Medical Radiography

- Another factor that can dictate which radioisotope to use is the chemistry of the radioisotope and how to complex it or stably bind it. Certain radionuclides can be administered close to their native form and will show uptake to the desired site naturally such as 89Sr to the bone and 131I to the thyroid. Others must be complexed using chelates or prosthetic groups and others may need to be complexed in microspheres or nanoparticles to provide high in vivo stability. Some metals have very rich chemistry such as rhenium with a large number of oxidation states that allows for a wide variety of complexes to be formed and with redox chemistry that if not controlled may be accessible to proteins in vivo and cause uptake in normal tissues. Rhenium, for instance, prefers to be in higher oxidation states and if not stably complexed can oxidize up to perrhenate which then can result in lower dose to the intended site. In certain applications such as palliation of bone pain this mechanism has been shown to lead to lower normal bone uptake and dose over time. Certain radioisotopes require very bulky groups that when conjugated to a biomolecule will dramatically change its in vivo behavior and thus are not effective. Others can be metabolized in vivo resulting in release and trapping in normal organs. Iodine for instance is often introduced by complexing it to functional groups of normal amino acids in proteins and peptides. These peptides and proteins can be degraded by enzymes to the individual amino acids, which are then released back into the body. Due to this when labeling peptides and proteins it is important to use attachment mechanisms that are residualizing or known to stay in the cell. 
- Finally, the cost and availability of the radionuclide must be considered. Many therapeutic isotopes can be produced most economically at medium and high-flux reactors (neutron flux > 4 · 1014 n/cm2 s−1) in neutron-induced nuclear reactions. A growing number of therapeutic radionuclides, charged-particle-induced nuclear reaction processes are needed, which are performed at dedicated accelerator sites.
- Prominent examples of radionuclides that transform via β− transformation are 131I, 177Lu, and 90Y. These and more are and will be discussed below in terms of nuclear transformation parameters, production pathways and other relevant aspects.

Automated Detection for Human Cancer Cell is one of the most effective applications of image processing and has obtained great attention in latest years, therefore. In this study, we propose an automated detection system for human cancer cells based on breast cancer cells. This study was conducted on a set of Fine Needle Aspiration (FNA) biopsy microscopic images that have been obtained from the

Breast cancer has emerged as a health problem is today’s world. There are many reasons for the occurrence of breast cancer. Early detection of the breast cancer is important for the proper treatment at the right time. There are many techniques to detect the breast cancer. But the best known is the mammography. Mammography is a low beam x-ray, so for the detection of the cancerous masses contrast enhancement is must in mammography. In this paper we are discussing some of the contrast enhancement techniques used in the mammograms.

- Because 99mTc IDA scintigraphy localises functioning hepatocytes and shows us where bile is being formed, it can be used to identify which parts of the liver are working normally
- Scintigraphy shows us where the bile is going to - both the normal and the abnormal pathways - so we can identify leaks, fistulae, obstructions and reflux of bile
- Both acute and chronic cholecystitis are associated with abnormal gall bladder function
- When sonography produces inconclusive or unexpected results, scintigraphy will often solve the problem
- With biliary tract disorders - as in other clinical areas - stressing the system is an effective way of identifying the cause of symptoms
- Abnormalities in the rate and direction of flow of bile are best shown scintigraphically

" X-rays are a cornerstone in diagnosing fractures and bone-related diseases. They provide clear images that help distinguish between different types of fractures, such as simple, compound, or stress fractures, allowing for accurate treatment plans. In addition to their role in detecting fractures, X-rays are essential in identifying bone diseases like osteoporosis, arthritis, and bone tumors. They also play a crucial part in assessing bone density and detecting early signs of conditions such as metabolic bone disorders. The ability of X-rays to reveal subtle abnormalities makes them an indispensable tool in modern orthopedic and medical imaging practices. "

Introduction: Osteoporosis is one of the non-communicable diseases linked to urbanisation. The foundation of osteoporotic fractures stems from childhood. Therefore, studies that promote maximising peak bone mass are strongly advocated. Studies have shown that there are differences in the incidence of osteoporotic fractures in rural and urban communities. No study has investigated urban-rural differences in BMD of Nigerian children. This study, therefore, aimed to investigate urban-rural differences in BMD of Nigerian children and the association with physical activity, demographic and anthropometric variables. Methods: In a cross-sectional, study in Enugu, Nigeria, estimated bone mineral density (eBMD) was measured at the calcaneum using the QUS densitometer, Hologic Sahara, in 457 urban (Enugu metropolis) and 559 rural (Nsukka community) children aged 6-14 years. Height, weight and physical activity were measured. Independent sample t-test was used for comparative analysis while Pearson correlation coefficients and multiple regression models were used to examine the relationship between the eBMD and the other parameters. Results: The mean (S.D) eBMD of the urban children [0.52(0.09) g/cm 2 ] were significantly higher (p < 0.05) than their age-and gender-matched counterparts in the rural areas [0.51(0.08) g/cm 2 ]. Age and weight predicted (p < 0.05) the eBMD in the urban subjects while only age was the predictor (p < 0.05) in the rural subjects. The physical activity pattern had no relationship with eBMD in both urban and rural children in Enugu, Nigeria. Conclusions: The eBMD of rural children is lower than that of their age-and gender-matched urban counterparts.

Objectives-The aim of our study was to establish a reference database of calcaneal broadband ultrasound attenuation (BUA) and the speed of sound (SOS) in a healthy Nigerian population of children aged 6 to 14 years and also to investigate the influence of sociodemographic and anthropometric variables on them. Methods-The BUA and SOS of 1016 healthy children were measured by quantitative ultrasound densitometry. Height and weight were measured with a stadiometer and weighing balance, respectively, whereas the sociodemographic parameters of the participants were collected on a semistructured questionnaire. A multiple regression model using stepwise selection and Pearson correlation coefficients were used to examine the relationship between the ultrasound parameters and sociodemographic and anthropometric parameters. Comparisons between various age groups were made by analysis of variance. Results-The mean BUA and SOS ± SD of the participants were 60.57 ± 16.21 dB/MHz and 1563.48 ± 21.74 m/s, respectively, for boys and 58.18 ± 15.16 dB/MHz and 1560.03 ± 22.32 m/s for girls. The BUA in both sexes was positively influenced by age and weight (P < .05), whereas only the father's educational status was its predictor (P < .05) in the boys. For the girls, the family size and mother's educational status were predictors of the BUA (P < .05). The SOS was marginally influenced by age and family size in the boys (P < .05), whereas the father's educational status was the predictor in the girls. Conclusions-The established BUA and SOS values in this study are baseline values, and further study, which would also involve data from a large number of Nigerian children, would be needed to support their effectiveness as reference values.

Job satisfaction is a critical factor influencing employee well-being, performance, and the overall quality of healthcare services. This study assesses the job satisfaction levels among radiographers and trainees at MMIMSR and MMDU Mullana Ambala. A total of 50 participants, including radiographers and trainees, were surveyed using a structured questionnaire. The results indicate that job satisfaction among radiographers is influenced by factors such as salary, working conditions, professional support, and career progression. Understanding the factors affecting job satisfaction in the radiography profession is essential for enhancing the quality of patient care and ensuring the well-being of healthcare workers.

MRI perfusion imaging is a non-invasive technique used to measure blood flow through tissues. By analyzing changes in MRI signal intensity over time, this method provides valuable insights into various physiological processes. This chapter explores the physics behind MRI perfusion, the different sequence types available, and its real-world applications in clinical medicine. Also explore the implementation of MRI perfusion sequences on various MRI machine brands (GE, Siemens, Philips, United Imaging). Understanding MRI perfusion is crucial for interpreting clinical studies and making informed treatment decisions.

Breast cancer is the second leading cause of cancer deaths for women. Mammography is the most effective technology presently available for breast cancer screening, despite the fact that there are still some limitations of the imaging technique, such as insufficient resolution, low local contrast and noise combined with the subtle nature of the usual radiographic findings. One of the most important radiographic findings associated to the existence of breast cancer is the clustered microcalcifications. Especially, it has been shown that some characteristics concerning the clustering parameters of microcalcifications are of great diagnostic value. However, the mentioned limitations of mammography make the detection and interpretation of microcalcifications a complicated task. The main purpose of this thesis is to develop Computer Aided Diagnosis (CAD) system in order to increase the efficiency of the mammographic screening process. The system may provide automated detection of microcal...

Medical image enhancement & classification play an important role in medical research area. To analyse the texture Computed Tomography (CT) images of lungs are taken to find the values of various parameters of texture. Mainly CT lung images are classified into normal and abnormal category. Classification of images depends on the features extracted from the images. Proposed system focusing on texture based features such as GLCM (Gray Level Cooccurrence Matrix) feature plays an important role in medical image analysis. Totally 12 different statistical features & 7 shape features will be extracted. To select the required features among them, use sequential forward selection algorithm. Afterwards prefer Bayesian classifier for the classification stage which gives perfect classification..

Osteoarthritis is a deteriorating joint disease affecting millions worldwide. Osteoarthritis is a chronic condition that develops over time due to joint wear and tears. The degeneration of joint cartilage is the underlying cause of osteoarthritis, resulting in bone-to-bone contact and contributing to stiffness, discomfort, and restricted movement. People with osteoarthritis struggle to perform simple tasks such as walking, standing, or climbing stairs. Moreover, osteoarthritis can also cause psychological distress, including depression and anxiety, due to the chronic pain and disability associated with the condition. Improving the quality of life requires the development of efficient methods for early detection. Our study aims to create a model that can effectively diagnose osteoarthritis in knee X-ray images at an early stage. The advanced deep learning-based Convolutional Neural Network (CNN) and several machine learning-based techniques are applied in comparison. A novel transfer learning-based feature engineering technique CRK (CNN Random forest K-neighbors) is proposed to detect osteoarthritis with high performance. Using a 2D-CNN, the proposed CRK smartly extracts the spatial features from the X-ray images. The spatial features are input to the random forest and k-neighbors techniques, creating a probabilistic feature set. The probabilistic feature set is utilized to build the applied machine learning-based techniques. Extensive study experiments demonstrate that the proposed model outperformed with a 99% accuracy score for predicting osteoarthritis. The performance of each applied model is validated using hyperparameter optimization and k-fold-based cross-validation. The proposed study has the potential to revolutionize the prediction of osteoarthritis from X-ray images with high-performance scores. INDEX TERMS osteoarthritis, X-ray images, knee X-ray, transfer learning, smart feature engineering, deep learning, machine learning. I. INTRODUCTION O STEOARTHRITIS is a deteriorating joint syndrome that affects the knee joint. Osteoarthritis is a form of arthritis, often called "wear and tear" arthritis [1]. Osteoarthritis of the knee occurs when the cartilage cushioning the knee joint wears away over time, resulting in bone-on-bone contact, which causes pain, swelling, stiffness, and reduced mobility. Factors that increase the risk of developing knee osteoarthritis include age, obesity, previous joint injury, and genetic factors [2]. Treatment for knee osteoarthritis may involve a combination of lifestyle modifications such as weight loss, exercise, physical therapy, and medications such as pain relievers and corticosteroid injections. Extreme situations may require surgical intervention, such as joint replacement, to alleviate discomfort and improve the range of motion. Osteoarthritis disease affects millions of people worldwide [3]. Osteoarthritis significantly impacts individuals, as it causes pain, stiffness, and decreased mobility, which can lead to disability. The cost of osteoarthritis in terms of healthcare and lost productivity is substantial and expected to rise as the population ages. Moreover, the disease can also negatively impact individuals' mental health, leading to depression and

Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. Early detection and diagnosis are crucial for successful clinical intervention and management to prevent severe complications, such as loss of mobility. In this paper, we propose an automated approach that employs the Swin Transformer to predict the severity of KOA. Our model uses publicly available radiographic datasets with Kellgren and Lawrence scores to enable early detection and severity assessment. To improve the accuracy of our model, we employ a multi-prediction head architecture that utilizes multi-layer perceptron classifiers. Additionally, we introduce a novel training approach that reduces the data drift between multiple datasets to ensure the generalization ability of the model. The results of our experiments demonstrate the effectiveness and feasibility of our approach in predicting KOA severity accurately.

In response to the launch of the HGSFHP in Nigeria, Osun State has recorded enormous achievements toward an effective and robust implementation of her ESFHP yet the health, nutritional, education, prosperity and food security of school children, their families and the community still tend to be threatened as some pupils sometimes bring lunch packs to the school, fetch water from neighbouring households, and exhibit unhealthy eating behaviours which could get them poisoned. Hence the study was undertaken to determine if these pitfalls. Three research questions, using the descriptive quantitative survey, were answered by the study. From a population of 50,204 elementary school pupils, a sample of 381 Grade IV pupils was selected using simple random sampling. A self-structured Beneficiary (Pupil) Questionnaire (BPQ) which was validated by experts with a reliability of 0.68 was used to collect data. Results revealed that pupils bringing food to the school is a challenge of the complementary nutrition activity. Moreover, pupils display healthful eating habits by washing their hands, eating utensils and eating all kinds of food served except for health reasons or personal choices but soaps, water or towels are sometimes absent for them to wash their hands before eating school food in Osun State. It was recommended that appropriate efforts should be taken to sustain the quality of food served to pupils.

Radiation Physics is making crucial contributions in many areas of the environment and industry for sustainable development. Radiation technology is being used to enhance the quality of life and to make industries cleaner and safer. Three effective methods are used for industrial operations: (i) Radiotracer and Gamma source techniques. (ii) Radiation processing/treatment technology. (iii) Industrial Gamma tomography and radiography. Industrial radiotracer and gamma source techniques are widely used to analyze industrial process systems. These techniques are critical in online monitoring, improved productivity, and process parameter optimization. New development work for preparing radiotracers for harsh industrial conditions is under progress. Multi-tracer applications for onshore and offshore oil field characterization are under development. Radiation processing applying gamma sources is a well-established technology, with about 160 gamma industrial irradiators worldwide. The main aim of this technology is the development of new materials, especially for environmental protection, healthcare, and advanced products (e.g. biotechnology, solar energy systems, etc.). The International Atomic Energy Agency's role in its development and transfer is worth appreciation. With the efforts of the IAEA, several radiation processing centers have been established in Malaysia, Egypt, Iran, Poland, Brazil, and Hungary. Gamma tomography is a complementary technology for optimizing industrial process design and operation in many industries. Digital Industrial Radiography testing has become a method of choice for online NDT inspection.

Feature learning refers to techniques that learn to transform raw data input into an effective representation for further higher-level processing in many computer vision tasks. This chapter presents the investigations and the results of feature learning using convolutional neural networks to automatically assess knee osteoarthritis (OA) severity and the associated clinical and diagnostic features of knee OA from radiographs (X-ray images). Also, this chapter demonstrates that feature learning in a supervised manner is more effective than using conventional handcrafted features for automatic detection of knee joints and fine-grained knee OA image classification. In the general machine learning approach to automatically assess knee OA severity, the first step is to localize the region of interest that is to detect and extract the knee joint regions from the radiographs, and the next step is to classify the localized knee joints based on a radiographic classification scheme such as Kellgren and Lawrence grades. First, the existing approaches for detecting (or localizing) the knee joint regions based on handcrafted features are reviewed and outlined in this chapter. Next, three new approaches are introduced: 1) to automatically detect the knee joint region using a fully convolutional network, 2) to automatically assess the radiographic knee OA using CNNs trained from scratch for classification and regression of knee joint images to predict KL grades in ordinal and continuous scales, and 3) to quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross entropy and mean-squared error using multi-objective convolutional learning. The results from these methods show progressive improvement in the overall quantification of the knee OA severity. Two public datasets: the OAI and the MOST are used to evaluate the approaches with promising results that outperform existing approaches. In summary, this work primarily contributes to the field of automated methods for localization (automatic detection) and quantification (image classification) of radiographic knee OA.

Objective: The objective of this study is to compare the perception of radiography lecturers and radiography students towards the proposed Doctor of Radiography curriculum. Methodology: This study was carried out in all universities of Nigeria offering radiography as a course of study. The study population comprised of 62 radiography lecturers and 40 radiography students. A standard, self-structured questionnaire was used in the collection of data. The questionnaire comprised three sections. Section A obtained information on the demographic data of the lecturers and the students. Section B elicited information on perceived knowledge of graduates of the D-RAD curriculum while section C captured information on perceived quality of radiographers that will be produced by the D-RAD curriculum. Results: A total of 62 lecturers comprising 51 males (82.3%) and 11 females (17.7%) and 40 students comprising 26 females (65%) and 14 females (35%) participated in the study. For the production of very knowledgeable radiographers, the lecturers and the students had means of 3.09 and 3.19 respectively. The lecturers and the students had means of 3.25 and 3.47 for the production of high quality radiographers. The means gotten were greater than the cutoff score that is 2.50 which was the mean derived from a 4-point Likert scale of Strongly Disagree-1 to Strongly Agree-4. Conclusion: The proposed D-RAD curriculum will cover more content than the basic Bachelor's curriculum and ensure for the production of high quality and knowledgeable radiographers hence it is a welcome development for radiography lecturers and students.

To compare radiography with multidetector computed tomography (MDCT) in the evaluation of graft integration following acetabular reconstruction for failed total hip arthroplasty (THA). Records of 5 men and 6 women aged 60 to 78 (mean, 71.8) years who underwent acetabular reconstruction using structural allografts for severe acetabular deficiency secondary to aseptic loosening (n=9) or septic loosening (n=2) were reviewed. The mean survival time of the THA was 136.4 (range, 12-360) months. Acetabular defects were classified as IIC (n=2), IIIA (n=3), or IIIB (n=6). Structural allografts were fixed with impaction followed by a reinforcement ring (n=10), an antiprotrusio cage (n=2), and/or an oblong cup (n=1) with gentamicineloaded cement. Cup loosening, graft integration, and graft resorption were evaluated using radiography. In addition, graft integration was evaluated using MDCT. At a mean follow-up of 4.2 (range, 2-11) years, the survival of the acetabular reconstruction was 90.9%. ...

Segmentation is the first step in osteoarthritis classification. Manual selection is time-consuming, tedious, and expensive. The system is designed to help medical doctors to determine the region of interest of visual characteristics found in knee Osteoarthritis (OA). We propose a fully automatic method without human interaction to segment Junction Space Area (JSA) for OA classification on impaired x-ray image. In this proposed system, right and left knee detection is performed using using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and template macthing. The row sum graph and moment methods are used to segment the junction space area of knee. Overall we evaluated 98 kneess of patients. Experimental results demonstrate an accuracy of the system of up to 100% for detection of both left and right knee and for junction space detection an accuracy 84.38% for the right knee and 85.42% for the left. The second experiment using gabor filter with parameter α=8, θ=0, Ψ=[0 Π/2], γ=0,8 and N=8 and row sum graph give an accuracy 92.63% for the right knee and 87.37% for the left. And the average time needs to process is 65.79 second. For obvious reasons we chose the results of the fourth to segment junction area in both right and the left knee. Keywordsknee osteoarthritis, segmentation, joint space width, CLAHE, gabor filter AbstrakSegmentasi image adalah tahap pertama dalam proses klasifikasi osteoarhtritis. Segmentasi manual memerlukan waktu yang lebih banyak serta memerlukan biaya yang lebih mahal. Sistem yang didesain ini diperuntukkan untuk membantu dokter menentukan region of interest pada penyakit osteoarthritis pada bagian lutut. Penelitian ini merekomendasikan metode untuk menentukan Junction Space Area (JSA) tanpa interaksi manusia untuk image x-ray. Kami mengusulkan penggunaan metode Contrast-limited adaptive histogram equalization (CLAHE) dan template matching untuk mensegmentasi kaki kanan dan kiri. Kombinasi metode Row sum graph, moment dan Gabor Kernel digunakan untuk mensegmentasi Junction Space Area (JSA) pada lutut. Seacara keseluruhan kami mengevaluasi 98 lutut pasien. Hasil eksperimen menunjukkan bahwa untuk mendeteksi kaki kanan dan kiri mempunyai akurasi 100%, sedangkan kemampuan untuk mensegmentasi Junction Space Area (JSA) untu kaki kanan dan kiri adalah 84,38% dan 85,42%. Untuk percobaan kedua kami menggunakan kombinasi Row sum graph, moment dan Gabor Kernel dengan nilai parameter α=8, θ=0, Ψ=[0 Π/2], γ=0,8 dan N=8 menghasilkan akurasi 92,63% untuk lutut kanan dan 87,37% untuk lutut kiri. Rata-rata waktu yang diperlukan selama proses adalah 65,79 second.

Objective: The objective of this study is to compare the perception of radiography lecturers and radiography students towards the proposed Doctor of Radiography curriculum. Methodology: This study was carried out in all universities of Nigeria offering radiography as a course of study. The study population comprised of 62 radiography lecturers and 40 radiography students. A standard, self-structured questionnaire was used in the collection of data. The questionnaire comprised three sections. Section A obtained information on the demographic data of the lecturers and the students. Section B elicited information on perceived knowledge of graduates of the D-RAD curriculum while section C captured information on perceived quality of radiographers that will be produced by the D-RAD curriculum. Results: A total of 62 lecturers comprising 51 males (82.3%) and 11 females (17.7%) and 40 students comprising 26 females (65%) and 14 females (35%) participated in the study. For the production of very knowledgeable radiographers, the lecturers and the students had means of 3.09 and 3.19 respectively. The lecturers and the students had means of 3.25 and 3.47 for the production of high quality radiographers. The means gotten were greater than the cutoff score that is 2.50 which was the mean derived from a 4-point Likert scale of Strongly Disagree-1 to Strongly Agree-4. Conclusion: The proposed D-RAD curriculum will cover more content than the basic Bachelor's curriculum and ensure for the production of high quality and knowledgeable radiographers hence it is a welcome development for radiography lecturers and students.

Breast cancer is the second leading cause of cancer deaths for women. Mammography is the most effective technology presently available for breast cancer screening, despite the fact that there are still some limitations of the imaging technique, such as insufficient resolution, low local contrast and noise combined with the subtle nature of the usual radiographic findings. One of the most important radiographic findings associated to the existence of breast cancer is the clustered microcalcifications. Especially, it has been shown that some characteristics concerning the clustering parameters of microcalcifications are of great diagnostic value. However, the mentioned limitations of mammography make the detection and interpretation of microcalcifications a complicated task. The main purpose of this thesis is to develop Computer Aided Diagnosis (CAD) system in order to increase the efficiency of the mammographic screening process. The system may provide automated detection of microcal...

Feature learning refers to techniques that learn to transform raw data input into an effective representation for further higher-level processing in many computer vision tasks. This chapter presents the investigations and the results of feature learning using convolutional neural networks to automatically assess knee osteoarthritis (OA) severity and the associated clinical and diagnostic features of knee OA from radiographs (X-ray images). Also, this chapter demonstrates that feature learning in a supervised manner is more effective than using conventional handcrafted features for automatic detection of knee joints and fine-grained knee OA image classification. In the general machine learning approach to automatically assess knee OA severity, the first step is to localize the region of interest that is to detect and extract the knee joint regions from the radiographs, and the next step is to classify the localized knee joints based on a radiographic classification scheme such as Kellgren and Lawrence grades. First, the existing approaches for detecting (or localizing) the knee joint regions based on handcrafted features are reviewed and outlined in this chapter. Next, three new approaches are introduced: 1) to automatically detect the knee joint region using a fully convolutional network, 2) to automatically assess the radiographic knee OA using CNNs trained from scratch for classification and regression of knee joint images to predict KL grades in ordinal and continuous scales, and 3) to quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross entropy and mean-squared error using multi-objective convolutional learning. The results from these methods show progressive improvement in the overall quantification of the knee OA severity. Two public datasets: the OAI and the MOST are used to evaluate the approaches with promising results that outperform existing approaches. In summary, this work primarily contributes to the field of automated methods for localization (automatic detection) and quantification (image classification) of radiographic knee OA.

Objective: The objective of this study is to compare the perception of radiography lecturers and radiography students towards the proposed Doctor of Radiography curriculum. Methodology: This study was carried out in all universities of Nigeria offering radiography as a course of study. The study population comprised of 62 radiography lecturers and 40 radiography students. A standard, self-structured questionnaire was used in the collection of data. The questionnaire comprised three sections. Section A obtained information on the demographic data of the lecturers and the students. Section B elicited information on perceived knowledge of graduates of the D-RAD curriculum while section C captured information on perceived quality of radiographers that will be produced by the D-RAD curriculum. Results: A total of 62 lecturers comprising 51 males (82.3%) and 11 females (17.7%) and 40 students comprising 26 females (65%) and 14 females (35%) participated in the study. For the production of very knowledgeable radiographers, the lecturers and the students had means of 3.09 and 3.19 respectively. The lecturers and the students had means of 3.25 and 3.47 for the production of high quality radiographers. The means gotten were greater than the cutoff score that is 2.50 which was the mean derived from a 4-point Likert scale of Strongly Disagree-1 to Strongly Agree-4. Conclusion: The proposed D-RAD curriculum will cover more content than the basic Bachelor's curriculum and ensure for the production of high quality and knowledgeable radiographers hence it is a welcome development for radiography lecturers and students.

ABSTRACT
School feeding is simply providing of food to children by using in-school meals or take-home rations in both developed and developing countries for achieving the sustainable development goal of universal primary education. The objective of the study was to assess the current practice and challenges for implementation of school feeding program at Addis Ababa public primary schools. Institutional based descriptive cross-sectional study of mixed method was implemented. The data collected from teachers, school directors, food handlers and regional coordinators with sample size of 383 by using structured and non-structured questionnaire. Descriptive statistics were used to summarize categorical and continuous variables. For qualitative content analysis was used. Based on the study community participation and involvement is not as expected, Infrastructures in the schools is not standardized and good enough. The fund is not enough and considers the current market. Decision-making process related to the program is not participatory and inclusive. Monitoring and Evaluation is not adequate and capable. Related to challenges lack of effective monitoring and evaluation system, absence of organizational structure and community involvement, legal framework and policy, absence of infrastructure and training, effective communication, manager and management team are challenges. To summarize and conclude; the practice of the school feeding program is not as a standard and expected. So concerned body should give attention and focused to standardize the program.
Key words: School feeding program, Practice, Challenge