Impact of CT Photon-Counting Virtual Monoenergetic Imaging on Visualization of Abdominal Arterial Vessels

PURPOSE

The novel photon-counting detector (PCD) technique acquires spectral data for virtual monoenergetic imaging (VMI) in every examination. The aim of this study was the evaluation of the impact of VMI of abdominal arterial vessels on quantitative and qualitative subjective image parameters.

METHODS

A total of 20 patients that underwent an arterial phase computed tomography (CT) scan of the abdomen with a novel PCD CT (Siemens NAEOTOM alpha) were analyzed regarding attenuation at different energy levels in virtual monoenergetic imaging. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were calculated and compared between the different virtual monoenergetic (VME) levels with correlation to vessel diameter. In addition, subjective image parameters (overall subjective image quality, subjective image noise and vessel contrast) were evaluated.

RESULTS

Our research showed decreasing attenuation levels with increasing energy levels in virtual monoenergetic imaging regardless of vessel diameter. CNR showed best overall results at 60 keV, and SNR at 70 keV with no significant difference to 60 keV (p = 0.294). Subjective image quality was rated best at 70 keV for overall image quality, vessel contrast and noise.

CONCLUSIONS

Our data suggest that VMI at 60-70 keV provides the best objective and subjective image quality concerning vessel contrast irrespective of vessel size.

Gene expression for biodosimetry and effect prediction purposes: promises, pitfalls and future directions - key session ConRad 2021

PURPOSE

In a nuclear or radiological event, an early diagnostic or prognostic tool is needed to distinguish unexposed from low- and highly exposed individuals with the latter requiring early and intensive medical care. Radiation-induced gene expression (GE) changes observed within hours and days after irradiation have shown potential to serve as biomarkers for either dose reconstruction (retrospective dosimetry) or the prediction of consecutively occurring acute or chronic health effects. The advantage of GE markers lies in their capability for early (1-3 days after irradiation), high-throughput, and point-of-care (POC) diagnosis required for the prediction of the acute radiation syndrome (ARS).

CONCLUSIONS

As a key session of the ConRad conference in 2021, experts from different institutions were invited to provide state-of-the-art information on a range of topics including: (1) Biodosimetry: What are the current efforts to enhance the applicability of this method to perform retrospective biodosimetry? (2) Effect prediction: Can we apply radiation-induced GE changes for prediction of acute health effects as an approach, complementary to and integrating retrospective dose estimation? (3) High-throughput and point-of-care diagnostics: What are the current developments to make the GE approach applicable as a high-throughput as well as a POC diagnostic platform? (4) Low level radiation: What is the lowest dose range where GE can be used for biodosimetry purposes? (5) Methodological considerations: Different aspects of radiation-induced GE related to more detailed analysis of exons, transcripts and next-generation sequencing (NGS) were reported.

Predicting the Radiation Sensitivity of Male and Female Rhesus Macaques Using Gene Expression

Radiosensitivity differs in humans and likely among closely-related primates. Reasons for variation in radiosensitivity are not well known. We examined preirradiation gene expression in peripheral blood among male and female rhesus macaques which did or did not survive (up to 60 days) after whole-body irradiation with 700 cGy (LD66/60). RNA samples originated from a blinded randomized Good Laboratory Practice study in 142 irradiated rhesus macaques. Animals were untreated (placebo), or treated using recombinant human IL-12, G-CSF or combination of the two. We evaluated gene expression in a two-phase study design where phase I was a whole genome screen [next generation sequencing (NGS)] for mRNAs (RNA-seq) using five RNA samples from untreated male and female animals per group of survivor and non-survivor (total n = 20). Differential gene expression (DGE) was defined as a statistically significant and ≥2-fold up- or downregulation of mRNA species and was calculated between groups of survivors and non-survivors (reference) and by gender. Altogether 659 genes were identified, but the overlapping number of differentially expressed genes (DGE) observed in both genders was small (n = 36). Fifty-eight candidate mRNAs were chosen for independent validation in phase II using the remaining samples (n = 122) evaluated with qRT-PCR. Among the 58 candidates, 16 were of significance or borderline significance (t test) by DGE. Univariate and multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis further refined and identified the most outstanding validated genes and gene combinations. For untreated male macaques, we identified EPX (P = 0.005, ROC=1.0), IGF2BP1 (P = 0.05, ROC=0.74) and the combination of EPX with SLC22A4 (P = 0.03, ROC=0.85) which appeared most predictive for the clinical outcome for treated and combined (untreated and treated) male macaque groups, respectively. For untreated, treated and both combined female macaque groups the same gene (MBOAT4, P = 0.0004, ROC = 0.81) was most predictive. Based on the probability function of the ROC curves, up to 74% of preirradiation RNA measurements predicted survival with a positive and negative predictive value ranging between 85-100% and associated odds ratios reflecting a 2-3-fold elevated risk for surviving per unit change (cycle threshold value) in gene expression. In conclusion, we identified gender-dependent genes and gene combinations in preirradiation blood samples for survival prediction after irradiation in rhesus macaques.