Evaluation of lateral epicondylopathy, posterior interosseous nerve compression, and plica syndrome as co-existing causes of chronic tennis elbow.

PURPOSE: A great number of patients that suffer from lateral epicondylitis, commonly called tennis elbow (TE), are not successfully treated, meaning, not getting adequate therapeutic effects and the main origin of the pain not being handled appropriately. The hypothesis of the present study is that the inefficiency of the treatment of the chronic TE may often be due to underdiagnosis of posterior interosseous nerve (PIN) entrapment or and plica syndrome, as the authors believe that those pathologies can often occur simultaneously. METHODS: A prospective cross sectional study was conducted. A total of 31 patients met the required criteria. RESULTS: Thirteen (40.7%) of the patients had more than one source of the lateral elbow pain. Five patients (15.6%) had all three examined pathologies. Six patients (18.8%) had TE and PIN syndrome. Two patients (6.3%) had TE and plica syndrome. CONCLUSION: The present study demonstrated concomitant potential sources of lateral elbow pain in patients diagnosed with chronic TE. Our analysis shows how important it is to systematically diagnose patients that present with lateral elbow pain. The clinical characteristics of the three most common causes of chronic lateral elbow pain, meaning, TE, PIN compression, and plicae syndrome were also analyzed. Having adequate knowledge about the clinical aspects of these pathologies can help with a more effective differentiation of the etiology of chronic lateral elbow pain, and with that, a more efficient and cost-effective treatment plan.

Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment.

The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced, investigated, and experimentally compared with state-of-the-art NR-IQA methods on two representative MRIQA benchmark datasets. One of the datasets is introduced in this work. As the experimental validation reveals, the proposed fusion of networks outperforms related approaches in terms of correlation with subjective opinions of a large number of experienced radiologists.

Differential Diagnosis of Cysts and Granulomas Supported by Texture Analysis of Intraoral Radiographs.

The aim of this study was to evaluate whether textural analysis could differentiate between the two common types of lytic lesions imaged with use of radiography. Sixty-two patients were enrolled in the study with intraoral radiograph images and a histological reference study. Full textural analysis was performed using MaZda software. For over 10,000 features, logistic regression models were applied. Fragments containing lesion edges were characterized by significant correlation of structural information. Although the input images were stored using lossy compression and their scale was not preserved, the obtained results confirmed the possibility of distinguishing between cysts and granulomas with use of textural analysis of intraoral radiographs. It was shown that the important information distinguishing the aforementioned types of lesions is located at the edges and not within the lesion.

No-reference image quality assessment of magnetic resonance images with high-boost filtering and local features.

PURPOSE: Subjective quality assessment of displayed magnetic resonance (MR) images plays a key role in diagnosis and the resultant treatment. Therefore, this study aims to introduce a new no-reference (NR) image quality assessment (IQA) method for the objective, automatic evaluation of MR images and compare its judgments with those of similar techniques. METHODS: A novel NR-IQA method was developed. The method uses a sequence of scaled images filtered to enhance high-frequency components and preserve low-frequency parts. Since the human visual system (HVS) is sensitive to local image variations and local features often mimic the attraction of the HVS to high-frequency image regions, they were detected in the filtered images and described. Then, the statistics of obtained descriptors were used to build a quality model via the Support Vector Regression method. RESULTS: The method was compared with 21 state-of-the-art techniques for NR-IQA on a new dataset of 70 distorted MR images assessed by 31 experienced radiologists, using typical evaluation criteria for the comparison of NR measures. The introduced method significantly outperforms the compared approaches, in terms of the correlation with human judgments. CONCLUSIONS: It is demonstrated that the presented NR-IQA method for the assessment of MR images is superior to the state-of-the-art NR techniques. The method would be beneficial for a wide range of image processing applications, assessing their outputs and affecting the directions of their development.

Interobserver variability in quality assessment of magnetic resonance images.

BACKGROUND: The perceptual quality of magnetic resonance (MR) images influences diagnosis and may compromise the treatment. The purpose of this study was to evaluate how the image quality changes influence the interobserver variability of their assessment. METHODS: For the variability evaluation, a dataset containing distorted MRI images was prepared and then assessed by 31 experienced medical professionals (radiologists). Differences between observers were analyzed using the Fleiss' kappa. However, since the kappa evaluates the agreement among radiologists taking into account aggregated decisions, a typically employed criterion of the image quality assessment (IQA) performance was used to provide a more thorough analysis. The IQA performance of radiologists was evaluated by comparing the Spearman correlation coefficients, ρ, between individual scores with the mean opinion scores (MOS) composed of the subjective opinions of the remaining professionals. RESULTS: The experiments show that there is a significant agreement among radiologists (κ=0.12; 95% confidence interval [CI]: 0.118, 0.121; P<0.001) on the quality of the assessed images. The resulted κ is strongly affected by the subjectivity of the assigned scores, separately presenting close scores. Therefore, the ρ was used to identify poor performance cases and to confirm the consistency of the majority of collected scores (ρmean = 0.5706). The results for interns (ρmean = 0.6868) supports the finding that the quality assessment of MR images can be successfully taught. CONCLUSIONS: The agreement observed among radiologists from different imaging centers confirms the subjectivity of the perception of MR images. It was shown that the image content and severity of distortions affect the IQA. Furthermore, the study highlights the importance of the psychosomatic condition of the observers and their attitude.

Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis.

An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in this paper, a novel blind image quality assessment (BIQA) method for the evaluation of MR images is introduced. It is observed that the result of filtering using non-maximum suppression (NMS) strongly depends on the perceptual quality of an input image. Hence, in the method, the image is first processed by the NMS with various levels of acceptable local intensity difference. Then, the quality is efficiently expressed by the entropy of a sequence of extrema numbers obtained with the thresholded NMS. The proposed BIQA approach is compared with ten state-of-the-art techniques on a dataset containing MR images and subjective scores provided by 31 experienced radiologists. The Pearson, Spearman, Kendall correlation coefficients and root mean square error for the method assessing images in the dataset were 0.6741, 0.3540, 0.2428, and 0.5375, respectively. The extensive experimental evaluation of the BIQA methods reveals that the introduced measure outperforms related techniques by a large margin as it correlates better with human scores.

Combining variational mode decomposition with regularisation techniques to denoise MRI data.

In this paper, we propose a novel method for removing noise from MRI data by exploiting regularisation techniques combined with variational mode decomposition. Variational mode decomposition is a new decomposition technique for sparse decomposition of a 1D or 2D signal into a set of modes. In turn, regularisation is a method that can translate the ill-posed problem (e.g., image denoising) into a well-posed problem. The proposed method aims to remove the noise from the image in two steps. In the first step, the MR imaging data are decomposed by the 2D variational mode decomposition algorithm. In the second step, for effective suppression of Rician noise from MRI data, we used the fused lasso signal approximator with all modes acquired from the medical scan. The performance of the proposed approach was compared with state-of-the-art reference methods based on different metrics, that is, the peak signal-to-noise ratio, the structural similarity index metrics, the high-frequency error norm, the quality index based on local variance, and the sharpness index. The experiments were performed on the basis of both simulated and real images. The presented results prove the high denoising performance of the proposed algorithm; particularly under heavy noise conditions.

Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing-A Review.

Artificial intelligence (AI) is currently becoming a leading field in data processing [...].

Sex Differentiation of Trabecular Bone Structure Based on Textural Analysis of Pelvic Radiographs.

Objectives: The purpose of this paper is to assess the determination of male and female sex from trabecular bone structures in the pelvic region. The study involved analyzing digital radiographs for 343 patients and identifying fourteen areas of interest based on their medical significance, with seven regions on each side of the body for symmetry. Methods: Textural parameters for each region were obtained using various methods, and a thorough investigation of data normalization was conducted. Feature selection approaches were then evaluated to determine a small set of the most representative features, which were input into several classification machine learning models. Results: The findings revealed a sex-dependent correlation in the bone structure observed in X-ray images, with the degree of dependency varying based on the anatomical location. Notably, the femoral neck and ischium regions exhibited distinctive characteristics between sexes. Conclusions: This insight is crucial for medical professionals seeking to estimate sex dependencies from such image data. For these four specific areas, the balanced accuracy exceeded 70%. The results demonstrated symmetry, confirming the genuine dependencies in the trabecular bone structures.

Advances in Musculoskeletal Imaging and Their Applications.

Modern medical imaging systems provide ever-more information about the patient's health condition [...].

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys.

Currently, bone age is assessed by X-rays. It enables the evaluation of the child's development and is an important diagnostic factor. However, it is not sufficient to diagnose a specific disease because the diagnoses and prognoses may arise depending on how much the given case differs from the norms of bone age. BACKGROUND: The use of magnetic resonance images (MRI) to assess the age of the patient would extend diagnostic possibilities. The bone age test could then become a routine screening test. Changing the method of determining the bone age would also prevent the patient from taking a dose of ionizing radiation, making the test less invasive. METHODS: The regions of interest containing the wrist area and the epiphyses of the radius are marked on the magnetic resonance imaging of the non-dominant hand of boys aged 9 to 17 years. Textural features are computed for these regions, as it is assumed that the texture of the wrist image contains information about bone age. RESULTS: The regression analysis revealed that there is a high correlation between the bone age of a patient and the MRI-derived textural features derived from MRI. For DICOM T1-weighted data, the best scores reached 0.94 R2, 0.46 RMSE, 0.21 MSE, and 0.33 MAE. CONCLUSIONS: The experiments performed have shown that using the MRI images gives reliable results in the assessment of bone age while not exposing the patient to ionizing radiation.

Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels.

BACKGROUND: Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk. METHODS: Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy. RESULTS: Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy. CONCLUSIONS: There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.

Deep Learning Algorithm for Differentiating Patients with a Healthy Liver from Patients with Liver Lesions Based on MR Images.

The problems in diagnosing the state of a vital organ such as the liver are complex and remain unresolved. These problems are underscored by frequently published studies on this issue. At the same time, demand for imaging diagnostics, preferably using a method that can detect the disease at the earliest possible stage, is constantly increasing. In this paper, we present liver diseases in the context of diagnosis, diagnostic problems, and possible elimination. We discuss the dataset and methods and present the stages of the pipeline we developed, leading to multiclass segmentation of the liver in multiparametric MR image into lesions and normal tissue. Finally, based on the processing results, each case is classified as either a healthy liver or a liver with lesions. For the training set, the AUC ROC is 0.925 (standard error 0.013 and a p-value less than 0.001), and for the test set, the AUC ROC is 0.852 (standard error 0.039 and a p-value less than 0.001). Further refinements to the proposed pipeline are also discussed. The proposed approach could be used in the detection of focal lesions in the liver and the description of liver tumors. Practical application of the developed multi-class segmentation method represents a key step toward standardizing the medical evaluation of focal lesions in the liver.

Calcification alters the viscoelastic properties of tendon fascicle bundles depending on matrix content.

Tendon fascicle bundles are often used as biological grafts and thus must meet certain quality requirements, such as excluding calcification, which alters the biomechanical properties of soft tissues. In this work, we investigate the influence of early-stage calcification on the mechanical and structural properties of tendon fascicle bundles with varying matrix content. The calcification process was modeled using sample incubation in concentrated simulated body fluid. Mechanical and structural properties were investigated using uniaxial tests with relaxation periods, dynamic mechanical analysis, as well as magnetic resonance imaging and atomic force microscopy. Mechanical tests showed that the initial phase of calcification causes an increase in the elasticity, storage, and loss modulus, as well as a drop in the normalized value of hysteresis. Further calcification of the samples results in decreased modulus of elasticity and a slight increase in the normalized value of hysteresis. Analysis via MRI and scanning electron microscopy showed that incubation alters fibrillar relationships within the tendon structure and the flow of body fluids. In the initial stage of calcification, calcium phosphate crystals are barely visible; however, extending the incubation time for the next 14 days results in the appearance of calcium phosphate crystals within the tendon structure and leads to damage in its structure. Our results show that the calcification process modifies the collagen-matrix relationships and leads to a change in their mechanical properties. These findings will help to understand the pathogenesis of clinical conditions caused by calcification process, leading to the development of effective treatments for these conditions. STATEMENT OF SIGNIFICANCE: This study investigates how calcium mineral deposition in tendons affects their mechanical response and which processes are responsible for this phenomenon. By analyzing the elastic and viscoelastic properties of animal fascicle bundles affected by calcification induced via incubation in concentrated simulated body fluid, the study sheds light on the relationship between structural and biochemical changes in tendons and their altered mechanical response. This understanding is crucial for optimizing tendinopathy treatment and preventing tendon injury. The findings provide insights into the calcification pathway and its resulting changes in the biomechanical behaviors of affected tendons, which have been previously unclear.

Effect of Matrix Size Reduction on Textural Information in Clinical Magnetic Resonance Imaging.

The selection of the matrix size is an important element of the magnetic resonance imaging (MRI) process, and has a significant impact on the acquired image quality. Signal to noise ratio, often used to assess MR image quality, has its limitations. Thus, for this purpose we propose a novel approach: the use of texture analysis as an index of the image quality that is sensitive for the change of matrix size. Image texture in biomedical images represents tissue and organ structures visualized via medical imaging modalities such as MRI. The correlation between texture parameters determined for the same tissues visualized in images acquired with different matrix sizes is analyzed to aid in the assessment of the selection of the optimal matrix size. T2-weighted coronal images of shoulders were acquired using five different matrix sizes while maintaining the same field of view; three regions of interest (bone, fat, and muscle) were considered. Lin's correlation coefficients were calculated for all possible pairs of the 310-element texture feature vectors evaluated for each matrix. The obtained results are discussed considering the image noise and blurring effect visible in images acquired with smaller matrices. Taking these phenomena into account, recommendations for the selection of the matrix size used for the MRI imaging were proposed.

Influence of Ultrasound Examination on Diagnosis and Treatment of Temporomandibular Disorders.

BACKGROUND: Disorders of the masticatory muscles and temporomandibular joints as well as the surrounding craniofacial structures are called temporomandibular disorders. These are dental diseases affecting an increasing number of people with a multifactorial etiology. Noninvasive ultrasonography imaging of temporomandibular joints was performed to obtain more detailed information on joint pathologies. MATERIAL AND METHODS: The aim of the study was to assess the influence of ultrasound examinations of the temporomandibular joints on the diagnosis and treatment planning in patients with temporomandibular disorders. The study included 110 patients examined with the use of the Research Diagnostic Criteria for Temporomandibular Disorders questionnaire, axis I and II, after which the initial treatment plan was created. All patients underwent an ultrasound examination of the temporomandibular joints. RESULTS: The results reveal numerous morphological changes within the joint structures in all treatment groups. Comparative statistical analyses of symptoms were performed between study groups. CONCLUSIONS: The number of pathologies in the myofascial pain group was much higher than expected and required introduction of additional treatment procedures. Further studies confirming these results and the effectiveness of ultrasound diagnostic of temporomandibular disorders are recommended.

Diffusion as a Natural Contrast in MR Imaging of Peripheral Artery Disease (PAD) Tissue Changes. A Case Study of the Clinical Application of DTI for a Patient with Chronic Calf Muscles Ischemia.

This paper reports a first application of diffusion tensor imaging with corrections by using the B-matrix spatial distribution method (BSD-DTI) for peripheral artery disease (PAD) detected in the changes of diffusion tensor parameters (DTPs). A 76-year-old male was diagnosed as having PAD, since he demonstrated in angiographic images of lower legs severe arterial stenosis and the presence of lateral and peripheral circulation and assigned to the double-blind RCT using mesenchymal stem cells (MSCs) or placebo for the regenerative treatment of implications of ischemic diseases. In order to indicate changes in diffusivity in calf muscles in comparison to a healthy control, a DTI methodology was developed. The main advantage of the applied protocol was decreased scanning time, which was achieved by reducing b-value and number of scans (to 1), while maintaining minimal number of diffusion gradient directions and high resolution. This was possible due to calibration via the BSD method, which reduced systematic errors and allowed quantitative analysis. In the course of PAD, diffusivities were elevated across the calf muscles in posterior compartment and lost their anisotropy. Different character was noticed for anterior compartment, in which diffusivities along and across muscles were decreased without a significant loss of anisotropy. After the intervention involving a series of injections, the improvement of DTPs and tractography was visible, but can be assigned neither to MSCs nor placebo before unblinding.

The dispersion of viscoelastic properties of fascicle bundles within the tendon results from the presence of interfascicular matrix and flow of body fluids.

In this work, we investigate differences in the mechanical and structural properties of tendon fascicle bundles dissected from different areas of bovine tendons. The properties of tendon fascicle bundles were investigated by means of uniaxial tests with relaxation periods and hysteresis, dynamic mechanical analysis (DMA), as well as magnetic resonance imaging (MRI). Uniaxial tests with relaxation periods revealed greater elastic modulus, hysteresis, as well as stress drop during the relaxation of samples dissected from the posterior side of the tendon. However, the normalized stress relaxation curves did not show a statistically significant difference in the stress drop between specimens cut from different zones or between different strain levels. Using dynamic mechanical analysis, we found that fascicle bundles dissected from the anterior side of the tendon had lower storage and loss moduli, which could result from altered fluid flow within the interfascicular matrix (IFM). The lower water content, diffusivity, and higher fractional anisotropy of the posterior part of the tendon, as observed using MRI, indicates a different structure of the IFM, which controls the flow of fluids within the tendon. Our results show that the viscoelastic response to dynamic loading is correlated with fluid flow within the IFM, which was confirmed during analysis of the MRI results. In contrast to this, the long-term relaxation of tendon fascicle bundles is controlled by viscoplasticity of the IFM and depends on the spatial distribution of the matrix within the tendon. Comparison of results from tensile tests, DMA, and MRI gives new insight into tendon mechanics and the role of the IFM. These findings may be useful in improving the diagnosis of tendon injury and effectiveness of medical treatments for tendinopathies.

Does image normalization and intensity resolution impact texture classification?

Image texture is a very important component in many types of images, including medical images. Medical images are often corrupted by noise and affected by artifacts. Some of the texture-based features that should describe the structure of the tissue under examination may also reflect, for example, the uneven sensitivity of the scanner within the tissue region. This in turn may lead to an inappropriate description of the tissue or incorrect classification. To limit these phenomena, the analyzed regions of interest are normalized. In texture analysis methods, image intensity normalization is usually followed by a reduction in the number of levels coding the intensity. The aim of this work was to analyze the impact of different image normalization methods and the number of intensity levels on texture classification, taking into account noise and artifacts related to uneven background brightness distribution. Analyses were performed on four sets of images: modified Brodatz textures, kidney images obtained by means of dynamic contrast-enhanced magnetic resonance imaging, shoulder images acquired as T2-weighted magnetic resonance images and CT heart and thorax images. The results will be of use for choosing a particular method of image normalization, based on the types of noise and distortion present in the images.

Novel Technique for Growth Plate Analysis Based on the Superposition of T1- and T2-weighted MR Imaging of Adolescent Wrists.

In this article, a new method of information extraction on the basis of the differentiation of T1- and T2-weighted MR images is proposed. It relies on a technique of superposition of T1- and T2-weighted MR images with use of statistical dominance algorithm. On the basis of implemented image analysis, a reproducible extraction of growth zone of adolescent boys' wrists is possible.