Medical Diagnostic Imaging Research Articles

Subjective assessments of cognition and affect and their relationship with objective performance: Individuals with high levels of cognitive failures or negative affect miss more rare visual targets.

The detection of particular targets is critical in applied contexts, such as identifying cancers in diagnostic medical imaging and finding weapons in airport baggage security screening. Missed targets can have dire consequences in such contexts. These contexts are also typically characterized by low prevalence or rare targets such that most searched-through images do not contain targets. A substantive body of evidence demonstrates that humans are much more likely to miss targets when they are rare. Therefore, it is critical to understand the factors that may mitigate or exacerbate this general tendency to miss rare targets. The present study considered the relative role of individual differences in cognitive failures, cognitive empathy, and negative affect (i.e., depression, anxiety, and stress) in predicting the detection of rare targets. Across two experiments, there was evidence that individuals experiencing elevated cognitive failures were more likely to miss the rare targets. In Experiment 1, negative affect was also related to performance, but it was only cognitive failures that made a unique contribution to explaining target-present accuracy when they were pitted against one another. There was no evidence that cognitive empathy was linked to performance. These findings have important theoretical implications and also highlight potential avenues for intervention to improve the detection of rare targets. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting.

Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation. However, this requires predictive models that are generalized to handle subject variability as seen clinically. Here, we highlight methods to improve test accuracy of an image classifier model for shrapnel identification using tissue phantom image sets. Using a previously developed image classifier neural network-termed ShrapML-blind test accuracy was less than 70% and was variable depending on the training/test data setup, as determined by a leave one subject out (LOSO) holdout methodology. Introduction of affine transformations for image augmentation or MixUp methodologies to generate additional training sets improved model performance and overall accuracy improved to 75%. Further improvements were made by aggregating predictions across five LOSO holdouts. This was done by bagging confidences or predictions from all LOSOs or the top-3 LOSO confidence models for each image prediction. Top-3 LOSO confidence bagging performed best, with test accuracy improved to greater than 85% accuracy for two different blind tissue phantoms. This was confirmed by gradient-weighted class activation mapping to highlight that the image classifier was tracking shrapnel in the image sets. Overall, data augmentation and ensemble prediction approaches were suitable for creating more generalized predictive models for ultrasound image analysis, a critical step for real-time diagnostic deployment.

Engineered Nonviral Protein Cages Modified for MR Imaging.

Diagnostic medical imaging utilizes magnetic resonance (MR) to provide anatomical, functional, and molecular information in a single scan. Nanoparticles are often labeled with Gd(III) complexes to amplify the MR signal of contrast agents (CAs) with large payloads and high proton relaxation efficiencies (relaxivity, r1). This study examined the MR performance of two structurally unique cages, AaLS-13 and OP, labeled with Gd(III). The cages have characteristics relevant for the development of theranostic platforms, including (i) well-defined structure, symmetry, and size; (ii) the amenability to extensive engineering; (iii) the adjustable loading of therapeutically relevant cargo molecules; (iv) high physical stability; and (v) facile manufacturing by microbial fermentation. The resulting conjugates showed significantly enhanced proton relaxivity (r1 = 11-18 mM-1 s-1 at 1.4 T) compared to the Gd(III) complex alone (r1 = 4 mM-1 s-1). Serum phantom images revealed 107% and 57% contrast enhancements for Gd(III)-labeled AaLS-13 and OP cages, respectively. Moreover, proton nuclear magnetic relaxation dispersion (1H NMRD) profiles showed maximum relaxivity values of 50 mM-1 s-1. Best-fit analyses of the 1H NMRD profiles attributed the high relaxivity of the Gd(III)-labeled cages to the slow molecular tumbling of the conjugates and restricted local motion of the conjugated Gd(III) complex.

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays.

Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients' right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model's accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.

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).

О некоторых задачах программного управления пучком траекторий. Часть I

This paper deals with problems of program control of a trajectory beam. We investigate formulations that arise when considering problems of charged particle beams control, as well as, for example, in image processing. In applied problems, it is often necessary to investigate the problem of the gravity center control of some set or changing the density distribution of particles according to some given one. The functionals proposed in the paper can be effectively used for the velocity field constructing in various image processing, in particular, medical diagnostic images. In this paper, the problem of constructing a velocity field is considered as a control and optimization problem, and, unlike the previous works of the authors, macroparameters characterizing the objects under study are used in optimization. In the article, variations of the studied functionals are obtained and the necessary optimality conditions are given.

Increased Sensitivity of Computed Tomography Scan for Neoplastic Tissues Using the Extracellular Vesicle Formulation of the Contrast Agent Iohexol.

Computed tomography (CT) is a diagnostic medical imaging modality commonly used to detect disease and injury. Contrast agents containing iodine, such as iohexol, are frequently used in CT examinations to more clearly differentiate anatomic structures and to detect and characterize abnormalities, including tumors. However, these contrast agents do not have a specific tropism for cancer cells, so the ability to detect tumors is severely limited by the degree of vascularization of the tumor itself. Identifying delivery systems allowing enrichment of contrast agents at the tumor site would increase the sensitivity of detection of tumors and metastases, potentially in organs that are normally inaccessible to contrast agents, such as the CNS. Recent work from our laboratory has identified cancer patient-derived extracellular vesicles (PDEVs) as effective delivery vehicles for targeting diagnostic drugs to patients' tumors. Based on this premise, we explored the possibility of introducing iohexol into PDEVs for targeted delivery to neoplastic tissue. Here, we provide preclinical proof-of-principle for the tumor-targeting ability of iohexol-loaded PDEVs, which resulted in an impressive accumulation of the contrast agent selectively into the neoplastic tissue, significantly improving the ability of the contrast agent to delineate tumor boundaries.

Weathering a demand shock: The impact of prior vertical scope on post‐shock firm response

AbstractResearch SummaryWe examine how and why pre‐existing vertical scope may cause differences in product market exit rates after sudden and exogenous decreases in demand. Our empirical context is the U.S. medical diagnostic imaging industry (2004–2009), wherein a major Medicare reform created a derived demand shock to equipment manufacturers. Using a difference‐in‐difference‐in‐difference design, we find integrated firms were more likely to exit than nonintegrated firms. Building on the literature conceptualizing firms' pre‐shock vertical scope as a representation of existing resources and governance choices, we explain that integrated and nonintegrated firms responded differently by leveraging their own distinctive capabilities. Our qualitative insights suggest that higher market exit of integrated firms was driven by their higher adjustment costs due to frictions across strategies for demand management versus cost reduction.Managerial SummaryThis study investigates whether having a dedicated sales force or utilizing third‐party distributors can help mitigate the adverse effect of an abrupt demand decrease on manufacturers. In the context of the US medical imaging equipment industry affected by the 2005 Deficit Reduction Act, we show that all firms implemented strategies for demand management, cost reduction, and product portfolio reconfiguration in response. Manufacturers using external distributors experienced fewer frictions across these strategies than those with an internal sales force and were less likely to exit. Thus, a firms' vertical scope can impact how they cope with environmental changes.

Highly Enriched Uranium-Free Medical Radioisotope Production Methods: An Integrative Review

The ever-growing need for radiopharmaceuticals, i.e., compounds containing pharmaceutical drugs and radioisotopes used for medical diagnostic imaging (SPECT/PET scan) and treating neoplasms, is significantly leading to an increased demand for such substances in hospitals and clinics worldwide. Currently, most large-scale productions of radioisotopes required for radiopharmaceuticals are carried out in research reactors, via the fission of highly enriched uranium. However, because large amounts of radioactive waste are produced as byproducts in this process, new greener methods are needed for radioisotope production. This work presents an integrative literature review and summarizes enriched uranium-free methods for radioisotope production, accomplished through the adoption of new reaction routes, distinct acceleration technologies, or by using other physical processes. This review considered forty-eight studies published from 2010 to 2021 on three established virtual databases. Among these selected works, a cyclotron is the most adopted HEU-free method for radioisotope production, and 44Sc, 68Ga, and 99mTc are the medical radioisotopes most often reported as produced by using the investigated HEU-free production methods.

Brain tumor segmentation using a deep Shuffled‐YOLO network

AbstractBrain tumor is an anomalous proliferation of cells in the brain that can evolve to malignant and benign tumors. Currently, segmentation of brain tumor is the most important surgical and pharmaceutical procedures. However, manually segmenting brain tumors is hard because it is hard to find erratically shaped tumors with only one modality; the MRI modalities are integrated to provide multi‐modal images with data that can be utilized to segment tumors. The recent developments in machine learning and the accessibility of medical diagnostic imaging have made it possible to tackle the challenges of segmenting brain tumors with deep neural networks. In this work, a novel Shuffled‐YOLO network has been proposed for segmenting brain tumors from multimodal MRI images. Initially, the scalable range‐based adaptive bilateral filer (SCRAB) pre‐processing technique was used to eliminate the noise artifacts from MRI while preserving the edges. In the segmentation phase, we propose a novel deep Shuffled‐YOLO architecture for segmenting the internal tumor structures that include non‐enhancing, edema, necrosis, and enhancing tumors from the multi‐modality MRI sequences. The experimental fallouts reveal that the proposed Shuffled‐YOLO network achieves a better accuracy range of 98.07% for BraTS 2020 and 97.04% for BraTS 2019 with very minimal computational complexity compared to the state‐of‐the‐art models.

Study of folate receptor-targeted macromolecular gadolinium complex tumor contrast agent

Targeted drug delivery can improve efficacy and reduce dose, so targeted drug delivery is a very ideal diagnostic imaging approach, and it is necessary to develop magnetic resonance imaging contrast agents with targeted effects. In this study, nuclear magnetic resonance contrast agent folic acid-chitosan/chitosan-diethylenetriaminepentaacetic acid-gadolinium nanoparticles (FA-CS/CS-DTPA-Gd NPs) was combined with folic acid (FA) group modified chitosan (CS) and diethylenetriaminepentaacetic acid-gadolinium complex (DTPA-Gd) modified chitosan. FA-CS/CS-DTPA-Gd NPs were characterized by fourier transform spectroscopy (FT-IR), nuclear magnetic resonance (1H NMR), dynamic light scattering (DLS) and transmission electron microscopy (TEM). The in vitro magnetic properties, cytotoxicity, and targeting to HeLa tumor cells of the nanoparticles were al so evaluated. The results showed that the prepared FA-CS/CS-DTPA-Gd NPs had uniform spheroid shape with particle size of 120.12 ± 1.29 nm; they had good magnetic properties with relaxation rate of 7.5 mM−1·s−1, which was 2.5 times that of DTPA-Gd; they had good in vitro cellular MR imaging and cell safety and could specifically target folic acid-overexpressing tumor cells. The results showed that nanoparticles could be used as targeted magnetic resonance imaging contrast agents in medical diagnostic imaging pathways.

Radiologist burnout: Trends in medical imaging utilization under the national health insurance system with the universal code bundling strategy in an academic tertiary medical centre

PurposeThe utilization of diagnostic medical imaging has been growing worldwide. However, no study has investigated the trend in image utilization and the corresponding workload of radiologists under the National Healthcare Insurance (NHI) system with a code-bundling-based reimbursement strategy. We will analyse the trend in diagnostic imaging utilization and the corresponding workload of the radiologists at a single tertiary medical centre using the NHI system. Materials and methodsThis was a retrospective study recruiting the diagnostic medical images, including X-rays, CT, and MR performed between 2005 and 2020 at a single medical centre. We investigated the change over time in image utilization and workload for interpreting the images. The two-sided Mann-Kendall test was used for the monotonic trend analysis and Sen’s slope estimate was calculated for the annual mean change with the 95% confidence interval (CI). A P value < 0.05 was considered significant. ResultsA total of 10,069,583 examinations were performed at our institute from 2005 to 2020, including 7,821,880 X-rays, 1,665,787 CT, and 581,916 MR examinations. The numbers of examinations of X-rays, CT, and MR increased with average annual changes of 13,411.3 (95% CI = 11,875.0–14,773.8), 9,496.7 (95% CI = 8,845.3–9,828.7), and 2,417.1 (95% CI = 2,209.8–2,668.9) respectively, all P < 0.001. The proportion of cases including multiple examinations increased, growing from 21.5% (6,627 in 30,878 cases) to 43.8% (39,417 in 90,032 cases) for CT and from 8.9% (1,316 in 14,791 cases) to 15.7% (6,083 in 38,865 cases) for MR. The average time spent on interpreting each diagnostic image decreased significantly from 16.0 to 2.9 sec. (P < 0.001). ConclusionImaging utilization increased significantly under the NHI system at a medical centre. The corresponding demand for image interpretation also placed a significant workload on radiologists, potentially contributing to radiologist burnout.

Checklist for Artificial Intelligence in Medical Imaging Reporting Adherence in Peer-Reviewed and Preprint Manuscripts With the Highest Altmetric Attention Scores: A Meta-Research Study.

Purpose: To establish reporting adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) in diagnostic accuracy AI studies with the highest Altmetric Attention Scores (AAS), and to compare completeness of reporting between peer-reviewed manuscripts and preprints. Methods: MEDLINE, EMBASE, arXiv, bioRxiv, and medRxiv were retrospectively searched for 100 diagnostic accuracy medical imaging AI studies in peer-reviewed journals and preprint platforms with the highest AAS since the release of CLAIM to June 24, 2021. Studies were evaluated for adherence to the 42-item CLAIM checklist with comparison between peer-reviewed manuscripts and preprints. The impact of additional factors was explored including body region, models on COVID-19 diagnosis and journal impact factor. Results: Median CLAIM adherence was 48% (20/42). The median CLAIM score of manuscripts published in peer-reviewed journals was higher than preprints, 57% (24/42) vs 40% (16/42), P < .0001. Chest radiology was the body region with the least complete reporting (P = .0352), with manuscripts on COVID-19 less complete than others (43% vs 54%, P = .0002). For studies published in peer-reviewed journals with an impact factor, the CLAIM score correlated with impact factor, rho = 0.43, P = .0040. Completeness of reporting based on CLAIM score had a positive correlation with a study's AAS, rho = 0.68, P < .0001. Conclusions: Overall reporting adherence to CLAIM is low in imaging diagnostic accuracy AI studies with the highest AAS, with preprints reporting fewer study details than peer-reviewed manuscripts. Improved CLAIM adherence could promote adoption of AI into clinical practice and facilitate investigators building upon prior works.

Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers.

Background: In the biotechnology and applied microbiology sectors, artificial intelligence (AI) has been extensively used in disease diagnostics, drug research and development, functional genomics, biomarker recognition, and medical imaging diagnostics. In our study, from 2000 to 2021, science publications focusing on AI in biotechnology were reviewed, and quantitative, qualitative, and modeling analyses were performed. Methods: On 6 May 2022, the Web of Science Core Collection (WoSCC) was screened for AI applications in biotechnology and applied microbiology; 3,529 studies were identified between 2000 and 2022, and analyzed. The following information was collected: publication, country or region, references, knowledgebase, institution, keywords, journal name, and research hotspots, and examined using VOSviewer and CiteSpace V bibliometric platforms. Results: We showed that 128 countries published articles related to AI in biotechnology and applied microbiology; the United States had the most publications. In addition, 584 global institutions contributed to publications, with the Chinese Academy of Science publishing the most. Reference clusters from studies were categorized into ten headings: deep learning, prediction, support vector machines (SVM), object detection, feature representation, synthetic biology, amyloid, human microRNA precursors, systems biology, and single cell RNA-Sequencing. Research frontier keywords were represented by microRNA (2012–2020) and protein-protein interactions (PPIs) (2012–2020). Conclusion: We systematically, objectively, and comprehensively analyzed AI-related biotechnology and applied microbiology literature, and additionally, identified current hot spots and future trends in this area. Our review provides researchers with a comprehensive overview of the dynamic evolution of AI in biotechnology and applied microbiology and identifies future key research areas.

Convolutional Neural Networks (CNN) with Quantum-Behaved Particle Swarm Optimization (QPSO)-Based Medical Image Fusion

Medical imaging fusion is the process of combining pictures from various imaging modalities to create a single image that may be used in clinical settings. Robust methods for merging image data from several modalities are being developed in the field of multimodal medical imaging. Deep learning (DL) has been widely researched in two areas: pattern recognition and image processing. We will demonstrate a multimodal image fusion with DL implementation that considers the characteristics of medical diagnostic imaging as well as the demands of clinical practice. For the past three years, pixel-level picture fusion has been a hot topic. This paper proposes a new multimodal medical picture fusion technique for a wide range of medical diagnostic challenges. Image fusion is crucial in biomedical research and clinical diagnostics for biomedical image processing and therapy planning. The most convincing argument for fusion is obtaining a significant amount of critical information from the input photographs. We show how a well-organized multimodal medical image fusion technique can be utilized to integrate computed tomography (CT) and magnetic resonance imaging (MRI) data in this study. Using convolutional neural networks (CNNs), the quantum-behaved particle swarm optimization (QPSO) algorithm was used to create a method for integrating multimodal medical pictures. In order to improve the overall quality and efficiency of QPSO, it was chosen to add the metrics of image entropy, standard deviation, average gradient (AG), spatial frequency (SF), and visual information fidelity (VIF). In experiments, multimodal medical images are utilized to evaluate a variety of parameters, including performance and algorithm stability. When compared to the other possibilities, the recommended technique outperformed them in the evaluations. On a range of quantitative metrics, this method outperforms the alternatives.

Indocyanine green for targeted imaging of the gall bladder and fluorescence navigation

Researchers nowadays have devoted extra attention to the different biomedical applications of indocyanine green (ICG), a US Food and Drug Administration-approved fluorescent compound in the fields such as drug delivery, medical imaging and disease diagnosis. In addition, hepatic function evaluation could be conducted by using ICG before surgical procedures and angiographic assessment of blood. Therefore, ICG will be expected to be excellent imaging and targeting agent in various preclinical and clinical model systems. However, whether ICG possesses the potential for the gall bladder's intraoperative imaging guidance needs to be further explored in vivo animal experiments. Herein, near-infrared fluorophores ICG can display the specific uptake by the gall bladder cells and tissues. The dynamic process of biodistribution and the clearance of ICG in vivo in mice are clearly shown in real-time live-body imaging. Furthermore, ICG was rapidly excreted into the bile and lately biodistributed to the stomach after treatment in mice. Meanwhile, the signal-to-background ratio of the gall bladder demonstrated a tremendously higher level compared to other organs (stomach, heart, liver, lung, pancreas, spleen, intestine and duodenum). In conclusion, fluorescence navigation using ICG fluorescence imaging will provide good visualization and detection of the target lesions (gall bladder) in clinics such as diagnostic medical imaging and intraoperative navigation.

What is Missing in the Diagnosis and Treatment of Osteoporosis Including the Impact of the SARS-CoV-2 Pandemic?

Osteoporosis is a civilization disease characterized by an increased risk of bone fractures as a result of progressive bone loss. The diagnosis of osteoporosis and the assessment of fracture risk are based on a medical examination, imaging diagnostics and laboratory tests. Identifying people at risk is a priority in preventive treatment, and people with already diagnosed osteoporosis require proper treatment. Osteoporotic fractures are the main cause of disability and thus a significant deterioration in the quality of life of patients, which is why the goals of osteoporosis treatment include the prevention of low-energy fractures and the overall improvement of the quality of life. Therefore, a long-term medical care strategy should be planned according to the individual risk of fracture for each patient. This article is an analysis of selected aspects of the principles of diagnostic and therapeutic procedures in osteoporosis, which due to the SARS-CoV-2 pandemic, were partially or completely omitted, or it was not possible to implement them properly.

Advanced insulating materials contributing to “carbon neutrality”: Opportunities, issues and challenges

Advanced insulating materials contributing to “carbon neutrality”: Opportunities, issues and challenges

Evaluation of Airflow Sensitivity to the Truncation Level of a Realistic Human Airway Model in an Accurate Numerical Simulation.

Background: The truncation level of human airways is an influential factor in the analysis of respiratory flow in numerical simulations. Due to computational limitations and limited resolution of diagnostic medical imaging equipment, a truncated geometry of airways is always investigated.Objective: This study aimed to employ image-based geometries with zero generation and 5th-generation truncation levels and assess bronchial airways truncation’s effect on tracheal airflow characteristics.Material and Methods: In this numerical study, computational fluid dynamics was employed to solve the respiratory flow in a realistic human airway model using the large eddy simulation technique coupling with the wall-adapting local eddy-viscosity (WALE) sub-grid scale model. The accuracy of numerical simulations was ensured by examining the large eddy simulation index of quality and Kolmogorov’s K-5/3 law.Results: The turbulent kinetic energy along the trachea has increased abnormally in the geometry with the zero-generation truncation level, and more severe fluctuations occurred in the velocity field of this geometry, which increased the tendency of each point to rotate. Compared to the extended model, the airflow’s more chaotic behavior prevented larger-scale vortices from forming in the geometry with the zero-generation truncation level. Larger-scale vortices in the extended model caused the primary flow passing next to the vortices to accelerate more intensely, increasing the wall shear stress peaks in this geometry.Conclusion: Eliminating the bronchial airways caused changes in tracheal airflow characteristics.

A global survey of healthcare professionals undertaking MRI of patients with cochlear implants: a heterogeneity of practice and opinions.

To capture practice and opinions around the current clinical use of MRI in patients with cochlear implants (CIs), and to characterise patient progression from referral to image reporting. An online survey recruited 237 healthcare professionals between 9 December 2019 and 9 September 2020. Descriptive statistics and informal thematic analyses were conducted. Respondents estimated that approximately 75% of CI users referred for an MRI proceeded to image acquisition, of which ~70% of cases comprised image acquisition on the head and the remaining cases on another area. They estimated that the proportion of these images that were usable was 93 and 99%, respectively. Confidence in most processes was high, with at least two-thirds of respondents reporting to be very or somewhat confident in obtaining consent and acquiring images. Conversely, fewer than half the respondents had the same confidence when splinting and bandaging the implant and troubleshooting any issues arising. Patient safety was rated of paramount importance, with patient comfort a clear second and image quality third. These findings highlight the need for consistent publication of clear, succinct, and standardised operating procedures for scanning patients with CIs and the requirement for regular training of radiographic and radiological healthcare professionals to address the heterogeneity of devices available. There is a need to improve the communication to radiography and radiology personnel regarding the nature of CIs, the heterogeneity of devices in existence, and the key differences between them. CI users risk being underserved by diagnostic medical imaging.