Characterization of statistical prior image constrained compressed sensing (PICCS): II. Application to dose reduction

Abstract

Purpose: The ionizing radiation imparted to patients during computed tomography exams is raising concerns. This paper studies the performance of a scheme called dose reduction using prior image constrained compressed sensing (DR-PICCS). The purpose of this study is to characterize the effects of a statistical model of x-ray detection in the DR-PICCS framework and its impact on spatial resolution. Methods: Both numerical simulations with known ground truth and in vivo animal dataset were used in this study. In numerical simulations, a phantom was simulated with Poisson noise and with varying levels of eccentricity. Both the conventional filtered backprojection (FBP) and the PICCS algorithms were used to reconstruct images. In PICCS reconstructions, the prior image was generated using two different denoising methods: a simple Gaussian blur and a more advanced diffusion filter. Due to the lack of shift-invariance in nonlinear image reconstruction such as the one studied in this paper, the concept of local spatial resolution was used to study the sharpness of a reconstructed image. Specifically, a directional metric of image sharpness, the so-called pseudopoint spread function (pseudo-PSF), was employed to investigate local spatial resolution. Results: In the numerical studies, the pseudo-PSF was reduced from twice the voxel width in the prior image down to less than 1.1 times the voxel width in DR-PICCS reconstructions when the statistical model was not included. At the same noise level, when statistical weighting was used, the pseudo-PSF width in DR-PICCS reconstructed images varied between 1.5 and 0.75 times the voxel width depending on the direction along which it was measured. However, this anisotropy was largely eliminated when the prior image was generated using diffusion filtering; the pseudo-PSF width was reduced to below one voxel width in that case. In the in vivo study, a fourfold improvement in CNR was achieved while qualitatively maintaining sharpness; images also had a qualitatively more uniform noise spatial distribution when including a statistical model. Conclusions: DR-PICCS enables to reconstruct CT images with lower noise than FBP and the loss of spatial resolution can be mitigated to a large extent. The introduction of statistical modeling in DR-PICCS may improve some noise characteristics, but it also leads to anisotropic spatial resolution properties. A denoising method, such as the directional diffusion filtering, has been demonstrated to reduce anisotropy in spatial resolution effectively when it was combined with DR-PICCS with statistical modeling.

A generalized Fourier penalty in prior-image-based reconstruction for cross-platform imaging

“…All of these situations present an excellent opportunity to apply prior-image-based reconstruction (PIBR) methods that leverage high-fidelity prior imaging studies to help improve the image quality or reduce the x-ray exposures in subsequent studies. A myriad of algorithms have been proposed that take advantage of high-fidelity prior images of a patient to reconstruct lower fidelity current measurements including Prior image constrained compressed sensing (PICCS) with statistical weighting [3], or in Prior Image Registration, PenalizedLikelihood Estimation (PIRPLE) [4]. Another class of PIBR methods seeks to reconstruct only the difference between the current anatomy and the prior image.…”

Sparse-view x-ray CT reconstruction via total generalized variation regularization

“…Among them, statistical iterative reconstruction (SIR) methods by modeling the measurement statistics and imaging geometry can significantly reduce radiation dose while maintaining image quality in various CT applications compared with the filtered back-projection (FBP) reconstruction algorithm (Elbakri and Fessler, 2002, Tang et al , 2009, Lauzier Thériault and Chen, 2013). Usually, the cost function of SIR consists of two components, i.e., the data-fidelity term and regularization term.…”

Improved CT Image Reconstruction Through Partial Fourier Sampling

“…The compressive sensing concept utilizes the sparseness of the natural signals and images in di erent domains such as time, space, and frequency in order to reconstruct the desired signal with less-than-Nyquist samples [4][5][6]. Specically, the Compressed Sensing (CS) method has found its application in CT imaging, being able to provide a low-radiation CT image reconstruction platform [7][8][9][10][11][12][13]. In fact, by using the CS approach in CT algorithms, the reconstruction process can be done with small number of data, reducing the health risk.…”

“…Another group of CS-based CT image reconstruction approaches use Total Variation (TV) for image reconstruction with incomplete set of measurements [9][10][11]. Bian et al exploited the sparsity of objects in the Total Variation (TV) domain, naming it the CSTV model [9].…”

“…Bian et al exploited the sparsity of objects in the Total Variation (TV) domain, naming it the CSTV model [9]. This work was completed later in [10,11] where the priori information was introduced as constraints that assisted the TV minimization formula to provide a high-quality image reconstruction.…”