Computational and human observer image quality evaluation of low dose, knowledge‐based CT iterative reconstruction

• Published in: Medical physics (Lancaster) 2015-10, Vol.42 (10), p.6098-6111
• Main Authors: Eck, Brendan L., Fahmi, Rachid, Brown, Kevin M., Zabic, Stanislav, Raihani, Nilgoun, Miao, Jun, Wilson, David L.
• Format: Article
• Language: English
• Subjects: 60 APPLIED LIFE SCIENCES; Biological material, e.g. blood, urine; Haemocytometers; Biomedical modeling; BIOMEDICAL RADIOGRAPHY; Computed tomography; Computer Simulation; Computerised tomographs; computerised tomography; COMPUTERIZED TOMOGRAPHY; Digital computing or data processing equipment or methods, specially adapted for specific applications; Dose‐volume analysis; dosimetry; Experiment design; Gaussian processes; Humans; Image data processing or generation, in general; image denoising; Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; IMAGE PROCESSING; Image Processing, Computer-Assisted - methods; image quality; image reconstruction; ITERATIVE METHODS; iterative reconstruction; Machine Learning; maximum likelihood estimation; Medical image noise; medical image processing; Medical image reconstruction; Medical X‐ray imaging; model observers; MONTE CARLO METHOD; Noise; Numerical optimization; Observer Variation; optimisation; OPTIMIZATION; PHANTOMS; Phantoms, Imaging; probability; Probability theory; Quality Control; Radiation Dosage; RADIATION DOSES; Radiation Imaging Physics; Reconstruction; Scintigraphy; Tomography, X-Ray Computed
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4929973

Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge‐based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d′) was evaluated in phantom studies across a range of conditions. Images were generated using a numerical CT simulator. Trained observers performed 4‐alternative forced choice (4‐AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five‐channel Laguerre–Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, PC. Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP‐IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4‐AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model‐k4, showed the best trade‐off between fit and model complexity according to AICc. With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP‐IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model‐k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x‐r