Medical Imaging Systems Research Articles

Physiological disease pattern as assessed by PPGI in vessels with FFR and iFR discordance

Abstract Background Fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) disagree on the hemodynamic significance of a coronary lesion in≈20% of cases. It is unclear whether the physiological pattern of disease is an influencing factor for this. This study assessed whether the physiological pattern of coronary artery disease (CAD) as assessed by angiography derived pullback pressure gradient index (PPGI) influences discordance between FFR and iFR measurement. Methods and results This study is a centralized off-line analysis by an independent academic core laboratory (CORRIB Core lab) of a prospective cohort of 444 vessels with both pressure wire-instantaneous wave free ratio (PW-iFR) and pressure wire-fractional flow reserve (PW-FFR) measurements. The study population consisted of a patients presenting with chronic coronary syndrome and having at least one epicardial coronary artery lesion with a 40% to 90% diameter stenosis by visual assessment on invasive coronary angiography. Three-hundred and ninety vessels (355 patients; mean age, 68±10 years; 70% men) with combined FFR, iFR, QFR and angiography derived PPGI were included. QFR was calculated using the QAngio XA 3D 2.1 software package (Medis Medical Imaging System BV, Leiden, The Netherlands). PPGI was calculated using the QFR 2.1.38.2 Research Edition software package. Cut points for hemodynamic significance were FFR ≤0.80 and iFR ≤0.89, respectively. Vessels were classified into FFR+/iFR+ (n=103; 26.4%), FFR-/iFR+ (n=27; 6.9%), FFR+/iFR- (n=38; 9.7%), and FFR-/iFR- (n=222; 57%) groups.. Median FFR, iFR, and QFR were 0.84 (0.77-0.90), 0.92 (0.88-0.97), and 0.83 (0.73-0.90) respectively. FFR disagreed with iFR in 16.7% (65 of 390). The Median PPGI was 0.75 (0.67-0.85). Vessels with PPGI ≥0.75 was considered to have predominantly focal disease pattern and vessels with PPGI <0.75 was considered to have predominantly diffuse disease pattern. The physiological pattern of disease was classified according to the angiography derived pullback pressure gradient index (PPGI) as predominantly physiologically focal (n=209; 53.6%) or predominantly physiologically diffuse (n=181; 46.4%). The median PPGI was significantly lower in FFR-/iFR+ vessels as compared to FFR+/iFR- vessels [0.65(0.60-0.69) vs 0.82(0.75-0.85), p<0.001). The physiological pattern of disease was the influencing factor relating to FFR/iFR discordance: predominantly physiologically focal was significantly associated with FFR+/iFR- (76.3% [29 of 38]), and predominantly physiologically diffuse was significantly associated with FFR-/iFR+ (96.3% [26 of 27]; P<0.001 for pattern of disease between FFR+/iFR- and FFR-/iFR+ groups). Conclusions The physiological pattern of coronary artery disease was an important influencing factor for FFR/iFR discordance.

A Multilayer Nonlinear Permutation Framework and Its Demonstration in Lightweight Image Encryption

As information systems become more widespread, data security becomes increasingly important. While traditional encryption methods provide effective protection against unauthorized access, they often struggle with multimedia data like images and videos. This necessitates specialized image encryption approaches. With the rise of mobile and Internet of Things (IoT) devices, lightweight image encryption algorithms are crucial for resource-constrained environments. These algorithms have applications in various domains, including medical imaging and surveillance systems. However, the biggest challenge of lightweight algorithms is balancing strong security with limited hardware resources. This work introduces a novel nonlinear matrix permutation approach applicable to both confusion and diffusion phases in lightweight image encryption. The proposed method utilizes three different chaotic maps in harmony, namely a 2D Zaslavsky map, 1D Chebyshev map, and 1D logistic map, to generate number sequences for permutation and diffusion. Evaluation using various metrics confirms the method’s efficiency and its potential as a robust encryption framework. The proposed scheme was tested with 14 color images in the SIPI dataset. This approach achieves high performance by processing each image in just one iteration. The developed scheme offers a significant advantage over its alternatives, with an average NPCR of 99.6122, UACI of 33.4690, and information entropy of 7.9993 for 14 test images, with an average correlation value as low as 0.0006 and a vast key space of 2800. The evaluation results demonstrated that the proposed approach is a viable and effective alternative for lightweight image encryption.

Tracing proliferating potential of lung tumours by [11C]Nicotinamide

Many tumours rely on aerobic glycolysis, known as the Warburg effect, to satisfy energy needs and sustain rapid proliferation [1]. High demands of glucose in tumours can be measured by [18F[fluorodeoxyglucose, [18F]FDG, positron emission tomography (PET). Nicotinamide adenine dinucleotide (NAD) is a coenzyme involved in redox reactions in many metabolic pathways, including glycolysis. To maintain the high rates of glycolysis and proliferation, tumours need increased NAD+ levels and use nicotinamide for the upregulated NAD+ salvage pathway that is the main source of NAD+ in proliferative cancer cells [2]. Herein, we show an improved synthesis method of [11C]Nicotinamide, [11C]NAM, and studied the uptake of [11C]NAM and [18F]FDG to measure proliferating capacity in a lung cancer model. [Carboxyl-11C]nicotinamide was synthesized by a one-pot radiolabeling method [3] on the SYNTHRA synthesizer (Synthra GmbH, Germany) (see Figure 1A). We used a KrasG12D/+ genetically engineered mouse model of lung cancer and evaluated the uptake of [11C]nicotinamide and [18F]FDG with dual-tracer sequential imaging protocol (see Figure 1B) using a NanoScan PET/MRI scanner (Mediso Medical Imaging Systems, Hungary). We obtained [carboxyl-11C]nicotinamide in a radiochemical yield of 15 ± 5 %, volumic activity of 50 ± 10 MBq/ml, and radiochemical purity was > 99 %. We optimized the imaging protocol and found the uptake of [11C]nicotinamide co-localized with the uptake of [18F]FDG in the KrasG12D/+ model of lung cancer as shown in Figure 1C suggesting the higher NAM uptake was associated with high NAD+ production in highly proliferating tumours. We demonstrated that metabolic PET/MRI imaging with [11C]NAM can complement [18F]FDG PET and be useful for probing proliferative potential in lung cancer. This method could be translated and applied in clinical practice to enable visualization of NAD+ metabolism in lung tumours and targeted therapeutically. Please click on the 'PDF' for the full abstract!

Medical Image Segmentation : A Comparative Study and Survey

Image segmentation involves dividing an image into distinct segments. It is important to distinguish this from image enhancement, which focuses on improving the image’s visual attributes, such as brightness, contrast, and texture. In segmentation, specific parts of the image are emphasized based on the problem being addressed. Clinical imaging techniques are continuously evolving, aiming to improve the quality of services in the healthcare industry. Methods such as interpolation, image registration, compression, and diagnosis need to be updated to meet the increasing demands of the field, especially with advancements in mobile and cloud computing technologies. The integration of medical devices with wearable technologies presents a promising area for further exploration. This paper provides valuable insights into the domain of medical imaging systems and aims to outline the future potential of work in this field.

Explainable AI for Transparent MRI Segmentation: Deep Learning and Visual Attribution in Clinical Decision Support

For medical diagnosis and therapy planning, the importance of accurate MRI segmentation cannot be overemphasized. Conversely, the inscrutability of deep learning models remains obstacles to their application in therapeutic contexts. In this article, an interpretability artificial intelligence framework is introduced. It combines an MRI segmentation model based on deep learning, visual attribution algorithms and natural language explanations. EXPERIMENT The dataset is consisting of plenty of different types of brain MRI scans, and used to test the architecture. The average of Dice score of our method is 88.7% and 92.3% for segmentation of tumor and categorization of tissues, respectively. Both are pretty epic scores. The insights extracted from both the visual attributions and textual explations improve our understanding of how the model arrives at its decisions, thereby increasing the transparency and interpretability of the model. believe this approach to explainable artificial intelligence can help to close the gap between state-of-the-art performance in MRI segmentation and clinical interpretability, by increasing the transparency of complex models and facilitating their implementation into a clinical workflow. Conclusion Our approach may have implications in the transparent and reliable development of AI-based decision support systems for medical imaging

An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow.

In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy. Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity. These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViTB- 16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95. This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.

Quantitative flow ratio versus fractional flow reserve for coronary revascularisation guidance (FAVOR III Europe): a multicentre, randomised, non-inferiority trial

Fractional flow reserve (FFR) or non-hyperaemic pressure ratios are recommended to assess functional relevance of intermediate coronary stenosis. Both diagnostic methods require the placement of a pressure wire in the coronary artery during invasive coronary angiography. Quantitative flow ratio (QFR) is an angiography-based computational method for the estimation of FFR that does not require the use of pressure wires. We aimed to investigate whether a QFR-based diagnostic strategy yields a non-inferior 12-month clinical outcome compared with an FFR-based strategy. FAVOR III Europe was a multicentre, randomised, open-label, non-inferiority trial comparing a QFR-based with an FFR-based diagnostic strategy for patients with intermediate coronary stenosis. Enrolment was performed in 34 centres across 11 European countries. Patients aged 18 years or older with either chronic coronary syndrome or stabilised acute coronary syndrome, and with at least one intermediate non-culprit stenosis (40-90% diameter stenosis by visual estimate; referred to here as a study lesion), were randomly assigned (1:1) to the QFR-guided or the FFR-guided group. Randomisation was done using a concealed web-based system and was stratified by diabetes and presence of a left anterior descending coronary artery study lesion. The primary endpoint was a composite of death, myocardial infarction, and unplanned revascularisation at 12 months. The predefined non-inferiority margin was 3·4% and the primary analysis was performed in the intention-to-treat population. The trial was registered with ClinicalTrials.gov (NCT03729739) and long-term follow-up is ongoing. Between Nov 6, 2018, and July 21, 2023, 2000 patients were enrolled and randomly assigned to the QFR-guided strategy (1008 patients) or the FFR-guided strategy (992 patients). The median age was 67·3 years (IQR 59·9-74·7); 1538 (76·9%) patients were male and 462 (23·1%) were female. Median follow-up time was 365 days (IQR 365-365). At 12 months, a primary endpoint event had occurred in 67 (6·7%) patients in the QFR group, and in 41 (4·2%) patients in the FFR group (hazard ratio 1·63 [95% CI 1·11-2·41]). The event proportion difference was 2·5% (90% two-sided CI 0·9-4·2). The upper limit of the 90% CI exceeded the prespecified non-inferiority margin of 3·4%. Therefore, QFR did not meet non-inferiority to FFR. A total of 18 (1·8%) patients in each group experienced an adverse procedural event, the most frequent being procedure-related myocardial infarction, which occurred in ten (1·0%) patients in the QFR group and seven (0·7%) in the FFR group. One patient in the QFR group died in relation to the index procedure. The results of the FAVOR III Europe trial do not support the use of QFR if FFR is available to guide revascularisation decisions in patients with intermediate coronary stenosis. This finding could have implications for current clinical guidelines recommending QFR for this purpose. Medis Medical Imaging Systems and Aarhus University.

An Improved Hyper Spectral Imaging for Accurate Disease Diagnosis in Sustainable Medical Environments

Hyper spectral imaging (HSI) has emerged as a powerful technique for accurate disease diagnosis in medical environments. It provides high-resolution images with detailed spectral information, making it possible to identify subtle differences between healthy and diseased tissues. However, current HSI systems face challenges in terms of accuracy and efficiency, limiting their widespread application in sustainable medical environments. To overcome these challenges, our team has developed an improved HSI system that utilizes state-of-the-art spectral imaging technology and machine learning algorithms. This system is capable of capturing and analyzing a wider range of spectral data, enabling more precise identification of disease-specific spectral signatures. Furthermore, our system has been optimized to operate with minimal power consumption, making it environmentally friendly and suitable for sustainable medical environments. The improved HSI system has been successfully tested in clinical settings and has shown promising results in accurately diagnosing various diseases such as cancer, dermatitis, and cardiovascular conditions. Its high accuracy and fast processing time make it a valuable tool for early disease detection and treatment planning. Moreover, the ability to operate with low energy consumption makes it a sustainable solution for medical facilities in resourcelimited areas. In addition to its accuracy and efficiency, our improved HSI system is also user-friendly and can be easily integrated into existing medical imaging systems.

Improvement of X-ray response of CZT films by post-annealing

CdZnTe (CZT) epitaxial films are promising for X-ray flat panel detectors due to their ability for large area fabrication, high absorption efficiency, and excellent detective quantum efficiency (DQE). However, challenges like image lag, ghosting, and reduced DQE due to long response times and high dark currents persist. This study introduces a novel post-in-situ annealing technique to enhance CZT film sensitivity and transient response. Results show that a brief 20 s annealing treatment increased surface Zn content, raising detector resistivity from 3.5 × 109 Ω·cm to 3.3 × 1010 Ω·cm while reducing leakage current at high voltages. This treatment also significantly reduced surface defects, improving CZT film detectors’ X-ray response time. Moreover, the electrons mobility-lifetime product (μτ)e improved from 2.30 × 10−5 cm2/V to 2.17 × 10−4 cm2/V, indicating enhanced charge carrier dynamics crucial for high-speed X-ray detection. Notably, the X-ray response sensitivity achieved a commendable 126 μC G−1· cm−2 a bias of 30 V. In conclusion, in-situ annealing represents a remarkable step forward in the development of CZT technology for X-ray flat panel imaging, addressing critical issues and potentially broadening its applications in medical and industrial imaging systems.

An Audit of Carotid Dopplers in Sligo University Hospital

Abstract Background Stroke is a common and often disabling condition impacting thousands of individuals in Ireland annually 1. Timely screening for underlying etiologies is important in all patients presenting with stroke or transient ischaemic attack (TIA) to reduce recurrence risk. Carotid artery disease is frequently implicated as the cause 3. The 2023 National Clinical Guideline for Stroke for the United Kingdom and Ireland recommends carotid imaging be completed within one week of diagnosis of ischemic stroke or TIA for patients in whom potential carotid intervention would be appropriate. We aimed to assess the timing of Carotid Doppler scans in our hospital for patients presenting clinically with stroke or TIA. Methods The National Integrated Medical Imaging System (NIMIS) was used to generate a list of patients that had a Carotid Doppler scan over a six- month period (1st July 2022 to 31st December 2022). NIMIS was also used to identify the indication, setting, requesting department and timeliness of scans. Results A total of 96 Carotid & Vertebral Dopplers were performed. Majority of studies were performed on males (65%). The mean age of patients was 70 (±11.5) years. Most studies (55%) were done in an inpatient setting. Of all Carotid Dopplers performed, 94 (97%) had an appropriate indication. With regards to timeliness of carotid imaging, the median time from order to study completion varied between inpatients [2 days (IQR 1-3)] and outpatients [16 days (IQR 9-38)]. The majority (95%) of inpatient studies were completed within 7-days of order, while only 14% of outpatient studies were performed in this timeframe. Of the untimely outpatient scans done to investigate stroke or TIA, 2/33 (6%) had significant carotid stenosis. Conclusion Majority of Carotid Dopplers were done for appropriate indications. While inpatients studies were largely done in a timely manner, there is a significant delay in the completion of most outpatient studies.

Radiation Detectors and Sensors in Medical Imaging.

Medical imaging instrumentation design and construction is based on radiation sources and radiation detectors/sensors. This review focuses on the detectors and sensors of medical imaging systems. These systems are subdivided into various categories depending on their structure, the type of radiation they capture, how the radiation is measured, how the images are formed, and the medical goals they serve. Related to medical goals, detectors fall into two major areas: (i) anatomical imaging, which mainly concerns the techniques of diagnostic radiology, and (ii) functional-molecular imaging, which mainly concerns nuclear medicine. An important parameter in the evaluation of the detectors is the combination of the quality of the diagnostic result they offer and the burden of the patient with radiation dose. The latter has to be minimized; thus, the input signal (radiation photon flux) must be kept at low levels. For this reason, the detective quantum efficiency (DQE), expressing signal-to-noise ratio transfer through an imaging system, is of primary importance. In diagnostic radiology, image quality is better than in nuclear medicine; however, in most cases, the dose is higher. On the other hand, nuclear medicine focuses on the detection of functional findings and not on the accurate spatial determination of anatomical data. Detectors are integrated into projection or tomographic imaging systems and are based on the use of scintillators with optical sensors, photoconductors, or semiconductors. Analysis and modeling of such systems can be performed employing theoretical models developed in the framework of cascaded linear systems analysis (LCSA), as well as within the signal detection theory (SDT) and information theory.

Scintillation in Liquid Xenon for Gamma-Ray Medical Imaging: From Single Time-over-Threshold to Multi-Time-over-Threshold PMT Signal Measurements.

In this paper, a new light event acquisition chain in a three-gamma liquid xenon prototype for medical nuclear imaging is presented. The prototype implements the Multi-Time-Over-Threshold (MTOT) method. This method surpasses the Single-Time-Over-Threshold (STOT) by precisely determining both the number of vacuum ultraviolet (VUV) photons detected by each photomultiplier tube (PMT) and their arrival times for light signal measurement. Based on both the experimental and simulated results, the MTOT method achieved a 70% improvement in reconstructing photoelectrons (PEs) and enhanced the precision of the arrival time estimation by 20-30% compared with STOT. These results will enable an upgrade of the XEMIS2 (Xenon Medical Imaging System) camera, improving its performance as the imaged activity increases.

Generative Adversarial Networks with Radiomics Supervision for Lung Lesion Generation.

Data-driven methods for lesion generation are quickly emerging due to the need for realistic imaging targets for image quality assessment and virtual clinical trials. We proposed a generative adversarial network (GAN) architecture for conditional generation of lung lesions based on user-specified classes of lesion size and solidity. The network consists of two discriminators, one for volumetric lesion data, and one for radiomics features derived from the lesion volume. A Wasserstein loss with gradient penalty was adopted for each discriminator. Training data were drawn from contoured and annotated lesions from a public lung CT database. Four quantitative evaluation methods were devised to assess the network performance: 1) overfitting (similarity between generated and real lesions), 2) diversity (similarity among generated lesions), 3) conditional consistency (capability of generating lesions according to user-specified classes), and 4) similarity in distributions of various lesion properties between the generated and real lesions. Ablation studies were also performed to investigate the importance of individual network component. The proposed network was found to generate lesions that resemble real lesions by visual inspection. Solid lesions are distinct from non-solid ones, and lesion sizes largely correspond to their specified classes. With a classifier trained on real lesions, the classification accuracies of generated and real lesions in both solid and non-solid classes are similar. Radiomics features of generated and real lesions were found to have similar distributions, indicated by the relatively low Kullback-Leibler (KL) divergence values. Furthermore, the correlations between pairwise radiomics features in generated lesions were comparable to those of real lesions. The proposed network presents a promising approach for generating realistic lesions with clinically relevant features crucial for the comprehensive assessment of medical imaging systems.

Synthesis of oleic acid – coated zinc – doped iron boride nanoparticles for biomedical applications

Although various iron-based magnetic materials have been extensively studied in biomedical field for many years, iron boride compounds with interesting chemical and magnetic properties are relatively less explored, and their potential applications are not as widely known. In this study, the synthesis, coating, surface modification, and cytotoxicity tests of the Fe–Zn–B system were presented. Iron boride-based nanoparticles (NPs) containing elemental zinc (Zn) were developed by using a direct chemical synthesis of FeCl3, ZnCl2 and NaBH4, and investigated for potential use in biomedical applications. Powders having the phases of pure FeB with small amount of elemental Zn were obtained with a uniform morphology and an average particle size of 68 nm. The NPs were then coated with oleic acid (OA) and surface modified with sodium tricitrate, to increase their stability and biocompatibility, and well-dispersed NPs were obtained with sizes below 30 nm. TEM investigations revealed the presence of hybrid clusters with nanoparticle – OA structures, indicating that FeB nanoparticles were stabilized by being embedded in OA clusters, forming both agglomerated sub-micron and free nano-sized structures. Obtained NPs showed ferromagnetic property, with a saturation magnetization of 25.9 emu/g and a low coercivity of 90 Oe. As a result of testing different types of healthy and cancer cell lines with NPs, Zn-doped-FeB@OA NPs exhibited a high biocompatibility. Results suggested that highly biocompatible and magnetic OA-coated Zn-doped FeB particles can be potential candidates for biomedical applications such as medical imaging or drug delivery systems.

A new adoption model for quality of experience assessed by radiologists using AI medical imaging technology

This study introduces a new adoption model for assessing the quality of experience (QoE) of radiologists using AI-based medical imaging technology. While AI has increasingly been used by radiologists for screening, diagnosis, and classification of medical images, previous investigations have primarily focused on metrics such as effectiveness, efficiency, and satisfaction. This research expands the evaluation criteria to include user experience and user interface (UX/UI) factors, integrating them within the broader QoE. QoE is conceptualized as a multifaceted construct influenced by both human and system factors, which affect cognitive perception, including hedonic and pragmatic aspects. Data were collected from 159 hospital radiologists with prior experience in using AI technology in medical imaging systems through a structured questionnaire. The data were then analyzed using structural equation modeling principles. The findings suggest that contextual content, human factors, and system characteristics significantly influence cognitive perception, which in turn affects the adoption and utilization of AI in medical imaging. This adoption model captures the radiologists' integration of AI throughout various stages of radiological procedures, including scheduling, scanning, acquisition, interpretation, reporting, and communication. The study also highlights the importance of data collection, storage, and sharing practices in compliance with privacy policies.

Improved air kerma determination in the radiation field of the X-ray tube used in medical imaging systems, considering the type and thickness of the filter

In diagnostic radiology, the air kerma is an essential parameter. Radiologists consider the air kerma, when calculating organ doses and dangers to patients. The intensity of the radiation beam is represented by the air kerma, which is the value of energy wasted by a photon as it travels through air. Because of the heel effect in X-ray sources, air kerma varies throughout the field of medical imaging systems. One possible contributor to this discrepancy is the X-ray tube's voltage. In this study, an approach has been proposed for predicting the air kerma anywhere inside the field of X-ray beams utilized in medical diagnostic imaging systems. As a first step, a diagnostic imaging system was modelled using the Monte Carlo N-Particle platform. We used a tungsten target and aluminum and beryllium filters of varying thicknesses to recreate the X-ray tube. The air kerma has been measured in different parts of the conical X-ray beam that is working at 30, 50, 70, 90, 110, 130, and 150 kV. This gives enough data for training neural networks. The voltage of the X-ray tube, filter type, filter thickness, and the coordinates of each point used to calculate the air kerma were all inputs to the MLP neural network. The MLP architecture, known for its significant advancements in research and expanding applications, was trained to predict the quantity of air kerma as its output. Specifically, by considering X-ray tube filters of varying thicknesses, the trained MLP model demonstrated its capability to accurately predict the air kerma at every point within the X-ray field for a range of X-ray tube voltages typically used in medical diagnostic radiography (30–150 kV).

An Efficient Image Encryption Scheme for Medical Image Security

In the contemporary landscape of digital healthcare, the confidentiality and integrity of medical images have become paramount concerns, necessitating the development of robust security measures. This research endeavors to address these concerns by proposing an innovative image encryption scheme tailored specifically for enhancing medical image security. The proposed scheme integrates a sophisticated blend of symmetric and asymmetric encryption techniques, complemented by a novel key management system, to fortify the protection of medical image data against unauthorized access and malicious tampering. The proposed DNA-based encryption algorithm leverages the unique properties of DNA encoding to securely scramble image data, providing an added layer of protection. By utilizing DNA sequences in the encryption and decryption processes, the scheme achieves a high level of data confusion and diffusion, significantly enhancing security. The efficacy of the proposed encryption scheme is validated through comprehensive experimental evaluations, which demonstrate its proficiency in ensuring data security while maintaining computational efficiency. The scheme's compatibility with existing medical imaging systems is also examined, affirming its seamless integration into contemporary healthcare infrastructures. This research contributes to the advancement of medical image security by proposing an efficient encryption scheme that strikes a balance between stringent security requirements and practical implementation considerations. The primary contributions include the development of a DNA-based encryption algorithm and a novel key management system, both of which significantly enhance the security of medical images. This research contributes to the advancement of medical image security by proposing an efficient encryption scheme that strikes a balance between stringent security requirements and practical implementation considerations. By safeguarding the confidentiality and integrity of medical images, the proposed scheme empowers healthcare providers to uphold patient privacy and trust in the digital age. Experimental results show that this approach ensures robust encryption without compromising image quality, making it suitable for sensitive medical imaging applications.

A secure and privacy-preserving technique based on coupled chaotic system and plaintext encryption for multimodal medical images

A secure and privacy-preserving technique based on coupled chaotic system and plaintext encryption for multimodal medical images

Edge reconstruction and feature enhancement‐driven architecture for blind super‐resolution in medical imaging systems

Edge reconstruction and feature enhancement‐driven architecture for blind super‐resolution in medical imaging systems

Cable-Driven Light-Weighting and Portable System for Robotic Medical Ultrasound Imaging

Cable-Driven Light-Weighting and Portable System for Robotic Medical Ultrasound Imaging