The increasing use of computed tomography (CT) has led to a rise in cumulative radiation dose due to medical imaging, raising concerns about potential long-term adverse effects. Large-scale epidemiological studies indicate a higher tumor incidence associated with CT examinations, but the underlying biological mechanisms remain largely unexplained. To gain further insights into the cellular response triggered by routine CT diagnostics, we investigated CT-induced changes of gene expression in peripheral blood cells using whole transcriptome sequencing. RNA was isolated from peripheral blood cells of 40 male patients with asymptomatic microhematuria, sampled before and after multi-phase abdominal CT (CTDIvol: 3.75–26.95 mGy, median: 6.55 mGy). On average, 22.11 million sequence reads (SD 5.71) per sample were generated to identify differentially expressed genes 6 h post-exposure by means of DESeq2. To assess the dose dependency of CT-induced effects, we additionally divided samples into three categories: low exposure (≤6.55 mGy, n = 20), medium exposure (>6.55 mGy and <12 mGy, n = 16), and high exposure (≥12 mGy, n = 4), and repeated gene expression analysis for each subset and their corresponding prae-exposure sample. CT exposure caused consistent and dose-dependent upregulation of six genes (EDA2R, AEN, FDXR, DDB2, PHLDA3, and MIR34AHG; padj < 0.1). These genes share several functional commonalities, including regulation by TP53 and involvement in the DNA damage response. The biological pathways highlighted by Gene Set Enrichment Analysis (GSEA) suggest a dose-dependent increase of cellular damage and metabolic particularities in the low-exposure subset, which may be related to a potential adaptive cellular response to low-dose irradiation. Irrespective of applied dose, AEN emerged as the most robust biomarker for CT exposure among all genes. Routine abdominal CT scans cause dose-dependent gene deregulation in association with DNA damage in peripheral blood cells after in vivo exposure. Regarding risk assessment of CT, our results support the commonly applied “As Low–As –Reasonably Achievable (ALARA)” principle. Evidence of additional gene expression changes associated with metabolic processes indicates a rather complex molecular response beyond DNA damage after CT exposure, and emphasizes the need for further targeted investigations.

Objectives: To evaluate the value of virtual monoenergetic images (VMI) from photon-counting detector CT (PCD-CT) for discriminability of severe lung injury and atelectasis in polytraumatized patients. Materials & Methods: Contrast-enhanced PCD-CT examinations of 20 polytraumatized patients with severe thoracic trauma were included in this retrospective study. Spectral PCD-CT data were reconstructed using a noise-optimized virtual monoenergetic imaging (VMI) algorithm with calculated VMIs ranging from 40 to 120 keV at 10 keV increments. Injury-to-atelectasis contrast-to-noise ratio (CNR) was calculated and compared at each energy level based on CT number measurements in severely injured as well as atelectatic lung areas. Three radiologists assessed subjective discriminability, noise perception, and overall image quality. Results: CT values for atelectasis decreased as photon energy increased from 40 keV to 120 keV (mean Hounsfield units (HU): 69 at 40 keV; 342 at 120 keV), whereas CT values for severe lung injury remained near-constant from 40 keV to 120 keV (mean HU: 42 at 40 keV; 44 at 120 keV) with significant differences at each keV level (p < 0.001). The optimal injury-to-atelectasis CNR was observed at 40 keV in comparison with the remaining energy levels (p < 0.001) except for 50 keV (p > 0.05). In line with this, VMIs at 40 keV were rated best regarding subjective discriminability. VMIs at 60–70 keV, however, provided the highest subjective observer parameters regarding subjective image noise as well as image quality. Conclusions: Discriminability between severely injured and atelectatic lung areas after thoracic trauma can be substantially improved by virtual monoenergetic imaging from PCD-CT with superior contrast and visual discriminability at 40–50 keV.

Computed tomography (CT) is a major source of low-dose ionizing radiation exposure in medical imaging. Risk assessment at this dose level is difficult and relies on the hypothetical linear no-threshold model. To address the response to such low doses in patients undergoing CT scans, we examined radiation-induced alterations at the transcriptomic and DNA damage levels in peripheral blood cells. Peripheral whole blood of 60 patients was collected before and after CT. Post-CT samples were obtained 4–6 h after scan (n = 28, in vivo incubation) or alternatively immediately after the CT scan, followed by ex vivo incubation (n = 32). The gene expression of known radiation-responsive genes (n = 9) was quantified using qRT-PCR. DNA double-strand breaks (DSB) were assessed in 12 patients through microscopic γ-H2AX + 53BP1 DSB focus staining. The mean dose–length product (DLP) across all scans was 561.9 ± 384.6 mGy·cm. Significant differences in the median differential gene expression (DGE) were detected between in vivo and ex vivo incubation conditions, implicating that ex vivo incubation masked the true effect in low-dose settings. The median DGE of in vivo-incubated samples showed a significant upregulation of EDA2R, MIR34AHG, PHLDA3, DDB2, FDXR, and AEN (p ranging from <0.001 to 0.041). In vivo, we observed a linear dose-dependent upregulation for several genes and an explained variance of 0.66 and 0.56 for AEN and FDXR, respectively. DSB focus analysis revealed a slight, non-significant increase in the average DSB damage post-exposure, at a mean DLP of 321.0 mGy·cm. Our findings demonstrate that transcriptional biomarkers are sensitive indicators of low-dose radiation exposure in medical imaging and could prove themselves as clinically applicable biodosimetry tools. Furthermore, the results underscore the need for dose optimization.