Virtual scatter modulation for X-ray CT scatter correction using primary modulator

Physics-based spectral compensation algorithm for x-ray CT with primary modulator

X-ray computed tomography (CT) scatter correction using primary modulator has been continuously developed over the past years, with progress in improving the performance of scatter correction. In this work, we further advance the primary modulator technique towards practical applications where the spectral nonuniformity caused by the modulator continues to be a challenging problem. A physics-based spectral compensation algorithm is proposed to adaptively correct for the spectral nonuniformity, and hence to reduce the resultant ring artifacts on reconstructed CT images. First, an initial spectrum of the CT system without the primary modulator is modeled using an understanding of x-ray CT physics, and optimized by an expectation maximization method; then, the optimized estimation of the initial spectrum is utilized to adaptively calculate the effective modulator thickness from measured transmissions of the primary modulator at each detector element, leading to a set of new spectra that is able to capture the nonuniform spectral distribution of the primary modulator; finally, using the modulator-modeled spectrum, a beam hardening mapping function is generated and beam hardening correction is applied to CT projections. A CatPhan600 phantom and an anthropomorphic thorax phantom were scanned with three different primary modulators to evaluate the approach. For the Catphan phantom, the spectral compensation algorithm efficiently removes the ring (and band) artifacts that otherwise dominate the reconstructed CT image. For the three modulators with nominal copper thickness of 52.5, 105 and 210 [Formula: see text]m, our method reduces the CT number nonuniformity from 147.9, 436.2 and 696.4 Hounsfield units (HU) to 14.6, 26.2 and 13.6 HU, respectively, close to that of the reference image (i.e. 7.5 HU). For the thorax phantom, the ring artifacts are also suppressed significantly on the transaxial image; on the sagittal image, the alternating black-and-white patterns are largely removed, with the CT number nonuniformity being reduced from 282.0 HU to 38.5 HU.

Fast shading correction for cone-beam CT via partitioned tissue classification

The quantitative use of cone beam computed tomography (CBCT) in radiation therapy is limited by severe shading artifacts, even with system embedded correction. We recently proposed effective shading correction methods, using planning CT (pCT) as prior information to estimate low-frequency errors in either the projection domain or image domain. In this work, we further improve the clinical practicality of our previous methods by removing the requirement of prior pCT images. Clinical CBCT images are typically composed of a limited number of tissues. By utilizing the low frequency characteristic of shading distribution, we first generate a 'shading-free' template image by enforcing uniformity on CBCT voxels of the same tissue type via a technique named partitioned tissue classification. Only a small subset of voxels in the template image are used in the correction process to generate sparse samples of shading artifacts. Local filtration, a Fourier transform based algorithm, is employed to efficiently process the sparse errors to compute a full-field distribution of shading artifacts for CBCT correction. We evaluate the method's performance using an anthropomorphic pelvis phantom and 6 pelvis patients. The proposed method improves the image quality of CBCT for both phantom and patients to a level matching that of pCT. On the pelvis phantom, the signal non-uniformity (SNU) is reduced from 12.11% to 3.11% and 8.40% to 2.21% on fat and muscle, respectively. The maximum CT number error is reduced from 70 to 10 HU and 73 to 11 HU on fat and muscle, respectively. On patients, the average SNU is reduced from 9.22% to 1.06% and 11.41% to 1.67% on fat and muscle, respectively. The maximum CT number error is reduced from 95 to 9 HU and 88 to 8 HU on fat and muscle, respectively. The typical processing time for one CBCT dataset is about 45 s on a standard PC.