Volume-of-interest imaging with dynamic fluence modulation using multiple aperture devices.
Wenying WangGrace Jianan GangJeffrey H SiewerdsenReuven LevinsonSatomi KawamotoJ Webster StaymanPublished in: Journal of medical imaging (Bellingham, Wash.) (2019)
Volume-of-interest (VOI) imaging is a strategy in computed tomography (CT) that restricts x-ray fluence to particular anatomical targets via dynamic beam modulation. This permits dose reduction while retaining image quality within the VOI. VOI-CT implementation has been challenged, in part, by a lack of hardware solutions for tailoring the incident fluence to the patient and anatomical site, as well as difficulties involving interior tomography reconstruction of truncated projection data. We propose a general VOI-CT imaging framework using multiple aperture devices (MADs), an emerging beam filtration scheme based on two binary x-ray filters. Location of the VOI is prescribed using two scout views at anterior-posterior (AP) and lateral perspectives. Based on a calibration of achievable fluence field patterns, MAD motion trajectories were designed using an optimization objective that seeks to maximize the relative fluence in the VOI subject to minimum fluence constraints. A modified penalized-likelihood method is developed for reconstruction of heavily truncated data using the full-field scout views to help solve the interior tomography problem. Physical experiments were conducted to show the feasibility of noncentered and elliptical VOI in two applications-spine and lung imaging. Improved dose utilization and retained image quality are validated with respect to standard full-field protocols. We observe that the contrast-to-noise ratio (CNR) is 40% higher compared with low-dose full-field scans at the same dose. The total dose reduction is 50% for equivalent image quality (CNR) within the VOI.
Keyphrases
- image quality
- dual energy
- computed tomography
- high resolution
- positron emission tomography
- low dose
- contrast enhanced
- magnetic resonance imaging
- healthcare
- cardiovascular disease
- primary care
- magnetic resonance
- type diabetes
- depressive symptoms
- electronic health record
- electron microscopy
- transcription factor
- big data
- high dose
- artificial intelligence
- air pollution
- ionic liquid
- mass spectrometry
- case report
- quality improvement
- deep learning