While the first century of medical x-ray imaging offered an unprecedented advance via projection imaging and computed tomography (CT), its second century will be marked by more intelligent application and utilization of x-ray spectral and tomographic information, novel source-detector configurations, and minimization of radiation dose. Such is evidenced by recent work in low-dose CT, dual-energy (DE) CT, and cone-beam CT (CBCT) and is crystal clear in recent scientific and popular communication of CT utilization. Application of DE-CT is seen in abdominal (e.g., renal calculi), musculoskeletal (MSK, e.g., arthritis), and neuro (e.g., perfusion) imaging. Key to the knowledgeable advance of spectral / tomographic techniques is a basis for performance assessment and optimization that considers not only the physics of x-ray detection and image reconstruction, but also the imaging task - and moreover - in a manner that maximizes task performance while minimizing radiation dose. This project renewal builds on the success of the original R01 in which a task-based framework for imaging performance was extended to DE imaging and CBCT, providing: i.) a bridge from fundamental Fourier-domain metrics such as noise-equivalent quanta (NEQ) to real and model observer performance;ii.) a quantitative framework for optimization of new DE and CBCT technologies;and iii.) an approach shown to accelerate translation of new technology knowledgeably to key clinical applications. By combining the models for DE imaging and CBCT, the current work aims to develop a framework for imaging performance in DE-CBCT, tackling challenges that beckon in regard to new detector technologies and image decomposition techniques and applying the results specifically to a novel DE-CBCT prototype for MSK imaging.
The aims of the proposal are to: 1.) Establish a model task-based framework for DE-CBCT performance that relates NEQ to specific detection / discrimination tasks and provides a basis for system optimization and dose minimization;2.) Extend the framework to energy-integrating and energy-discriminating detector technology (viz., flat-panel detectors and photon counters) and validate performance versus benchtop measurements in phantom and cadaver;3.) Extend the framework to reconstruction-based and projection-based DE-CBCT image decomposition, including noise reduction algorithms, to elucidate the advantages of each and optimize implementation in specific imaging tasks;and 4.) Translate the results to a prototype MSK extremities CBCT scanner (already constructed in a separate project) to implement and evaluate optimal DE-CBCT techniques in MSK imaging tasks (e.g., calcium-iodine, calcium-uric acid, and intrinsic soft-tissue discrimination). Successful completion of the work offers a quantitative framework for evaluation of new and existing DE-CT/CBCT systems, a guide to knowledgeable translation of this rapidly proliferating technology, a quantitative basis for minimizing radiation dose with respect to the imaging task, and a specific advance in application to MSK imaging (e.g., osteoarthritis, gout, soft-tissue and bone tumors, and tissue impingement syndromes).

Public Health Relevance

Emerging technologies for x-ray spectral tomography (commonly, dual-energy CT) offer to transcend conventional limitations of contrast resolution in single-energy CT and present a significant advance to diagnosis and therapy assessment in a broad range of pathologies characterized by distinct biochemical change - e.g., arthritis (including osteoarthritis, gout, and rheumatoid arthritis), kidney stones, and vascular or intra-articular contrast enhancement using iodine or novel contrast agents. As such technology advances in broader proliferation (e.g., in Gout Centers established around the country), there is an immediate need for an image science basis for quantitatively understanding the factors that govern imaging performance, identifying optimal imaging techniques, and minimizing radiation dose in a manner that maintains performance of the imaging task. This proposal advances an image science framework for spectral tomographic imaging to yield a validated model for technique optimization, guiding the development of new dual-energy CT detectors and algorithms, and translating the understanding specifically to a scanner prototype for musculoskeletal extremities (e.g., imaging of arthritis, gout, cartilage degeneration, and tissue impingement syndromes).

National Institute of Health (NIH)
Research Project (R01)
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Biomedical Imaging Technology Study Section (BMIT)
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Zhang, Yantian
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Johns Hopkins University
Biomedical Engineering
Schools of Medicine
United States
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