How Modern Imaging is Changing Cardio or Neuro Interventions- A Practical Guide

New imaging technology has completely revolutionized cardio or neuro procedures to attain accuracy unimaginable even a couple of decades back. MRI is now capable of providing non-invasive, high-definition images necessary to diagnose brain tumors and strokes, and CT scans can be completed within minutes, rendering them invaluable in emergency situations where seconds count.

We have witnessed valuable breakthroughs in cardiology imaging and neurology diagnostic imaging. Functional MRI detects alterations in blood flow to mirror neuronal activity, offering valuable information for pre-surgical evaluation. Likewise, PET scans can detect early alterations in glucose metabolism, invaluable to early intervention in diseases like Alzheimer's.

AI integration within cardiology and neuro imaging is also increasingly enhancing diagnostic results and streamlining clinical processes. Digital subtraction angiography with flat panel detectors now offers enhanced visualization with lower doses of radiation significant leap for complex procedures.

Throughout this handbook, we will outline how these imaging modalities are revolutionizing interventional practice, summarize their clinical indications, address issues of implementation, and point to future directions such as mixed reality and 3D modeling that will continue to transform care of the patient.

Evolution of imaging in cardiology and neurology

 The history of medical imaging started back in 1895 when Wilhelm Conrad Roentgen invented X-rays, the first doctor's ability to look inside the human body without the need for surgery. This discovery revolutionized diagnosis, first being able to assess bone fractures, dental problems, and lung conditions. But in the 19th century, medical physicians had mostly diagnosed illnesses by studying symptoms and signs, with diagnostic instruments such as ophthalmoscopes and stethoscopes emerging only in the 1850s.

From X-rays to high-tech MRI and CT

 Following Roentgen's Nobel Prize-winning finding, medical imaging progressed in separate stages. X-rays or 'plane films' were first used to diagnose bone fracture and chest disease. The first computed tomography (CT) scanner arrived in the 1970s, which enabled high-resolution cross-sectional imaging of the human body. This technology was revolutionary in that it allowed clinicians to create three-dimensional visualizations of internal anatomy.

The earliest magnetic resonance imaging (MRI) views of the human heart were obtained in 1981, though initially inferior to other methods. Yet, as hardware and dual-inversion technology developed through the next decades, imaging performance was unmatched. MRI has superior imaging performance to identify most cardiac diseases, which is primarily:

Congenital malformations, providing high-resolution anatomy images for preoperative planning valvular disease, morphologic and functional study

Heart failure assessment by using detailed information on myocardial structure and function

In cardiology in particular, the history of echocardiography started in 1880 but only became clinically significant in the 1950s. It was initially created as a two-dimensional echocardiography in 1967 with a system of rotating mirrors, and then the dynamic real-time transthoracic 3D imaging with high-frame rates in the early 2000s.

Why imaging is central to modern interventions

Contemporary medical imaging is a key component of the entire healthcare so pervasive is the use of imaging throughout the spectrum of health, from wellness and screening to early detection, treatment selection, and follow-up. It encompasses the triage of patients in acute care, management of chronic diseases, intervention guided by imaging, and optimization of treatment planning.

CT scans are also gaining more significance in emergency cases due to their effectiveness and speed. For stroke patients, CT scans can differentiate between hemorrhagic and ischemic stroke, which is of utmost significance in the choice of therapy. CT perfusion in the detection and evaluation of cerebral stroke has also widened the therapeutic window, providing much greater scope for successful embolectomy to far more patients.

In cardiology, modern imaging has typically improved patient care and diagnostic precision. For instance, a comparison between echocardiography and cardiac MRI in the diagnosis of ventricular thrombus saw it concluded that MRI was highly sensitive (88%) and specific (99%) compared to 24% sensitivity of echocardiography.

Additionally, cardiovascular imaging (echocardiography, CT, MRI, nuclear cardiology) has decreased the necessity of diagnostic cardiac catheterization, whereas catheter-based percutaneous interventions have decreased the necessity of surgery in most congenital, valvular, and coronary heart disease. The actual benefit of these new imaging modalities is to provide patient-specific data-driven decision-making, rather than conventional imaging practices.

Most influential imaging methods revolutionizing interventions

Advanced imaging methods form the core of contemporary cardio or neuro intervention. As we examine the most powerful technologies, we will see how each one is an integral part of diagnosis and treatment.

MRI and fMRI: Structural and functional insights

Magnetic resonance imaging (MRI) is now a routine device in cardiology and neurology, providing the ultimate resolution of tissue detail without the use of ionizing radiation. MRI is most ideally suited to image myocardial structure and function with greater sensitivity to ascertain congenital defects, valvular disease, and heart failure.

Functional MRI (fMRI) enhances this technique by tracking changes in blood flow that are related to neural activity. Unlike standard MRI that does anatomical snapshots, fMRI creates dynamic accounts of metabolic activity over time. The technique relies on Blood Oxygen Level Dependent (BOLD) contrast that measures very small changes in blood flow that are associated with brain activity.

They are observed as brighter areas on fMRI images, which reflect which areas are most active while performing a specific task. fMRI has also particularly been helpful in surgical planning. The method through which the brain is mapped prior to surgery to identify regions that regulate fundamental functions like movement and speech is accomplished with fMRI. Surgeons are able to identify regions that can be spared by having patients perform simple actions while undergoing scanning—e.g., tapping fingers. This has significantly reduced post-surgical complications and preserved neurological function.

CT and PET: Metabolic and rapid imaging

Computed tomography (CT) gives rapid imaging that is needed in emergencies. At the same time, positron emission tomography (PET) gives unmatched metabolic data by following radioactive tracers in the body.

The combination of these technologies, PET/CT, is a valuable imaging tool for the interpretation of molecular and physiological information in the context of anatomy. Of special interest are developments in digital PET/CT systems with long axial field of view (LAFOV) that have increased system sensitivity by 10-40-fold, greatly improving image quality as well as whole-body coverage.

In neurology, fluorodeoxyglucose (FDG) remains the agent of first choice for clinical brain imaging. FDG PET has been 92% to 95% accurate in differentiating Alzheimer's disease, frontotemporal dementia, and dementia with Lewy bodies from normal patients. In epilepsy, FDG PET has been more sensitive than MRI for the detection of seizures originating from the temporal lobes, with laterality and localization adding in another 60% to 90% of patients.

DTI and MRS: Cutting-edge neuroimaging technology

Diffusion tensor imaging (DTI) is a valuable new imaging method of white matter organization. DTI measures the directional movement (anisotropy) of water molecules in brain tissue, which is directional along myelinated axons and less directional in grey matter.

Fractional anisotropy (FA), the most ubiquitous scalar in DTI, is a measure of the amount of diffusional asymmetry in a voxel and ranges from 0 (theoretically infinite isotropy) to 1 (theoretically infinite anisotropy). Decreased FA in white matter has been correlated with histological evidence of traumatic axonal injury in experimental models. In human patients, longitudinal increases in FA in white matter are correlated with recovery from neurological impairment.

Magnetic resonance spectroscopy (MRS) offers a further strong component of neuroimaging by detecting specific chemicals in the brain. MRS quantitates metabolites like N-acetylaspartate (NAA) for neuronal integrity, creatine (Cr) for cellular energy metabolism, choline (Cho) for membrane turnover, and lactate (Lac) for anaerobic metabolism. These are critical measurements of brain metabolism that cannot be achieved with standard imaging.

In cardiological practice, MRS provides an opportunity to identify various characteristics of myocardial metabolism in vivo based on the detection of hydrogen-1, carbon-13, and phosphorus-31 signals. Hydrogen-1 MRS can identify cellular triglycerides, while carbon-13 MRS can assess the components of glycolysis, tricarboxylic acid cycle, or β-oxidation.

Together, these new imaging modalities have transformed the practice of cardio or neuro interventions by offering previously unattainable structural, functional, and metabolic data to guide more precise and effective interventions.

Clinical applications in diagnosis and treatment

Imaging technologies have progressed from their role as diagnostic tools to become integral parts that influence directly clinical decisions in neurology and in cardiology. The technologies now influence directly significant parts of patient care from initial diagnosis to treatment planning and follow-up.

Early diagnosis of neurological disorders

High-resolution imaging enables detection of neurological disease before clinical presentation. In patients with mild cognitive impairment, those with MRI hippocampal atrophy progressed to Alzheimer's disease at 38%, but only 5% of those without atrophy progressed. Amyloid and tau PET imaging combined have been shown to be highly effective at differentiating Alzheimer's from other degenerative disorders of the neurons, with 98% sensitivity and 88% specificity.

In Parkinson's disease, dopamine transporter (DaT) scanning with SPECT has proven very useful and shown a pooled sensitivity of 98.6% and specificity of 93.6% in differentiating PD from other parkinsonian disorders. Resting state functional MRI is now capable of distinguishing early Parkinson's patients from controls with an accuracy of 91.7% and allow intervention to be initiated earlier.

Cardiac imaging for diagnosing heart disease

Cardiac imaging has several clinical applications -

●      Screening for early detection of cardiac diseases

●      Diagnosing established cardiac problems

●      Evaluation of the degree of damage after heart attacks

●      Validity of treatment monitoring

These technologies enable diagnosis of most conditions like arrhythmias, coronary artery disease, heart failure, and valvular disease. Coronary CT angiography is now a first-line imaging test for suspected coronary artery disease and provides excellent imaging of vessel obstruction and plaque composition.

Long-term follow-up of treatment response

Diffusion-weighted MRI is of particular interest as a treatment response biomarker because it detects alterations at the cellular level before alterations in anatomy. Successful treatment in most tumor types is interpreted as an initial increase in apparent diffusion coefficient (ADC) values.

Parametric Response Map (PRM) technique allows voxel-level ADC change quantification to produce three-dimensional treatment response-associated visualizations. In glioma patients, research showed that early PRMADC changes correlate with conventional clinical outcome measures and can potentially allow for earlier optimization of treatment.

Planning neurosurgical and cardiac procedures

Innovative imaging technology has a profound effect on surgical strategy and decision-making long before the patient steps into the operating room. In neurosurgery, advanced imaging such as functional MRI, diffusion tractography, and intraoperative MRI allow neurosurgeons to identify and map functional areas of importance and provide a safer surgical approach to the brain lesion in a neurosurgical procedure.

In cardiology, the introduction of CT-derived fractional flow reserve (CT-FFR) as a pre-procedural planning tool provides a non-invasive approach to identify hemodynamically significant lesions. The technology of CT-FFR allows surgeons to model the post-stenting outcomes, leading to better decision-making processes for revascularization strategy.

It is also possible to utilize the same CT technology to estimate guide wire crossing success rates in total coronary occlusion (TCO) interventions, including the way and way procedural approaches must occur to address a cardiac event.

However, even though both surgical specialties use advanced imaging technology, some challenges may remain in using the imaging technology or disturbingly, interpreting it into practice for both cardiac and neurosurgical approaches.

Precision and False Positives/Negatives

Myocardial perfusion scintigraphy has an estimated accuracy of between 85% to >98% in the detection of major coronary artery disease. Reversible perfusion defects have been observed in up to 27% of selected patients presenting with angina symptoms and had normal coronary angiograms. When interpreting false negatives from PET-CT imaging in non-malignant conditions, myocardial 18F-FDG substrate has a high level of accumulation in normal tissue.

There may be scant affinity for malignant conditions toward the tracer target. The potential for misinformation and malpractice liability costs may surpass the expense of identification and treatment for the long list of patients referred for coronary catheterization.

Availability and cost of imaging across different geographic regions.

Approximately two-thirds of the global population lacks access to diagnostic imaging. The difference is stark, as low and middle-income countries have less than 1 CT scanner per million persons (mil), whereas high-income countries have nearly 40 CT scanners, and the disparity is even more pronounced for MRI and nuclear medicine. Lost access to patients who need services and cost imposed on society are a consequence of unsuccessful or inappropriate imaging.

In the US, patients had the highest out-of-pocket costs for advanced imaging than any other in-network benefit across health insurance plans in the country. The out-of-pocket costs patients incurred increased by 41.1% from $97.97 in 2000 to $138.25 in 2009, and again from $138.05 in 2017 to $185.91 in 2019.

Need for specialized training and skills

The use of cardiac CT and MRI is rapidly expanding based on compelling evidence from large international studies; however, the number of physicians who are trained to accurately interpret these images may not be able to keep pace with demand. Societies have established training requirements, but these have been recently updated to reflect the increased breadth of competency because of the continued advancement of imaging.

In neuroimaging as well, there are neither established guidelines about how one might become competent researchers or consumers of neuroimaging data nor published studies indicating the status of training among practitioners.

AI in cardiology image analysis and neurology diagnosis

AI algorithms can already analyze massive amounts of real time image data, flagging potential issues for the clinician which they may have overlooked based on their observation of the image alone. In cardiology imaging, for example, AI systems are already starting to analyze routine chest CT scans for findings that may not even be radiologically related.

One example is the detection of coronary artery calcification levels as determined from CT chest scans, to identify patients at risk for heart disease who may not even know they are at risk. In essence, patients with lung problems may also get a cardiac screening, during their lung CT scan without the clinician or their permission.

In neurology, again AI has successfully identified clinically relevant information in brain images to assist the physician in diagnosing diseases such as amyotrophic lateral sclerosis (ALS) as well as facilitating long-term care. These algorithms will act as a first-pass mechanism to identify images with results the radiologist will want to review.

Predictive diagnosis and personalized medicine

AI augmented predictive diagnostics will provide more personalized therapeutic insights from the patient’s imaging data, combined with other clinical data, to help clinicians understand disease trajectory and how a patient will respond to a specific therapy approach. This can aid clinicians in making treatment decisions based on individual patient characteristics.

AI-enabled image analysis data that provides diagnostic insights based on molecular, genomic and other data for a complete profile of the patient, has the potential for developing personalized therapeutic approaches. While issues regarding data privacy, transparency of the algorithms and clinical validation of the care are still barriers to adoption, the ability to provide accurate, non-invasive diagnosis, will lead to better patient outcomes.

Mixed reality and 3D modelling in planning

 Mixed reality (MR) technology aids specialists while carrying out complicated interventional procedures by embedding relevant patient-specific data in real-time insights into a single scene. MR has shown great success in cardiac interventions in particular at the time of constructing vena cava philtres and the treatment of pulmonary artery stenosis and associated percutaneous interventions with reduced radiation exposure and dose of contrast medium.

In addition to MR, advanced 3D modelling predicts what the post-surgical function of organs will be. This differs from typical anatomical models because a physiological model incorporates cardiac imaging and patient-specific measurements to demonstrate how a heart functions post surgically. This model provides surgeons with insight into the future making it possible to select the best surgical strategy for each patient and avoid high-risk procedures with poor outcomes.

Conclusion

 Advanced imaging modalities have undoubtedly impacted the field of cardiac and neuro interventions. In this guide, we discussed how modalities such as MRI, CT, PET, and fMRI have provided clinicians with a previously unattainable viewpoint into the human body and its physiology. These advanced imaging modalities now form the basis for evaluating, treating, and assessing outcomes for individuals with neurological and cardiac conditions.

Despite the advances in imaging modalities, there are still many challenges that lie ahead. Primarily, issues regarding the accuracy of advanced imaging are prevalent, evidenced with false-positives mentioned in myocardial perfusion imaging, and as it pertains to the false positives found in PET-CT studies [42-45]. Furthermore, the continued disparity in access to advanced imaging technologies between high and low-income areas of the world continues as it relates to a healthcare gap [46-47].

Lastly, as it pertains to advanced imaging, through training in individual interpreting of the images, this aspect of training also appears to lag significantly behind the implementation of the recent technological advancements [51-53].

Looking forward, advances in medical imaging may offer significant approaches to these challenges. The implementation of AI in the detection of cardiology and neurology will likely improve the accuracy of the initial detection while reducing the time involved to interpret studies.

The advancements of predictive diagnostics will provide a more personalized treatment approach by adding data from images to molecular and genomic data [57-58]. As it relates to surgical planning in particular, mixed realities and 3D modelling will allow physicians to visualize a person’s potential surgical outcome before making his or her first incision [59-62].

Medical imaging has continued to advance significantly since Roentgen discovered the X-ray in 1895. The advent from plain old radiograph

FAQs

 Q1. How is artificial intelligence shaping the future of cardiac and neurological imaging?

AI improves accuracy and efficiency in cardiac and neurological imaging. AI can analyze vast amounts of data in seconds. AI can identify the potential for a cardiovascular event that may be missed by human eyes. In cardiology, emerging AI systems can perform CVIT analysis of chest CT images using just coronary artery calcification, which is routinely obtained. In neurology, AI is being used to diagnose disease (e.g. amyotrophic lateral sclerosis (ALS), as well as for long-term care).

Q2. What are the most recent advancements in cardiac imaging technology?

Recent developments in cardiac imaging include the use of artificial intelligence to combine cardiovascular imaging with "big data". This may result in knowledge discovery, improved clinical disease characterization and tailored therapy. Another key development in imaging is an X-ray technique (hierarchical phase-contrast tomography, HiP-CT), which allows CT imaging of the heart with unprecedented detail, which can let you zoom in to the cellular level of the heart.

Q3: How is medical imaging changing the way cardiology and neurology are diagnosed? 

Medical imaging is changing the way diagnoses are made, by improving accuracy of visualization of structures inside the body. This facilitates the evaluation of disease, importantly the planning of therapies. Advanced imaging also facilitates non-invasive assessment for diseases such as cardiac diseases or neurodegenerative conditions (MRI).