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X-ray technicians play a pivotal role in the healthcare field, particularly in diagnostic radiology, where they ensure the accurate, safe, and efficient use of radiographic equipment. As integral members of the healthcare team, these professionals are tasked with optimizing radiation safety protocols, ensuring high-quality images for accurate diagnosis, and mitigating risks associated with radiation exposure to both patients and staff. This paper explores the critical responsibilities of X-ray technicians, focusing on their role in radiation safety, image quality assurance, patient care, and their contributions to enhancing diagnostic radiology outcomes. We discuss best practices, the importance of continued education, and the impact of advanced technologies on the role of X-ray technicians in modern healthcare. [ABSTRACT FROM AUTHOR]

This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR) by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR) research and application. [ABSTRACT FROM AUTHOR]

Background: Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale. Methods: Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to. Results: We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block. Discussion: This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls. [ABSTRACT FROM AUTHOR]

The environmental impact of training has been poorly recognised for many years. With the emergence of high-profile climate activists and a wider appreciation of the need for sustainable healthcare, training within radiology can no longer be excused from its responsibility to consider the environment in its actions. In this paper, we aim to evaluate the environmental impact of the travel undertaken by trainees within the Peninsula training programme, with the aim of developing practices and providing suggestions (evidence-based where possible) on how to improve the impact on the environment of trainee travel. We envisage that many of the lessons and suggestions may be transferrable to other training schemes in the UK and further afield. During the early months of 2020, in addition to the environmental crisis, COVID-19 escalated to a pandemic resulting in the alteration of working practices across the UK (and the rest of the world). This led to many environmentally beneficial working practices being adopted in Radiology in the South West Peninsula Deanery, and throughout this paper we have evaluated these changes and used our collective experience of these to inform our suggestions on how to improve the environmental sustainability of Medical and Radiological training. [ABSTRACT FROM AUTHOR]

Our study aimed to evaluate the trends of post retraction citations of articles reporting a radiology-imaging diagnostic method and to find if a different pattern exists between manuscripts reporting an ultrasound method and those reporting other radiology diagnostic methods. This study reviewed retractions stored in PubMed on the subject of radiology-imaging diagnosis to identify the motivation, time from publication to retraction, and citations before and after retraction. The PubMed database was searched on June 2017 to retrieve the retracted articles, and the Scopus database was screened to identify the post-retraction citations. The full text was screened to see the type of post-retraction citation (positive/negative) and whether the cited article appears or not as retracted. One hundred and two retractions were identified, representing 3.5% of the retracted articles indexed by PubMed, out of which 54 were included in the analysis. Half of the articles were retracted in the first 24 months after publication, and the number of post retraction citations was higher than the number of citations before retraction in 30 out of 54 cases (US methods: 9/20, other diagnostic methods 21/34, P-value = 0.2312). The plagiarism was the most common reason for retraction (31%), followed by repetitive publication (26%), and errors in data/manuscript (24%). In less than 2% of cases, the retracted articles appear as retracted in the text or reference list, while the negative citation is observed in 4.84% among manuscripts reporting an US diagnostic method and 0.32% among manuscripts reporting a diagnostic method other than US (P-value = 0.0004). No significant differences were observed when post retraction weighted citation index (WCI, no. of citations weighted by citation window) was compared to WCI prior retraction (P-value = 0.5972). In light of the reported results, we enumerated some recommendations that could potentially minimize the referral to retracted studies as valid. [ABSTRACT FROM AUTHOR]

Analysis of functional magnetic resonance imaging (fMRI) data has revealed that brain regions can be grouped into functional brain networks (fBNs) or communities. A community in fMRI analysis signifies a group of brain regions coupled functionally with one another. In neuroimaging, functional connectivity (FC) measure can be utilized to quantify such functionally connected regions for disease diagnosis and hence, signifies the need of devising novel FC estimation methods. In this paper, we propose a novel method of learning FC by constraining its rank and the sum of non-zero coefficients. The underlying idea is that fBNs are sparse and can be embedded in a relatively lower dimension space. In addition, we propose to extract overlapping networks. In many instances, communities are characterized as combinations of disjoint brain regions, although recent studies indicate that brain regions may participate in more than one community. In this paper, large-scale overlapping fBNs are identified on resting state fMRI data by employing non-negative matrix factorization. Our findings support the existence of overlapping brain networks. [ABSTRACT FROM AUTHOR]

This paper describes and presents a stable and reliable set of stereo-photogrammetric normative data for global and spino-pelvic sagittal alignment, as a proven reference system for evaluating/measuring a fully unconstrained natural upright neutral standing attitude in a young healthy adult population. The methodological features described in this article will enable future studies to replicate and/or directly compare a wide range of different postural tests and/or sagittal alignment assessment procedures including the study of sagittal spine shape variations occurring during gait performance. To date, the quantitative evaluation of adult spinal deformity (ASD) has been mainly confined to the X-ray imaging approach and, more recently, to 3D X-ray reconstruction. Within the existing evaluation framework an opportunity exists for an additional approach: a quantitative evaluation procedure which is easy, accurate, relatively speedy and non-ionising, in order to monitor and track the progress of patients in the areas of both surgical and non-surgical treatment. The resources and methodology described in this paper have been proven to meet all these criteria. They have enabled full 3D posture (including 3D spine shape and sagittal alignment of the skeleton) to be consistently and successfully measured in adult volunteers. All the measurement/evaluation procedures and outcomes carried out were based entirely on the new non-ionising 3D opto-electronic stereo-photogrammetric approach described in this article. The protocol for this methodology was based on a standard set of 27 pre-selected anatomical “landmarks” on the human body, providing standard reference points for observation and measurement. A total of 124 healthy subjects were successfully assessed and, for each subject, 27 individual markers were applied to the corresponding locations on his/her body. Statistical tests to investigate gender differences were also carried out. Descriptive statistics are provided for all 15 of the spino-pelvic parameters under consideration. Results indicated significant differences between genders in five sets of parameters: Kyphosis tilt, Head tilt, Pelvic tilt, Spino-pelvic angle and T1-pelvic angle. The data also demonstrate a high degree of congruity with results obtained using the X-ray method, as evidenced by the existing literature in the field. In summary, the current study presents a new stereo-photogrammetric opto-electronic technology which can be used successfully for ASD evaluation and introduces a comprehensive set of normative data analogous to those proposed in X-ray analysis for sagittal spino-pelvic and total body alignment. [ABSTRACT FROM AUTHOR]

Introduction: The superior labial artery (SLA) is a facial artery (FA) that drains into the peri-oral region (dangerous area of face). Owing to the recent rise in the demand for reconstructive procedures and filler injections in this region, it is important to understand its arterial topography. This paper aims to study the embranchment pattern of the labial arteries in the eastern Indian population. Method: An observational study using conventional dissection and dry dye injection methods was conducted to visualize the facial and superior labial arteries in 56 hemifaces. The origin, morphometry (length and diameter), branching pattern, and termination of the arteries were recorded and compared with the existing data. Results: Two hemifaces were excluded from analysis (vessels damaged in dissection); in the remaining 54, a single SLA was present in all samples originating at a mean distance of 1.29 ± 0.32 cm from oral commissure (68.51% originating above). Lee type II (independent SLA giving off alar branch) was the predominant pattern (56.2%), followed by type I (independent SLA and alar branches, 33%) and type III (FA terminating as SLA, 10.8%). The average length of SLA was 4.75 ± 1.28 cm and 4.56 ± 0.78 cm on the right and left sides, respectively. Conclusion: The SLA is highly variable in occurrence, course, and depth, sometimes even occurring unilaterally; therefore, any intervention in this region should be done with caution. Since the SLA was not found subcutaneously at the vermillion border, the intradermal and the subcutaneous injections used here are relatively safer. Level of Evidence IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

Background: Alzheimer’s disease neuroimaging initiative (ADNI) is a landmark imaging and omics study in AD. ADNI research literature has increased substantially over the past decade, which poses challenges for effectively communicating information about the results and impact of ADNI-related studies. In this work, we employed advanced information visualization techniques to perform a comprehensive and systematic mapping of the ADNI scientific growth and impact over a period of 12 years. Methods: Citation information of ADNI-related publications from 01/01/2003 to 05/12/2015 were downloaded from the Scopus database. Five fields, including authors, years, affiliations, sources (journals), and keywords, were extracted and preprocessed. Statistical analyses were performed on basic publication data as well as journal and citations information. Science mapping workflows were conducted using the Science of Science (Sci2) Tool to generate geospatial, topical, and collaboration visualizations at the micro (individual) to macro (global) levels such as geospatial layouts of institutional collaboration networks, keyword co-occurrence networks, and author collaboration networks evolving over time. Results: During the studied period, 996 ADNI manuscripts were published across 233 journals and conference proceedings. The number of publications grew linearly from 2008 to 2015, so did the number of involved institutions. ADNI publications received much more citations than typical papers from the same set of journals. Collaborations were visualized at multiple levels, including authors, institutions, and research areas. The evolution of key ADNI research topics was also plotted over the studied period. Conclusions: Both statistical and visualization results demonstrate the increasing attention of ADNI research, strong citation impact of ADNI publications, the expanding collaboration networks among researchers, institutions and ADNI core areas, and the dynamic evolution of ADNI research topics. The visualizations presented here can help improve daily decision making based on a deep understanding of existing patterns and trends using proven and replicable data analysis and visualization methods. They have great potential to provide new insights and actionable knowledge for helping translational research in AD. [ABSTRACT FROM AUTHOR]

Background: Detection of pulmonary nodules is an important aspect of an automatic detection system. Incomputer-aided diagnosis (CAD) systems, the ability to detect pulmonary nodules is highly important, which plays an important role in the diagnosis and early treatment of lung cancer. Currently, the detection of pulmonary nodules depends mainly on doctor experience, which varies. This paper aims to address the challenge of pulmonary nodule detection more effectively. Methods: A method for detecting pulmonary nodules based on an improved neural network is presented in this paper. Nodules are clusters of tissue with a diameter of 3 mm to 30 mm in the pulmonary parenchyma. Because pulmonary nodules are similar to other lung structures and have a low density, false positive nodules often occur. Thus, our team proposed an improved convolutional neural network (CNN) framework to detect nodules. First, a nonsharpening mask is used to enhance the nodules in computed tomography (CT) images; then, CT images of 512×512 pixels are segmented into smaller images of 96×96 pixels. Second, in the 96×96 pixel images which contain or exclude pulmonary nodules, the plaques corresponding to positive and negative samples are segmented. Third, CT images segmented into 96×96 pixels are down-sampled to 64×64 and 32×32 size respectively. Fourth, an improved fusion neural network structure is constructed that consists of three three-dimensional convolutional neural networks, designated as CNN-1, CNN-2, and CNN-3, to detect false positive pulmonary nodules. The networks’ input sizes are 32×32×32, 64×64×64, and 96×96×96 and include 5, 7, and 9 layers, respectively. Finally, we use the AdaBoost classifier to fuse the results of CNN-1, CNN-2, and CNN-3. We call this new neural network framework the Amalgamated-Convolutional Neural Network (A-CNN) and use it to detect pulmonary nodules. Findings: Our team trained A-CNN using the LUNA16 and Ali Tianchi datasets and evaluated its performance using the LUNA16 dataset. We discarded nodules less than 5mm in diameter. When the average number of false positives per scan was 0.125 and 0.25, the sensitivity of A-CNN reached as high as 81.7% and 85.1%, respectively. [ABSTRACT FROM AUTHOR]

This paper presents the histological characterisation of a selection of worked bone artefacts from Middle Stone Age layers at Sibudu cave, South Africa. Histographic rendering is achieved using high-resolution Computed Tomography, which is non-destructive and facilitates three-dimensional histologic analysis. Excellent congruency in image quality was achieved with previous studies using this method. The results show that most of the artefact fragments contain mostly primary lamellar tissue, which is the bone tissue best adapted to withstand impact stresses. This indicates that bone with greater elastic properties was chosen. Histological characterisation allows the identification of animal taxa. Based on the sample analysed in this paper, Perissodactyla bone was used predominantly in the older layers at the site. Artiodactyla are represented throughout but appear far more frequently in the later (post-Howiesons Poort onwards) layers. Some of the Artiodactyla specimens have high proportions of Haversian tissue, reducing elasticity. The higher percentages of Haversian tissue in the post-Howiesons Poort artefacts relative to Holocene examples from southern Africa suggests that people may have started experimenting with bone from different animal taxa at this time and had not yet learned to eliminate the mechanically weaker secondary tissue. Apart from mechanical considerations, possible cultural constraints governing raw material selection is also explored. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a disease which is widely diagnosed using (colour fundus) images. Efficiency and accuracy are critical in diagnosing DR as lack of timely intervention can lead to irreversible visual impairment. In this paper, we examine strategies for scrutinizing images which affect diagnostic performance of medical practitioners via an eye-tracking study. A total of 56 subjects with 0 to 18 years of experience participated in the study. Every subject was asked to detect DR from 40 images. The findings indicate that practitioners use mainly two types of strategies characterized by either higher dwell duration or longer track length. The main findings of the study are that higher dwell-based strategy led to higher average accuracy (> 85%) in diagnosis, irrespective of the expertise of practitioner; whereas, the average obtained accuracy with a long-track length-based strategy was dependent on the expertise of the practitioner. In the second part of the paper, we use the experimental findings to recommend a scanning strategy for fast and accurate diagnosis of DR that can be potentially used by image readers. This is derived by combining the eye-tracking gaze maps of medical experts in a novel manner based on a set of rules. This strategy requires scrutiny of images in a manner which is consistent with spatial preferences found in human perception in general and in the domain of fundus images in particular. The Levenshtein distance-based assessment of gaze patterns also establish the effectiveness of the derived scanning pattern and is thus recommended for image readers. [ABSTRACT FROM AUTHOR]

In this paper we investigate the changes in the functional connectivity intensity, and some related properties, in healthy people, across the life span and at resting state. For the explicit computation of the functional connectivity we exploit a recently proposed model, that bases not only on the correlations data provided by the acquisition equipment, but also on different parameters, such as the anatomical distances between nodes and their degrees. The leading purpose of the paper is to show that the proposed approach is able to recover the main aspects of resting state condition known from the available literature, as well as to suggest new insights, perspectives and speculations from a neurobiological point of view. Our study involves 133 subjects, both males and females of different ages, with no evidence of neurological diseases or systemic disorders. First, we show how the model applies to the sample, where the subjects are grouped into 28 different groups (14 of males and 14 of females), according to their age. This leads to the construction of two graphs (one for males and one for females), that can be realistically interpreted as representative of the neural network during the resting state. Second, following the idea that the brain network is better understood by focusing on specific nodes having a kind of centrality, we refine the two output graphs by introducing a new metric that favours the selection of nodes having higher degrees. As a third step, we extensively comment and discuss the obtained results. In particular, it is remarkable that, despite a great overlapping exists between the outcomes concerning males and females, some intriguing differences appear. This motivates a deeper local investigation, which represents the fourth part of the paper, carried out through a thorough statistical analysis. As a result, we are enabled to support that, for two special age groups, a few links contribute in differentiating the behaviour of males and females. In addition, we performed an average-based comparison between the proposed model and the traditional statistical correlation-based approach, then discussing and commenting the main outlined discrepancies. [ABSTRACT FROM AUTHOR]

In this paper the possibility of classification of X-ray images of the cervical vertebrae is studied. The images should be classified into one of the following classes—the images of healthy vertebrae and the images of vertebrae with syndesmophytes. The vertebra contours, described unambiguously by using the generalized shape language, are the basis of the analysis. As a result, the contour is represented as a chain of sinquads that determine switches. The found switches are the characteristic points of the analyzed contour. In these points additional features of the contour are determined. On the basis of these features two aforementioned classes of images are defined as fuzzy sets. Such an approach allows us to create a hierarchical algorithm of classification based on the syntactic and fuzzy description of the contour. [ABSTRACT FROM AUTHOR]

Accurate estimation of motion field in respiration-correlated 4DCT images, is a precondition for the analysis of patient-specific breathing dynamics and subsequent image-supported treatment planning. However, the lung motion estimation often suffers from the sliding motion. In this paper, a novel lung motion method based on the non-rigid registration of point clouds is proposed, and the tangent-plane distance is used to represent the distance term, which describes the difference between two point clouds. Local affine transformation model is used to express the non-rigid deformation of the lung motion. The final objective function is expressed in the Frobenius norm formation, and matrix optimization scheme is carried out to find out the optimal transformation parameters that minimize the objective function. A key advantage of our proposed method is that it alleviates the requirement that the source point cloud and the reference point cloud should be in one-to-one corresponding relationship, and the requirement is difficult to be satisfied in practical application. Furthermore, the proposed method takes the sliding motion of the lung into consideration and improves the registration accuracy by reducing the constraint of the motion along the tangent direction. Non-rigid registration experiments are carried out to validate the performance of the proposed method using popi-model data. The results demonstrate that the proposed method outperforms the traditional method with about 20% accuracy increase. [ABSTRACT FROM AUTHOR]

Glioma detection and segmentation is a challenging task for radiologists and clinicians. The research reported in this paper seeks to develop a better clinical decision support algorithm for clinicians diagnosis. This paper presents a probabilistic method for detection and segmentation between abnormal tissue regions and brain tumour (tumour core and edema) portions from Magnetic Resonance Imaging (MRI). A framework is constructed to learn structure of undirected graphical models that can represent the spatial relationships among variables and apply it to glioma segmentation. Compared with the pixel of image, the superpixel is more consistent with human visual cognition and contains less redundancy, thus, the superpixels are considered as the basic unit of structure learning and glioma segmentation scheme. ℓ1-regularization techniques are applied to learn the appropriate structure for modeling graphical models. Conditional Random Fields (CRF) are used to model the spatial interactions among image superpixel regions and their measurements. A number of features including statistics features, the combined features from the local binary pattern as well as gray level run length, curve features, and fractal features were extracted from each superpixel. The features are then passed by ℓ1-regularization to ensure a robust classification. The proposed method is compared with support vector machine and Fuzzy c-means to classify each superpixel into normal and abnormal tissue. The proposed system is tested for the presence of low grade as well as high grade glioma tumors on images collected from BRATS2013, BRATS2015 data set and Henan Provincial People’s Hospital (HNPPH) data set. The experiments performed provides similarity between segmented and truth image up to 91.5% by correlation method. [ABSTRACT FROM AUTHOR]

We present a finite difference time domain (FDTD) model for computation of A line scans in time domain optical coherence tomography (OCT). The OCT output signal is created using two different simulations for the reference and sample arms, with a successive computation of the interference signal with external software. In this paper we present the model applied to two different samples: a glass rod filled with water-sucrose solution at different concentrations and a peripheral nerve. This work aims to understand to what extent time domain OCT can be used for non-invasive, direct optical monitoring of peripheral nerve activity. [ABSTRACT FROM AUTHOR]

The human pulmonary valve, one of the key cardiac structures, plays an important role in circulatory system. However, there are few mathematical models to accurately simulate it. In this paper, we establish a geometric model of the normal human pulmonary valve from a mathematical perspective in the fully opening case. Based on the statistical data of the human pulmonary valves, we assume that the motions of the three cusps are symmetrical in the cardiac cycle. Thus, we first propose that each cusp is a part of the cylindrical shell according to its structure and physiological feature. The parameters for the pulmonary valve cusps in three-dimensional space are obtained by the fitting functions. We verify the accuracy of our results by comparing the areas of the pulmonary valve and pulmonary valve flap. [ABSTRACT FROM AUTHOR]

Objective: Gestural interfaces allow accessing and manipulating Electronic Medical Records (EMR) in hospitals while keeping a complete sterile environment. Particularly, in the Operating Room (OR), these interfaces enable surgeons to browse Picture Archiving and Communication System (PACS) without the need of delegating functions to the surgical staff. Existing gesture based medical interfaces rely on a suboptimal and an arbitrary small set of gestures that are mapped to a few commands available in PACS software. The objective of this work is to discuss a method to determine the most suitable set of gestures based on surgeon’s acceptability. To achieve this goal, the paper introduces two key innovations: (a) a novel methodology to incorporate gestures’ semantic properties into the agreement analysis, and (b) a new agreement metric to determine the most suitable gesture set for a PACS. Materials and methods: Three neurosurgical diagnostic tasks were conducted by nine neurosurgeons. The set of commands and gesture lexicons were determined using a Wizard of Oz paradigm. The gestures were decomposed into a set of 55 semantic properties based on the motion trajectory, orientation and pose of the surgeons’ hands and their ground truth values were manually annotated. Finally, a new agreement metric was developed, using the known Jaccard similarity to measure consensus between users over a gesture set. Results: A set of 34 PACS commands were found to be a sufficient number of actions for PACS manipulation. In addition, it was found that there is a level of agreement of 0.29 among the surgeons over the gestures found. Two statistical tests including paired t-test and Mann Whitney Wilcoxon test were conducted between the proposed metric and the traditional agreement metric. It was found that the agreement values computed using the former metric are significantly higher (p < 0.001) for both tests. Conclusions: This study reveals that the level of agreement among surgeons over the best gestures for PACS operation is higher than the previously reported metric (0.29 vs 0.13). This observation is based on the fact that the agreement focuses on main features of the gestures rather than the gestures themselves. The level of agreement is not very high, yet indicates a majority preference, and is better than using gestures based on authoritarian or arbitrary approaches. The methods described in this paper provide a guiding framework for the design of future gesture based PACS systems for the OR. [ABSTRACT FROM AUTHOR]

Ultrasound is an efficacious, versatile and affordable imaging technique in emergencies, but has limited utility without expert interpretation. Telesonography, in which experts may remotely support the use of ultrasound through a telecommunications link, may broaden access to ultrasound and improve patient outcomes, particularly in remote settings. This review assesses the literature regarding telesonography in emergency medicine, focussing on evidence of feasibility, diagnostic accuracy and clinical utility. A systematic search was performed for articles published from 1946 to February 2017 using the Cochrane, Medline, EMBASE, and CINAHL databases. Further searches utilising Scopus, Google Scholar, and citation lists were conducted. 4388 titles were identified and screened against inclusion criteria which resulted in the inclusion of 28 papers. These included feasibility, diagnostic accuracy and clinical pilot studies. Study design, methodology and quality were heterogeneous. There was good evidence of feasibility from multiple studies. Where sufficient bandwidth and high quality components were used, diagnostic accuracy was slightly reduced by image transmission. There was evidence of clinical utility in remote hospitals and low-resource settings, although reliability was infrequently reported. Further exploratory research is required to determine minimum requirements for image quality, bandwidth, frame rate and to assess diagnostic accuracy. Clinical trials in remote settings are justifiable. Telecommunication options will depend on local requirements; no one system conveys universal advantages. The methodological quality of research in this field must improve: studies should be designed to minimise bias, and must include details of their methods to allow replication. Analysis of cost effectiveness and sustainability should be provided. [ABSTRACT FROM AUTHOR]

Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing literature, support vector machine (SVM) has always been applied to distinguish between AD and healthy controls (HC) based on neuroimaging data. But previous studies have only used a single SVM to classify AD and HC, and the accuracy is not very high and generally less than 90%. The method of random support vector machine cluster was proposed to classify AD and HC in this paper. From the Alzheimer's Disease Neuroimaging Initiative database, the subjects including 25 AD individuals and 35 HC individuals were obtained. The classification accuracy could reach to 94.44% in the results. Furthermore, the method could also be used for feature selection and the accuracy could be maintained at the level of 94.44%. In addition, we could also find out abnormal brain regions (inferior frontal gyrus, superior frontal gyrus, precentral gyrus and cingulate cortex). It is worth noting that the proposed random support vector machine cluster could be a new insight to help the diagnosis of AD. [ABSTRACT FROM AUTHOR]

Today, while many researchers focus on the improvement of the regularization term in IR algorithms, they pay less concern to the improvement of the fidelity term. In this paper, we hypothesize that improving the fidelity term will further improve IR image quality in low-dose scanning, which typically causes more noise. The purpose of this paper is to systematically test and examine the role of high-fidelity system models using raw data in the performance of iterative image reconstruction approach minimizing energy functional. We first isolated the fidelity term and analyzed the importance of using focal spot area modeling, flying focal spot location modeling, and active detector area modeling as opposed to just flying focal spot motion. We then compared images using different permutations of all three factors. Next, we tested the ability of the fidelity terms to retain signals upon application of the regularization term with all three factors. We then compared the differences between images generated by the proposed method and Filtered-Back-Projection. Lastly, we compared images of low-dose in vivo data using Filtered-Back-Projection, Iterative Reconstruction in Image Space, and the proposed method using raw data. The initial comparison of difference maps of images constructed showed that the focal spot area model and the active detector area model also have significant impacts on the quality of images produced. Upon application of the regularization term, images generated using all three factors were able to substantially decrease model mismatch error, artifacts, and noise. When the images generated by the proposed method were tested, conspicuity greatly increased, noise standard deviation decreased by 90% in homogeneous regions, and resolution also greatly improved. In conclusion, the improvement of the fidelity term to model clinical scanners is essential to generating higher quality images in low-dose imaging. [ABSTRACT FROM AUTHOR]

The mechanical and radiation shielding properties for the PbO–MoO3–Li2O–B2O3 glass system were theoretically investigated in this paper. The PbO–MoO3–Li2O–B2O3 glass system (coded as investigated glasses) was fabricated using the melt quenching mechanism. The optical packing density (OPD) increases from 75.563 to 84.366, and oxygen molar volume (OMV) decreases from 13.234 to 11.853 cm3/mol when increasing the PbO concentration. The values of elastic moduli decreased from 47.06 to 39.67 GPa for Young, from 33.51 to 32.41 GPa for bulk, from 19.82 to 16.29 GPa for shear and from 59.94 to 54.14 GPa for longitudinal moduli as the PbO is increased. The radiation attenuation characteristics were reported at the photon energies used in diagnostic radiology. The mass attenuation coefficient (MAC) was evaluated using the three photoatomic data libraries EPICS2017, EPDL97, and XCOM, available in the EpiXS and Phy-X programs. The MAC for the five investigated glasses at 20 keV was much higher than the MAC at 40, 60 and 80 keV. The MAC for investigated glasses increased with the addition of PbO, with Pb-S1 demonstrating the lowest MAC, and Pb-S5 demonstrating the highest MAC. Additionally, the rate of the increment of MAC at 20 keV as the concentration of PbO increased was higher than that at 40, 60 and 80 keV. The effective atomic number (EAN) was determined using Phy-X program. The EAN follows the trend: Pb-S5 > Pb-S4 > Pb-S3 > Pb-S2 > Pb-S1. The EAN results proved that the glass with low amounts of B2O3 and higher amounts of PbO had good attenuation features. The EAN had the maximum values of 73.55–76.67 at 20 keV, whereas the lowest values occurred at 80 keV and varied between 53.63 and 63.39. The half-value layer (HVL) results showed that the Pb-S1 glass had the greatest HVL, while Pb-S5 had the least. There is a higher discrepancy between the tenth-value layer values at 80 keV than at 20 keV. At 20 keV, the difference between the highest and lowest TVL values (Pb-S1 and Pb-S5) was only 0.004 cm, while the difference at 80 keV was 0.152 cm. Pb-S5 is the most space-efficient radiation shield. [ABSTRACT FROM AUTHOR]

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This paper presents a region-based active contour method for the segmentation of intensity inhomogeneous images using an energy functional based on local and global fitted images. A square image fitted model is defined by using both local and global fitted differences. Moreover, local and global signed pressure force functions are introduced in the solution of the energy functional to stabilize the gradient descent flow. In the final gradient descent solution, the local fitted term helps extract regions with intensity inhomogeneity, whereas the global fitted term targets homogeneous regions. A Gaussian kernel is applied to regularize the contour at each step, which not only smoothes it but also avoids the computationally expensive re-initialization. Intensity inhomogeneous images contain undesired smooth intensity variations (bias field) that alter the results of intensity-based segmentation methods. The bias field is approximated with a Gaussian distribution and the bias of intensity inhomogeneous regions is corrected by dividing the original image by the approximated bias field. In this paper, a two-phase model is first derived and then extended to a four-phase model to segment brain magnetic resonance (MR) images into the desired regions of interest. Experimental results with both synthetic and real brain MR images are used for a quantitative and qualitative comparison with state-of-the-art active contour methods to show the advantages of the proposed segmentation technique in practical terms. [ABSTRACT FROM AUTHOR]

For analyzing multireader multicase (MRMC) diagnostic imaging data when the reader performance measure of interest is the area under the receiver-operating-characteristic curve (AUC), two popular methods of analysis that allow conclusions to generalize to both the reader and case populations are the method developed by Obuchowski, Rockette and Hillis (ORH) and the method primarily developed by Gallas (Gallas). While the ORH method is a general method that is applicable to most reader performance metrics, the Gallas method is limited to those metrics for which an unbiased variance estimate exists. Previously it was not known if the ORH method could be adapted so as to produce the same variance estimate as the Gallas method. In this paper, I show that a recently proposed version of the OR method produces the same unconstrained variance statistic as the Gallas method. However, the two methods differ in their approaches to constraining the variance estimate to be nonnegative and in their degrees-of-freedom estimates. These two differences are discussed and recommendations given. In addition, several contributions to the development of the ORH method are made, including determining sufficient conditions for unbiased variance estimates and providing justification for the ORH variance constraints and covariance estimation method. [ABSTRACT FROM AUTHOR]

Background: QuADRANT was a study funded by the European Commission to evaluate clinical audit uptake and implementation across Europe, with an emphasis on clinical audit as mandated within the BSSD (Basic Safety Standards Directive). AIMS: QuADRANT objectives—obtain an overview of European clinical audit activity; identify good practices and resources, barriers and challenges; provide guidance and recommendations going forwards; identify the potential for European Union action on quality and safety in the three core project specialties, radiology, radiotherapy and nuclear medicine. Findings and recommendations: QuADRANT identified that developments in national clinical audit infrastructure are required. National professional societies can be pivotal in improving clinical audit implementation, but resource allocation and national prioritisation of clinical audit are needed in many countries. Lack of staff time and expertise are also barriers. Enablers to enhance clinical audit participation are not widely employed. Development of hospital accreditation programmes can facilitate clinical audit uptake. An active and formalised role for patients in clinical audit practice and policy development is recommended. There is persisting variation in European awareness of BSSD clinical audit requirements. Work is needed to improve dissemination of information on the legislative requirements relating to clinical audit in the BSSD and in relation to inspection processes to ensure these include clinical audit and that they encompass all clinics and specialties involved in medical applications using ionising radiation. Conclusion: QuADRANT provides an important step towards enhancing clinical audit uptake and implementation across Europe and improving patient safety and outcomes. Key points: QuADRANT was a multiprofessional study, on behalf of the European Commission, examining clinical audit across three core specialties—radiology, radiotherapy and nuclear medicine—with an emphasis on radiation protection. QuADRANT aimed to provide an overview of the status of European clinical audit uptake and implementation and to identify barriers and good practices. QuADRANT recommendations include increased prioritisation and resourcing of clinical audit, use of enablers, motivation of staff and developing the role of the national professional societies in clinical audit uptake and promotion. [ABSTRACT FROM AUTHOR]

Background: The increased use of ionizing radiation for diagnostic purpose has resulted in an increase in the world population dose. Patient dosimetry in X-ray diagnostic radiology is required to establish diagnostic reference levels (DRLs) and to assess the average dose received by organs and tissues. International bodies have recommended DRLs to be based on dosimetric quantities. Aim: To cater to the increased requirement for dosimetry in diagnostic radiology, international guidelines are provided to establish and disseminate traceable calibration for dosimeters used in X-ray diagnostic radiology. X-ray diagnostic beams established are standardized using a diagnostic range free-air ionization chamber (DFAIC) (20-150 kV). Materials and Methods: Characterization of the DFAIC and determination of the correction factors for the air kerma measurements were evaluated experimentally and by theoretical calculations. Results: The paper details the establishment of 18 diagnostic beam qualities using DFAIC along with the associated uncertainties. The overall uncertainty for the air kerma measurements was within ±0.5% at 1 sigma level. Eight diagnostic range air kerma measurements using DFAIC were compared with the medium energy primary standard FAIC (50-300 kV) maintained in the laboratory. Conclusion: The air kerma rates agreed within ±1% and are within the overall standard uncertainty of both the chambers at the time of the comparison. Dissemination to the users in the field of diagnostic radiology in the country has been carried out by calibrating their ionization chambers and solid-state detector-based instruments against the DFAIC. The methodology followed to standardize the beams using DFAIC and calibration of dosimeters is presented in this work. [ABSTRACT FROM AUTHOR]

This position paper was produced by a working party set up by the Radiology Special Interest Group of the ACPSEM in 2001. It is designed to give the consensus view of College members in Australia and New Zealand on the nature and frequency of tests which should be performed on diagnostic x-ray equipment to maintain adequate quality control of imaging performance and radiation safety. Tests on mammographic equipment have been excluded having been covered in a previous ACPSEM position paper (Australas Phys Eng Sci Med, 24(3):107–131, 2001). Detailed descriptions of test procedures are not given but it is intended that a series of workbooks should be produced giving College recommended test methods for each imaging modality. The recommendations are produced here in an easy-toread, tabular form giving the nature and purpose of each test and the implications of non-compliance with regard to image quality and radiation safety. [ABSTRACT FROM AUTHOR]

Purpose: Overweight and underweight conditions are considered relative contraindications to lung transplantation due to their association with excess mortality. Yet, recent work suggests that body mass index (BMI) does not accurately reflect adipose tissue mass in adults with advanced lung diseases. Alternative and more accurate measures of adiposity are needed. Chest fat estimation by routine computed tomography (CT) imaging may therefore be important for identifying high-risk lung transplant candidates. In this paper, an approach to chest fat quantification and quality assessment based on a recently formulated concept of standardized anatomic space (SAS) is presented. The goal of the paper is to seek answers to several key questions related to chest fat quantity and quality assessment based on a single slice CT (whether in the chest, abdomen, or thigh) versus a volumetric CT, which have not been addressed in the literature. Methods: Unenhanced chest CT image data sets from 40 adult lung transplant candidates (age 58 ± 12 yrs and BMI 26.4 ± 4.3 kg/m2), 16 with chronic obstructive pulmonary disease (COPD), 16 with idiopathic pulmonary fibrosis (IPF), and the remainder with other conditions were analyzed together with a single slice acquired for each patient at the L5 vertebral level and mid-thigh level. The thoracic body region and the interface between subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in the chest were consistently defined in all patients and delineated using Live Wire tools. The SAT and VAT components of chest were then segmented guided by this interface. The SAS approach was used to identify the corresponding anatomic slices in each chest CT study, and SAT and VAT areas in each slice as well as their whole volumes were quantified. Similarly, the SAT and VAT components were segmented in the abdomen and thigh slices. Key parameters of the attenuation (Hounsfield unit (HU) distributions) were determined from each chest slice and from the whole chest volume separately for SAT and VAT components. The same parameters were also computed from the single abdominal and thigh slices. The ability of the slice at each anatomic location in the chest (and abdomen and thigh) to act as a marker of the measures derived from the whole chest volume was assessed via Pearson correlation coefficient (PCC) analysis. Results: The SAS approach correctly identified slice locations in different subjects in terms of vertebral levels. PCC between chest fat volume and chest slice fat area was maximal at the T8 level for SAT (0.97) and at the T7 level for VAT (0.86), and was modest between chest fat volume and abdominal slice fat area for SAT and VAT (0.73 and 0.75, respectively). However, correlation was weak for chest fat volume and thigh slice fat area for SAT and VAT (0.52 and 0.37, respectively), and for chest fat volume for SAT and VAT and BMI (0.65 and 0.28, respectively). These same single slice locations with maximal PCC were found for SAT and VAT within both COPD and IPF groups. Most of the attenuation properties derived from the whole chest volume and single best chest slice for VAT (but not for SAT) were significantly different between COPD and IPF groups. Conclusions: This study demonstrates a new way of optimally selecting slices whose measurements may be used as markers of similar measurements made on the whole chest volume. The results suggest that one or two slices imaged at T7 and T8 vertebral levels may be enough to estimate reliably the total SAT and VAT components of chest fat and the quality of chest fat as determined by attenuation distributions in the entire chest volume. [ABSTRACT FROM AUTHOR]

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system. [ABSTRACT FROM AUTHOR]

Purpose: Many studies have reported that carbohydrate antigen 153 (CA153) in breast secretions (BS) can discriminate breast cancer (BC) patients from healthy individuals, indicating CA153 in BS as a potential index for BC. This meta-analysis aimed to evaluate the actual diagnostic value of CA153 in BS. Methods: Related papers were obtained from Pubmed, Embase, Scopus, Ovid, Sciverse, the Cochrane library, Chinese Biomedical literature Database (CBM), Technology of Chongqing (VIP), Wan Fang Data, and Chinese National Knowledge Infrastructure (CNKI). Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of CA153 in BS for BC diagnosis were analyzed with the random effect model. SROC and the area under the curve (AUC) were applied to assess overall diagnostic efficiency. Results: This meta-analysis included five studies with a total of 329 BC patients and 381 healthy subjects. For CA153 in BS, the summary sensitivity, specificity, and DOR to diagnose BC were 0.63 (95% confidence interval (CI): 0.57∼0.68), 0.82 (95% CI: 0.78∼0.86), and 9.18 (95% CI: 4.22∼19.95), respectively. Furthermore, the AUC of BS CA153 in the diagnosis of BC was 0.8614. Conclusions: CA153 in BS is a valuable molecular marker in diagnosing BC and should be applied in standard clinical practices of BC screening upon confirmation of our findings in a larger prospective study. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) theory asserts that we can reconstruct signals and images with only a small number of samples or measurements. Recent works exploiting the nonlocal similarity have led to better results in various CS studies. To better exploit the nonlocal similarity, in this paper, we propose a non-convex smoothed rank function based model for CS image reconstruction. We also propose an efficient alternating minimization method to solve the proposed model, which reduces a difficult and coupled problem to two tractable subproblems. Experimental results have shown that the proposed method performs better than several existing state-of-the-art CS methods for image reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: Quantitative image analysis in previous research in obstructive sleep apnea syndrome (OSAS) has focused on the upper airway or several objects in its immediate vicinity and measures of object size. In this paper, we take a more general approach of considering all major objects in the upper airway region and measures pertaining to their individual morphological properties, their tissue characteristics revealed by image intensities, and the 3D architecture of the object assembly. We propose a novel methodology to select a small set of salient features from this large collection of measures and demonstrate the ability of these features to discriminate with very high prediction accuracy between obese OSAS and obese non-OSAS groups. Materials and Methods: Thirty children were involved in this study with 15 in the obese OSAS group with an apnea-hypopnea index (AHI) = 14.4 ± 10.7) and 15 in the obese non-OSAS group with an AHI = 1.0 ± 1.0 (p<0.001). Subjects were between 8–17 years and underwent T1- and T2-weighted magnetic resonance imaging (MRI) of the upper airway during wakefulness. Fourteen objects in the vicinity of the upper airways were segmented in these images and a total of 159 measurements were derived from each subject image which included object size, surface area, volume, sphericity, standardized T2-weighted image intensity value, and inter-object distances. A small set of discriminating features was identified from this set in several steps. First, a subset of measures that have a low level of correlation among the measures was determined. A heat map visualization technique that allows grouping of parameters based on correlations among them was used for this purpose. Then, through T-tests, another subset of measures which are capable of separating the two groups was identified. The intersection of these subsets yielded the final feature set. The accuracy of these features to perform classification of unseen images into the two patient groups was tested by using logistic regression and multi-fold cross validation. Results: A set of 16 features identified with low inter-feature correlation (< 0.36) yielded a high classification accuracy of 96% with sensitivity and specificity of 97.8% and 94.4%, respectively. In addition to the previously observed increase in linear size, surface area, and volume of adenoid, tonsils, and fat pad in OSAS, the following new markers have been found. Standardized T2-weighted image intensities differed between the two groups for the entire neck body region, pharynx, and nasopharynx, possibly indicating changes in object tissue characteristics. Fat pad and oropharynx become less round or more complex in shape in OSAS. Fat pad and tongue move closer in OSAS, and so also oropharynx and tonsils and fat pad and tonsils. In contrast, fat pad and oropharynx move farther apart from the skin object. Conclusions: The study has found several new anatomic bio-markers of OSAS. Changes in standardized T2-weighted image intensities in objects may imply that intrinsic tissue composition undergoes changes in OSAS. The results on inter-object distances imply that treatment methods should respect the relationships that exist among objects and not just their size. The proposed method of analysis may lead to an improved understanding of the mechanisms underlying OSAS. [ABSTRACT FROM AUTHOR]

Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance. The proposed framework consists of three steps. First, we augment the tumor region and use the augmented tumor region as the region of interest to incorporate informative contextual information. Second, the augmented tumor region is split into subregions by an adaptive spatial division method based on intensity orders; within each subregion, we extract raw image patches as local features. Third, we apply the Fisher kernel framework to aggregate the local features of each subregion into a respective single vector representation and concatenate these per-subregion vector representations to obtain an image-level signature. After feature extraction, a closed-form metric learning algorithm is applied to measure the similarity between the query image and database images. Extensive experiments are conducted on a large dataset of 3604 images with three types of brain tumors, namely, meningiomas, gliomas, and pituitary tumors. The mean average precision can reach 94.68%. Experimental results demonstrate the power of the proposed algorithm against some related state-of-the-art methods on the same dataset. [ABSTRACT FROM AUTHOR]

Recently, the Coherent Point Drift (CPD) algorithm has become a very popular and efficient method for point set registration. However, this method does not take into consideration the neighborhood structure information of points to find the correspondence and requires a manual assignment of the outlier ratio. Therefore, CPD is not robust for large degrees of degradation. In this paper, an improved method is proposed to overcome the two limitations of CPD. A structure descriptor, such as shape context, is used to perform the auxiliary calculation of the correspondence, and the proportion of each GMM component is adjusted by the similarity. The outlier ratio is formulated in the EM framework so that it can be automatically calculated and optimized iteratively. The experimental results on both synthetic data and real data demonstrate that the proposed method described here is more robust to deformation, noise, occlusion, and outliers than CPD and other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Air kerma length product and air kerma area product are special quantities used in diagnostic radiology. They are measured using special measurement devices – CT-chambers and KAP-meters, in order to calculate quantities related to patient exposure. Appropriate calibration of all measurement devices is of vital importance, and comparisons between calibration laboratories are necessary to prove competence. It is usually considered adequate to participate in air kerma comparisons to prove capabilities for special quantities, but the literature shows that some problems in calibration procedure can remain unknown. The comparisons directly in terms of special quantities provide additional burden to laboratories, and require special transfer instruments, but they allow checking the whole calibration procedures. This paper describes a comparison between two calibration laboratories in terms of both air kerma length product and air kerma area product. Both laboratories achieved good results for all radiation qualities, considering the measurement uncertainty. Transfer instruments' linearity, field size dependence and energy dependence were investigated. Even though the metrological properties of the transfer instruments are worse than the ionization chambers, they can be taken into account by introducing additional measurement uncertainty, performing appropriate corrections or choosing calibration points for the comparison for the values of influence quantities where the transfer instrument response is relatively flat. These comparisons provide additional value to calibration laboratories, but there are still several challenges related to their organization and execution. • Comparison of calibration factors for KAP-meters and CT-chambers was organized. • All the results for all radiation qualities were acceptable. • KAP-meters and CT-chambers have significant energy and field-size dependence. • Though hard to organize, comparisons for special quantities provide additional value. • Air kerma comparisons should also be accepted, especially for established laboratories. [ABSTRACT FROM AUTHOR]

In recent years, several high-dimensional, accurate, and effective classification methods have been proposed for the automatic discrimination of the subject between Alzheimer’s disease (AD) or its prodromal phase {i.e., mild cognitive impairment (MCI)} and healthy control (HC) persons based on T1-weighted structural magnetic resonance imaging (sMRI). These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low. However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects. In this paper, we propose a novel classification technique that precisely distinguishes individuals with AD, aAD (stable MCI, who had not converted to AD within a 36-month time period), and mAD (MCI caused by AD, who had converted to AD within a 36-month time period) from HC individuals. The proposed method combines three different features extracted from structural MR (sMR) images using voxel-based morphometry (VBM), hippocampal volume (HV), and cortical and subcortical segmented region techniques. Three classification experiments were performed (AD vs. HC, aAD vs. mAD, and HC vs. mAD) with 326 subjects (171 elderly controls and 81 AD, 35 aAD, and 39 mAD patients). For the development and validation of the proposed classification method, we acquired the sMR images from the dataset of the National Research Center for Dementia (NRCD). A five-fold cross-validation technique was applied to find the optimal hyperparameters for the classifier, and the classification performance was compared by using three well-known classifiers: K-nearest neighbor, support vector machine, and random forest. Overall, the proposed model with the SVM classifier achieved the best performance on the NRCD dataset. For the individual feature, the VBM technique provided the best results followed by the HV technique. However, the use of combined features improved the classification accuracy and predictive power for the early classification of AD compared to the use of individual features. The most stable and reliable classification results were achieved when combining all extracted features. Additionally, to analyze the efficiency of the proposed model, we used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to compare the classification performance of the proposed model with those of several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The transport and distribution of charged molecules in polyelectrolyte solutions are of both fundamental and practical importance. A practical example, which is the specific subject addressed in the present paper, is the transport and distribution of charged species into cartilage. The charged species could be a contrast agent or a drug molecule involved in diagnosis or treatment of the widespread degenerative disease osteoarthritis, which leads to degradation of articular cartilage. Associated scientific issues include the rate of transport and the equilibrium concentrations of the charged species in the cartilage and the synovial fluid. To address these questions, we present results from magnetic resonance micro-imaging experiments on a model system of articular cartilage. The experiments yield temporally and spatially resolved data on the transport of a negatively charged contrast agent (charge = -2), used in medical examinations of cartilage, into a polyelectrolyte solution, which is designed to capture the electrostatic interactions in cartilage. Also presented is a theoretical analysis of the transport where the relevant differential equations are solved using finite element techniques as well as treated with approximate analytical expressions. In the analysis, non-ideal effects are included in the treatment of the mobile species in the system. This is made possible by using results from previous Monte Carlo simulations. The results demonstrate the importance of taking non-idealities into account when data from measurements of transport of charged solutes in a system with fixed charges from biological polyelectrolytes are analyzed. [ABSTRACT FROM AUTHOR]