A comprehensive review of dual-energy and multi-spectral computed tomography

In vitro and in vivo evaluation of biohybrid tissue-engineered vascular grafts with transformative 1H/19F MRI traceable scaffolds

Biohybrid tissue-engineered vascular grafts (TEVGs) promise long-term durability due to their ability to adapt to hosts' needs. However, the latter calls for sensitive non-invasive imaging approaches to longitudinally monitor their functionality, integrity, and positioning. Here, we present an imaging approach comprising the labeling of non-degradable and degradable TEVGs' components for their in vitro and in vivo monitoring by hybrid 1H/19F MRI. TEVGs (inner diameter 1.5 mm) consisted of biodegradable poly(lactic-co-glycolic acid) (PLGA) fibers passively incorporating superparamagnetic iron oxide nanoparticles (SPIONs), non-degradable polyvinylidene fluoride scaffolds labeled with highly fluorinated thermoplastic polyurethane (19F-TPU) fibers, a smooth muscle cells containing fibrin blend, and endothelial cells. 1H/19F MRI of TEVGs in bioreactors, and after subcutaneous and infrarenal implantation in rats, revealed that PLGA degradation could be faithfully monitored by the decreasing SPIONs signal. The 19F signal of 19F-TPU remained constant over weeks. PLGA degradation was compensated by cells' collagen and α-smooth-muscle-actin deposition. Interestingly, only TEVGs implanted on the abdominal aorta contained elastin. XTT and histology proved that our imaging markers did not influence extracellular matrix deposition and host immune reaction. This concept of non-invasive longitudinal assessment of cardiovascular implants using 1H/19F MRI might be applicable to various biohybrid tissue-engineered implants, facilitating their clinical translation.

A study on the vertebral column of the dice snake Natrix tessellata (Serpentes, Natricidae) from Denizli (western Anatolia, Turkey)

The vertebral anatomy of snakes has attracted the attention of researchers for decades and numerous studies have been made for extinct and extant species. The present study investigated the morphological variations in vertebral structure among different vertebral regions in the dice snake Natrix tessellata, and provides a detailed anatomical and microstructural description of the vertebral column. Vertebrae were analyzed and compared using x-ray imaging, scanning electron microscopy, micro-computed tomography, and histological techniques. The vertebral column of N. tessellata is divided into three regions: precloacal, cloacal, and caudal. Unlike in many other tetrapods and snakes, the atlas of N. tessellata does not form a complete ring. It has a flat and roughly trilobate shape with a prominent middle lobe. The axis has two hypapophyses. The anterior precloacal region of the vertebral column has longer and more paddle-shaped hypapophyses, distinguishing it from the posterior and mid-trunk vertebrae. The anterior cloacal vertebrae have a short hypapophysis rather than a hemal keel, and the lymphapophysis extends outward, curving slightly. The cotyle and condyle of the caudal vertebrae exhibited a closer resemblance to a rounded shape, while the pleurapophysis extended ventrolaterally and curved ventrally near its distal end. Paired hemapophyses were present at the posterior-most point of the centrum instead of a hypapophysis. In light of previous fossil findings, our anatomical comparison of the vertebral and transverse processes indicates that the extant Natrix has a more flexible and less rigid spine than its ancestors. Overall, the vertebral differences among snake anatomical regions or taxa are a testament to the remarkable diversity and adaptability of these fascinating reptiles.

Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study

OBJECTIVES

To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms.

METHODS

An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions.

RESULTS

NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy.

CONCLUSIONS

DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'.

Super-resolution dual-layer CBCT imaging with model-guided deep learning

Objective.This study aims at investigating a novel super resolution CBCT imaging approach with a dual-layer flat panel detector (DL-FPD).Approach.With DL-FPD, the low-energy and high-energy projections acquired from the top and bottom detector layers contain over-sampled spatial information, from which super-resolution CT images can be reconstructed. A simple mathematical model is proposed to explain the signal formation procedure in DL-FPD, and a dedicated recurrent neural network, named suRi-Net, is developed based upon the above imaging model to nonlinearly retrieve the high-resolution dual-energy information. Physical benchtop experiments are conducted to validate the performance of this newly developed super-resolution CBCT imaging method.Main Results.The results demonstrate that the proposed suRi-Net can accurately retrieve high spatial resolution information from the low-energy and high-energy projections of low spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images from the top and bottom detector layers is increased by about 45% and 54%, respectively.Significance.In the future, suRi-Net will provide a new approach to perform high spatial resolution dual-energy imaging in DL-FPD-based CBCT systems.

Density-discriminating chromatic x-ray imaging based on metal halide nanocrystal scintillators

X-ray imaging based on a single gray level shows visual blind parts and affects accurate judgment in some situations. Color-cognized x-ray imaging will boost the recognition capability, which has not yet been reported. Here, we propose a quartz-assisted chromatic x-ray imaging model based on metal halide nanocrystal (NC) stacked scintillators. Mutually inactive (BA)2PbBr4:Mn and Cs3Cu2I5:Tl enable x-ray energy- or density-dependent radioluminescence (RL) color variation. The upper scintillator light yield and the bottom scintillator transmittance are enhanced by elaborate in situ passivation of phenethylamine bromide and NC orientation regulation, respectively. Imaging targets with different densities are distinguished on RL spectra, and the color coordinates shift linearly on CIE 1931. An algorithm balances the image details of different gray areas and enhances the visual perception by color filling. This work provides color recognition between objects with different densities and takes a step toward chromatic x-ray imaging applied to practical scenarios.

Dual-Energy CT Evaluation of Gastrointestinal Bleeding

Gastrointestinal (GI) bleeding is a potentially life-threatening condition accounting for more than 300 000 annual hospitalizations. Multidetector abdominopelvic CT angiography is commonly used in the evaluation of patients with GI bleeding. Given that many patients with severe overt GI bleeding are unlikely to tolerate bowel preparation, and inpatient colonoscopy is frequently limited by suboptimal preparation obscuring mucosal visibility, CT angiography is recommended as a first-line diagnostic test in patients with severe hematochezia to localize a source of bleeding. Assessment of these patients with conventional single-energy CT systems typically requires the performance of a noncontrast series followed by imaging during multiple postcontrast phases. Dual-energy CT (DECT) offers several potential advantages for performing these examinations. DECT may eliminate the need for a noncontrast acquisition by allowing the creation of virtual noncontrast (VNC) images from contrast-enhanced data, affording significant radiation dose reduction while maintaining diagnostic accuracy. VNC images can help radiologists to differentiate active bleeding, hyperattenuating enteric contents, hematomas, and enhancing masses. Additional postprocessing techniques such as low-kiloelectron voltage virtual monoenergetic images, iodine maps, and iodine overlay images can increase the conspicuity of contrast material extravasation and improve the visibility of subtle causes of GI bleeding, thereby increasing diagnostic confidence and assisting with problem solving. GI bleeding can also be diagnosed with routine single-phase DECT scans by constructing VNC images and iodine maps. Radiologists should also be aware of the potential pitfalls and limitations of DECT. ^©RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

Printable Perovskite Diodes for Broad-Spectrum Multienergy X-Ray Detection

Multienergy X-ray detection is critical to effectively differentiate materials in a variety of diagnostic radiology and nondestructive testing applications. Silicon and selenium X-ray detectors are the most common for multienergy detection; however, these present poor energy discrimination across the broad X-ray spectrum and exhibit limited spatial resolution due to the high thicknesses required for radiation attenuation. Here, an X-ray detector based on solution-processed thin-film metal halide perovskite that overcomes these challenges is introduced. By harnessing an optimized n-i-p diode configuration, operation is achieved across a broad range of soft and hard X-ray energies stemming from 0.1 to 10's of keV. Through detailed experimental and simulation work, it is shown that optimized Cs_0.1 FA_0.9 PbI₃ perovskites effectively attenuate soft and hard X-rays, while also possessing excellent electrical properties to result in X-ray detectors with high sensitivity factors that exceed 5 × 10³ µ C   G y Vac - 1   cm - 2 $\mu {\rm{C}}\;{{\bf Gy}}_{{\rm{Vac}}}^{ - 1}\;{\rm{c}}{{\rm{m}}^{ - 2}}$ and 6 × 10⁴ µC Gy^-1 cm^-2 within soft and hard X-ray regimes, respectively. Harnessing the solution-processable nature of the perovskites, roll-to-roll printable X-ray detectors on flexible substrates are also demonstrated.

Multi-Energy and Fast-Convergence Iterative Reconstruction Algorithm for Organic Material Identification Using X-ray Computed Tomography

In order to significantly reduce the computing time while, at the same time, keeping the accuracy and precision when determining the local values of the density and effective atomic number necessary for identifying various organic material, including explosives and narcotics, a specialized multi-stage procedure based on a multi-energy computed tomography investigation within the 20-160 keV domain was elaborated. It consisted of a compensation for beam hardening and other non-linear effects that affect the energy dependency of the linear attenuation coefficient (LAC) in the chosen energy domain, followed by a 3D fast reconstruction algorithm capable of reconstructing the local LAC values for 64 energy values from 19.8 to 158.4 keV, and, finally, the creation of a set of algorithms permitting the simultaneous determination of the density and effective atomic number of the investigated materials. This enabled determining both the density and effective atomic number of complex objects in approximately 24 s, with an accuracy and precision of less than 3%, which is a significantly better performance with respect to the reported literature values.

Pros and Cons of Dual-Energy CT Systems: "One Does Not Fit All"

Dual-energy computed tomography (DECT) uses different energy spectrum x-ray beams for differentiating materials with similar attenuation at a certain energy. Compared with single-energy CT, it provides images with better diagnostic performance and a potential reduction of contrast agent and radiation doses. There are different commercially available DECT technologies, with machines that may display two x-ray sources and two detectors, a single source capable of fast switching between two energy levels, a specialized detector capable of acquiring high- and low-energy data sets, and a filter splitting the beam into high- and low-energy beams at the output. Sequential acquisition at different tube voltages is an alternative approach. This narrative review describes the DECT technique using a Q&A format and visual representations. Physical concepts, parameters influencing image quality, postprocessing methods, applicability in daily routine workflow, and radiation considerations are discussed. Differences between scanners are described, regarding design, image quality variabilities, and their advantages and limitations. Additionally, current clinical applications are listed, and future perspectives for spectral CT imaging are addressed. Acknowledging the strengths and weaknesses of different DECT scanners is important, as these could be adapted to each patient, clinical scenario, and financial capability. This technology is undoubtedly valuable and will certainly keep improving.

X-ray computed tomography for 3D plant imaging

X-ray computed tomography (CT) is a valuable tool for 3D imaging of plant tissues and organs. Applications include the study of plant development and organ morphogenesis, as well as modeling of transport processes in plants. Some challenges remain, however, including attaining higher contrast for easier quantification, increasing the resolution for imaging subcellular features, and decreasing image acquisition and processing time for high-throughput phenotyping. In addition, phase contrast, multispectral, dark-field, soft X-ray, and time-resolved imaging are emerging. At the same time, a large amount of 3D image data are becoming available, posing challenges for data management. We review recent advances in the area of X-ray CT for plant imaging, and describe opportunities for using such images for studying transport processes in plants.