Photon-counting computed tomography of lanthanide contrast agents with a high-flux 330-μm-pitch cadmium zinc telluride detector in a table-top system

Surface Bio-engineered Polymeric Nanoparticles

Surface bio-engineering of polymeric nanoparticles (PNPs) has emerged as a cornerstone in contemporary biomedical research, presenting a transformative avenue that can revolutionize diagnostics, therapies, and drug delivery systems. The approach involves integrating bioactive elements on the surfaces of PNPs, aiming to provide them with functionalities to enable precise, targeted, and favorable interactions with biological components within cellular environments. However, the full potential of surface bio-engineered PNPs in biomedicine is hampered by obstacles, including precise control over surface modifications, stability in biological environments, and lasting targeted interactions with cells or tissues. Concerns like scalability, reproducibility, and long-term safety also impede translation to clinical practice. In this review, these challenges in the context of recent breakthroughs in developing surface-biofunctionalized PNPs for various applications, from biosensing and bioimaging to targeted delivery of therapeutics are discussed. Particular attention is given to bonding mechanisms that underlie the attachment of bioactive moieties to PNP surfaces. The stability and efficacy of surface-bioengineered PNPs are critically reviewed in disease detection, diagnostics, and treatment, both in vitro and in vivo settings. Insights into existing challenges and limitations impeding progress are provided, and a forward-looking discussion on the field's future is presented. The paper concludes with recommendations to accelerate the clinical translation of surface bio-engineered PNPs.

Material decomposition with a prototype photon-counting detector CT system: expanding a stoichiometric dual-energy CT method via energy bin optimization and K-edge imaging

Objective.Computed tomography (CT) has advanced since its inception, with breakthroughs such as dual-energy CT (DECT), which extracts additional information by acquiring two sets of data at different energies. As high-flux photon-counting detectors (PCDs) become available, PCD-CT is also becoming a reality. PCD-CT can acquire multi-energy data sets in a single scan by spectrally binning the incident x-ray beam. With this, K-edge imaging becomes possible, allowing high atomic number (high-Z) contrast materials to be distinguished and quantified. In this study, we demonstrated that DECT methods can be converted to PCD-CT systems by extending the method of Bourqueet al(2014). We optimized the energy bins of the PCD for this purpose and expanded the capabilities by employing K-edge subtraction imaging to separate a high-atomic number contrast material.Approach.The method decomposes materials into their effective atomic number (Zeff) and electron density relative to water (ρe). The model was calibrated and evaluated using tissue-equivalent materials from the RMI Gammex electron density phantom with knownρevalues and elemental compositions. TheoreticalZeffvalues were found for the appropriate energy ranges using the elemental composition of the materials.Zeffvaried slightly with energy but was considered a systematic error. Anex vivobovine tissue sample was decomposed to evaluate the model further and was injected with gold chloride to demonstrate the separation of a K-edge contrast agent.Main results.The mean root mean squared percent errors on the extractedZeffandρefor PCD-CT were 0.76% and 0.72%, respectively and 1.77% and 1.98% for DECT. The tissue types in theex vivobovine tissue sample were also correctly identified after decomposition. Additionally, gold chloride was separated from theex vivotissue sample with K-edge imaging.Significance.PCD-CT offers the ability to employ DECT material decomposition methods, along with providing additional capabilities such as K-edge imaging.

Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential

Range uncertainty has been a key factor preventing particle radiotherapy from reaching its full physical potential. One of the main contributing sources is the uncertainty in estimating particle stopping power (ρ_s) within patients. Currently, theρ_sdistribution in a patient is derived from a single-energy CT (SECT) scan acquired for treatment planning by converting CT number expressed in Hounsfield units (HU) of each voxel toρ_susing a Hounsfield look-up table (HLUT), also known as the CT calibration curve. HU andρ_sshare a linear relationship with electron density but differ in their additional dependence on elemental composition through different physical properties, i.e. effective atomic number and mean excitation energy, respectively. Because of that, the HLUT approach is particularly sensitive to differences in elemental composition between real human tissues and tissue surrogates as well as tissue variations within and among individual patients. The use of dual-energy CT (DECT) forρ_sprediction has been shown to be effective in reducing the uncertainty inρ_sestimation compared to SECT. The acquisition of CT data over different x-ray spectra yields additional information on the material elemental composition. Recently, multi-energy CT (MECT) has been explored to deduct material-specific information with higher dimensionality, which has the potential to further improve the accuracy ofρ_sestimation. Even though various DECT and MECT methods have been proposed and evaluated over the years, these approaches are still only scarcely implemented in routine clinical practice. In this topical review, we aim at accelerating this translation process by providing: (1) a comprehensive review of the existing DECT/MECT methods forρ_sestimation with their respective strengths and weaknesses; (2) a general review of uncertainties associated with DECT/MECT methods; (3) a general review of different aspects related to clinical implementation of DECT/MECT methods; (4) other potential advanced DECT/MECT applications beyondρ_sestimation.

Next generation high resolution perovskite direct conversion detector: Monte Carlo design optimisation and virtual clinical trial

We implement virtual clinical integration of next-generation perovskite detectors into common x-ray imaging devices. This was achieved by performing Monte Carlo (MC) optimisation of the design and benchmarking of low cost, high spatial resolution, direct conversion perovskite crystal x-ray flat panel imagers for a next generation of breast-, MV-, and kV-cone beam CT detectors. Semiconductor methylammonium lead bromide perovskite crystals energy deposition efficiencies calculated in TOPAS were benchmarked against four common detector materials for twelve detector crystal thicknesses between 40 to 15 mm and ten beam energies ranging from 20 keV to 6 MeV. Based on these simulations, Koning's dedicated breast CT, and Varian's Truebeam kV- and MV-cone beam CT systems were designated as suitable applications for perovskite detectors. System specific Fastcat hybrid MC cone beam CT image simulation was subsequently used to optimise the perovskite detector design and conduct virtual clinical trials. Device-specific optimal perovskite crystal thicknesses were calculated to be 0.30, 0.86, and 1.99 mm for Koning breast CT and Truebeam kV- and MV-cone beam CT systems, respectively. Replacing the current detectors on these machines with low cost perovskite crystal detectors could be advantageous as it would simultaneously yield 12.1%, 9.5% and 86.1% improvements in detective quantum efficiency as well as increases in contrast to noise ratio in brain, lung, and bone tissues.

New Composite Contrast Agents Based on Ln and Graphene Matrix for Multi-Energy Computed Tomography

The subject of the current research study is aimed at the development of novel types of contrast agents (CAs) for multi-energy computed tomography (CT) based on Ln-graphene composites, which include Ln (Ln = La, Nd, and Gd) nanoparticles with a size of 2-3 nm, acting as key contrasting elements, and graphene nanoflakes (GNFs) acting as the matrix. The synthesis and surface modifications of the GNFs and the properties of the new CAs are presented herein. The samples have had their characteristics determined using X-ray photoelectron spectroscopy, X-Ray diffraction, transmission electron microscopy, thermogravimetric analysis, and Raman spectroscopy. Multi-energy CT images of the La-, Nd-, and Gd-based CAs demonstrating their visualization and discriminative properties, as well as the possibility of a quantitative analysis, are presented.

A detective quantum efficiency for spectroscopic X-ray imaging detectors

PURPOSE

Spectroscopic X-ray detectors (SXDs) are under development for X-ray imaging applications. Recent efforts to extend the detective quantum efficiency (DQE) to SXDs impose a barrier to experimentation and/or do not provide a task-independent measure of detector performance. The purpose of this article is to define a task-independent DQE for SXDs that can be measured using a modest extension of established DQE-metrology methods.

METHODS

We defined a task-independent spectroscopic DQE and performed a simulation study to determine the relationship between the zero-frequency DQE and the ideal-observer signal-to-noise ratio (SNR) of low-frequency soft-tissue, bone, iodine, and gadolinium signals. In our simulations, we used calibrated models of the spatioenergetic response of cadmium telluride (CdTe) and cadmium-zinc-telluride (CdZnTe) SXDs. We also measured the zero-frequency DQE of a CdTe detector with two energy bins and of a CdZnTe detector with up to six energy bins for an RQA9 spectrum and compared with model predictions.

RESULTS

The spectroscopic DQE accounts for spectral distortions, energy-bin-dependent spatial resolution, interbin spatial noise correlations, and intrabin spatial noise correlations; it is mathematically equivalent to the squared SNR per unit fluence of the generalized least-squares estimate of the height of an X-ray impulse in a uniform noisy background. The zero-frequency DQE has a strong linear relationship with the ideal-observer SNR of low-frequency soft-tissue, bone, iodine, and gadolinium signals, and can be expressed in terms of the product of the quantum efficiency and a Swank noise factor that accounts for DQE degradation due to, for example, charge sharing (CS) and electronic noise. The spectroscopic Swank noise factor of the CdTe detector was measured to be 0.81 ± 0.04 and 0.83 ± 0.04 with and without anticoincidence logic for CS suppression, respectively. The spectroscopic Swank noise factor of the CdZnTe detector operated with four energy bins was measured to be 0.82 ± 0.02 which is within 5% of the theoretical value.

CONCLUSIONS

The spectroscopic DQE defined here is (1) task-independent, (2) can be measured using a modest extension of existing DQE-metrology methods, and (3) is predictive of the ideal-observer SNR of soft-tissue, bone, iodine, and gadolinium signals. For CT applications, the combination of CS and electronic noise in CdZnTe spectroscopic detectors will degrade the zero-frequency DQE by 10 %-20 % depending on the electronic noise level and pixel size.