Review is provided of a number of low-dose, low dose rate situations that in study require advances in the development of dosimetric facilities. Using a clinical linac set up to provide doses down to the few mGy level, the performance of a real-time radioluminescence system has then been illustrated, accommodating pulsed as well as continuous dose delivery. The system gate times provide for tracking of the pattern of dose delivery, allowing detailed account of dose and dose-rate variations. The system has been tested in both x-ray and electron mode dose delivery.
Ferroelectric poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) copolymer 70/30 thin films are prepared by spin coating. The crystalline structure of these films is investigated by varying the annealing temperature from the ferroelectric phase to the paraelectric phase. A hot plate was used to produce a direct and an efficient annealing effect on the thin film. The dielectric, ferroelectric and pyroelectric properties of the P(VDF-TrFE) thin films are measured as a function of different annealing temperatures (80 to 140 °C). It was found that an annealing temperature of 100 °C (slightly above the Curie temperature, Tc) has induced a highly crystalline β phase with a rod-like crystal structure, as examined by X-ray. Such a crystal structure yields a high remanent polarization, Pr = 94 mC/m2, and pyroelectric constant, p = 24 μC/m2K. A higher annealing temperature exhibits an elongated needle-like crystal domain, resulting in a decrease in the crystalline structure and the functional electrical properties. This study revealed that highly crystalline P(VDF-TrFE) thin films could be induced at 100 °C by annealing the thin film with a simple and cheap method.
A low-energy plasma focus device was used as an electron beam source. A technique was developed to simultaneously measure the electron beam intensity and energy. The system was operated in Argon filling at an optimum pressure of 1.7 mbar. A Faraday cup was used together with an array of filtered PIN diodes. The beam-target X-rays were registered through X-ray spectrometry. Copper and lead line radiations were registered upon usage as targets. The maximum electron beam charge and density were estimated to be 0.31 μC and 13.5 × 10(16)/m(3), respectively. The average energy of the electron beam was 500 keV. The high flux of the electron beam can be potentially applicable in material sciences.
There is a resurgence of tuberculosis globally but lesions affecting the skull are rare. Cases reported are of single, focal lesions as seen on plain x-rays. We report a 34 yearold patient with tuberculosis of the skull where multiple punched out lesions are seen, mimicking that of multiple myeloma.
Two organometallic compounds known as (E)-1-ferrocenyl-(3-fluorophenyl)prop-2-en-1-one (Fc1) and (E)-1-ferrocenyl-(3-fluoro-4-methoxyphenyl)prop-2-en-1-one (Fc2) are designed and synthesized for application in dye-sensitized solar cell (DSSC) based on a donor-π-acceptor (D-π-A) architecture. By strategically introducing a methoxy group into the acceptor side of the compound, Fc2 which has adopted a D-π-A-AD structure are compared with the basic D-π-A structure of Fc1. Both compounds were characterized by utilizing the IR, NMR and UV-Vis methods. Target compounds were further investigated by X-ray analysis and studied computationally using Density Functional Theory (DFT) and Time-Dependent DFT (TD-DFT) approaches to explore their potential performances in DSSCs. An additional methoxy group has been proven in enhancing intramolecular charge transfer (ICT) by improving the planarity of Fc2 backbone. This good electronic communication leads to higher HOMO energy level, larger dipole moment and better short-circuit current density (Jsc) values. Eventually, the presence of methoxy group in Fc2 has improved the conversion efficiency as in comparison to Fc1 under the same conditions.
For x- and gamma- irradiations delivering entrance doses from 2- up to 1000 Gy to commercial 1.0 mm thick borosilicate glass microscope slides, study has been made of their thermoluminescence yield. With an effective atomic number of 10.6 (approximating bone equivalence), photon energy dependency is apparent in the low x-ray energy range, with interplay between the photoelectric effect and attenuation. As an example, over the examined dose range, at 120 kVp the photon sensitivity has been found to be some 5× that of 60Co gamma irradiations, also with repeatability to within ~1%. The glow-curves, taking the form of a single prominent broad peak, have been deconvolved yielding at best fit a total of five peaks, the associated activation energies and frequency factors also being obtained. The results indicate borosilicate glass slides to offer promising performance as a low-cost passive radiation dosimeter, with utility for both radiotherapy and industrial applications.
In primary care, chest X-rays are commonly performed to assess patients presenting with a prolonged
cough. However, the extent to which the flms are accurately interpreted depends on the skill of the
doctors. Doctors with insufcient experience may miss an exact diagnosis when evaluating a flm,
especially in patients with nonspecifc symptoms, such as in the case discussed in this paper.
( Copied from article ).
Introduction: Various medium and high tube potentials were utilized to conduct chest x-rays. There
are advantages and disadvantages with regards to image quality and radiation dose when using
medium and high kilovoltage (kVp) technique. However, radiographers have misconstrued
understanding pertaining to the choice of tube potential as well as grid usage when performing chest radiography. Methods: The experimental study was conducted using the PBU-50 phantom by exposing it with medium kVp utilizing grid and non-grid as well as high kVp with grid. All images obtained were evaluated using the modified evaluation criteria for PA chest established by the Commission of European Communities, 1996 whilst the dose area product (DAP) was determined using the Dose Area Product (DAP) meter. The value obtained from the DAP meter was converted to entrance surface dose (ESD) usingCALDOSE_X5.0 software and mathematical formula. Results: The Wilcoxon Signed-Rank Test indicated a significant difference in ESD when using medium and high kVp; Z= -2.666, p
X-ray is produced in form of divergent beam. The beam divergence results to blurring effect that influences image diagnosis. Thus, the blurring effect assessment should be enrolled within the quality control (QC) program of an imaging unit.
The ideal imaging system that is providing a good quality image of minimal radiation dose. There are many parameters that influenced image quality and radiation dose on clinical radiography. This study has identified some of the problems whereby there are practitioners do not select the proper size of image receptor (IR) and collimation during the examination. The re-usable of the IR and imaging plate also need to be concerned whether multiple exposures may affect the image
quality or not. The purpose of this study is to investigate the effect of different exposure settings; kVp, mAs, collimation field sizes and different IR’ sizes for image quality and radiation dose. Methods: The wall-mounted x-ray machine act as a sources of radiation which exposed the acrylic cylinder that placed over the IR. The examination is repeated with different kVp, mAs, collimation field sizes and IR’s sizes. The source to image distance (SID) is fixed to 100 cm distance and put Nano dot dosimeter similar level with the top of acrylic to measure the dose. The result analysed by using software ImageJ to measure the Contrast to Noise Ratio (CNR). Results: The percentage of CNR1 and CNR2 reduced as the kVp is increased from (CNR1=77.25, CNR2=64.45), (CNR1=73.47, CNR2=61.22) and (CNR1=62.80, CNR2=57.32) for 50 kVp, 75 kVp and 100 kVp respectively and fluctuate when mAs increased. The CNR and entrance skin dose (ESD) shows higher when x-ray beam collimate according to IR’s size. Conclusions: Overall, the manipulative effect of exposure settings on image quality and ESD shows some positive results. The result also shows inconsistent readings in the CNR and ESD. The percentage of CNR decreased when kVp increases and slightly fluctuate when mAs increased. The ESD reading depicts higher when the kVp and mAs increase as well as when x-ray beam collimated according to IR’s sizes.
The Malaysian Association of Medical Physics (MAMP) was set up in the year 2000 to promote and further develop the field that was relatively new in Malaysia. The article briefly summarises key developments in medical physics since the first discovery of x-rays in 1895. The resulted rapid progress in the field was also highlighted and related to the pace of development in Malaysia. Key activities organised by MAMP were also addressed. The international practices related to the field and the profession were highlighted and compared to the current status in Malaysia. Although the field has progressed well in the country, there are several gaps identified to further improve the field and the profession in Malaysia.
COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.
Penekanan saraf lateral kutaneus femoral merupakan punca kepada penyakit meralgia parestetika. Gejala penyakit ini ialah kesakitan dan perubahan sensori pada bahagian lateral paha. Namun, gejala penyakit ini seakan menyerupai gejala penyakit lain seperti radikulopati lumbar, penyempitan ruang femoro-acetabular, bursitis trokanterik dan beberapa contoh lain. Meralgia parestetika merupakan diagnosis secara pengecualian setelah penyebab kepada kesakitan pada bahagian lateral paha tidak dapat dibuktikan melalui hasil penyiasatan yang terperinci. Pengetahuan anatomi tentang saraf yang mensarafi bahagian paha adalah amat penting untuk mengenalpasti punca kesakitan yang dialami. Kami ingin melaporkan satu kes yang melibatkan seorang pesakit lelaki berumur 46 tahun, telah didiagnos menghidap kencing manis, darah tinggi, masalah jantung yang telah datang ke Jabatan Kecemasan dan Trauma dengan aduan kesakitan akut pada bahagian lateral paha kanan. Kesakitan yang dialami digambarkan sebagai rasa seperti terbakar, dicucuk dan disertai dengan rasa kebas. Selain itu, terdapat pengurangan sensasi rasa pada bahagian paha yang sakit. Tiada aduan berkaitan sakit pada rangsangan ringan (allodinia) atau sakit yang berlebihan pada rangsangan kuat (hiperalgesia). Bacaan gula darah kapilari ialah 8.4 mmol/l dan keputusan HbA1c ialah 7%. Diagnosis meralgia parestetika telah disahkan setelah semua kemungkinan diagnosis lain tidak dapat dibuktikan melalui pemeriksaan fizikal, ujian makmal dan radiologi (x-ray, ultrasound dan MRI). Keadaan pesakit bertambah baik selepas diberikan rawatan ubat secara oral dan menjalani sesi fisioterapi.
The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.
Heel Effect is the well known phenomena in x-ray production. It contributes the effect to image
formation and as well as scattered radiation. But there is paucity in the study related to heel effect.
This study is for mapping and profiling the dose on the surface of water phantom by using mobile
C-arm unit Toshiba SXT-1000A. Based on the result the dose profile is increasing up to at least
about 57% from anode to cathode bound of the irradiated area. This result and information can be
used as a guide to manipulate this phenomenon for better image quality and radiation safety for
this specific and dedicated fluoroscopy unit.
One of the non-destructive methods used for the identification and verification of metals is by the energy-dispersive X-ray fluorescence (EDXRF) technique. EDXRF analysis provides several important advantages such as simultaneous determination of the elements present, enable to analyse a very wide concentration range, fast analysis with no tedious sample preparation. The paper shows how this technique is developed and applied in the identification and verification of different grades of stainless steels. Comparison of the results obtained from this analysis with certified reference standards show very small differences between them.
Microwave breast imaging has been reported as having the most potential to become an alternative or additional tool to the existing X-ray mammography technique for detecting breast tumors. Microwave antenna sensor performance plays a significant role in microwave imaging system applications because the image quality is mostly affected by the microwave antenna sensor array properties like the number of antenna sensors in the array and the size of the antenna sensors. In this paper, a new system for successful early detection of a breast tumor using a balanced slotted antipodal Vivaldi Antenna (BSAVA) sensor is presented. The designed antenna sensor has an overall dimension of 0.401λ × 0.401λ × 0.016λ at the first resonant frequency and operates between 3.01 to 11 GHz under 10 dB. The radiating fins are modified by etching three slots on both fins which increases the operating bandwidth, directionality of radiation pattern, gain and efficiency. The antenna sensor performance of both the frequency domain and time domain scenarios and high-fidelity factor with NFD is also investigated. The antenna sensor can send and receive short electromagnetic pulses in the near field with low loss, little distortion and highly directionality. A realistic homogenous breast phantom is fabricated, and a breast phantom measurement system is developed where a two antennas sensor is placed on the breast model rotated by a mechanical scanner. The tumor response was investigated by analyzing the backscattering signals and successful image construction proves that the proposed microwave antenna sensor can be a suitable candidate for a high-resolution microwave breast imaging system.
The values of beam quality correction factor kQ that were experimentally determined from year 2002 to 2008 were analysed. As kQ is the function of ionization chamber and beam quality, the analysis were based on three cases namely (a) kQ(NE2571, 6 MV x-rays) that were determined from 17 measurements in the duration of 69 months at 6 radiotherapy centres, (b) kQ(NE2571, 10 MV x-rays) from 7 measurements in the duration of 12 months at one radiotherapy centre, and (c) kQ(NE2581, 6 MV x-rays) from 5 measurements in the duration of 5 months also at one radiotherapy centre. The purpose is to examine, in each case, the variation kQ for all the measurements. In other words, to see variation kQ with time. Results obtained are 0.993(NE2571, 6 MV), 0.986(NE2571, 10 MV) and 0.986(NE2581, 6 MV). This shows that in each case, despite the difference in the experimental data in getting kQ for all measurement, kQ remains constant with time. Reasons for this are explained.
Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the "black-box" nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.
According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical facilities are under pressure due to case numbers. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep-representations over a Gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. The work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. The approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifiers which take the latent vector as input. We experimented with publicly available datasets having three classes: COVID-19, normal and pneumonia yielding an overall accuracy and AUC of 0.91 and 0.97, respectively. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset to classify among Atelectasis, Effusion, Infiltration, Nodule, and Pneumonia classes. The results demonstrate that the proposed model has better understanding of the X-ray images which make the network more generic to be later used with other domains of medical image analysis.