Grace J. Gang obtained her Ph.D. in Biomedical Engineering from the University of Toronto in 2014 and is currently Research Associate Faculty at Johns Hopkins University. Her main research interests involve image quality modeling, assessment, and optimization for advanced technologies in CT and cone-beam CT. Her work spans a wide range of topics including detector physics, image reconstruction, post-processing, and statistical decision theory.
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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.
Comparison of deep learning and human observer performance for lesion detection and characterization
Methods: Novel predictors for estimation of local spatial resolution and noise properties are developed for PL reconstruction that include a physical model for blur and correlated noise in flat-panel cone-beam CT (CBCT) acquisitions. Prospective predictions (e.g., without reconstruction) of local point spread function and and local noise power spectrum (NPS) model are applied, compared, and validated using a flat-panel CBCT test bench. Imaging conditions investigated include two acquisition strategies (an unmodulated X-ray technique and automatic exposure control) as well as varying regularization strength.
Results: Comparisons between prediction and physical measurements show excellent agreement for both spatial resolution and noise properties. In comparison, traditional prediction methods (that ignore blur/correlation found in flat-panel data) fail to capture important data characteristics and show significant mismatch.
Conclusion: Novel image property predictors permit prospective assessment of flat-panel CBCT using MBIR. Such predictors enable standard and task-based performance assessments, and are well-suited to evaluation, control, and optimization of the CT imaging chain (e.g., x-ray technique, reconstruction parameters, novel data acquisition methods, etc.) for improved imaging performance and/or dose utilization.
Joint optimization of fluence field modulation and regularization in task-driven computed tomography
Methods: Experiments involved a cone-beam CT (CBCT) bench system, a robotic C-arm, and three phantoms. A robust 3D-2D registration process was used to compute the 9 degree of freedom (DOF) transformation between each projection and an existing 3D image by maximizing normalized gradient information with a digitally reconstructed radiograph (DRR) of the 3D volume. The quality of the resulting “self-calibration” was evaluated in terms of the agreement with an established calibration method using a BB phantom as well as image quality in the resulting CBCT reconstruction.
Results: The self-calibration yielded CBCT images without significant difference in spatial resolution from the standard (“true”) calibration methods (p-value >0.05 for all three phantoms), and the differences between CBCT images reconstructed using the “self” and “true” calibration methods were on the order of 10-3 mm-1. Maximum error in magnification was 3.2%, and back-projection ray placement was within 0.5 mm.
Conclusion: The proposed geometric “self” calibration provides a means for 3D imaging on general noncircular orbits in CBCT systems for which a geometric calibration is either not available or not reproducible. The method forms the basis of advanced “task-based” 3D imaging methods now in development for robotic C-arms.
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