Fluence field modulated computed tomography (FFMCT) presents a novel approach for acquiring CT images, whereby a patient model guides dynamically changing fluence patterns in an attempt to achieve task-based, user-prescribed, regional variations in image quality, while also controlling dose to the patient. This work aims to compare the relative effectiveness of FFMCT applied to different thoracic imaging tasks (routine diagnostic CT, lung cancer screening, and cardiac CT) when the modulator is subject to limiting constraints, such as might be present in realistic implementations.
An image quality plan was defined for a simulated anthropomorphic chest slice, including regions of high and low image quality, for each of the thoracic imaging tasks. Modulated fluence patterns were generated using a simulated annealing optimization script, which attempts to achieve the image quality plan under a global dosimetric constraint. Optimization was repeated under different types of modulation constraints (e.g., fixed or gantry angle dependent patterns, continuous or comprised of discrete apertures) with the most limiting case being a fixed conventional bowtie filter. For each thoracic imaging task, an image quality map (IQMsd) representing the regionally varying standard deviation is predicted for each modulation method and compared to the prescribed image quality plan as well as against results from uniform fluence fields. Relative integral dose measures were also compared.
Each IQMsd resulting from FFMCT showed improved agreement with planned objectives compared to those from uniform fluence fields for all cases. Dynamically changing modulation patterns yielded better uniformity, improved image quality, and lower dose compared to fixed filter patterns with optimized tube current. For the latter fixed filter cases, the optimal choice of tube current modulation was found to depend heavily on the task. Average integral dose reduction compared to a uniform fluence field ranged from 10% using a bowtie filter to 40% or greater using an idealized modulator.
The results support that FFMCT may achieve regionally varying image quality distributions in good agreement with user-prescribed values, while limiting dose. The imposition of constraints inhibits dose reduction capacity and agreement with image quality plans but still yields significant improvement over what is afforded by conventional dose minimization techniques. These results suggest that FFMCT can be implemented effectively even when the modulator has limited modulation capabilities.
This research has been funded in part by the Orey and Mary Fidani Family, a postgraduate scholarship from the Natural Sciences and Engineering Research Council of Canada (NSERC), and grants from Elekta Inc. and the Ontario Consortium for Adaptive Interventions in Radiation Oncology (OCAIRO).
III. METHODS AND MATERIALS
III.A. FFMCT framework
III.B. Simulated thoracic phantom
III.C. Optimization and image quality metrics
III.D. Weight selection
III.D.1. Uniform image quality priority
III.D.2. Minimum image quality priority
III.E. Imaging tasks and image quality prescriptions
III.F. Modulation constraints
III.F.1. 64 Modulation bins per projection
III.F.2. 16 Modulation bins per projection
III.F.3. Modulation constrained to a bowtie filter
III.F.4. Two exposures: One full field exposure plus one collimated exposure
III.F.5. Modulation constrained to a custom patient and task specific filter
III.G.1. Uniformity priority
III.G.2. Minimum image quality priority
IV.A. Image quality uniformity priority
IV.A.1. Modulation profiles
IV.A.2. Image quality
IV.B. Minimum image quality priority
IV.B.1. Image quality
- Medical imaging
- Medical image quality
- Computed tomography
- Medical image noise
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