Dual Energy CT Physics-A Primer for the Emergency Radiologist

Dual-energy CT applications in musculoskeletal disorders

Dual-energy CT (DECT) is an exciting application in CT technology conferring many advantages over conventional single-energy CT at no additional with comparable radiation dose to the patient. Various emerging and increasingly established clinical DECT applications in musculoskeletal (MSK) imaging such as bone marrow oedema detection, metal artefact reduction, monosodium urate analysis, and collagen analysis for ligamentous, meniscal, and disc injuries are made possible through its advanced DECT post-processing capabilities. These provide superior information on tissue composition, artefact reduction and image optimization. Newer DECT applications to evaluate fat fraction for sarcopenia, Rho/Z application for soft tissue calcification differentiation, 3D rendering, and AI integration are being assessed for future use. In this article, we will discuss the established and developing applications of DECT in the setting of MSK radiology as well as the basic principles of DECT which facilitate them.

Radiological Insights into Sacroiliitis: A Narrative Review

Sacroiliitis is the inflammation of the sacroiliac joint, the largest axial joint in the human body, contributing to 25% of lower back pain cases. It can be detected using various imaging techniques like radiography, MRI, and CT scans. Treatments range from conservative methods to invasive procedures. Recent advancements in artificial intelligence offer precise detection of this condition through imaging. Treatment options range from physical therapy and medications to invasive methods like joint injections and surgery. Future management looks promising with advanced imaging, regenerative medicine, and biologic therapies, especially for conditions like ankylosing spondylitis. We conducted a review on sacroiliitis using imaging data from sources like PubMed and Scopus. Only English studies focusing on sacroiliitis's radiological aspects were included. The findings were organized and presented narratively.

Accuracy of iodine quantification in dual energy CT: A phantom study across 3 different CT systems

INTRODUCTION

No study has rigorously compared the performances of iodine quantification on recent CT systems employing different emission-based technologies, depending on the manufacturers and models.

METHODS

A specific bespoke phantom was used for this study, with 12 known concentrations of iodinated contrast agent: 0.4, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 10.0, 15.0, 20.0, 30.0 and 50.0 mg/mL. Three different dual-energy scanners were tested: one system using dual-source acquisition (CT#1) and two systems using Fast kilovolt-peak switching technology ± artificial intelligence (AI) reconstruction methods (CT#2 and #3) from two different manufacturers. For each system, helical scans were performed following recommended clinical protocols. Four acquisitions were performed per iodine concentration (mg/mL), and measurements were made on iodine-maps using ROIs. Mean measured values were compared to the known concentrations, and the absolute quantification error (AQE) and the relative percentage error (RPE) were used to compare the performances of each CT.

RESULTS

The accuracy of the obtained measurements varied depending on the studied model but not on the acquisition mode (dual-source vs kVp switch ± AI). The quantification was more precise at high concentrations. RPE values were below 10 % with CT#2 (kVp switch) and below 25 % with CT#1 (dual-source), but were significantly higher with CT#3 (kVp switch + AI), exceeding 50 % at low concentrations (<3 mg/mL).

CONCLUSIONS

With the help of a phantom, we identified variability in the results accuracy depending on the CT model, with sometimes significant deviation. Considering the performances of the different DECT technologies in iodine mapping, dual-source (CT#1) and kVp switch (CT#2) technologies appear more accurate than kVp switch technology combined with deep-learning-based reconstruction (CT#3) especially at low concentrations (<3 mg/mL).

IMPLICATIONS FOR PRACTICE

As the primary and daily user of medical imaging devices, the radiographer role is to be attentive to the performance of imaging systems, particularly when performing quantitative acquisitions like iodine-quantification. In CT quantitative imaging (iodine map), it's essential for radiographers to consider their CT systems as measuring tools, and to be aware of their accuracies and limits.

Pediatric Applications of Dual-Energy Computed Tomography

There is renewed interest in novel pediatric dual-energy computed tomography (DECT) applications that can image awake patients faster and at low radiation doses. DECT enables the simultaneous acquisition of 2 data sets at different energy levels, allowing for better material characterization and unique image reconstructions that enhance image analysis and provide quantitative and qualitative information about tissue composition. Pediatric DECT reduces radiation doses further while accelerating image acquisition and improving motion robustness. Current applications include the improved evaluation of congenital and acquired cardiovascular anomalies, lung perfusion and ventilation, renal stone composition, tumor extension and treatment response, and gastrointestinal diseases.

Can virtual non-contrast imaging replace true non-contrast imaging in multiphase scanning of the neck region?

Background

Dual-energy computed tomography (DECT) is an advanced imaging method that enables reconstruction of virtual non-contrast (VNC) images from a contrast-enhanced acquisition. This has the potential to reduce radiation exposure by eliminating the need for a true non-contrast (TNC) phase.

Purpose

The purpose is to evaluate the feasibility of VNC images in the neck region.

Materials and methods

A total of 100 patients underwent a DECT scan as part of diagnostic workup of primary hyperparathyroidism. VNC images were reconstructed from 30 s (arterial) and 50 s (venous) post-contrast scans. Regions of interest (ROIs) were placed in thyroid tissue, lymph node, carotid artery, jugular vein, fat, and sternocleidomastoid muscle. Mean densities of all anatomical structures were compared between VNC and TNC images.

Results

For all anatomical structures except the thyroid gland, the difference in mean density between TNC and VNC images was less than 15 HU. The mean difference in density between TNC and VNC images of the thyroid was 53.2 HU (95% CI 46.8; 59.6, p = <0.001).

Conclusion

This study demonstrated an acceptable agreement in density between true non-contrast and virtual non-contrast images for most anatomical structures in the neck region. Therefore, VNC images may have the potential to replace TNC images in the neck. However, due to significant differences in CT density of thyroid tissue, true non-contrast imaging cannot be directly substituted by virtual non-contrast imaging when examining the thyroid and its surrounding tissue.

Microbiome-Driven Proline Biogenesis in Plants under Stress: Perspectives for Balanced Diet to Minimize Depression Disorders in Humans

According to the World Health Organization (WHO), depression is a leading cause of disability worldwide and a major contributor to the overall global burden of mental disorders. An increasing number of studies have revealed that among 20 different amino acids, high proline consumption is a dietary factor with the strongest impact on depression in humans and animals, including insects. Recent studies acknowledged that gut microbiota play a key role in proline-related pathophysiology of depression. In addition, the multi-omics approach has alleged that a high level of metabolite proline is directly linked to depression severity, while variations in levels of circulating proline are dependent on microbiome composition. The gut–brain axis proline analysis is a gut microbiome model of studying depression, highlighting the critical importance of diet, but nothing is known about the role of the plant microbiome–food axis in determining proline concentration in the diet and thus about preventing excessive proline intake through food consumption. In this paper, we discuss the protocooperative potential of a holistic study approach combining the microbiota–gut–brain axis with the microbiota–plant–food–diet axis, as both are involved in proline biogenesis and metabolism and thus on in its effect on mood and cognitive function. In preharvest agriculture, the main scientific focus must be directed towards plant symbiotic endophytes, as scavengers of abiotic stresses in plants and modulators of high proline concentration in crops/legumes/vegetables under climate change. It is also implied that postharvest agriculture—including industrial food processing—may be critical in designing a proline-balanced diet, especially if corroborated with microbiome-based preharvest agriculture, within a circular agrifood system. The microbiome is suggested as a target for selecting beneficial plant endophytes in aiming for a balanced dietary proline content, as it is involved in the physiology and energy metabolism of eukaryotic plant/human/animal/insect hosts, i.e., in core aspects of this amino acid network, while opening new venues for an efficient treatment of depression that can be adapted to vast groups of consumers and patients. In that regard, the use of artificial intelligence (AI) and molecular biomarkers combined with rapid and non-destructive imaging technologies were also discussed in the scope of enhancing integrative science outcomes, agricultural efficiencies, and diagnostic medical precisions.