Medical X-ray Research Articles

Stable perovskite single-crystal X-ray imaging detectors with single-photon sensitivity

A major thrust of medical X-ray imaging is to minimize the X-ray dose acquired by the patient, down to single-photon sensitivity. Such characteristics have been demonstrated with only a few direct-detection semiconductor materials such as CdTe and Si; nonetheless, their industrial deployment in medical diagnostics is still impeded by elaborate and costly fabrication processes. Hybrid lead halide perovskites can be a viable alternative owing to their facile solution growth. However, hybrid perovskites are unstable under high-field biasing in X-ray detectors, owing to structural lability and mixed electronic–ionic conductivity. Here we show that both single-photon-counting and long-term stable performance of perovskite X-ray detectors are attained in the photovoltaic mode of operation at zero-voltage bias, employing thick and uniform methylammonium lead iodide single-crystal films (up to 300 µm) and solution directly grown on hole-transporting electrodes. The operational device stability exceeded one year. Detection efficiency of 88% and noise-equivalent dose of 90 pGyair are obtained with 18 keV X-rays, allowing single-photon-sensitive, low-dose and energy-resolved X-ray imaging. Array detectors demonstrate high spatial resolution up to 11 lp mm−1. These findings pave the path for the implementation of hybrid perovskites in low-cost, low-dose commercial detector arrays for X-ray imaging.

Air Kerma Calculation in Diagnostic Medical Imaging Devices Using Group Method of Data Handling Network

The air kerma, which is the amount of energy given off by a radioactive substance, is essential for medical specialists who use radiation to diagnose cancer problems. The amount of energy that a photon has when it hits something can be described as the air kerma (the amount of energy that was deposited in the air when the photon passed through it). Radiation beam intensity is represented by this value. Hospital X-ray equipment has to account for the heel effect, which means that the borders of the picture obtain a lesser radiation dosage than the center, and that air kerma is not symmetrical. The voltage of the X-ray machine can also affect the uniformity of the radiation. This work presents a model-based approach to predict air kerma at various locations inside the radiation field of medical imaging instruments, making use of just a small number of measurements. Group Method of Data Handling (GMDH) neural networks are suggested for this purpose. Firstly, a medical X-ray tube was modeled using Monte Carlo N Particle (MCNP) code simulation algorithm. X-ray tubes and detectors make up medical X-ray CT imaging systems. An X-ray tube's electron filament, thin wire, and metal target produce a picture of the electrons' target. A small rectangular electron source modeled electron filaments. An electron source target was a thin, 19,290 kg/m3 tungsten cube in a tubular hoover chamber. The electron source-object axis of the simulation object is 20° from the vertical. For most medical X-ray imaging applications, the kerma of the air was calculated at a variety of discrete locations within the conical X-ray beam, providing an accurate data set for network training. Various locations were taken into account in the aforementioned voltages inside the radiation field as the input of the GMDH network. For diagnostic radiology applications, the trained GMDH model could determine the air kerma at any location in the X-ray field of view and for a wide range of X-ray tube voltages with a Mean Relative Error (MRE) of less than 0.25%. This study yielded the following results: (1) The heel effect is included when calculating air kerma. (2) Computing the air kerma using an artificial neural network trained with minimal data. (3) An artificial neural network quickly and reliably calculated air kerma. (4) Figuring out the air kerma for the operating voltage of medical tubes. The high accuracy of the trained neural network in determining air kerma guarantees the usability of the presented method in operational conditions.

Unconventional High-Value Utilization of Metallurgical Iron-Bearing Dust as Shielding Composite for Medical X-rays

Iron-bearing dust is one of the main solid wastes in the metallurgical industry, and currently, it is mainly disposed of according to accumulation, which brings great environmental risks. Therefore, this paper proposes a method of preparing X-ray shielding materials by hot pressing using iron-bearing dust as the filler and polyimide resin powder as the matrix. A CT imaging system was used to test the X-ray shielding performance of the materials. The results demonstrated that shielding material I-95 had a shielding percentage of more than 95% at a tube voltage of 55 kVp and a tube current of 2 mA, and the thickness of the half-value layer was less than 0.68 mm. The shielding percentage and mass attenuation coefficient of the composites showed an increasing trend with increased filler addition, tube voltage, and tube current intensity, while the half-value layer thickness showed the opposite trend. Furthermore, the shielding percentage of composites with different fillers was affected by the voltage and hardly affected by the current variation. The dominant part of the shielding material interaction across the tested tube voltage range was photoelectric absorption. The prepared composite is a low-cost material and has high efficiency and is an ideal medical X-ray shielding material.

Radiation dose to staff from medical X-ray scatter in the orthopaedic theatre

PurposeGiven the growing demand for intraoperative imaging, there is increased concern for radiation dose for orthopaedic surgical staff. This study sought to determine the distribution of scatter radiation from fluoroscopic imaging in the orthopaedic surgical environment, with particular emphasis on the positions of personnel and the type of orthopaedic surgery performed.MethodsA radiation survey detector was deployed at various angles and distances around an anthropomorphic phantom. The scatter dose rate in microsieverts per hour (µSv/h) was recorded using consistent exposure parameters for five common surgical procedures. A C-arm unit produced radiation for the hip arthroscopy, hip replacement and knee simulations, whilst a mini C-arm unit produced fluoroscopy for the foot and hand simulations.ResultsReadings were tabulated, and coloured heatmaps were generated from scatter measurements for each of the five procedures. Positions corresponding to the typical location of the surgical staff (surgeon, surgical assistant, anaesthetist, instrument (scrub) nurse, circulation (scout) nurse and anaesthetic nurse) were superimposed on heatmaps. The surgeon’s proximity to the radiation source meant this position experienced the greatest amount of radiation in all five surgical procedures. Mini C-arm doses were considered low in all procedures for positions, with and without lead protection.ConclusionThis investigation demonstrated the distribution of scattered radiation dose experienced at different positions within the orthopaedic surgical theatre. It reinforces the importance of staff increasing their distance from the primary beam where possible, reducing exposure time and increasing shielding with lead protection.

Effective doses and risks from medical diagnostic x-ray examinations for male and female patients from childhood to old age

The consideration of risks from medical diagnostic x-ray examinations and their justification commonly relies on estimates of effective dose, although the quantity is actually a health-detriment-weighted summation of organ/tissue-absorbed doses rather than a measure of risk. In its 2007 Recommendations, the International Commission on Radiological Protection (ICRP) defines effective dose in relation to a nominal value of stochastic detriment following low-level exposure of 5.7 × 10−2 Sv−1, as an average over both sexes, all ages, and two fixed composite populations (Asian and Euro-American). Effective dose represents the overall (whole-body) dose received by a person from a particular exposure, which can be used for the purposes of radiological protection as set out by ICRP, but it does not provide a measure that is specific to the characteristics of the exposed individual. However, the cancer incidence risk models used by ICRP can be used to provide estimates of risk separately for males and females, as a function of age-at-exposure, and for the two composite populations. Here, these organ/tissue-specific risk models are applied to estimates of organ/tissue-specific absorbed doses from a range of diagnostic procedures to derive lifetime excess cancer incidence risk estimates; the degree of heterogeneity in the distribution of absorbed doses between organs/tissues will depend on the procedure. Depending on the organs/tissues exposed, risks are generally higher in females and notably higher for younger ages-at-exposure. Comparing lifetime cancer incidence risks per Sv effective dose from the different procedures shows that overall risks are higher by about a factor of two to three for the youngest age-at-exposure group, 0–9 yr, than for 30–39 yr adults, and lower by a similar factor for an age-at-exposure of 60–69 yr. Taking into account these differences in risk per Sv, and noting the substantial uncertainties associated with risk estimates, effective dose as currently formulated provides a reasonable basis for assessing the potential risks from medical diagnostic examinations.

A study of off-focal radiation in transmission geometry x-ray sources

Off-focal x rays are an undesirable feature in industrial and medical x-ray computed-tomography scanners that contributes the effects of a large defocused source. They lead to artefacts in tomographic volumes that degrade contrast and resolution. A simple model of an x-ray tube with a transmission target has been developed in Topas, a Monte Carlo (MC) simulation extension to Geant4, to improve the understanding of the origin of off-focal x rays. From the analysis of the MC simulations we are able to identify the origin of various types of off-focal x rays and characterise their spatial distribution and energy spectra. We can then propose and test different mitigation methods, based both on hardware additions as well as improvements in the image reconstruction or data postprocessing.

On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories

The identification and characterization of lung diseases is one of the most interesting research topics in recent years. They require accurate and rapid diagnosis. Although lung imaging techniques have many advantages for disease diagnosis, the interpretation of medial lung images has always been a major problem for physicians and radiologists due to diagnostic errors. This has encouraged the use of modern artificial intelligence techniques such as deep learning. In this paper, a deep learning architecture based on EfficientNetB7, known as the most advanced architecture among convolutional networks, has been constructed for classification of medical X-ray and CT images of lungs into three classes namely: common pneumonia, coronavirus pneumonia and normal cases. In terms of accuracy, the proposed model is compared with recent pneumonia detection techniques. The results provided robust and consistent features to this system for pneumonia detection with predictive accuracy according to the three classes mentioned above for both imaging modalities: radiography at 99.81% and CT at 99.88%. This work implements an accurate computer-aided system for the analysis of radiographic and CT medical images. The results of the classification are promising and will certainly improve the diagnosis and decision making of lung diseases that keep appearing over time.

Impact of the implementation of the new radiation quantities recommended by ICRU/ICRP for practical use in interventional radiology: a Monte Carlo study

The International Commission on Radiation Units and Measurements (ICRU) proposed a new set of operational quantities for radiation protection for external radiation in its Report Committee 26 (ICRU 95). The new proposal aims to improve the coherence between the operational quantities and the definitions of the protection quantities in the recommendations of the International Commission on Radiological Protection set out in 2007 (Ann. ICRP 37). It is expected that this change in operational quantities will impact current dosimeter designs. Although for many photon energies, the conversion coefficients from physical field quantities to the new operational quantities will change relatively little, for radiation fields with low energy photon components, such as medical x-ray applications, there will be a significant decrease in the values of the conversion coefficients. This means that the numerical values of the new operational quantities will be much lower for the same radiation field. These values will be closer to the effective dose, but this change can still cause confusion for medical staff. It is important to examine the effect of the new set of dose conversion coefficients on the personal dose in realistic radiation fields. We performed a study to assess the effect of changing the definition of the operational quantity, personal dose equivalent (), in realistic radiation fields in interventional radiology (IR) workplaces. The x-ray tube kilovoltage peak (kVp) in IR ranges between 60 and 120 kV. The medical staff is exposed to the scattered photons which have a wide range of energies depending on the beam configuration and the patient size. The objective of this study is to ‘quantitatively’ estimate the impact of implementing the new ICRU quantities of Report 95 in IR radiation fields using Monte Carlo simulations. Simulations of 560 different configurations in IR were performed using MCNPX to calculate fluence binned per energy and angle of incidence. and were then calculated for each configuration using dose conversion coefficients from fluence given by ICRU Reports 95 and 57, respectively. The results show that the mean of the ratio, , is 1.6 for all simulated scenarios. This reduction will correct the current overestimation of the effective dose and should result in better compliance with the dose limits in IR. However, it may also have negative consequences on the safety culture among the medical staff. Special care will be needed when interpreting these lower doses.

Characterization of two-phase flow in a faulted sandstone

Fault zones present in some underground CO2 storage formations can impact on injectivity, storage capacity, and the path taken by migrating CO2, including possible leakage to overlying formations and the escape of a plume beyond the area of a demarcated storage lease licence. A thorough understanding of the 2-phase flow in fault zones is therefore essential to de-risk any storage location where faults could be present. Many previous studies have addressed top-seal risks from fault reactivation, but the influence of intra-reservoir faults on flow within the target storage formations has received less attention. Studies of intraformational faults in sandstones have typically used plugs cored from outcrops or from subsurface boreholes, where important aspects of the geological history such as the stress conditions during faulting, and the degree of post-faulting compaction and cementation may be poorly constrained. Moreover, only intact fault rocks can be cored and re-loaded under stress with this approach, so that it is very difficult to establish the interacting effects or rock fractures and cataclastic gouge in a single experiment using borehole/outcrop samples.This study investigates newly created fault rock in sandstone that has been deformed under controlled conditions and without the effects of secondary cementation. We used a large direct shear rig to deform a large, water-saturated Bentheimer sandstone block to sufficient displacement (40mm) and under enough overburden pressure (25 MPa) to create a zone of cataclastic gouge several mm thick bounded by a damage zone of fractured rock. The sheared block was cored at an inclined angle while deep frozen to extract an oriented plug containing a complete and intact section of fault rock that could be reloaded under hydrostatic conditions in an x-ray transparent core holder. The faulted plug of Bentheimer was used for running steady-state drainage and imbibition 2-phase flow experiments. The fluid saturation and its spatial distribution in 3D inside the faulted plug were dynamically monitored during all stages of the experiment in a medical x-ray CT scanner.Analysis of the flow patterns revealed from the X-ray CT images collected over time enables us to characterize in detail, the influence of recently formed fault core and damage zones on fluid flow both during drainage as gas breaks across the fault zone, and as water re-invades during imbibition. The fault core acts as a semi-permeable membrane that is only permeable to the wetting phase (water) under pressure differentials less than the capillary entry pressure (Pce) of the membrane. During drainage stages, the high Pce of the fault core led to backfilling of the upstream end of the core and almost to compartmentalization of the sample. The fracture network that exists in the damage zone played a critical role in keeping the upstream side of the fault core connected to the downstream side. During imbibition stages, the high Pce of the fault core led to significant gas trapping at the upstream side of the fault core adjacent to the fault core-damage zone interface. While the fault core formed a barrier to flow, the fracture network can provide bypass pathways for gas flow. In this particular sample bypassing through fractures was sufficient such that for a particular value of average gas saturation the flow rate was faster in the faulted rock than in the unfaulted reservoir rock.

Digital Medical X-ray Imaging, CAD in Lung Cancer and Radiomics in Colorectal Cancer: Past, Present and Future

Computed tomography (CT) introduced medicine to digital imaging. This occurred in the early 1970s and it was the start of the digital medical imaging revolution. The resulting changes and improvements in health care associated with digital imaging have been marked, are occurring now, and are likely to continue into the future. Before CT, medical images were acquired, stored, and displayed in analog form (i.e., on film). Now essentially all medical images are acquired and stored digitally. When they are not viewed by computer, they are converted to an analog image to be seen. The application of computer algorithms and the processing of digital medical images improves the visualization of diagnostically important details and aids diagnosis by extracting significant quantitative information. Examples of this can be seen with CAD and radiomics applications in the diagnosis of lung and colorectal cancer, respectively. The objectives of this article are to point out the key aspects of the digital medical imaging revolution, to review its current status, to discuss its clinical translation in two major areas: lung and colorectal cancer, and to provide future directions and challenges of these techniques.

Research of a High Voltage Generator for Medical Diagnostic X-ray Equipment

A kind of designing scheme of high voltage generator for medical diagnostic X-ray equipment is proposed. A X-ray HV generator, which has output voltage from 60kV to 160kV and tube current from 1mA to 10mA, is developed successfully by applying the technology of LC series resonant soft-switching, high voltage doubling and rectifying, full bridge inverter and pulse width modulation. The topologic structure and simulating result are listed detailed, including high frequency inverter circuit, doubling and rectifying circuit and the controlling system. At last, the experimental data and waveform of the X-ray high voltage generator are presented, verifying the validity of the design scheme.

XMAM:X-raying models with a matrix to reveal backdoor attacks for federated learning

Federated Learning (FL), a burgeoning technology, has received increasing attention due to its privacy protection capability. However, the base algorithm FedAvg is vulnerable when it suffers from so-called backdoor attacks. Former researchers proposed several robust aggregation methods. Unfortunately, due to the hidden characteristic of backdoor attacks, many of these aggregation methods are unable to defend against backdoor attacks. What's more, the attackers recently have proposed some hiding methods that further improve backdoor attacks' stealthiness, making all the existing robust aggregation methods fail.To tackle the threat of backdoor attacks, we propose a new aggregation method, X-raying Models with A Matrix (XMAM), to reveal the malicious local model updates submitted by the backdoor attackers. Since we observe that the output of the Softmax layer exhibits distinguishable patterns between malicious and benign updates, unlike the existing aggregation algorithms, we focus on the Softmax layer's output in which the backdoor attackers are difficult to hide their malicious behavior. Specifically, like medical X-ray examinations, we investigate the collected local model updates by using a matrix as an input to get their Softmax layer's outputs. Then, we preclude updates whose outputs are abnormal by clustering. Without any training dataset in the server, the extensive evaluations show that our XMAM can effectively distinguish malicious local model updates from benign ones. For instance, when other methods fail to defend against the backdoor attacks at no more than 20% malicious clients, our method can tolerate 45% malicious clients in the black-box mode and about 30% in Projected Gradient Descent (PGD) mode. Besides, under adaptive attacks, the results demonstrate that XMAM can still complete the global model training task even when there are 40% malicious clients. Finally, we analyze our method's screening complexity and compare the real screening time with other methods. The results show that XMAM is about 10–10000 times faster than the existing methods.

Informational Needs for Dental-Oriented Electronic Health Records from Dentists' Perspectives.

Information technology is vital to support dental care services and is yet to be thoroughly investigated. This study aims to assess the dentists' needs and requirements for health records from dental care providers' perspectives. In-depth interviews were conducted with dentists during clinic practices. This qualitative research method involves exploring the information and functions dentists use to obtain information from EHR. The target population is the dental staff interacting with the patients and accessing the electronic health records in the government and private sectors. Thirty-five dentists were interviewed directly after the treatment session and asked four pre-defined questions, the dentists' needs were collected, and the met and unmet needs were presented. The interview results revealed 42 needs (15 were met and 27 were unmet), with an average of 1.17 needs per session. The information needs were categorized into foreground and background information needs and reported in nine main themes. The interviews were analyzed, and as a result, nine themes were generated: chief complaints and symptoms, medical and health history, medications, visual representations of the problem, treatment procedures, X-ray services, needs related to advanced features, needs related to insurance coverage, and finally, information needs related to the treatment environment. The required information and functions mentioned by dentists in the study emphasize the need for integrated modules for oral and medical care services. Generally, it is evident that dentists have substantial unmet needs, and the desired EHR should have functions that cover all dentists' needs. The study's findings demonstrate gaps between current and desired EHR to serve dentists' needs. Dentists need better access to patient history and medical information, progress notes, and X-rays to provide visualization tools for problems and patient charts. Moreover, essential needs were related to messaging capability, educational tools, availability of tutorial videos, and accessing external resources. Information needs were described and should be considered when designing EHR to meet all dentists' needs.

Emerging Nanoagents for Medical X-ray Imaging.

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTEmerging Nanoagents for Medical X-ray ImagingQinxia WuQinxia WuMOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350002, ChinaMore by Qinxia WuView Biography, Qianyu ZhengQianyu ZhengMOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350002, ChinaMore by Qianyu ZhengView Biography, Yu HeYu HeMOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350002, ChinaMore by Yu HeView Biography, Qiushui Chen*Qiushui ChenMOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350002, ChinaFujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350108, P. R. China*Email: [email protected]More by Qiushui ChenView Biography, and Huanghao Yang*Huanghao YangMOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350002, ChinaFujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350108, P. R. China*Email: [email protected]More by Huanghao YangView Biographyhttps://orcid.org/0000-0001-5894-0909Cite this: Anal. Chem. 2023, 95, 1, 33–48Publication Date (Web):January 10, 2023Publication History Published online10 January 2023Published inissue 10 January 2023https://doi.org/10.1021/acs.analchem.2c04602Copyright © 2023 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views231Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (4 MB) Get e-AlertsSUBJECTS:Biological imaging,Imaging agents,Luminescence,Nanoparticles,X-rays Get e-Alerts

Proposing Intelligent Approach to Predicting Air Kerma within Radiation Beams of Medical X-ray Imaging Systems.

The air kerma is a key parameter in medical diagnostic radiology. Radiologists use the air kerma parameter to evaluate organ doses and any associated patient hazards. The air kerma can be simply described as the deposited kinetic energy once a photon passes through the air, and it represents the intensity of the radiation beam. Due to the heel effect in the X-ray sources of medical imaging systems, the air kerma is not uniform within the X-ray beam's field of view. Additionally, the X-ray tube voltage can also affect this nonuniformity. In this investigation, an intelligent technique based on the radial basis function neural network (RBFNN) is presented to predict the air kerma at every point within the fields of view of the X-ray beams of medical diagnostic imaging systems based on discrete and limited measured data. First, a diagnostic imaging system was modeled with the help of the Monte Carlo N Particle X version (MCNPX) code. It should be noted that a tungsten target and beryllium window with a thickness of 1 mm (no extra filter was applied) were used for modeling the X-ray tube. Second, the air kerma was calculated at various discrete positions within the conical X-ray beam for tube voltages of 40 kV, 60 kV, 80 kV, 100 kV, 120 kV, and 140 kV (this range covers most medical X-ray imaging applications) to provide the adequate dataset for training the network. The X-ray tube voltage and location of each point at which the air kerma was calculated were used as the RBFNN inputs. The calculated air kerma was also assigned as the output. The trained RBFNN model was capable of estimating the air kerma at any random position within the X-ray beam's field of view for X-ray tube voltages within the range of medical diagnostic radiology (20-140 kV).

Radiation dosimetry with a Triple-GEM detector

We studied the properties of the gaseous detector triple-GEM (triple Gas Electron Multiplier) as a dosimeter with different ionizing radiation sources used in medical applications. The detector was calibrated in energy with an Iron-55 radioactive source. We measured doses of radiation from different radioactive sources as well as from a medical portable X-ray machine, and compared them to reference values. The detector presents a linear dependence between radiation intensity and measured dose. A calibration factor of 1.13x104 was found, independently of the radiation source. These results allow us to conclude that the triple-GEM detector has the potential to be used as a dosimeter in medical applications.

A medical waste X-ray film based triboelectric nanogenerator for self-powered devices, sensors, and smart buildings

Medical waste X-ray films are used to fabricate a triboelectric nanogenerator (TENG). The high-power density of (1.39 W m−2) TENG demonstrated its potential applications in self-powered devices, sensors, and smart buildings.

Hot-pressed CH3NH3PbI3 polycrystalline wafers for near-infrared bioimaging and medical X-ray imaging

Near-infrared bioimaging and medical X-ray imaging have been realized by hot-pressed CH3NH3PbI3 polycrystalline wafers.

The effects of medical linear accelerator X-rays on human peripheral blood lymphocytes in the presence of glucosamine.

Glucosamine is widely prescribed as a dietary supplement used to treat arthritis. In this study, the radioprotective ability of glucosamine was evaluated against radiation-induced genotoxicity and cytotoxicity in human peripheral blood lymphocytes. Blood samples were collected from five healthy male donors and were divided into four groups. Isolated lymphocytes and blood samples were treated with 10µM of glucosamine for 2h before exposure to 2Gy radiation. The radioprotective potential of glucosamine was assessed by micronucleus assay, reactive oxygen species (ROS) level analysis, and flow cytometry. Irradiation significantly increased the micronuclei frequency as compared to the control group. Contrary to that pretreatment with glucosamine before irradiation significantly reduced the frequency of micronuclei. Furthermore, pretreatment with glucosamine significantly prevented the percentage of apoptotic lymphocytes. Also, glucosamine pretreatment significantly reduced the production of ROS in irradiated lymphocytes. This study shows glucosamine to be a potent radioprotector against radiation that induces DNA damage and apoptosis in human lymphocytes. Several additional in vivo and in vitro studies are needed before glucosamine can be considered as a radioprotective candidate in patients undergoing radiation therapy.

PreRadE: Pretraining Tasks on Radiology Images and Reports Evaluation Framework

Recently, self-supervised pretraining of transformers has gained considerable attention in analyzing electronic medical records. However, systematic evaluation of different pretraining tasks in radiology applications using both images and radiology reports is still lacking. We propose PreRadE, a simple proof of concept framework that enables novel evaluation of pretraining tasks in a controlled environment. We investigated three most-commonly used pretraining tasks (MLM—Masked Language Modelling, MFR—Masked Feature Regression, and ITM—Image to Text Matching) and their combinations against downstream radiology classification on MIMIC-CXR, a medical chest X-ray imaging and radiology text report dataset. Our experiments in the multimodal setting show that (1) pretraining with MLM yields the greatest benefit to classification performance, largely due to the task-relevant information learned from the radiology reports. (2) Pretraining with only a single task can introduce variation in classification performance across different fine-tuning episodes, suggesting that composite task objectives incorporating both image and text modalities are better suited to generating reliably performant models.