Thoracic Diseases Clinical Trial
Official title:
Design and Evaluation of Innovative Mobile X-Ray Technology for Rapid and Accurate Diagnosis of Thoracic Disease in Critically Ill Patients
Computed tomography (CT) is the most accurate test for evaluating patients with thoracic disease. However, access to CT is limited due to long wait times and for the sickest patients in Hospital who cannot be transported from the ward to the CT scanner. The investigators propose to modify a standard X-ray unit to provide more detailed information of the chest such that a CT scan is not required for all patients.
Dual-energy (DE) imaging consists of acquiring paired "low" and "high"-energy x-ray images.
The use of DE to improve radiological contrast was first described by Jacobson et al in 1958;
followed by Mistretta et al and Alvarez and Macovski in the mid-1970s. With the advent of new
digital X-ray detectors that provide high dose efficiency and rapid readout of digital X-ray
projections, there has been renewed interest in using DE x-ray imaging for lung nodule
detection. More recently, portable x-ray detectors have made bedside DE imaging possible.
Jabri et. al. presented a portable DE system with novel respiratory and cardiac gating, and
Hoggarth et. al. investigated the potential for DE subtraction in improving the visualization
of lung tumors while performing image-guided radiotherapy.
The investigators group is experienced in investigating DE for lung nodule detection, this
theoretical framework was instrumental in optimizing a clinical prototype for
high-performance DE chest X-ray. It identified optimal DE image acquisition and decomposition
techniques, and validated the approach in comparison to human observer performance. The
analysis further demonstrated that - given a high-performance flat plate detector, optimal
acquisition and decomposition - DE chest X-ray is possible at the same dose as conventional
computer radiography (CR) and digital radiography (DR) chest X-ray, while significantly
improving conspicuity of subtle lung nodules by the reduction of overlying background noise.
The DE work undertaken by our group is unique in terms of providing a clear theoretical
framework for optimizing a clinical prototype for best performance in terms of image quality
and patient exposure to ionizing radiation.
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