Background: Effective thermal ablation of liver tumors requires precise monitoring of the ablation zone. Computed tomography (CT) thermometry can non-invasively monitor lethal temperatures but suffers from metal artifacts caused by ablation equipment. Purpose: This study assesses spectral CT thermometry's applicability during microwave ablation, comparing the reproducibility, precision, and accuracy of attenuation-based versus physical density-based thermometry. Furthermore, it identifies optimal metal artifact reduction (MAR) methods: O-MAR, deep learning-MAR, spectral CT, and combinations thereof. Methods: Four gel phantoms embedded with temperature sensors underwent a 10- minute, 60 W microwave ablation imaged by dual-layer spectral CT scanner in 23 scans over time. For each scan attenuation-based and physical density-based temperature maps were reconstructed. Attenuation-based and physical density-based thermometry models were tested for reproducibility over three repetitions; a fourth repetition focused on accuracy. MAR techniques were applied to one repetition to evaluate temperature precision in artifact-corrupted slices. Results: The correlation between CT value and temperature was highly linear with an R-squared value exceeding 96 %. Model parameters for attenuation-based and physical density-based thermometry were −0.38 HU/°C and 0.00039 °C⁻¹, with coefficients of variation of 2.3 % and 6.7 %, respectively. Physical density maps improved temperature precision in presence of needle artifacts by 73 % compared to attenuation images. O-MAR improved temperature precision with 49 % compared to no MAR. Attenuation-based thermometry yielded narrower Bland-Altman limits-of-agreement (−7.7 °C to 5.3 °C) than physical density-based thermometry. Conclusions: Spectral physical density-based CT thermometry at 150 keV, utilized alongside O-MAR, enhances temperature precision in presence of metal artifacts and achieves reproducible temperature measurements with high accuracy.

Objective: To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR). Methods: PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made. Results: Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was − 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was − 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was − 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence. Conclusions: There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR. Key Points: CT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR).DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images.DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values.