Radiology embraces FFR-CT and AI plaque analysis as cardiac imaging evolves

CCTA moves from anatomy to physiology

Historically, coronary CT angiography (CCTA) has focused on identifying stenosis, or narrowing of coronary arteries. However, these anatomical findings alone do not always indicate whether a lesion is actually impairing blood flow. The addition of FFR-CT changes that paradigm by enabling noninvasive physiologic assessment with an image-based technology, rather than needing an invasive cath lab wire-based FFR.

“Now, instead of just being limited to saying, ‘Oh, there’s a stenosis, we’re not sure what it means,’ the addition of FFR-CT and the physiologic information that we get to it really refines the care that we’re giving to patients,” he said.

FFR-CT has a decade of validation

Morris said his team has used FFR-CT for nearly a decade, during which time the evidence base has expanded considerably. The technology also has gained endorsement in both U.S. and European clinical guidelines, helping drive broader adoption. Inclusion in American College of Cardiology and American Heart Association clinical guidelines has increasingly made CCTA a frontline tool for evaluating chest pain.

Despite growing acceptance among cardiologists, Morris noted that wider adoption within radiology still depends heavily on education.

“The challenge for us has been disseminating that knowledge more broadly into the radiology community,” he said.  

CCTA reimbursement spurs adoption

One of the key inflection points for FFR-CT adoption has been reimbursement. While the technology was previously covered under temporary codes, the introduction of a Category 1 CPT code has made it easier to incorporate it into routine practice.

“That’s certainly one of the limits of adoption—having a technology that can be reimbursed,” Morris said. “Now that we have that Level 1 code, certainly the adoption is a lot easier.”

A similar pathway is now expected for AI-based plaque analysis, which had its own Category 1 CPT code go into effect in January.  

AI-driven plaque quantification is emerging as another major advancement in cardiac imaging. This goes beyond basic calcified plaque assessment, offering more detailed quantification of soft plaques that pose higher cardiovascular risk. These tools can automatically identify the type and measure of plaque burden, including high-risk ones that may lead to adverse cardiac events.

Soft plaque analysis is seen as a way to catch patients at risk for coronary disease much earlier when the disease progression can be halted. These plaque assessments also are being used increasingly for preplanning percutaneous coronary interventions.

A notable shift in responsibility

“Historically we read the study and we defer to the clinicians how to manage the patients,” Morris said. “But now, we’re responsible for helping...drive treatment decision-making.”

AI enables rapid, reproducible analysis that would otherwise be too time-consuming to perform manually, opening the door to more precise risk stratification and longitudinal patient monitoring.

Evidence drives clinical integration

Morris emphasized that FFR-CT and plaque analysis stand out in a crowded AI landscape because of their strong clinical validation. While hundreds of imaging algorithms have received regulatory clearance, relatively few have demonstrated clear impact on outcomes.

“We’re in a sea of AI algorithms, but what we’re really missing is that validation to understand what impact they have on patient care,” he said.

He added that robust clinical evidence has been key to securing reimbursement and driving real-world use, a model he believes other AI developers should follow.