Robert Monsoura; Mudit Duttaa; Ahmed-Zayn Mohameda; Andrew Borkowski, MDa,b; and Narayan A. Viswanadhan, MDa,b Correspondence:Robert Monsour (rcm80@georgetown.edu)
aUniversity of South Florida Morsani College of Medicine, Tampa, Florida bJames A. Haley Veterans’ Hospital, Tampa, Florida
Author disclosures
The authors report no actual or potential conflicts of interest or outside sources of funding with regard to this article.
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AI has the ability to read CT, MRI, and positron emission tomography (PET) with reduced or without contrast without significant loss in sensitivity for detecting lesions. With MRI, gadolinium-based contrast can cause injection site reactions, allergic reactions, metal deposition throughout the body, and nephrogenic systemic fibrosis in the most severe instances.14 DL has been applied to brain MRIs performed with 10% of a full dose of contrast without significant degradation of image quality. Neuroradiologists did not rate the AI-synthesized images for several MRI indications lower than their full-dose counterparts.15 Low-dose contrast imaging, regardless of modality, generates greater noise with a significantly reduced signal. However, with AI applied, researchers found that the software suppressed motion and aliasing artifacts and improved image quality, perhaps evidence that this low-dose modality is less vulnerable to the most common pitfalls of MRI.
Recently, low-dose MRI moved into the spotlight when Subtle Medical SubtleGAD software received a National Institutes of Health grant and an expedited pathway to phase 2 clinical trials.16 SubtleGAD, a DL AI that enables low-dose MRI interpretation, might allow contrast MRI for patients with advanced kidney disease or contrast allergies. At some point, contrast with MRI might not be necessary because DL AI applied to noncontrast MRs for detecting MS lesions was found to be preliminarily effective with 78% lesion detection sensitivity.17
PET-MRI combines simultaneous PET and MRI and has been used to evaluate neurologic disorders. PET-MRI can detect amyloid plaques in Alzheimer disease 10 to 20 years before clinical signs of dementia emerge.18 PET-MRI has sparked DL AI development to decrease the dose of the IV radioactive tracer 18F-florbetaben used in imaging to reduce radiation exposure and imaging costs.This reduction is critical if PET-MRI is to become used widely.19-21
An initial CNN could reconstruct low-dose amyloid scans to full-dose resolution, albeit with a greater susceptibility to some artifacts and motion blurring.22 Similar to the synthetic multi-orientation resolution enhancement CNN, this program showed signal blurring from the L2 loss function, which was corrected in a later AI that used a generative adversarial network to minimize perceptual loss.23 This new AI demonstrated greater image resolution, feature preservation, and radiologist rating over the previous AI and was capable of reconstructing low-dose PET scans to full-dose resolution without an accompanying MRI. Applications of this algorithm are far-reaching, potentially allowing neuroimaging of brain tumors at more frequent intervals with higher resolution and lower total radiation exposure.
AI also has been applied to neurologic CT to reduce radiation exposure.24 Because it is critical to abide by the principles of ALARA (as low as reasonably achievable), the ability of AI to reduce radiation exposure holds significant promise. A CNN has been used to transform low-dose CTs of anthropomorphic models with calcium inserts and cardiac patients to normal-dose CTs, with the goal of improving the SNR.25 By training a noise-discriminating CNN and a noise-generating CNN together in a generative adversarial network, the AI improved image feature preservation during transformation. This algorithm has a direct application in imaging cerebral vasculature, including calcification that can explain lacunar infarcts and tracking systemic atherosclerosis.26
Another CNN has been applied to remove more complex noise patterns from the phenomena of beam hardening and photon starvation common in low-dose CT. This algorithm extracts the directional components of artifacts and compares them to known artifact patterns, allowing for highly specific suppression of unwanted signals.27 In June 2019, the US Food and Drug Administration (FDA) approved ClariPi, a deep CNN program for advanced denoising and resolution improvement of low- and ultra low-dose CTs.28 Aside from only low-dose settings, this AI could reduce artifacts in all CT imaging modalities and improve therapeutic value of procedures, including cerebral angiograms and emergency cranial scans. As the average CT radiation dose decreased from 12 mSv in 2009 to 1.5 mSv in 2014 and continues to fall, these algorithms will become increasingly necessary to retain the high resolution and diagnostic power expected of neurologic CTs.29,30
Downstream Applications
Downstream applications refer to AI use after a radiologic study is acquired, mostly image interpretation. More than 70% of FDA-approved AI medical devices are in radiology, and many of these relate to image analysis.6,31 Although AI is not limited to black-and-white image interpretation, it is hypothesized that one of the reasons radiology is inviting to AI is because gray-scale images lend themselves to standardization.3 Moreover, most radiology departments already use AI-friendly picture archiving and communication systems.31,32
AI has been applied to a range of radiologic modalities, including MRI, CT, ultrasonography, PET, and mammography.32-38 AI also has been specifically applied to radiography, including the interpretation of tuberculosis, pneumonia, lung lesions, and COVID-19.33,39-45 AI also can assist triage, patient screening, providing a “second opinion” rapidly, shortening the time needed for attaining a diagnosis, monitoring disease progression, and predicting prognosis.37-39,43,45-47 Downstream applications of AI in neuroradiology and neurology include using CT to aid in detecting hemorrhage or ischemic stroke; using MRI to automatically segment lesions, such as tumors or MS lesions; assisting in early diagnosis and predicting prognosis in MS; assisting in treating paralysis, including from spinal cord injury; determining seizure type and localizing area of seizure onset; and using cameras, wearable devices, and smartphone applications to diagnose and assess treatment response in neurodegenerative disorders, such as Parkinson or Alzheimer diseases (Figure).37,48-56
Several AI tools have been deployed in the clinical setting, particularly triaging intracranial hemorrhage and moving these studies to the top of the radiologist’s worklist. In 2020 the Centers for Medicare and Medicaid Services (CMS) began reimbursing Viz.ai software’s AI-based Viz ContaCT (Viz LVO) with a new International Statistical Classification of Diseases, Tenth Revision procedure code.57
