individuals with generalized anxiety disorder (GAD), a condition characterized by daily excessive worry lasting at least six months, have a high relapse rate even after receiving treatment. Artificial intelligence (AI)...

Accelerating NHS Digital Maturity: Paper…

Digitised clinical noting at South Tees Hospitals NHS Foundation Trust is creating efficiencies for busy doctors and nurses. The trust’s CCIO Dr Andrew Adair, deputy CCIO Dr John Greenaway, and...

AI can Open Up Beds in the ICU

At the height of the COVID-19 pandemic, hospitals frequently ran short of beds in intensive care units. But even earlier, ICUs faced challenges in keeping beds available. With an aging...

Mobile App Tracking Blood Pressure Helps…

The AHOMKA platform, an innovative mobile app for patient-to-provider communication that developed through a collaboration between the School of Engineering and leading medical institutions in Ghana, has yielded positive results...

Can AI Help Detect Cognitive Impairment?

Mild cognitive impairment (MCI) can be an early indicator of Alzheimer's disease or dementia, so identifying those with cognitive issues early could lead to interventions and better outcomes. But diagnosing...

Customized Smartphone App Shows Promise …

A growing body of research indicates that older adults in assisted living facilities can delay or even prevent cognitive decline through interventions that combine multiple activities, such as improving diet...

AI Model Predicting Two-Year Risk of Com…

AFib (short for atrial fibrillation), a common heart rhythm disorder in adults, can have disastrous consequences including life-threatening blood clots and stroke if left undetected or untreated. A new study...

New Study Shows Promise for Gamified mHe…

A new study published in Multiple Sclerosis and Related Disorders highlights the potential of More Stamina, a gamified mobile health (mHealth) app designed to help people with Multiple Sclerosis (MS)...

Patients' Affinity for AI Messages …

In a Duke Health-led survey, patients who were shown messages written either by artificial intelligence (AI) or human clinicians indicated a preference for responses drafted by AI over a human...

New Research Explores How AI can Build T…

In today’s economy, many workers have transitioned from manual labor toward knowledge work, a move driven primarily by technological advances, and workers in this domain face challenges around managing non-routine...