Despite universal consensus that computed tomography (CT) overwhelmingly benefits patients when used for appropriate indications, concerns have been raised regarding the potential risk of cancer induction from CT due to the exponentially increased use of CT in medicine. Keeping radiation dose as low as reasonably achievable, consistent with the diagnostic task, remains the most important strategy for decreasing this potential risk. This article summarizes the general technical strategies that are commonly used for radiation dose management in CT. Dose-management strategies for pediatric CT, cardiac CT, dual-energy CT, CT perfusion and interventional CT are specifically discussed, and future perspectives on CT dose reduction are presented.

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Low-contrast detectability performance increased with increasing object size, object contrast, dose index, section thickness, and ADMIRE strength. Compared with FBP, ADMIRE allows a substantial radiation dose reduction while preserving low-contrast detectability. Online supplemental material is available for this article.

Purpose To determine the ex vivo ability of dual-energy, dual-source computed tomography (DE-DSCT) with additional tin filtration to differentiate between five groups of human renal stone types. Methods Forty-three renal stones of ten types were categorized into five primary groups based on effective atomic numbers, which were calculated as the weighted average of the atomic numbers of constituent atoms. Stones were embedded in porcine kidneys and placed in a 35cm water phantom. DE-DSCT scans were performed with and without tin filtration at 80/140kV. The CT number ratio [CTR=CT(low)/CT(high)] was calculated on a volumetric voxel-by-voxel basis for each stone. Statistical analysis was performed and receiver operating characteristic (ROC) curves were plotted to compare the difference in CTR with and without tin filtration, and to measure the discrimination between stone groups. Results CTR of non-uric acid stones increased on average by 0.17 (range 0.03–0.36) with tin filtration. The CTR values for non-uric acid stone groups were not significantly different (p>0.05) between any of the two adjacent groups without tin filtration. Use of the additional tin filtration on the high-energy x-ray tube significantly improved the separation of non-uric acid stone types by CTR (p<0.05). The area under the ROC curve increased from 0.78–0.84 without fin filtration to 0.89–0.95 with tin filtration. Conclusion Our results demonstrated better separation between different stone types when additional tin filtration was used on DE-DSCT. The increased spectral separation allowed a 5-group stone classification scheme. Some overlapping between particular stone types still exists, including brushite and calcium oxalate.

Purpose: To present and evaluate a new image reconstruction method for dynamic CT based on a nonconvex prior image constrained compressed sensing ͑NCPICCS͒ algorithm. The authors systematically compared the undersampling potential, functional information recovery, and solution convergence speed of four compressed sensing ͑CS͒ based image reconstruction methods using perfusion CT data: Standard ᐉ 1 -based CS, nonconvex CS ͑NCCS͒, and ᐉ 1 -based and nonconvex CS, including an additional constraint based on a prior image ͑PICCS and NCPICCS, respectively͒. Methods: The Shepp-Logan phantom was modified such that its uppermost ellipses changed attenuation through time, simulating both an arterial input function ͑AIF͒ and a homogeneous tissue perfusion region. Data were simulated with and without Poisson noise added to the projection data and subsequently reconstructed with all four CS-based methods at four levels of undersampling: 20, 12, 6, and 4 projections. Root mean squared ͑RMS͒ error of reconstructed images and recovered time attenuation curves ͑TACs͒ were assessed as well as convergence speed. The performance of both PICCS and NCPICCS methods were also evaluated using a kidney perfusion animal experiment data set. Results: All four CS-based methods were able to reconstruct the phantoms with 20 projections, with similar results on the RMS error of the recovered TACs. NCCS allowed accurate reconstructions with as few as 12 projections, PICCS with as few as six projections, and NCPICCS with as few as four projections. These results were consistent for noise-free and noisy data. NCPICCS required the fewest iterations to converge across all simulation conditions, followed by PICCS, NCCS, and then CS. On animal data, at the lowest level of undersampling tested ͑16 projections͒, the image quality of NCPICCS was better than PICCS with fewer streaking artifacts, while the TAC accuracy on the selected region of interest was comparable. Conclusions:The authors have presented a novel method for image reconstruction using highly undersampled dynamic CT data. The NCPICCS method takes advantage of the information provided by a prior image, as in PICCS, but employs a more general nonconvex sparsity measure ͓such as the ᐉ p -norm ͑0 Ͻ p Յ 1͔͒ rather than the conventional convex ᐉ 1 -norm. Despite the lack of guarantees of a globally optimal solution, the proposed nonconvex extension of PICCS consistently allowed for image reconstruction from fewer samples than the analogous ᐉ 1 -based PICCS method. Both nonconvex sparsity measures as well as prior image information ͑when available͒ significantly reduced the number of iterations required for convergence, potentially providing computational advantages for practical implementation of CS-based image reconstruction techniques.