Statistical iterative reconstruction algorithms have shown potential to improve cone-beam CT (CBCT) image quality. Most iterative reconstruction algorithms utilize prior knowledge as a penalty term in the objective function. The penalty term greatly affects the performance of a reconstruction algorithm. The total variation (TV) penalty has demonstrated great ability in suppressing noise and improving image quality. However, calculated from the first-order derivatives, the TV penalty leads to the well-known staircase effect, which sometimes makes the reconstructed images oversharpen and unnatural. In this study, we proposed to use a second-order derivative penalty that involves the Frobenius norm of the Hessian matrix of an image for CBCT reconstruction. The second-order penalty retains some of the most favorable properties of the TV penalty like convexity, homogeneity, and rotation and translation invariance, and has a better ability in preserving the structures of gradual transition in the reconstructed images. An effective algorithm was developed to minimize the objective function with the majorization-minimization (MM) approach. The experiments on a digital phantom and two physical phantoms demonstrated the priority of the proposed penalty, particularly in suppressing the staircase effect of the TV penalty.

The proposed method effectively eliminated motion-induced artifacts in head CT scans. Since only measured raw data are needed for the motion estimation and compensation, the proposed method can be applied retrospectively to clinical helical CT scans affected by motion.

Previously, we proposed using an interpolated average CT (IACT) method for attenuation correction (AC) in positron emission tomography (PET), which is a good, low-dose approximation of cine average CT (CACT) to reduce misalignments and improve quantification in PET/CT. This study aims to evaluate the performance of IACT for different motion amplitudes. We used the digital four-dimensional (4-D) extended cardiac-torso phantom (XCAT) to simulate maximum of 2, 3, and 4 cm respiratory motions. The respiratory cycle was divided into 13 phases, with average activity and attenuation maps to represent 18F-fluorodeoxyglucose (18F-FDG) distributions with average respiratory motions and CACT, respectively. The end-inspiration, end-expiration, and midrespiratory phases of the XCAT attenuation maps represented three different helical CTs (i.e., HCT-1, HCT-5, and HCT-8). The IACTs were generated using: 1) 2 extreme+11 interpolated phases (IACT 2o ); 2) 2 phases right after the extreme phases+11 interpolated phases (IACT 2s); 3) 4 original+9 interpolated phases (IACT 4o). A spherical lesion with a target-to-background ratio (TBR) of 4:1 and a diameter of 25 mm was placed in the base of right lung. The noise-free and noisy sinograms with attenuation modeling were generated and reconstructed with different noise-free and noisy AC maps (CACT, HCTs, and IACTs) by Software for Tomographic Image Reconstruction, respectively, using ordered subset expectation maximization(OS-EM) with up to 300 updates. Normalized mean-square error, mutual information (MI), TBR, image profile, and noise-contrast tradeoff were analyzed. The PET reconstructed images with AC using CACT showed least difference as compared to the original phantom, followed by IACT 4o, IACT 2o , IACT 2s, HCT-5, HCT-8, and HCT-1. Significant artifacts were observed in the reconstructed images using HCTs for AC. The MI differences between IACT 2o and IACT 4o /CACT were<0.41% and <2.17%, respectively. With a slight misplacement of the two extreme phases, IACT 2s was still comparable to IACT 2o with MI difference of <2.23%. The IACT is a robust and accurate low-dose alternate to CACT.

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IACT-ABC reduces respiratory artifacts, PET/CT misregistration and enhances lesion quantitation. This technique is a robust and low dose AC protocol for clinical oncology application especially in the thoracic region.

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Correction for rigid object motion in helical CT can be achieved by reconstructing from a modified source-detector orbit, determined by the object motion during the scan. This ensures that all projections are consistent, but it does not guarantee that the projections are complete in the sense of being sufficient for exact reconstruction. We have previously shown with phantom measurements that motion-corrected helical CT scans can suffer from data-insufficiency, in particular for severe motions and at high pitch. To study whether such data-insufficiency artefacts could also affect the motion-corrected CT images of patients undergoing head CT scans, we used an optical motion tracking system to record the head movements of 10 healthy volunteers while they executed each of the 4 different types of motion ('no', slight, moderate and severe) for 60 s. From these data we simulated 354 motion-affected CT scans of a voxelized human head phantom and reconstructed them with and without motion correction. For each simulation, motion-corrected (MC) images were compared with the motion-free reference, by visual inspection and with quantitative similarity metrics. Motion correction improved similarity metrics in all simulations. Of the 270 simulations performed with moderate or less motion, only 2 resulted in visible residual artefacts in the MC images. The maximum range of motion in these simulations would encompass that encountered in the vast majority of clinical scans. With severe motion, residual artefacts were observed in about 60% of the simulations. We also evaluated a new method of mapping local data sufficiency based on the degree to which Tuy's condition is locally satisfied, and observed that areas with high Tuy values corresponded to the locations of residual artefacts in the MC images. We conclude that our method can provide accurate and artefact-free MC images with most types of head motion likely to be encountered in CT imaging, provided that the motion can be accurately determined.

Motion of the myocardium deteriorates the quality and quantitative accuracy of cardiac PET images. We present a method for MR-based cardiac and respiratory motion correction of cardiac PET data and evaluate its impact on estimation of activity and kinetic parameters in human subjects. Three healthy subjects underwent simultaneous dynamic 18F-FDG PET and MRI on a hybrid PET/MR scanner. A cardiorespiratory motion field was determined for each subject using navigator, tagging and golden-angle radial MR acquisitions. Acquired coincidence events were binned into cardiac and respiratory phases using electrocardiogram and list mode-driven signals, respectively. Dynamic PET images were reconstructed with MR-based motion correction (MC) and without motion correction (NMC). Parametric images of 18F-FDG consumption rates (Ki) were estimated using Patlak’s method for both MC and NMC images. MC alleviated motion artifacts in PET images, resulting in improved spatial resolution, improved recovery of activity in the myocardium wall and reduced spillover from the myocardium to the left ventricle cavity. Significantly higher myocardium contrast-to-noise ratio and lower apparent wall thickness were obtained in MC versus NMC images. Likewise, parametric images of Ki calculated with MC data had improved spatial resolution as compared to those obtained with NMC. Consistent with an increase in reconstructed activity concentration in the frames used during kinetic analyses, MC led to the estimation of higher Ki values almost everywhere in the myocardium, with up to 18% increase (mean across subjects) in the septum as compared to NMC. This study shows that MR-based motion correction of cardiac PET results in improved image quality that can benefit both static and dynamic studies.