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

• Published in: Medical physics (Lancaster) Vol. 40; no. 2; pp. 021902 - n/a
• Main Authors: Lauzier, Pascal Thériault, Chen, Guang-Hong
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01.02.2013
• Subjects: 60 APPLIED LIFE SCIENCES; Algorithms; Animals; ANISOTROPY; CAT SCANNING; compressed sensing; Computed tomography; Computerised tomographs; computerised tomography; COMPUTERIZED SIMULATION; Contrast; CT dose reduction; data compression; DIFFUSION; Digital computing or data processing equipment or methods, specially adapted for specific applications; DOSES; EVALUATION; FILTERS; image coding; Image coding, e.g. from bit‐mapped to non bit‐mapped; 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 evaluation; image reconstruction; IN VIVO; Medical image noise; medical image processing; Medical image reconstruction; Medical image spatial resolution; Medical imaging; Methods or arrangements for processing data by operating upon the order or content of the data handled; Noise; optical transfer function; PATIENTS; PHANTOMS; Radiation Dosage; Radiation Imaging Physics; RADIOLOGY AND NUCLEAR MEDICINE; Reconstruction; Signal-To-Noise Ratio; SPATIAL DISTRIBUTION; SPATIAL RESOLUTION; statistical image reconstruction; Statistical model calculations; STATISTICAL MODELS; Statistics as Topic; Tomography, X-Ray Computed - methods; X-RAY DETECTION; X‐ray imaging; computed tomography; CT dose reduction; image quality evaluation; compressed sensing; statistical image reconstruction
• ISSN: 0094-2405; 2473-4209; 1522-8541; 2473-4209; 0094-2405
• DOI: 10.1118/1.4773866

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 andin 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 thein 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.