dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images

Abstract

Sequential imaging studies are conducted in many clinical scenarios. Prior images from previous studies contain a great deal of patient-specific anatomical information and can be used in conjunction with subsequent imaging acquisitions to maintain image quality while enabling radiation dose reduction (e.g., through sparse angular sampling, reduction in fluence, etc.). However, patient motion between images in such sequences results in misregistration between the prior image and current anatomy. Existing prior-image-based approaches often include only a simple rigid registration step that can be insufficient for capturing complex anatomical motion, introducing detrimental effects in subsequent image reconstruction. In this work, we propose a joint framework that estimates the 3D deformation between an unregistered prior image and the current anatomy (based on a subsequent data acquisition) and reconstructs the current anatomical image using a model-based reconstruction approach that includes regularization based on the deformed prior image. This framework is referred to as deformable prior image registration, penalized-likelihood estimation (dPIRPLE). Central to this framework is the inclusion of a 3D B-spline-based free-form-deformation model into the joint registration-reconstruction objective function. The proposed framework is solved using a maximization strategy whereby alternating updates to the registration parameters and image estimates are applied allowing for improvements in both the registration and reconstruction throughout the optimization process. Cadaver experiments were conducted on a cone-beam CT testbench emulating a lung nodule surveillance scenario. Superior reconstruction accuracy and image quality were demonstrated using the dPIRPLE algorithm as compared to more traditional reconstruction methods including filtered backprojection, penalized-likelihood estimation (PLE), prior image penalized-likelihood estimation (PIPLE) without registration, and prior image penalized-likelihood estimation with rigid registration of a prior image (PIRPLE) over a wide range of sampling sparsity and exposure levels.

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

“…Examples include longitudinal monitoring of brain hemorrhage in the intensive care unit (ICU) (where acquisition of multiple head CT scans over the course of ICU monitoring is common) and populations at high risk of head injury in sports and military theatres. Such patient-specific prior images can be incorporated into the PWLS * reconstruction in the form of previously developed prior image regularization (Stayman et al 2013, Dang et al 2014) to maximize the conspicuity of low-contrast hemorrhages and increase the sensitivity to subtle anatomical changes. The previously developed methods (Stayman et al 2013, Dang et al 2014) also jointly register the patient-specific prior image to the current anatomy in the course of image reconstruction so that the corresponding anatomical structures are well aligned for correct prior image regularization.…”

Prospective image quality analysis and control for prior-image-based reconstruction of low-dose CT

“…The PIRPLE method has previously been proposed 4,5 and may be written as: $$\widehat{\mu }=argmax\mathit{L}(\mu ;y)-{\beta }_R{\Vert \mathbf{\Psi }\mu \Vert }_1-{\beta }_P{\Vert \mu -\mathit{T}{\mu }_P\Vert }_1$$…”

“…1–8 For example, in sequential CT studies such as lung nodule surveillance, a high-quality patient-specific prior image can be incorporated into the reconstruction of subsequent data acquisitions to achieve order-of-magnitude exposure reduction. 5,8 However, there are major challenges with the application of PIBR. For example, while integration of prior image knowledge can have a dramatic effect on the apparent image quality of the reconstruction, traditional image quality metrics like spatial resolution do not capture biases associated with the reconstruction.…”

“…While we focus on the prior image registration, penalized likelihood estimation (PIRPLE) method, 4,5 we expect the same strategy can be extended to other PIBR methods. We apply the analytic framework in both simulation and physical experiments to demonstrate reliable prediction and control of performance at varying fluence levels and levels of sampling.…”

Regularization design and control of change admission in prior-image-based reconstruction

“…The prior image term seeks to encourage similarity of current estimate μ with a previously acquired prior image μ P . Without considering the simultaneous registration 4,7 , the PIRPLE objective can be written as $${\hat{\mu }}_{PIRPLE}=\underset{\mu }{\arg \max }L(y;\mu )-{\beta }_R\Vert {\Psi }_R\mu {\Vert }_{p_R}^{p_R}-{\beta }_P\Vert {\Psi }_P(\mu -{\mu }_P){\Vert }_{p_P}^{p_P}$$ where β P is the prior image regularization strength that we want to estimate in this work, β R is image roughness strength, ψ R and ψ P are sparsifying operators, y is measurements, and p R and p P are modified p -norm, which is quadratic within a small neighborhood ± δ of zero and a shifted p -norm outside ± δ such that the function and derivative match at ± δ .…”