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

Giving Doctors an AI-Powered Head Start …

Detection of melanoma and a range of other skin diseases will be faster and more accurate with a new artificial intelligence (AI) powered tool that analyses multiple imaging types simultaneously...

AI Agents for Oncology

Clinical decision-making in oncology is challenging and requires the analysis of various data types - from medical imaging and genetic information to patient records and treatment guidelines. To effectively support...

AI Medical Receptionist Modernizing Doct…

A virtual medical receptionist named "Cassie," developed through research at Texas A&M University, is transforming the way patients interact with health care providers. Cassie is a digital-human assistant created by Humanate...

Using Data and AI to Create Better Healt…

Academic medical centers could transform patient care by adopting principles from learning health systems principles, according to researchers from Weill Cornell Medicine and the University of California, San Diego. In...

AI Tool Set to Transform Characterisatio…

A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterise the diversity of individual cells within...

AI Detects Hidden Heart Disease Using Ex…

Mass General Brigham researchers have developed a new AI tool in collaboration with the United States Department of Veterans Affairs (VA) to probe through previously collected CT scans and identify...

Human-AI Collectives Make the Most Accur…

Diagnostic errors are among the most serious problems in everyday medical practice. AI systems - especially large language models (LLMs) like ChatGPT-4, Gemini, or Claude 3 - offer new ways...

Northern Ireland Completes Nationwide Ro…

Go-lives at Western and Southern health and social care trusts mean every pathology service is using the same laboratory information management system; improving efficiency and quality. An ambitious technology project to...

Highland Marketing Announced as Official…

Highland Marketing has been named, for the second year running, the official communications partner for HETT Show 2025, the UK's leading digital health conference and exhibition. Taking place 7-8 October...

MHP-Net: A Revolutionary AI Model for Ac…

Liver cancer is the sixth most common cancer globally and a leading cause of cancer-related deaths. Accurate segmentation of liver tumors is a crucial step for the management of the...

Groundbreaking TACIT Algorithm Offers Ne…

Researchers at VCU Massey Comprehensive Cancer Center have developed a novel algorithm that could provide a revolutionary tool for determining the best options for patients - both in the treatment...

The Many Ways that AI Enters Rheumatolog…

High-resolution computed tomography (HRCT) is the standard to diagnose and assess progression in interstitial lung disease (ILD), a key feature in systemic sclerosis (SSc). But AI-assisted interpretation has the potential...