Since VIT components are modeled in a modular fashion, they need to be integrated with each other to conduct a complete VIT. Images of same phantom show simulated CT at 50 ( A), 25 ( B), and 5 ( C) mAs, as well as simulated chest radiograph ( D, blue border). 1-Simulated CT images of coronavirus disease 2019 (COVID-19) on 4D extended cardiac-torso phantom developed at Duke University. Such patient models can be used to design optimal imaging protocols for the diagnosis and staging of COVID-19, as well as other diseases.įig. These lesions are incorporated into an existing virtual phantom library, creating a population of virtual patients with COVID-19 abnormalities.
The shapes and density of the COVID-19 lesions are modeled based on the segmentation of real data. These pathological models are based on clinical CT images of reverse transcription–polymerase chain reaction confirmed COVID-19 cases (Fig.
As the current phantoms are getting more anatomically realistic and diverse, more efforts are being made to model targeted pathologies for specific imaging trial questions.Īn evident example is the recently developed human models with coronavirus disease 2019 (COVID-19) pathology. To be relevant for specific clinical applications, virtual patients need to be representative in terms of anatomy, physiology, patient-to-patient variability, and pathology. Virtual patients have been modeled and advanced for several decades. New Models for DiseaseĪ major part of a VIT is the virtual patient cohort. Computer-based algorithms, set to mimic radiologist tasks, provide a means to interpret the simulated images. The last component of a VIT is the virtual interpretation in which a medical image is quantitatively assessed for a specific clinical task (e.g., lesion detection or disease quantification). They are designed to virtually image the computational phantoms, aiming to incorporate realistic models of the geometrical, physical, and processing attributes of different scanners. Another component in a VIT is the virtual imaging systems. These mathematically defined phantoms model the patient’s anatomy and physiology, typically based on clinical images and morphometrical and physiologic measurements. In a VIT, patients are defined with anthropomorphic, computational phantoms. This is done by mimicking the chain of clinical imaging trial processes, which includes patients, scanners, and interpretations. Virtual imaging trials (VITs) involve conducting imaging research in silicousing mathematical models and computer simulations.
In this article, we briefly introduce virtual clinical trials in the context of medical imaging, highlighting some of the recent advancements and ongoing projects in the field. Virtual imaging trials offer an alternative solution to overcome these limitations by providing a mechanism to conduct trials in silico, as summarized in a recent review paper, as well as an editorial that highlights an existential need for virtual imaging in medicine. Beside this, the ground truth or baseline state of the patient is often unknown, which significantly limits researchers from assessing technologies in an accurate and precise manner. When a trial is designed for human subjects, there are various factors that need to be considered to minimize the risk to the patients, which limits the thoroughness of the study. These trials are usually expensive and time-consuming. In the context of a diagnostic method, and particularly in medical imaging, clinical trial development and applications have been limited due to multiple factors. The medical procedure could be a vaccine, a drug treatment, a diagnosis tool, etc. These experiments are usually aimed to assess or optimize the efficacy and safety of a particular medical procedure. Clinical trials are an indispensable component in medicine.