- The technology revolution of the last 20 years has spurred an evolution in cardiac imaging
- Among current trends is a greater role for radiologists in outpatient imaging, taking over some of the diagnostics normally performed by cardiologists
- As new, safer, and more helpful imaging tests have been developed, older technologies have fallen out of favor
- The biggest overall trend is non-invasive tools, and CCTA, cMRI, and cPET are taking the lead
- Hybrid and fusion imaging makes things clearer, and AI and ML will lead the way to further advances in imaging that improve diagnoses, treatment, and outcomes
Cardiovascular imaging has significantly evolved in this century. Over the last two decades, there have been advances in every method used to image the heart, including echocardiography, cardiac magnetic resonance, cardiac CT, and nuclear cardiology. There has also been a large increase in hybrid and fusion modalities that leverage the unique capabilities of two imaging techniques simultaneously as well as the incorporation of artificial intelligence (AI) and machine learning (ML) into the clinical workflow.
Among trends in cardiac imaging are advances in non-invasive imaging, which has helped guided patient management and improved outcomes. The technological advances of the past 20 years have also given rise to new imaging subspecialties and increased the demand for dedicated cardiac imagers who are cross-trained in multiple modalities, and there is further room for innovation.
While cardiologists still control most aspects of cardiac imaging, coronary computed tomography angiography (CCTA) exams are increasingly being performed by radiologists, with the largest growth – 355% – seen in outpatient settings. This marks a shift in heart imaging from a hospital inpatient to outpatient model. In this blog, we’ll look at the trends shaping cardiac imaging today as well as the future.
Imaging techniques go in and out of favor
The rates of use of transthoracic echocardiography (TTE), stress echo (SE), and transesophageal echocardiography (TEE) have decreased 3% since 2010, while nuclear myocardial perfusion imaging (MPI) decreased by 36%. This large decrease in MPI was the result of advances in CT technology. While MPI is between 44% and 70% accurate, CT has an accuracy of 91% to 96%.
In the same time period, other technologies have come to the forefront: The use of cardiac positron emission computed tomography (cPET) has increased 193%, CCTA by 84%, and cardiac magnetic resonance imaging (MRI) by 125%.
Concurrently, these alternative and more accurate methods are finding increasing usage: In-office imaging by cardiologists fell by 52% and imaging performed in hospital outpatient imaging departments rose 71%. The increasing rate of CCTAs performed by radiologists has been encouraging collaboration between cardiologists and radiologists, as demonstrated at Thomas Jefferson University Hospital, where they are reading exams together in an effort to improve patient care.
What all this means is an increased emphasis on fast and non-invasive cardiac screening tools. Let’s take a closer look.
The rise of cPET
The rate of cPET imaging in cardiology offices has increased because it enables extremely clear images of heart muscle defects and is much faster than its predecessor, single-photon emission computed tomography (SPECT). A PET exam is performed in about 75 seconds as compared to more than an hour with SPECT. PET also uses a small, on-demand isotope generator instead of the cyclotron required by SPECT.
The growth of radiology-office cMRIs
Hospital outpatient cMRIs saw growth for cardiologists and radiologists. However, the trend is with radiologists: The rate of cMRIs done in cardiology offices fell by 50%, while those performed in radiology offices grew by more than 40%, and that trend is expected to continue.
Multimodality imaging advances
CCTA, cMRI, and cPET have been the main drivers impacting non-invasive evaluations of cardiovascular disease in this century as well as guideposts for clinical management and improved patient outcomes. The use of AI and ML has had an impact as well, allowing identification of previously unrecognized disease patterns, refined risk assessment, and personalized care.
The technological revolution has caused an evolution in multimodality in cardiac imaging. In CCTA, for example, improvements in scanners, acquisition methods, and modeling software have all contributed to minimizing radiation exposure while increasing the capabilities of CCTA. Today, ML applied to datasets provides more information without an invasive procedure.
cMRI is an effective, unparalleled diagnostic tool when it comes to evaluating cardiomyopathy and myocarditis. Mapping techniques that have evolved over the past decade demonstrate a marked improvement over normal T1, T2, and T2* images because they allow for the visualization as well as the quantification of focal and diffuse disease.
cPET has replaced single-photon emission computer tomography (SPECT) because PET can show true quantification of myocardial perfusion globally as well as regionally. The risk with SPECT is that it provides a relative assessment that does not always reveal underlying disease. PET can generate an absolute measurement of myocardial blood flow, which is useful to detect epicardial coronary artery disease. These blood flow measurements also have an increasingly important role in managing patients who are suspected of having coronary microvascular dysfunction.
Hybrid and fusion imaging
Hybrid cardiac imaging using PET and CCTA means a single assessment can identify flow-limiting coronary stenosis and ischemia qualifications, which can inform clinical management. When nuclear myocardial perfusion imaging is used with CCTA, the approach minimizes attenuation artifacts and
greatly improves the robustness of both the diagnosis and prognosis.
Fusion images are created when images obtained with different modalities are fused into a composite image, and this fusing provides a better understanding of the relationship between anatomy and devices to increase the efficiency of procedures as well as improve outcomes.
AI and ML will lead to further advances
AI is currently being used for image integration and analysis of data, applications that will result in new discoveries, better disease characterization, and more personalized therapies. Costs should decrease and value increase when it comes to image acquisition and interpretation, as well as reporting and decision making.
ML gives AI the means to learn by extracting patterns in data. This deep learning informs associations based on previous experience to train the AI process to increase the probability of correct classification.
Research employing radiomics – the means of obtaining quantitative metrics from images to create data sets – may further improve the prediction of cardiac risk and optimize outcomes in those with coronary artery disease.
As developments continue, AI armed with large imaging registries and repositories will boost diagnostic and prognostic performance.
Just as today’s cardiac imaging with its ability to provide diagnostic, prognostic, and risk assessment were unimaginable just two decades ago, the future is expected to expand on current capabilities to offer increasing insight into the human body.
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