Cone-Beam CT of Traumatic Brain Injury Using Statistical Reconstruction with a Post-Artifact-Correction Noise Model

Prospective regularization design in prior-image-based reconstruction

Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g. through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in phantoms where the optimal parameters vary spatially by an order of magnitude or more. In a series of studies designed to explore potential unknowns associated with accurate PIBR, optimal prior image strength was found to vary with attenuation differences associated with anatomical change but exhibited only small variations as a function of the shape and size of the change. The results suggest that, given a target change attenuation, prospective patient-, change-, and data-specific customization of the prior image strength can be performed to ensure reliable reconstruction of specific anatomical changes.

Statistical reconstruction for cone-beam CT with a post-artifact-correction noise model: application to high-quality head imaging

Non-contrast CT reliably detects fresh blood in the brain and is the current front-line imaging modality for intracranial hemorrhage such as that occurring in acute traumatic brain injury (contrast ~40-80 HU, size  >  1 mm). We are developing flat-panel detector (FPD) cone-beam CT (CBCT) to facilitate such diagnosis in a low-cost, mobile platform suitable for point-of-care deployment. Such a system may offer benefits in the ICU, urgent care/concussion clinic, ambulance, and sports and military theatres. However, current FPD-CBCT systems face significant challenges that confound low-contrast, soft-tissue imaging. Artifact correction can overcome major sources of bias in FPD-CBCT but imparts noise amplification in filtered backprojection (FBP). Model-based reconstruction improves soft-tissue image quality compared to FBP by leveraging a high-fidelity forward model and image regularization. In this work, we develop a novel penalized weighted least-squares (PWLS) image reconstruction method with a noise model that includes accurate modeling of the noise characteristics associated with the two dominant artifact corrections (scatter and beam-hardening) in CBCT and utilizes modified weights to compensate for noise amplification imparted by each correction. Experiments included real data acquired on a FPD-CBCT test-bench and an anthropomorphic head phantom emulating intra-parenchymal hemorrhage. The proposed PWLS method demonstrated superior noise-resolution tradeoffs in comparison to FBP and PWLS with conventional weights (viz. at matched 0.50 mm spatial resolution, CNR = 11.9 compared to CNR = 5.6 and CNR = 9.9, respectively) and substantially reduced image noise especially in challenging regions such as skull base. The results support the hypothesis that with high-fidelity artifact correction and statistical reconstruction using an accurate post-artifact-correction noise model, FPD-CBCT can achieve image quality allowing reliable detection of intracranial hemorrhage.