Medical Magnetic Resonance Imaging Research Articles

Epidemiological, clinical and therapeutic characteristics of metastatic spinal cord compression in prostate cancer patients in two tertiary hospitals in Cameroon

Introductionprostate cancer represents the 3rd primary neoplasia responsible for metastatic spinal cord compression (MSCC). MSCC is an extreme oncological emergency, because it involves both functional and vital prognosis. The present study aimed to establish a pattern of MSCC in prostate cancer patients in Douala and Yaoundé general hospitals (Cameroon).Methodsthis was a descriptive and retrospective study in the Radiotherapy and Medical Oncology services at both Douala and Yaoundé General Hospitals. The explored variables were general characteristics of the study population, clinical and paraclinical features, management and outcomes. Furthermore 5-year survival was analyzed by the Kaplan-Meier method. Logistic regression by determining the odd ratios and their 95% confidence was done using “Statistical Package for Social Sciences” (SPSS 23) software. The difference was considered significant at p < 0.05.Resultsour series consisted of 151 patients out of which the mean age was 66.88 (SD: 8.71) years (95% CI: 44-88). Pain was the most common clinical symptom (53.33%; n= 80) and fracture-settlement accounted for majority (60%; n= 90.61) of the pain. Thoracic spine damage was encountered by 47.02% (n= 71). Patients received a total doses of irradiation between 20 and 30 gray (Gy). The main toxicity due to radiotherapy were asthenia (45.70%; n= 69.11). The overall survival at 5 years was 90.11%. Factors associated with fracture-settlement were smoking (aOR 10.04, 95% CI: 2.09-48.12; p = 0.004) and the localization of MSCC occurred (aOR 0.21, 95% CI: 0.05-0.77; p = 0.02).Conclusionin summary the average age for developing the condition is 66.88 years and factors associated with fracture-settlement were smoking and the localization of MSCC. Back pain was the most common clinical sign and fracture-settlement was the first type of injury on medical magnetic resonance imaging. Therefore, we recommend that emphasis should be placed on increasing awareness of the population on the importance of early diagnosis.

Dynamic boundary-insensitive loss for magnetic resonance medical image segmentation.

A deep learning method has achieved great success in MR medical image segmentation. One challenge in applying deep learning segmentation models to clinical practice is their poor generalization mainly due to limited labeled training samples, inter-site heterogeneity of different datasets, and ambiguous boundary definition, etc. The objective of this work is to develop a dynamic boundary-insensitive (DBI) loss to address this poor generalization caused by an uncertain boundary. The DBI loss is designed to assign higher penalties to misclassified voxels farther from the boundaries in each training iteration to reduce the sensitivity of the segmentation model to the uncertain boundary. The weighting factor of the DBI loss can be adjusted adaptively without any manual setting and adjustment. Extensive experiments were conducted to verify the performance of our DBI loss and its variant, DiceDBI, on four heterogeneous prostate MRI datasets for prostate zonal segmentation and whole prostate segmentation. Experimental results show that our DBI loss, when combined with Dice loss, outperforms all competing loss functions in dice similarity coefficient (DSC) and improves the segmentation performance across all datasets consistently, especially on unseen datasets and when segmenting small or narrow targets. The proposed DiceDBI loss will be valuable for enhancement of the generalization performance of the segmentation model.

Radiomics, deep learning and early diagnosis in oncology.

Medical imaging, including X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response prediction of cancer. To ease radiologists’ task and help with challenging cases, computer-aided diagnosis has been developing rapidly in the past decade, pioneered by radiomics early on, and more recently, driven by deep learning. In this mini-review, I use breast cancer as an example and review how medical imaging and its quantitative modeling, including radiomics and deep learning, have improved the early detection and treatment response prediction of breast cancer. I also outline what radiomics and deep learning share in common and how they differ in terms of modeling procedure, sample size requirement, and computational implementation. Finally, I discuss the challenges and efforts entailed to integrate deep learning models and software in clinical practice.

Unlocking the Potentials of Helium in Nigeria

With the dwindling price of crude oil globally and its negative consequences on the economy and environment, there is an urgent need to diversify in the search for alternative sources of revenue for the country particularly in the Oil and Gas sector.&nbsp; The discovery of radiogenic helium in commercial quantity serves to augment the gross domestic product of any nation due to the various roles it plays in our everyday life; from entertainment to the medical field, where it is used for cooling superconducting magnets in medical Magnetic Resonance Imaging (MRI) scanners and Nuclear Magnetic Resonance (NMR) spectrometers to industrial applications as an agent for in maintaining controlled atmosphere and in detecting leakages. It is also useful in supersonic wind tunnels because of its stability, thermal and caloric perfect nature as well as its high speed of sound and high heat capacity ratio. Just recently, it was discovered that helium is being used to create hard drives in computers. Helium has no known substitution in cryogenic uses. It is obvious that there is a great need for the indigenous sourcing of this valued resource which is non-renewable. The discovery of large quantities of helium in Tanzania has heightened the search for this noble gas in Nigeria.&nbsp; This paper is therefore aimed at reviewing the generation, migration and reservoir accumulation of the gas from both biogenic and abiogenic sources.in order to support and likewise make vital contribution towards sourcing for helium in Nigeria.

Lanthanide complexes with polyaminopolycarboxylates as prospective NMR/MRI diagnostic probes: peculiarities of molecular structure, dynamics and paramagnetic properties.

The paramagnetic lanthanide complexes with polyaminopolycarboxylate (PAPC) ligands attract considerable attention from the standpoint of potential applications thereof as relaxation agents used in medical magnetic resonance imaging (MRI) and in luminescent materials, as well as owing to promising use thereof as paramagnetic labels for studying the properties of biopolymers since they exhibit thermodynamic stability, good solubility in aqueous media and moderate toxicity. For the last decades, the NMR methods have been used to determine the physical and chemical properties of paramagnetic Ln compounds. The studies concerning paramagnetic NMR lanthanide-induced shifts (LISs) in dissolved Ln complexes, as well as the analysis of band shape as a function of temperature make it possible to obtain valuable information on the structure, intra- and intermolecular dynamics and paramagnetic properties thereof. This review is devoted solely to the following features: firstly, the processes of intramolecular dynamics of lanthanide complexes with polyamino-polycarboxylate ligands such as DOTA, EDTA and DTPA and their derivatives studied by NMR; secondly, the LISs of lanthanide complexes with EDTA, DOTA, DTPA and some of their derivatives depending on temperature and pH. Moreover, in this review, for the first time, the dependence of the activation energy of molecular dynamics in complexes with polydentate ligands on the atomic number of the lanthanide cation is analyzed and a monotonic change in energy is detected, which is due to the effect of lanthanide contraction. It should be noted that this phenomenon is quite general and may also appear in the future in many other series of lanthanide complexes with both other multidentate ligands and with bidentate and monodentate ligands. In the future, it is possible to predict the dependence of the properties of certain lanthanide complexes on the ionic radius of the lanthanide cation based on the approaches presented in the review. In this review, we have also presented the dynamic NMR as the main research method widely used to analyze the processes of molecular dynamics, and the structural studies based on the NMR relaxation spectroscopy and LIS analysis.

Preexisting brain lesions in patients with post stroke pusher behavior and their association with the recovery period: A one year retrospective cohort study in a rehabilitation setting

The presence of preexisting brain lesions due to previous stroke and cerebral small vessel disease has been reported to influence stroke related disability or rehabilitation outcomes. However, there is no data about the impact of such lesions on the recovery period after pusher behavior (PB). This retrospective cohort study aimed to determine the influence of preexisting brain lesions on PB recovery time. Nineteen patients who were suffering from PB were included in the study. The presence of preexisting brain lesions, including previous stroke, silent brain infarcts, microbleed, white matter hyperintensity, and enlarged perivascular spaces were assessed using medical history reports, radiological reports, and magnetic resonance imaging data. The lesion score, ranging from 0 to 5, was calculated based on each preexisting brain lesion. The time to recovery from PB was assessed using the Scale for Contraversive Pushing. Based on the median value of the lesion score, we divided patients into those with a lesion score < 2 and those with a lesion score ≥ 2. A Kaplan Meier survival analysis was performed between these two groups. A multivariable Cox proportional hazards analysis was also performed using the side with hemiparesis and the score of preexisting brain lesions as covariates to determine the hazard ratio. The results showed that the group with a lesion score ≥ 2 had significantly delayed recovery from PB and the hazard ratio of preexisting brain lesions score was 0.458 (95% confidence interval: 0.221, 0.949), while the side of hemiparesis was not identified a significant covariate. Our results indicated that patients with PB having higher score of preexisting brain abnormalities might require a longer time to recover, and this might be useful in planning inpatient rehabilitation and treatment goals for patients with PB.

DiaMe: IoMT deep predictive model based on threshold aware region growing technique

Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.

Dissolved rubidium to strontium ratio as a conservative tracer for wastewater effluent-sourced contaminant inputs near a major urban wastewater treatment plant

Municipal wastewater (MWW) effluent discharges can introduce contaminants to receiving waters which may have adverse impacts on local ecosystems and human health. Conservative chemical constituents specific to the MWW effluent stream can be used to quantify and trace wastewater effluent-sourced contaminant inputs. Gadolinium (Gd), a rare earth element used as a contrasting agent in medical magnetic resonance imaging, can be found in urban MWW streams. Dissolved anthropogenic Gd has been shown to be an indicator and potential conservative tracer for MWW effluent in receiving waters. Like other known MWW tracers, it can be difficult and expensive to measure. Dissolved rubidium (Rb) to strontium (Sr) ratio enrichment in biological materials such as blood and urine can lead to enriched Rb/Sr values in MWW effluent relative to natural waters. This ratio is relatively easy and inexpensive to measure and represents a promising additional indicator for MWW effluent in receiving waters in urbanized freshwater systems. In July 2015 and 2016 surface water samples were collected from sites in the tidal-fresh Potomac River in the vicinity of the Blue Plains Advanced Wastewater Treatment Plant (BPAWWTP) outfall near Washington, DC USA along with treated MWW effluent samples from the BPAWWTP. Dissolved Rb/Sr ratios were measured in these waters and compared to dissolved Gd concentrations in order to demonstrate the potential of the dissolved Rb/Sr ratio as a conservative indicator for MWW effluent. Results suggest the dissolved Rb/Sr ratio represents a simple and cost-effective indicator and conservative tracer for MWW effluent. It can be used with, or in place of, other proven tracers to investigate wastewater impacts in highly-urbanized, anthropogenically-impacted freshwater systems like the tidal fresh Potomac River and perhaps in a wider range of geologic settings than previously thought. A case study is presented as an example to demonstrate the potential of using dissolved Rb/Sr ratios to trace MWW-sourced nutrient inputs from a major WWTP like BPAWWTP to the receiving waters of tidal-fresh Potomac River.

Medical Image Segmentation Algorithm Based on Multi-scale Color Wavelet Texture

In order to improve the segmentation accuracy of brain tumor magnetic resonance medical image, a segmentation method of brain tumor magnetic resonance medical image based on multi-scale color wavelet texture features is proposed. The segmentation model of brain tumor magnetic resonance medical image is established, and the motion damage information of brain tumor magnetic resonance medical image is adaptively fused in the ultrasound imaging environment. The medical image information is enhanced by using the motion skeletal muscle block matching technology. According to the suspicious point feature matching method of brain tumor, the fusion detection and processing of brain tumor magnetic resonance medical image are carried out. The multi-scale color wavelet texture feature detection method is used to extract the image features of brain tumor MRI points, and the CT bright spot features are used to analyze the features of brain tumor MRI medical images. Combined with the adaptive neural network training method, the automatic detection of brain tumor magnetic resonance medical image is completed, and the suspected brain tumor points are extracted, so as to realize the segmentation of brain tumor magnetic resonance medical image. Simulation results show that the proposed method can effectively improve the segmentation accuracy of brain tumor MRI medical image, and has high resolution and accuracy for suspicious brain tumor detection.

Residual dense network for medical magnetic resonance images super-resolution

Background and objectiveHigh-resolution magnetic resonance images (MRI) help experts to localize lesions and diagnose diseases, but it is difficult to obtain high-resolution MRI. Furthermore, image super-resolution technology based on deep learning can effectively improve image resolution. MethodsIn this work, we propose a medical magnetic resonance (MR) image super-resolution reconstruction method based on residual dense network (MRDN). Firstly, we input the convolutional features of the shallow layer into the residual dense block to obtain global and local features. Secondly, each layer in the residual dense block is directly connected to the previous layer to achieve reuse of features. Finally, we use sub-pixel convolution layer for upsampling and super-resolution reconstruction to get a clear high-resolution image. ResultsFor the 2 ×, 3 ×, and 4 × enlargement, we propose the MRDN method shows the superiority over the state-of-the-art methods on the Set5, Set14, and Urban100 benchmark datasets, extensive benchmark experiment and analysis show that the superiority of our MRDN algorithm in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index indicators (SSIM). ConclusionQuantitative experiments are conducted on three public datasets: Set5, Set14 and Urban10, evaluate with commonly used evaluation metrics, and the experimental results show that the method in this paper is more effective. In addition, we reconstruct the public MR datasets, and the reconstructed high-resolution MR image has a clear structure and rich texture details.

High-performance Ba1−xKxFe2As2 superconducting tapes with grain texture engineered via a scalable fabrication

Nowadays the development of high-field magnets strongly relies on the performance of superconducting materials. Iron-based superconductors exhibit high upper critical fields and low electromagnetic anisotropy, making them particularly attractive for high-field applications, especially in particle accelerator magnets, nuclear magnetic resonance spectrometers, medical magnetic resonance imaging systems and nuclear fusion reactors. Herein, through an industrially scalable and cost-effective manufacturing strategy, a practical level critical current density up to 1.1x10^5 A/cm2 at 4.2 K in an external magnetic field of 10 T was achieved in Cu/Ag composite sheathed Ba0.6K0.4Fe2As2 superconducting tapes. The preparation strategy combines flat rolling to induce grain texture and a subsequent hot-isostatic-pressing densification. By varying the parameters of rolling, the degree of grain texture was engineered. It is found that the transport properties of the Ba1-xKxFe2As2 tapes can be enhanced by applying a large amount of deformation during rolling, which can be attributed to the improved degree of c-axis texture. Microstructure characterizations on the highest-performance tape demonstrate that the Ba1-xKxFe2As2 phase has a uniform element distribution and small grains with good connectivity. Grain boundary pinning is consequently enhanced as proved by large currents circulating through the sample even at 25 K. Our work proves that Cu/Ag composite sheathed Ba1-xKxFe2As2 superconducting tapes can be a promising competitor for practical high-field applications in terms of the viable, scalable and cost-effective fabrication strategy applied and the high transport properties achieved in this work.

Numerical Evaluation of Screening Current-Induced Magnetic Field in No-Insulation REBCO Coil Systems

We have been developing a no-insulation (NI) REBCO coil which is expected to be applied to medical accelerators and magnetic resonance imaging as a technology that can achieve both high thermal stability and high current density. Due to the flat shape and much greater, than the thickness, width of a REBCO-coated conductor, the screening currents are remarkably induced by changing the magnetic flux that penetrates the tape vertically. For insulated (INS) coils, this screening current causes an irregular magnetic field (screening current-induced magnetic field (SCIF)). Also, in the NI coil, an excitation delay occurs because of the current flowing in the radial direction with coil excitation. Therefore, as the cause of irregular magnetic field, it is necessary to simultaneously consider the influence of both the excitation delay and SCIF. In this study, we numerically investigate the magnetic field in the NI coil. The SCIF and an excitation delay reduce magnetic accuracy in the NI coil. Thus, we propose an energization waveform that can improve the magnetic field accuracy even if there is an excitation delay. Besides, we verified its effectiveness.

Medical Images Enhanced by Using Fuzzy Logic Depending on Contrast Stretch Membership Function

Medical images are often adversely affected by a lack of clarity due to the limited representation of the color gamut. In this research, three types of medical images microscopic, magnetic resonance and x-ray images were enhanced by using a Fuzzy Logic by Stretch Membership Function (FLSMF). The Stretch Membership Function increased the dynamic range for the compounds red, green and blue in the medical images which have a few ranges. The FLSMF algorithm was compared with other methods by calculating the entropy value, wavelet quality evaluator and lightness order error. The analysis of the results showed that the proposed method succeeded in enhancing the contrast of the different types of medical images where it had high average values for the entropy (6.95) and wavelet quality evaluator (0.08), and a small average value for the lightness order error (60.77).

Anthropogenic gadolinium in estuaries and tropical Atlantic coastal waters from Fortaleza, Northeast Brazil

Gadolinium-based contrast agents are worldly used for medical magnetic resonance imaging and are an emerging contaminant in natural waters. We investigated the dissolved fraction of Gd in coastal waters from Fortaleza city and observe positive Gd anomalies in the wastewater outfall area as well as in two local river estuaries indicating that the city is a significant source of anthropogenic Gd (Gdanth) to the ocean. Based on this synoptic study and on the conservative behavior of Gdanth we trace a highly concentrated sewage-based source which accounts for 2200 pmol kg−1 and to an annual discharge of 25 kg of Gd to the ocean. We also trace minor sources from the two rivers and estimate that the levels of wastewater dilution within freshwater prior to mixing with seawater accounted for 4.8%–14% of the Cocó River discharge and 1.4%–3.9% of the Ceará River discharge at the time of the sampling. Gd is consequently a suitable and promising tracer for water management and forensic purposes in Fortaleza. In order to guide the application of this method to other coastal waters impacted by metropolitan areas in the world, we propose a conceptual model for Gdanth behavior within salinity gradients and apply it to revisit previous studies.

Commissioning of an MRI test facility for CFD-grade flow experiments in replicas of nuclear fuel assemblies and other reactor components

A new test facility is presented, enabling single-phase isothermal fluid mechanics experiments to support nuclear reactor safety studies. The applied measurement technique Magnetic Resonance Velocimetry (MRV) is based on the medical imaging modality Magnetic Resonance Imaging (MRI). The experiments in this laboratory aim at providing the mean velocity field in models of nuclear fuel assemblies using isothermal water. Additionally, turbulence statistics in terms of the Reynolds stress tensor can be acquired. Since MRV is a non-optical measurement technique, there are no requirements on optical properties, and the investigated models can be quickly built with additive manufacturing techniques. Even though MRV offers a relatively low temporal and spatial resolution compared to laser-optical techniques and has some limitations regarding the test section materials, e.g., no metals, the main gain is that MRV provides a full three-dimensional representation of the flow field in short times. Both the model preparation and the measurement time are significantly faster than with laser-optical measurement techniques. The full-field experimental data obtained with MRV is often referred to as 'CFD grade data' as it enables a full-field validation of numerical solutions obtained with Computational Fluid Dynamics (CFD). As a commissioning experiment, measurements inside a 5x5 fuel element model of a pressurized water reactor (PWR) are conducted and compared with CFD results. The measured mean velocity field inside the PWR replica comprised of 2.9 Million velocity vectors was acquired in 79 min. In addition, turbulence measurements in a periodic hill channel demonstrate how MRV can be used to obtain all components of the Reynolds stress tensor. Although the measurements in this proof-of-concept study suffer from some measurement errors, all major error contributions are well known, and validated methods exist to overcome these shortcomings. In conclusion, this study shows how the velocity field in a specific fuel assembly configuration can be obtained highly efficiently using a specialized MRI laboratory.

Adverse cardiovascular ageing among older women and cardiometabolic ageing

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): National Medical Research Council (NMRC) Background Despite longer life expectancies, women may experience reduced ‘health-span’ (period of life spent in good health) due to accumulation of risks over their longer life spans. Metabolic factors present in diet and lifestyle may provide modifiable solutions to tackle burdens of cardiovascular (CV) ageing in women. Objective We aim to study gender differences in CV structure and function among community older adults without clinical CV disease, as well as metabolic perturbations in their study samples. Methods We examined a prospective cohort study of older adults, obtaining their medical history, serum sampling, echocardiography and cardiac magnetic resonance (CMR) imaging on a single day visit. Echo E/A ratio was computed as a ratio of peak velocity flow in early diastole E (m/s) to peak velocity flow in late diastole by atrial contraction A (m/s) mitral inflow velocities. Longitudinal strain (ε) at any time point (t) in the cardiac cycle from end-diastole (time 0) was calculated as: ε(t)=(L(t)-L0)/L0, obtaining left atrial (LA) reservoir strain (εs), conduit strain (εe) and booster strain (εa) by CMR. Metabolomics profiling was performed by standard mass spectrometry techniques. Results Among n = 492 (49.6%, n = 244 women, mean age 73 ± 4 years) participants, women had lower prevalence of hypertension (52% vs 58%, p &amp;lt; 0.0001), smoking (4% vs 35%, p &amp;lt; 0.0001), but higher prevalence of dyslipidemia (52% vs 50%, p = 0.001) compared to men. Women had lower left ventricular mass index (69 vs 71 g/m2, p &amp;lt; 0.0001) compared to men. However, women had more adverse CV function, such as lower E/A ratio (0.78 vs 0.85, p &amp;lt; 0.0001), lower LA booster (εa) (16.5 vs 17.8, p = 0.027) and lower LA reservoir (εs) strain rate (1.5 vs 1.6, p = 0.012), compared to men. Amino acids such as alanine, arginine, aspartate, citrulline, glycine, phenylalanine and valine were similar between gender. However, women had higher levels of tyrosine (76 vs 68 μM, p = 0.015). On multivariate adjustment, apart from age, tyrosine was independently associated with E/A ratio (β=0.002, 95%CI 0.00-0.004, p = 0.044) and εs (β=0.004, 95%CI 0.00-0.009, p = 0.048) among women. Conclusion Utilizing conventional and sensitive CV imaging, women appeared to have more adverse CV functions with ageing. As a branched chain amino acid linked to future risk of CV disease, tyrosine may be linked to adverse CV function. Factors that contribute to these cardiometabolic profiles, such as menopause, diet or lifestyle warrant further research.

Atrofia das glândulas lacrimais e olho seco relacionados aisotretinoína, androgênio e prolactina: diagnóstico diferencial com a síndrome de Sjögren

This report is of three cases of sicca syndrome, initially suspected to be Sjögren's syndrome, which was ruled out by clinical and laboratory investigations. The patients were a 24-year-old woman, a 32-year-old man, and a 77-year-old woman with chronic symptoms of sicca syndrome, including dry eye syndrome. The first case was associated with the use of isotretinoin, a retinoic acid. The second was associated with the use of anabolic androgenic steroids, and the third was related to a prolactin- secreting pituitary adenoma. All cases manifested sicca, including dry eye syndrome, after those events, and the manifestations persisted. Magnetic resonance imaging revealed bilateral atrophy of the lacrimal gland. The medical history, ocular examinations, laboratory exams, and magnetic resonance images confirmed dry eye syndrome; however, the exams were all negative for Sjögren's syndrome. The lacrimal gland was absent on magnetic resonance imaging in all three cases. The clinical history revealed that the signs and symptoms appeared after chronic exposure to retinoic acid, anabolic androgenic steroids, and a prolactin-secreting pituitary adenoma, respectively. Chronic isotretinoin, anabolic androgenic steroids, and prolactin-secreting pituitary adenoma or, in this last case, its inhibitory treatment, can cause lacrimal gland atrophy, sicca syndrome, and dry eye syndrome, and a differential diagnosis of Sjögren's syndrome. Further studies on doses, time, and other susceptibilities to the long-lasting adverse effects of retinoic acid, anabolic androgenic steroids, and the repercussions of prolactin-secreting pituitary adenoma are necessary to confirm and expand upon these associations.

DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images

Alzheimer's Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one's ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classifying cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconsistent due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) is utilized to create a framework that can be used to detect specific Alzheimer's disease characteristics from MRI images. By considering four stages of dementia and conducting a particular diagnosis, the proposed model generates high-resolution disease probability maps from the local brain structure to a multilayer perceptron and provides accurate, intuitive visualizations of individual Alzheimer's disease risk. To avoid the problem of class imbalance, the samples should be evenly distributed among the classes. The obtained MRI image dataset from Kaggle has a major class imbalance problem. A DEMentia NETwork (DEMNET) is proposed to detect the dementia stages from MRI. The DEMNET achieves an accuracy of 95.23%, Area Under Curve (AUC) of 97% and Cohen's Kappa value of 0.93 from the Kaggle dataset, which is superior to existing methods. We also used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to predict AD classes in order to assess the efficacy of the proposed model.

Nonlinear acoustic characteristics of multilayer magnetic microbubbles

The combination of superparamagnetic iron oxide nanoparticles (SPIOs) with ultrasonic contrast agent (UCA) microbubble is called magnetic microbubble (MMB) and has been used to produce multimodal contrast agents to enhance medical ultrasound and magnetic resonance imaging. The nanoparticles are either covalently linked to the shell or physically entrapped into the shell. Considering the effect of the volume fraction of SPIOs on the shell density and viscosity, a nonlinear dynamic equation of magnetic microbubbles (MMBs) with multilayer membrane structure is constructed based on the basic theory of bubble dynamics. The influences of the driving sound pressure and frequency, particle volume fraction, shell thickness and surface tension on the acoustic-dynamics behavior of microbubbles are numerically analyzed. The results show that when the volume fraction of magnetic particles is small and &lt;i&gt;α&lt;/i&gt; ≤ 0.1, the acoustic properties of magnetic microbubbles are similar to those of ordinary UCA microbubbles. The acoustic response of the microbubble depends on its initial size and driving pressure. The critical sound pressure of microbubble vibration instability is lowest when the driving sound field frequency is twice the magnetic microbubble resonance frequency &lt;i&gt;f&lt;/i&gt;&lt;sub&gt;0&lt;/sub&gt; (&lt;i&gt;f&lt;/i&gt; = 2&lt;i&gt;f&lt;/i&gt;&lt;sub&gt;0&lt;/sub&gt;). The presence of magnetic particles inhibits the bubbles from expanding and contracting, but the inhibition effect is very limited. The surface tension parameter &lt;i&gt;K&lt;/i&gt; of the outer film material and thickness of the shell also affect the vibration of the microbubble. When &lt;i&gt;K&lt;/i&gt; and film thickness are 0.2–0.4 N/m and 50–150 nm respectively, it is observed that the bubble has an unstable vibration response region.

Application of Image Registration Techniques to Medical CT and MR Images (Dept.E)

Image registration has become one of the most widely used techniques in computer vision. Its applications include optical flow, notion analysis, tracking, face detection, and biomedical image registration. In the present work, three different techniques of image registration were implemented and applied to both Computed Tomography (CT) and Magnetic Resonance (MR) images. The first technique is based on Cross Correlation (CC). The second approach depends on Control Points' Selection (CPS) from both the reference and the input images. The last technique is based on Maximization of Mutual Information (MMI) between We two images. The registrability is calculated for each image to measure its ability to provide unambiguous registration, by providing clear correlation peaks when registered with other subimage. Then, the three registration techniques were evaluated and compared using both the Weighted Peak Signal to Noise Ratio (WPSNR) and the Normalized Cross Correlation Coefficient (NCCC). The application of the selected techniques to CT and MR images has shown that registration based on MMI has given the best results and can be used efficiently for alignment of CT and MR images.