Guidelines and recommendations for radiographer education from the EU-REST project

This book places an emphasis on the basic principles and recent advances in medical imaging technologies. After an introduction, the rest of the text can be categorized into three main parts. Part 1 provides a comprehensive tutorial on the fundamentals of medical imaging modalities and image processing methodologies. Part 2 highlights the cutting-edge imaging modalities for radiography and the recent developments of information technology in biomedicine. Part 3 provides some case studies and applications in radiation therapy and computer-aided diagnosis along with a perspective on future trends in medical and molecular imaging. The book is well organized and well presented. A concise summary before each chapter highlights the most important points of the text. This book can be used as a textbook for graduate courses and as an excellent reference for radiologists, neurologists, biomedical engineers, and researchers who are interested in imaging processing and modeling.

The similarities and crossovers between geophysical and medical imaging are well known and documented (see President's Pages by D. Lumley and J. Zhang in the March 2021 and June 2022 issues, respectively, for two recent examples from this journal). Each of the two industries has introduced technology of tremendous value to the other, and one can only imagine what future advancements in medical imaging may bring to exploration geophysics — and vice versa. Welcome to the wonderland of the intersection of geophysical imaging and medical imaging.

Medical imaging is becoming indispensable for patients' healthcare. Machine learning plays an essential role in the medical imaging field, including computer-aided diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotation, and image database retrieval. With advances in medical imaging, new imaging modalities and methodologies such as cone-beam/multislice CT, 3D ultrasound imaging, tomosynthesis, diffusion-weighted magnetic resonance imaging (MRI), positron-emission tomography (PET)/CT, electrical impedance tomography, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Because of large variations and complexityit is generally difficult to derive analytic solutions or simple equations to represent objects such as lesions and anatomy in medical images. Therefore, tasks in medical imaging require “learning from examples” for accurate representation of data and prior knowledge. Because of its essential needs, machine learning in medical imaging is one of the most promising, growing fields. The main aim of this special issue is to help advance the scientific research within the broad field of machine learning in medical imaging. The special issue was planned in conjunction with the International Workshop on Machine Learning in Medical Imaging (MLMI 2010) [1], which was the first workshop on this topic, held at the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010) in September, 2010, in Beijing, China. This special issue is one in a series of special issues of journals on this topic [2]; it focuses on major trends and challenges in this area, and it presents work aimed at identifying new cutting-edge techniques and their use in medical imaging. The quality level of the submissions for this special issue was very high. A total of 17 papers were submitted to this issue in response to the call for papers. Based on a rigorous review process, 10 papers (59%) were accepted for publication in the special issue. The special issue starts by a review of studies on a class of machine-learning techniques, called pixel/voxel-based machine learning, in medical imaging by K. Suzuki. A series of medical imaging applications of machine-learning techniques are presented. A large variety of applications are well represented here, including organ modeling by D. Wang et al. and X. Qiao and Y.-W. Chen, brain function estimation by V. Michel et al., image reconstruction by H. Shouno et al., lesion classification by P. Wighton et al., modality classification by X.-H. Han and Y.-W. Chen, lesion segmentation by M. Zortea et al., organ segmentation by S. Alzubi et al., and visualization of molecular signals by F. Mattoli et al. Also, the issue covers various biomedical imaging modalities, including MRI by D. Wang et al., CT by X. Qiao and Y.-W. Chen and H. Shouno et al., functional MRI by V. Michel et al., dermoscopy by P. Wighton et al. and M. Zortea et al., scintigraphy by X.-H. Han and Y.-W. Chen, ultrasound imaging by X.-H. Han and Y.-W. Chen, radiography by X.-H. Han and Y.-W. Chen, MR angiography by S. Alzubi et al., and microscopy by F. Mattoli et al. as well as a variety of organs, including the kidneys by D. Wang et al., liver by X. Qiao and Y.-W. Chen, brain by V. Michel et al. and F. Mattoli et al., chest by S. Alzubi et al., skin by P. Wighton et al. and M. Zortea et al., and heart by F. Mattoli et al. Various machine-learning techniques were developed/used to solve the respective problems, including structured dictionary learning by D. Wang et al., generalized N-dimensional principal component analysis by X. Qiao et al., multiclass sparse Bayesian regression by V. Michel et al., Bayesian hyperparameter inference by H. Shouno et al., supervised learning of probabilistic models based on maximum aposteriori estimation and conditional random fields by P. Wighton et al., joint kernel equal contribution in support vector classification by X.-H. Han and Y.-W. Chen, and iterative hybrid classification by M. Zortea et al. We are grateful to all authors for their excellent contributions to this special issue and to all reviewers for their reviews and constructive suggestions. We hope that this special issue will inspire further ideas for creative research, advance the field of machine learning in medical imaging, and facilitate the translation of the research from bench to bedside. Kenji Suzuki Pingkun Yan Fei Wang Dinggang Shen

Chapter 12 - Modeling People and Populations: Exploring Medical Visualization Through Immersive Interactive Virtual Environments

The development of PACS image databases has long been thought of as a major technological challenge, due to the amount of data to be managed. On the contrary, the authors think that despite major improvements in storage technology, new data management techniques must be proposed to make image databases medically useful in PACS environments. More precisely, image databases must contain not only images per se, but also the description of all objects used in medical imaging, in order to permit the remote processing, analysis, and interpretation of images. In several other papers, the authors explain why they adopted an object-oriented approach to model information in medical imaging. In this paper, the focus is on the inventory of objects manipulated in medical imaging from a qualitative viewpoint. For this purpose, a large number of representative imaging procedures were selected. The authors characterized how they are asked for by clinicians, realized in imaging departments, and consumed by requiring physicians and surgeons, in three French university hospitals. On the basis of this inventory, a set of image data -- i.e. of objects used in medical imaging -- was defined to show that this set must evolve with advances in medical imaging, and to point out that relational DBMS concepts cannot represent all image data.

This article outlines the radiographer workforce and staffing recommendations developed by the European Commission-funded EU-REST (European Union Radiation, Education, Staffing and Training) project. Focusing on radiographers in medical imaging, nuclear medicine, and radiotherapy, the project identified gaps in workforce planning across EU member states. Evidence-based guidelines to harmonise radiographer staffing levels and improve safety and quality in medical settings are proposed. A structured, workload-based approach for radiographer staffing using the World Health Organization’s Workload Indicators of Staffing Need (WISN) method, offering a comprehensive framework to optimise workforce planning, is outlined. The study emphasises the critical role of radiographers, who are increasingly involved in advanced and extended roles, driven by technological advancements, demographic changes, and evolving healthcare demands. However, challenges in defining workforce requirements persist due to the lack of standardised methodologies, leading to fragmented staffing guidelines. National registries to track workforce data and the recognition of emerging roles for radiographers, such as clinical research radiographers, academics, and advanced practitioners, are also recommended. By addressing these emerging needs and incorporating them into workforce planning, the study aims to enhance professional development and improve patient outcomes across EU member states. General recommendations for all professional groups include the maintenance of a central registry for each professional group and for relevant equipment by each EU Member State.Critical relevance statementThe radiographer workforce/staffing guidelines and recommendations developed by the EU-REST project propose a standardised approach to calculate radiographer staffing levels and provide recommendations regarding new and emerging professional roles and the need for centralised workforce data registries.Key PointsLimited data is available to facilitate comprehensive radiographer workforce planning across Europe.A structured, workload-based approach for radiographer staffing using the World Health Organization’s Workload Indicators of Staffing Need method is proposed.Improved data will support workforce planning and the appropriate recognition and utilisation of emerging and advanced professional roles.

The EU Member States have to implement the new Basic Safety Standards Directive (BSS) by May 2000. The Title VII of the Directive applies in particular to radon in workplaces. The Member States are required to identify workplaces which may be of concern, to set up appropriate means for monitoring radon exposures in the identified workplaces and, as necessary, to apply all or part of the system of radiological protection for practices or interventions. The BSS provisions on natural radiation are based on the ICRP 1990 recommendations. These recommendations were considered in the Finnish radiation legislation already in 1992, which resulted in establishing controls on radon in all types qf workplaces. In this paper issues are discussed on the practical implementation of the BSS concerning occupational exposures to radon basing on the Finnish experiences in monitoring radon in workplaces during the past seven years.

Advances in medical imaging and image processing are paving the way for personalised cardiac biomechanical modelling. Models provide the capacity to relate kinematics to dynamics and—through patient-specific modelling—derived material parameters to underlying cardiac muscle pathologies. However, for clinical utility to be achieved, model-based analyses mandate robust model selection and parameterisation. In this paper, we introduce a patient-specific biomechanical model for the left ventricle aiming to balance model fidelity with parameter identifiability. Using non-invasive data and common clinical surrogates, we illustrate unique identifiability of passive and active parameters over the full cardiac cycle. Identifiability and accuracy of the estimates in the presence of controlled noise are verified with a number of in silico datasets. Unique parametrisation is then obtained for three datasets acquired in vivo. The model predictions show good agreement with the data extracted from the images providing a pipeline for personalised biomechanical analysis.

Competency-based education is still at its early stages considering the lack of commonly accepted best practices to develop, implement and most importantly evaluate competency-based curriculum. This chapter proposes an evaluation framework for the competency-based curriculum as a result of authors' experience with a fully developed and implemented competency-based curriculum in a Master of Healthcare Administration program. In this chapter, authors will revisit the important concepts about competency-based education and curriculum evaluation. Authors will provide operational definitions and a framework for the evaluation of competency-based curriculum. The goal of this chapter is to provide guidance to the competency-based degree programs during the evaluation of competency-based curriculum for continuous quality improvement.

Medical imaging uses various types of electromagnetic radiation to produce images which reveal exquisite detail about the structures and functions of the human body. As medical imaging evolves, it takes on new roles beyond the detection and assessment of disease. Advances in medical imaging are stimulating medical scientists to develop new devices and techniques which characterize and treat diseases. This is often achieved by incorporating imaging in new roles for guiding, delivering, monitoring and assessing interventional procedures, frequently in real time during the procedure. This presentation will illustrate how this medical imaging “vision” is stimulating new advances in cardiology, tumour detection and biopsy, cancer treatment, and assessment and treatment of diseases of the brain.

The practice of medicine has become extremely technology dependent, and this is especially the case for radiology [1]. Advances in medical imaging and information systems technologies have transformed radiology practice through the creation of new medical imaging techniques (e.g., molecular imaging), data delivery and presentation [e.g., picture archival and communication system (PACS)], and clinical decision support (e.g., CAD). Yet, despite the long-standing history of medical imaging and information technology (IT) innovation, the past few years have been relatively barren. This recent lack of innovation has led to a trend towards commoditization, which is particularly evident in information system technologies, such as PACS and RIS, which just a decade ago, were viewed as groundbreaking and innovative technologies [2]. The recent economic decline has served as a deterrent to R & D expenditures, further perpetuating the cycle of innovation decline [3]. In order to combat this trend in declining innovation, it is necessary to understand the principles of innovation, learn from past successes and failures, and proactively apply this knowledge to ongoing trends in medical practice and economics for guiding future innovation.

This chapter discusses the importance of machine learning in the field of medical imaging for reconstructing medical images from the measured raw data. Besides discussing the use of machine learning for medical image reconstruction, a general overview is also provided on all the existing techniques of medical imaging. Mathematical models are provided in order to understand better the use of machine learning for reconstruction purposes. We discuss both unsupervised techniques like dictionary learning, auto-encoders, and supervised techniques which include learning of hyperparameters and various regularization methods used in deep learning models that replace various steps in iterative algorithms used for image reconstruction such as for image enhancement.

A huge amount of medical images is generated through advanced medical imaging in hospitals. Medical image compression plays a key role in telemedicine to have high image quality for medical images with relevant diagnostic information and with less storage requirements, so that it can be easily transmitted over a network and can be accessed within a limited time. This paper discusses about the performance analysis of different image compression for the medical images their applications in telemedicine. The performance variations of compression algorithm can be determined using objective parameters such as MSE, PSNR and SNR at different bit rates.

Objective To probe cultivation model of high-qualified personnel in medical image of comprehensive university. Methods International standard of medical education was modeled and the combination research teaching model of study, practice, exploration together with three-in-one combination was enforced. Namely, quality training of three three-in-one: ①The combination of medical science, technical and humanities unify. ② The combination of professional teaching, professional ethics and medical ethics education. ③ The combination of class teaching, independence study and lifelong education. Teaching model of three three-in-one: ① The combination of medical imaging, clinic and pathology. ② The combination of X-ray, CT and MRI. ③ The combination of radiology, ultrasound and nuclear medicine. Capability of three three-in-one: ① The combination of professional theory, film reading and practice. ② The combination of capability cultivation of film reading. comparative imaging, evidence-based imaging. ③ The combination of three basic knowledge, frontier knowledge and creative talent. Results and Conclusion By reforming of personnel training programs, a student-centered, teacher-led teaching philosophy is created, a medical imaging high-quality personnel training model is built, medical imaging professional teaching quality is improved. Key words: International standards; Medical image; Creative talent; Training model

PurposeTo spotlight the trends and hot topics looming from the highly cited papers in the subject category of Radiology, Nuclear Medicine & Medical Imaging with bibliometric analysis.Materials and methodsBased on the Essential Science Indicators, this study employed a bibliometric method to examine the highly cited papers in the subject category of Radiology, Nuclear Medicine & Medical Imaging in Web of Science (WoS) Categories, both quantitatively and qualitatively. In total, 1325 highly cited papers were retrieved and assessed spanning from the years of 2011 to 2021. In particular, the bibliometric information of the highly cited papers based on WoS database such as the main publication venues, the most productive countries, and the top cited publications was presented. An corpus was built to help identify the most frequently explored topics. VoSviewer was used to visualize the co-occurrence networks of author keywords.ResultsThe top three active journals are Neuroimage, Radiology and IEEE T Med Imaging. The United States, Germany and England have the most influential publications. The top cited publications unrelated to COVID-19 can be grouped in three categories: recommendations or guidelines, processing software, and analysis methods. The top cited publications on COVID-19 are dominantly in China. The most frequently explored topics based on the corpus and the author keywords with the great link strengths overlap to a great extent. Specifically, phrases such as magnetic resonance imaging, deep learning, prostate cancer, chest CT, computed tomography, CT images, coronavirus disease, convolutional neural network(s) are among the most frequently mentioned.ConclusionThe bibliometric analysis of the highly cited papers provided the most updated trends and hot topics which may provide insights and research directions for medical researchers and healthcare practitioners in the future.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11604-022-01268-z.