Feasibility of Four-Dimensional Similarity Filter For Radiation Dose Reduction in Dynamic Myocardial Computed Tomography Perfusion Imaging

Purpose: We aimed to evaluate the impact of four-dimensional noise reduction filtering using a similarity algorithm (4D-SF) on image noise during dynamic myocardial computed tomography perfusion (CTP) to simulate the reduction of radiation dose.Methods: A total of 43 patients who underwent dynamic myocardial CTP using 320-row CT were included in the study. The original images were reconstructed using iterative reconstruction (IR); three different CTP datasets with simulated noise, which corresponded to 25%, 50%, and 75% reduction of the original dose (= 300mA), were reconstructed using a combination of IR and 4D-SF. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were assessed, and CT-derived myocardial blood flow (CT-MBF) was quantified. The results were compared between the original and simulated images with radiation dose reduction.Results: The original, 25%-, 50%-, and 75%-dose reduced images with 4D-SF showed an SNR of 8.3 (6.5–10.2), 16.5 (11.9–21.7), 15.6 (11.0–20.1), and 12.8 (8.8–18.1) and a CNR of 4.4 (3.2–5.8), 6.7 (4.6–10.3), 6.6 (4.3–10.1), and 5.5 (3.5–9.1), respectively. Compared to the original images, the 25%-, 50%-, and 75%-dose reduced-simulated images showed significant improvement in both SNR and CNR with 4D-SF. There was no significant difference in CT-MBF between the original and 25%- or 50%-dose reduced-simulated images with 4D-SF, however, there was a significant difference in CT-MBF between the original and 75%-dose reduced-simulated images.Conclusion: 4D-SF has the potential to reduce the radiation dose associated with dynamic myocardial CTP imaging by half, without impairing the robustness of MBF quantification.

Usefulness of a Metal Artifact Reduction Algorithm in Digital Tomosynthesis Using a Combination of Hybrid Generative Adversarial Networks

In this study, a novel combination of hybrid generative adversarial networks (GANs) comprising cycle-consistent GAN, pix2pix, and (mask pyramid network) MPN (CGpM-metal artifact reduction [MAR]), was developed using projection data to reduce metal artifacts and the radiation dose during digital tomosynthesis. The CGpM-MAR algorithm was compared with the conventional filtered back projection (FBP) without MAR, FBP with MAR, and convolutional neural network MAR. The MAR rates were compared using the artifact index (AI) and Gumbel distribution of the largest variation analysis using a prosthesis phantom at various radiation doses. The novel CGpM-MAR yielded an adequately effective overall performance in terms of AI. The resulting images yielded good results independently of the type of metal used in the prosthesis phantom (p < 0.05) and good artifact removal at 55% radiation-dose reduction. Furthermore, the CGpM-MAR represented the minimum in the model with the largest variation at 55% radiation-dose reduction. Regarding the AI and Gumbel distribution analysis, the novel CGpM-MAR yielded superior MAR when compared with the conventional reconstruction algorithms with and without MAR at 55% radiation-dose reduction and presented features most similar to the reference FBP. CGpM-MAR presents a promising method for metal artifact and radiation-dose reduction in clinical practice.