Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis

Abstract

Purpose: Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means to overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors' goal was to develop techniques for applying statistical IR to tomosynthesis imaging data. Methods: These techniques include the following: a physics model with a local voxel-pair based prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction. Results: IR improved the detectability of low-contrast and small microcalcifications, reduced crossplane artifacts, improved spatial resolution, and lowered noise in reconstructed images. Conclusions: Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications. C 2015 American Association of Physicists in Medicine.

Assessment of prior image induced nonlocal means regularization for low‐dose CT reconstruction: Change in anatomy

“…13 To minimize patient exposure while optimizing the utility of the radiation dose, a normal-dose/high-quality scan can be performed first to establish a reference, followed by a series of low-dose CT (LDCT) scans. The LDCT scans can be reconstructed independently by statistical image reconstruction (SIR) methods [14][15][16][17][18][19][20][21][22][23] to suppress noise and streak artifacts, using information from current acquisition only. Nevertheless, the previously acquired normal-dose image can be exploited as prior information due to similarities between the normal-dose image and the series of reconstructed images from these scans.…”

Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction

“…One potential limitation of this work is that we demonstrated through‐plane blurring artifacts in DBT images could be reduced, resulting in a significant reduction in anatomical clutter when a specific SIR implementation is used; yet we have not demonstrated this can be achieved with other SIR methods in general. However, work from many other groups has previously shown that a reduction in through‐plane blurring artifacts can be achieved using other SIR methods . Based upon our finding in this paper that the reduced anatomical clutter is associated with the reduction of through‐plane blurring artifacts, it is anticipated that other SIR methods might also be used to reduce anatomical clutter as long as the parameters in these alternative methods are optimized to achieve the goal of through‐plane artifacts reduction.…”

“…Given the argument that a reduction in through‐plane artifacts level would lead to a potential reduction in anatomical clutter and previous results from other investigators demonstrated that a reduction of through‐plane artifacts can be achieved in DBT with some statistical image reconstruction (SIR) methods, one may wonder, if the use of SIR in DBT would result in a more pronounced reduction in β values than β ≈3.1 from the current clinical reconstructions? If the answer is yes, then what would be the quantitative β values for a specific SIR?…”

Polyenergetic known-component CT reconstruction with unknown material compositions and unknown x-ray spectra

“…A location-dependent weighting (Xu et al 2015) of the penalty function was applied to encourage more uniform resolution, and the regularization parameter β =5 e − 2 was chosen to qualitatively achieve a comparable resolution to FBP. The initial guess for the background anatomy for KCR was the FBP volume truncated at an upper value of 0.03 mm −1 (in a coarse attempt to remove the pedicle screw from the anatomy).…”