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Background: Prior studies have reported that curcumin is inversely associated with reduced markers of atherosclerosis risk, including carotid intima-media thickness (CIMT). This study was designed to assess the effects of curcumin on CIMT and pulse wave velocity (PWV) in diabetic hemodialysis (HD) patients. Methods: This randomized, double-blinded, placebo-controlled trial was conducted on 39 diabetic HD patients. People were assigned to receive curcumin or placebo (starch) for 24 weeks. Individuals in the curcumin group (n = 26) received 80 mg/day. CIMT and PWV levels were taken at baseline and after 24 weeks of intervention. Results: After 24 weeks of intervention, curcumin intake did not affect mean levels of left (P = 0.83) and right (P = 0.47) CIMT and maximum levels of left (P = 0.84) and right (P = 0.11) CIMT, and PWV (P = 0.12) compared to the placebo. Furthermore, within-group difference demonstrated a significant reduction in mean levels of PWV (P = 0.01) in the curcumin group. We did not observe any significant change in C-reactive protein (CRP) concentrations after curcumin intake (P = 0.69). Conclusions: Curcumin intake did not affect mean levels of left and right CIMT and maximum levels of left and right CIMT, PWV, and CRP levels compared to the placebo. Additionally, within-group difference demonstrated a significant reduction in mean levels of PWV in the curcumin group.This trial was registered at www.irct.ir as http://www.irct.ir: IRCT20200527047584N1.
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BACKGROUND AND PURPOSE: Considering recent iodinated contrast shortages and a focus on reducing waste, developing protocols with lower contrast dosing while maintaining image quality through artificial intelligence is needed. This study compared reduced iodinated contrast media and standard dose CTP acquisitions, and the impact of deep learning denoising on CTP image quality in preclinical and clinical studies. The effect of reduced X-ray mAs dose was also investigated in preclinical studies. MATERIALS AND METHODS: Twelve swine underwent 9 CTP examinations each, performed at combinations of 3 different x-ray (37, 67, and 127 mAs) and iodinated contrast media doses (10, 15, and 20 mL). Clinical CTP acquisitions performed before and during the iodinated contrast media shortage and protocol change (from 40 to 30 mL) were retrospectively included. Eleven patients with reduced iodinated contrast media dosages and 11 propensity-score-matched controls with the standard iodinated contrast media dosages were included. A residual encoder-decoder convolutional neural network (RED-CNN) was trained for CTP denoising using k-space-weighted image average filtered CTP images as the target. The standard, RED-CNN-denoised, and k-space-weighted image average noise-filtered images for animal and human studies were compared for quantitative SNR and qualitative image evaluation. RESULTS: The SNR of animal CTP images decreased with reductions in iodinated contrast media and milliampere-second doses. Contrast dose reduction had a greater effect on SNR than milliampere-second reduction. Noise-filtering by k-space-weighted image average and RED-CNN denoising progressively improved the SNR of CTP maps, with RED-CNN resulting in the highest SNR. The SNR of clinical CTP images was generally lower with a reduced iodinated contrast media dose, which was improved by the k-space-weighted image average and RED-CNN denoising (P < .05). Qualitative readings consistently rated RED-CNN denoised CTP as the best quality, followed by k-space-weighted image average and then standard CTP images. CONCLUSIONS: Deep learning-denoising can improve image quality for low iodinated contrast media CTP protocols, and could approximate standard iodinated contrast media dose CTP, in addition to potentially improving image quality for low milliampere-second acquisitions.