Axial Magnetic Resonance Imaging Images Research Articles

Is the Caudate, Putamen, and Globus Pallidus the Delusional Disorder's Trio? A Texture Analysis Study.

The neurobiological basis of delusional disorder is less explored through neuroimaging techniques than in other psychotic disorders. This study aims to provide information about the neural origins of delusional disorder (DD) by examining the neuroanatomical features of some basal nuclei with magnetic resonance imaging (MRI) texture analysis. Twenty DD patients and 20 healthy individuals were included in the study. Globus pallidus, putamen, and caudate nuclei were selected individually with a region of interest (ROI) on the axial MRI images. The entire texture analysis algorithm applied to all selected ROIs was done with an in-house software. Nuclei on both sides were taken as separate samples. There were no significant differences between groups in terms of age and gender. The average "mean, median and maximum" values of all three nuclei were decreased in DD patients. The small putamen area and the differences detected in different tissue parameters for all three nuclei in delusional disorder patients indicate that they differ in delusional disorder from normal controls (p < 0.05). The differences detected in the texture parameters for all three nuclei indicate that there is something different in the DD from in the normal controls. Neuroimaging studies with larger samples and different techniques in the future may shed light on the etiology of delusional disorder.

Brain Tumor Recognition Using Artificial Intelligence Neural-Networks (BRAIN): A Cost-Effective Clean-Energy Platform

Brain tumors necessitate swift detection and classification for optimal patient outcomes. Deep learning has been extensively utilized to recognize complex tumor patterns in magnetic resonance imaging (MRI) images, aiding in tumor diagnosis, treatment, and prognostication. However, model complexity and limited generalizability with unfamiliar data hinder appropriate clinical integration. The objective of this study is to develop a clean-energy cloud-based deep learning platform to classify brain tumors. Three datasets of a total of 2611 axial MRI images were used to train our multi-layer convolutional neural network (CNN). Our platform automatically optimized every transfer learning and data augmentation feature combination to provide the highest predictive accuracy for our classification task. Our proposed system identified and classified brain tumors successfully and efficiently with an overall precision value of 96.8% [95% CI; 93.8–97.6]. Using clean energy supercomputing resources and cloud platforms cut our workflow to 103 min, $0 in total cost, and a negligible carbon footprint (0.0014 kg eq CO2). By leveraging automated optimized learning, we developed a cost-effective deep learning (DL) platform that accurately classified brain tumors from axial MRI images of different levels. Although studies have identified machine learning tools to overcome these obstacles, only some are cost-effective, generalizable, and usable regardless of experience.

The Success of Deep Learning Modalities in Evaluating Modic Changes

BackgroundModic changes are pathologies that are common in the population and cause low back pain. The aim of the study is to analyze the modic changes detected in magnetic resonance imaging (MRI) using deep learning modalities. MethodsThe sagittal T1, sagittal and axial T2-weighted lumbar MRI images of 307 patients, of which 125 were female and 182 were male, aged 19-86 years, who underwent MRI examination between 2016-2021 were analyzed. Modic changes (MC) were categorized and marked according to signal changes. Our study consists of two independent stages: classification and segmentation. The categorized data was first classified using convolutional neural network (CNN) architectures such as DenseNet-121, DenseNet-169, and VGG-19. In the next stage, masks were removed by segmentation using U-Net, which is the CNN architecture, with image processing programs on the marked images. ResultsDuring the classification stage, the success rates for modic type 1, type 2, and type 3 changes were 98%, 96%, 100% in DenseNet-121, 100%, 94%, 100% in DenseNet-169, and 98%, 92%, 97% in VGG-19, respectively. At the segmentation phase, the success rate was 71% with the U-Net architecture. ConclusionEvaluation of MRI findings of MC in the etiology of lower back pain with deep learning architectures can significantly reduce the workload of the radiologist by providing ease of diagnosis.

Radiomics for the Detection of Active Sacroiliitis Using MR Imaging.

Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in detecting acute inflammatory findings as these methods primarily identify chronic structural lesions. Conversely, Magnetic Resonance Imaging (MRI) is the preferred technique for detecting bone marrow edema, although it is a complex process requiring extensive expertise. Additionally, ascertaining the origin of lesions can be challenging, even for experienced medical professionals. Machine learning (ML) has showcased its proficiency in various fields by uncovering patterns that are not easily perceived from multi-dimensional datasets derived from medical imaging. The aim of this study is to develop a radiomic signature to aid clinicians in diagnosing active sacroiliitis. A total of 354 sacroiliac joints were segmented from axial fluid-sensitive MRI images, and their radiomic features were extracted. After selecting the most informative features, a number of ML algorithms were utilized to identify the optimal method for detecting active sacroiliitis, leading to the selection of an Extreme Gradient Boosting (XGBoost) model that accomplished an Area Under the Receiver-Operating Characteristic curve (AUC-ROC) of 0.71, thus further showcasing the potential of radiomics in the field.

Two-Dimensional Magnetic Resonance Imaging in Preparation for Autograft Anterior Cruciate Ligament Reconstruction Demonstrates Quadriceps Tendon Is Thicker Than Patellar Tendon

The purpose of this study was to assess patellar tendon (PT) and quadriceps tendon (QT) thickness on preoperative magnetic resonance imaging (MRI), in both the sagittal and axial planes, at multiple points along each tendon, and to correlate these findings to anthropometric patient data before anterior cruciate ligament (ACL) surgery. Patients who underwent PT or QT autograft ACL reconstruction between 2020 and 2022 and who had preoperative MRIs with adequate visualization of the proximal QT and distal PT were retrospectively identified. Patient demographics were recorded (age, height, weight, sex, injury side). Preoperative MRI measurements were performed by 3 independent examiners using standardized protocol. Preoperative MRI measurements were the QT anterior-posterior (AP) thickness at 1, 2, and 4 cm from the proximal patella on axial and sagittal MRI images at the central aspect of the tendon, as well as PT AP thickness at 1, 2, and 4 cm from the distal patella on axial and sagittal MRI images at the central aspect of the tendon. Forty-one patients (21 females, 20 males) were evaluated, with a mean age of 33.4 years. The quadriceps tendon was significantly thicker than the patellar tendon at all measured locations (P < .0001) with average QT versus PT thickness (in mm) at each level sagittal 1 cm (7.13 vs 4.35), sagittal 2 cm (7.41 vs 4.44), sagittal 4 cm (7.26 vs 4.81), axial 1 cm (7.35 vs 4.50), axial 2 cm (7.63 vs 4.47), axial 4 cm (7.46 vs 4.62), respectively. There were no significant correlations between tendon size and patient body mass index. The quadriceps tendon is significantly thicker than the patellar tendon at 1, 2, and 4 cm from the patella in both males and females based on preoperative MRI before ACL surgery. Investigating the thickness of the tendons available for autograft harvest before surgery will give us a better understanding of tendon anatomy in the setting of ACL reconstruction.

Cerebral Malaria and Cytotoxic Lesions of the Corpus Callosum.

To the Editors: Malaria is a common cause of morbidity and mortality worldwide. According to the WHO, 229 million cases and 409,000 deaths were reported worldwide in 2020.1 Children account for a high burden of malaria-related mortality and are at a higher risk of developing cerebral malaria (CM).1 A 12-year-old, previously healthy female from Guinea-Bissau was admitted 1 day after arriving in Portugal with a 2-day history of headache, generalized myalgia, and high-grade fever. Physical examination was unremarkable, except for pallor, icteric sclerae, and nontender hepatomegaly (4 cm). The patient was hemodynamically stable (BP, 101/52 mmHg; HR, 111 bpm), but shortly after admission, vomiting, lethargy, and impaired consciousness were observed (Glasgow coma score 11). She presented with anemia (hemoglobin, 10.0 g/dL; normal reference [NR]: 12.0–16.0), thrombocytopenia (78 × 109/L; NR: 150–450) and significant elevation of inflammatory markers (C-reactive protein, 301.0 mg/L; NR, <5; and procalcitonin, >100 ng/mL; NR, <0.1). No electrolyte disturbances, hypoglycemia, or acidosis. Plasmodium falciparum blood smear was positive, and severe malaria was diagnosed due to high parasitemia (28.5%/erythrocytes–11.872.000 parasites/µL) and central nervous system (CNS) involvement other infections were excluded. The cerebrospinal fluid revealed pleocytosis (15 cells), generalized slowing on the electroencephalogram, and a lesion in the splenium of the corpus callosum with T2 hypersignal and restricted diffusion (Fig. 1), compatible with a cytotoxic lesion of the corpus callosum (CLOCCs) on brain magnetic resonance imaging (MRI). CM was diagnosed based on severely impaired consciousness, CLOCCs, and no other identifiable causes of coma.FIGURE 1.: MRI axial images T2 weighted (A), DWI (B) and ADC map (C): oval lesion in the splenium of the corpus callosum (arrow) with T2 hypersignal and diffusion restriction with DWI hypersignal and ADC map hyposignal, corresponding to cytotoxic edema.The patient was treated with parenteral quinine and clindamycin, due to the immediate unavailability of artesunate, plus ceftriaxone. She became afebrile with progressive improvement in neurological status after 48 h and complete parasite clearance on day 3. Artemether-lumefantrine was initiated on day 5, and the patient was discharged on day 8. CM is defined as a disturbance of consciousness in a patient with Plasmodium parasitemia and no other identifiable cause.2 Despite the reported low mortality rate of pediatric imported malaria in Europe (<1%), CM entails a risk of life-threatening acute CNS effects with diffuse encephalopathy and often rapidly progressive coma, as observed in our patient.3 Distinctive brain MRI features involving both white and gray matter structures have been reported in a large cohort of Malawian children with CM. Increased volume, T2 hyperintensity, and Diffusion-weighted imaging (DWI) abnormalities affecting the cortical area (61.7%), subcortical and periventricular white matter (71.7%), basal ganglia (84.2%) and the corpus callosum (49.2%) were the most frequent features.4 CLOCCS was a common MRI finding in CM, as suggested by DWI/Apparent diffusion coefficient mismatch with DWI increased signal and Apparent diffusion coefficient low signal,4 as observed in our patient (Fig. 1). The corpus callosum, mostly the splenium, is a region vulnerable to inflammatory cytokinopathy, driven by malaria, seizures or other causes.5 The higher density of cytokines and glutamate receptors in this area leads to high extracellular glutamate levels, inducing cytotoxic edema upon infection, which is frequently reversible.5 CM in children is the most severe complication of P. falciparum infection and needs a high suspicion index in nonendemic countries. Altered MRI and CLOCCs, although unspecific, are associated with CM.

Sensitivity and Specificity of MRI versus CBCT to Detect Vertical Root Fractures Using MicroCT as a Reference Standard

IntroductionVertical root fracture (VRF) in root-canal–treated teeth frequently results in tooth loss, partly because VRFs are difficult to diagnose and when detected the fracture is often beyond the point of preservation with surgical intervention. Nonionizing magnetic resonance imaging (MRI) has demonstrated the ability to detect small VRFs, but it is unknown how its diagnostic capabilities compare with the current imaging standard for VRF detection, cone-beam computed tomography (CBCT). This investigation aimed to compare the sensitivity and specificity between MRI and CBCT for detecting VRF, using micro-computed tomography (microCT) as a reference. MethodsA total of 120 extracted human tooth roots were root canal treated using common techniques, and VRFs were mechanically induced in a proportion. Samples were imaged using MRI, CBCT, and microCT. Axial MRI and CBCT images were examined by 3 board-certified endodontists, who evaluated VRF status (yes/no) and gave a confidence assessment for that decision, from which a receiver operating characteristic curve was generated. Intra- and inter-rater reliability were calculated, sensitivity and specificity, and area under the curve. ResultsIntra-rater reliability was 0.29–0.48 for MRI and 0.30–0.44 for CBCT. Inter-rater reliability for MRI was 0.37 and for CBCT 0.49. Sensitivity was 0.66 (95% confidence interval [CI], 0.53–0.78) and 0.58 (95% CI, 0.45–0.70), and specificity 0.72 (95% CI, 0.58–0.83) and 0.87 (95% CI, 0.75–0.95) for MRI and CBCT, respectively. Area under the curve was 0.74 (95% CI, 0.65–0.83) for MRI and 0.75 (95% CI, 0.66–0.84) for CBCT. ConclusionsThere was no significant difference in sensitivity or specificity between MRI and CBCT in detecting VRF, despite the early-stage development of MRI.

An MRI image automatic diagnosis model for lumbar disc herniation using semi-supervised learning

Lumbar disc herniation is a common disease that causes low back pain. Due to the high cost of medical diagnosis, as well as a shortage and uneven distribution of medical resources, a system that can automatically analyze and diagnose lumbar spine Magnetic Resonance Imaging (MRI) is becoming an urgent need. This study uses deep learning methods to establish a classifier to diagnose lumbar disc herniation. An MRI classification dataset of lumbar disc herniation consisting of public MRI images is presented and is used to train the proposed classifier. Because a common difficulty in applying computer vision technology to medical images is labeling training data, we use a semi-supervised model training method, while multilayer transverse axial MRI images are used as the model input. In this method, we first use unlabelled MRI images for random self-supervised pre-training and the pre-trained model as a feature extractor for MRI images. Then, all marked cross-sections of each intervertebral disc are used to calculate the feature vector through the feature extractor. The information of all feature vectors is integrated, while a multilayer perceptron is used for classification training. After training, the model achieved 87.11% accuracy, 87.50% sensitivity, 86.72% specificity and 0.9487 AUC (Area Under the ROC Curve) index on the test set. To analyze the rationality of the diagnostic results more quickly, we output the severity of degenerative changes in each region using a heatmap.

Spinal Cord Delineation Based on Computed Tomography Myelogram Versus T2 Magnetic Resonance Imaging in Spinal Stereotactic Body Radiation Therapy

Spinal cord delineation is critical to the delivery of stereotactic body radiation therapy (SBRT). Although underestimating the spinal cord can lead to irreversible myelopathy, overestimating the spinal cord may compromise the planning target volume coverage. We compare spinal cord contours based on computed tomography (CT) simulation with a myelogram to spinal cord contours based on fused axial T2 magnetic resonance imaging (MRI). Eight patients with 9 spinal metastases treated with spinal SBRT were contoured by 8 radiation oncologists, neurosurgeons, and physicists, with spinal cord definition based on (1) fused axial T2 MRI and (2) CT-myelogram simulation images, yielding 72 sets of spinal cord contours. The spinal cord volume was contoured at the target vertebral body volume based on both images. The mixed-effect model assessed comparisons of T2 MRI- to myelogram-defined spinal cord in centroid deviations (deviations in the center point of the cord) through the vertebral body target volume, spinal cord volumes, and maximum doses (0.035 cc point) to the spinal cord applying the patient's SBRT treatment plan, in addition to in-between and within-subject variabilities. The estimate for the fixed effect from the mixed model showed that the mean difference between 72 CT volumes and 72 MRI volumes was 0.06 cc and was not statistically significant (95% confidence interval, -0.034, 0.153; P=.1832). The mixed model showed that the mean dose at 0.035 cc for CT-defined spinal cord contours was 1.24 Gy lower than that of MRI-defined spinal cord contours and was statistically significant (95% confidence interval, -2.292, -0.180; P=.0271). Also, the mixed model indicated no statistical significance for deviations in any of the axes between MRI-defined spinal cord contours and CT-defined spinal cord contours. CT myelogram may not be required when MRI imaging is feasible, although uncertainty at the cord-to-treatment volume interface may result in overcontouring and hence higher estimated cord dose-maximums with axial T2 MRI-based cord definition.

Grading of gliomas using transfer learning on MRI images.

Despite the critical role of Magnetic Resonance Imaging (MRI) in the diagnosis of brain tumours, there are still many pitfalls in the exact grading of them, in particular, gliomas. In this regard, it was aimed to examine the potential of Transfer Learning (TL) and Machine Learning (ML) algorithms in the accurate grading of gliomas on MRI images. Dataset has included four types of axial MRI images of glioma brain tumours with grades I-IV: T1-weighted, T2-weighted, FLAIR, and T1-weighted Contrast-Enhanced (T1-CE). Images were resized, normalized, and randomly split into training, validation, and test sets. ImageNet pre-trained Convolutional Neural Networks (CNNs) were utilized for feature extraction and classification, using Adam and SGD optimizers. Logistic Regression (LR) and Support Vector Machine (SVM) methods were also implemented for classification instead of Fully Connected (FC) layers taking advantage of features extracted by each CNN. Evaluation metrics were computed to find the model with the best performance, and the highest overall accuracy of 99.38% was achieved for the model containing an SVM classifier and features extracted by pre-trained VGG-16. It was demonstrated that developing Computer-aided Diagnosis (CAD) systems using pre-trained CNNs and classification algorithms is a functional approach to automatically specify the grade of glioma brain tumours in MRI images. Using these models is an excellent alternative to invasive methods and helps doctors diagnose more accurately before treatment.

Une petite taille et une faible distance entre le rebord acétabulaire antérieur et le nerf fémoral sont des facteurs de risque de paralysie du nerf fémoral après une arthroplastie totale de hanche primaire par l’approche modifiée de Watson-Jones

BackgroundNerve palsy following total hip arthroplasty (THA) critically impacts patient clinical function. However, few studies have focused on femoral nerve palsy (FNP) following THA via the modified Watson-Jones approach. Previous reports have suggested that THA, regardless of the approach, is associated with several FNP risk factors, including female gender, hip dysplasia, revision surgery, and short stature. Magnetic resonance imaging (MRI) has suggested that a shorter distance between the femoral nerve and the anterior acetabular edge (dFN) is related to FNP after THA. The purposes of this study were: 1) to determine the presumed risk factors through a retrospective investigation of FNP clinical courses; and 2) to identify the relationships between FNP occurrence and the short dFN following primary THA via the modified Watson-Jones approach. HypothesisShort stature is a risk factor for femoral nerve palsy following THA, i.e. a significant difference in dFN exists between patients with and without FNP. Patients and methodsThis retrospective case-control study was performed at a single university hospital. From January 2016 to December 2020, 676 THAs were performed via the modified Watson-Jones approach at our institution. These included 495 THAs performed in the supine position and 181 in the lateral position. In this study, FNP was defined as weakness of the quadriceps femoris (manual muscle test<grade 3) with or without sensory disturbance over the anteromedial aspect of the thigh. The incidence of FNP was calculated. Patient background factors (age, sex, preoperative diagnosis, surgical position, height, weight, body mass index, surgeon experience, type of components, the method of anesthesia, leg lengthening during the surgery, and operation time) were compared between the FNP group and a non-FNP control group. The dFN was measured in T1-weighted MRI axial images at the level of the hip center. The distance between the femoral nerve and the anterior acetabular edges, where retractors are commonly placed during surgery, was also measured and compared between the FNP group and the non-FNP control group. The FNP group and non-FNP control group were extracted by 1:4 matching of patient height and weight. All data were statistically evaluated using the Mann-Whitney U test, and p-values less than 0.05 were considered statistically significant. ResultsFNP occurred in 6 out of 676 joints (0.88 %) following primary THA via the modified Watson-Jones approach. In all 6 cases, the motor deficit recovered completely within a year. Patient height was significantly shorter in the FNP group than in the non-FNP control group (148.4±3.3cm vs. 155.4±8.1cm (p=0.01)). The dFN was significantly shorter in the FNP group (16.3±4.1mm vs. 21.5±4.0mm (p=0.034)). ConclusionShort stature and short dFN are risk factors for FNP after THA using the modified Watson-Jones approach. Level of evidenceIII, case-control study.

Short stature and short distance between the anterior acetabular rim to the femoral nerve are risk factors for femoral nerve palsy following primary total hip arthroplasty using the modified Watson-Jones approach

BackgroundNerve palsy following total hip arthroplasty (THA) critically impacts patient clinical function. However, few studies have focused on femoral nerve palsy (FNP) following THA via the modified Watson-Jones approach. Previous reports have suggested that THA, regardless of the approach, is associated with several FNP risk factors, including female gender, hip dysplasia, revision surgery, and short stature. Magnetic resonance imaging (MRI) has suggested that a shorter distance between the femoral nerve and the anterior acetabular edge (dFN) is related to FNP after THA. The purposes of this study were: 1) to determine the presumed risk factors through a retrospective investigation of FNP clinical courses, and 2) to identify the relationships between FNP occurrence and the short dFN following primary THA via the modified Watson-Jones approach. HypothesisShort stature is a risk factor for femoral nerve palsy following THA. i.e. a significant difference in dFN exists between patients with and without FNP. Patients and methodsThis retrospective case-control study was performed at a single university hospital. From January 2016 to December 2020, 676 THAs were performed via the modified Watson-Jones approach at our institution. These included 495 THAs performed in the supine position and 181 in the lateral position. In this study, FNP was defined as weakness of the quadriceps femoris (manual muscle test<grade 3) with or without sensory disturbance over the anteromedial aspect of the thigh. The incidence of FNP was calculated. Patient background factors (age, sex, preoperative diagnosis, surgical position, height, weight, body mass index, surgeon experience, type of components, the method of anesthesia, leg lengthening during the surgery, and operation time) were compared between the FNP group and a non-FNP control group. The dFN was measured in T1-weighted MRI axial images at the level of the hip center. The distance between the femoral nerve and the anterior acetabular edges, where retractors are commonly placed during surgery, was also measured and compared between the FNP group and the non-FNP control group. The FNP group and non-FNP control group were extracted by 1:4 matching of patient height and weight. All data were statistically evaluated using the Mann-Whitney U test, and p values less than 0.05 were considered statistically significant. ResultsFNP occurred in 6 out of 676 joints (0.88%) following primary THA via the modified Watson-Jones approach. In all 6 cases, the motor deficit recovered completely within a year. Patient height was significantly shorter in the FNP group than in the non-FNP control group (148.4±3.3cm vs. 155.4±8.1cm [p=0.01]). The dFN was significantly shorter in the FNP group (16.3±4.1mm vs. 21.5±4.0mm [p=0.034]). ConclusionShort stature and short dFN are risk factors for FNP after THA using the modified Watson-Jones approach. Level of evidenceIII, case-control study.

An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ

Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients.

The Risk of Iatrogenic Peroneal Nerve Injury in Lateral Meniscal Repair and Safe Zone to Minimize the Risk Based on Actual Arthroscopic Position: An MRI Study

Background: Lateral meniscal repair using an all–inside meniscal repair device involves a risk of iatrogenic peroneal nerve injury. To our knowledge, there have been no previous studies evaluating the risk of injury with the knee in the standard operational figure-of-4 position with joint dilatation in arthroscopic lateral meniscal repair. Purpose: To evaluate and compare the risk of peroneal nerve injury and establish the safe and danger zones in repairing the lateral meniscus through the anteromedial, anterolateral, or transpatellar portal in relation to the medial and lateral borders of the popliteal tendon (PT). Study Design: Descriptive laboratory study. Methods: Using axial magnetic resonance imaging (MRI) studies of knees in the figure-of-4 position with joint fluid dilatation at the level of the lateral meniscus, we drew direct lines to simulate a straight all–inside meniscal repair device deployed from the anteromedial, anterolateral, and transpatellar portals to the medial and lateral borders of the PT. If the line passed through or touched the peroneal nerve, a risk of iatrogenic peroneal nerve injury was noted, and measurements were made to determine the safe and danger zones for peroneal nerve injury in relation to the medial or lateral border of the PT. Results: Axial MRI images of 29 adult patients were reviewed. Repairing the lateral meniscus through the anteromedial portal in relation to the lateral border of the PT and through the anterolateral portal in relation to the medial border of the PT had a 0% risk of peroneal nerve injury. The “safe zone” in relation to the medial border of the PT through the anterolateral portal was between the medial border of the PT and 9.62 ± 4.60 mm medially from the same border. Conclusion: It is safe to repair the body of the lateral meniscus through the anteromedial portal in the area lateral to the lateral border of the PT or through the anterolateral portal in the area medial to the medial border of the PT. Clinical Relevance: There is a risk of iatrogenic peroneal nerve injury during lateral meniscal repair. Thus, we recommend repairing the lateral meniscal tissue through the anteromedial portal in the area lateral to the lateral border of the PT and using the anterolateral portal in the area medial to the medial border of the PT, as neither of these approaches resulted in peroneal nerve injury. Additionally, the surgeon can decrease this risk by repairing the meniscal tissue using the all–inside meniscal device in the safe zone area.

Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI

Nakagami distribution and related imaging methods are very efficient in diagnostic ultrasonography for visualization and characterization of tissues for years. Abnormalities in tissues are distinguished from surrounding cells by application of the distribution ruled by the Nakagami m-parameter. The potential of discrimination in ultrasonography enables intelligent segmentation of lesions by other diagnostic tools and the imaging technique is very promising in other areas of medicine, like magnetic resonance imaging (MRI) for brain lesion identification, as presented in this paper. Therefore, we propose a novel Nakagami-Fuzzy imaging framework for intelligent and fully automated suspicious region segmentation from axial FLAIR MRI images exhibiting brain tumor characteristics to satisfy ground truth images with different precision levels. The images from MRI data set are processed by applying Nakagami distribution from pre-Rayleigh to post-Rayleigh for adjusting m-parameter. Amorphous and non-homogenous suspicious regions revealed by Nakagami imaging are segmented using customized Fuzzy 2-means to compare with two types of binary ground truths. The framework we propose is an outstanding example of fuzzy-based expert systems providing an average of 92.61% dice score for the main clinical experiment we conducted using the images and two types of ground truths provided by University of Hospital, Hradec Kralove. We also tested our framework by the BraTS 2012 and BraTS 2020 datasets and achieved an average of 91.88% and 89.25% dice scores respectively, which are competitive among the relevant researches.

Validation of exophthalmos magnetic resonance imaging measurements in patients with Graves' orbitopathy, compared to ophthalmometry results.

PurposeAlthough assessment of the orbital structures using magnetic resonance imaging (MRI) is well described in the literature, there is no consensus as to which measurement method is the most useful in exophthalmos assessment. The aim of the study was to correlate 2 MRI methods of exophthalmos measurement with exophthalmometry results and to determine a proper technique of exophthalmos measurement.Material and methodsFifty-four patients (108 orbits) with exophthalmos in the course of Graves’ orbitopathy were enrolled in the study. Two measurements on axial T2W orbital MRI images were performed by 2 independent radiologists: the distance from the interzygomatic line to the anterior surface of the globe (AD) and the distance from the interzygomatic line to the posterior sclera (PD). Within 4 weeks, an exophthalmometry was performed by an ophthalmologist using a Hertel exophthalmometer. The inter-observer variation was assessed using the Pearson correlation coefficient. Values were presented as mean and standard deviation, and the differences in values were explored with Student’s t-test.ResultsThe mean AD measured on MRI by the first observer was 20.6 ± 3 mm, and 20.6 ± 2.9 mm by the second observer. PD values were 2.9 ± 2.8 mm and 3.4 ± 2.8 mm, respectively. The mean exophthalmometry result was 21 ± 3.3 mm. The correlation was very high between observers for AD measurements (r = 0.98, p = 0.01) and high for PD measurements (r = 0.95, p = 0.01). AD measurements on MRI and exophthalmometry results were strongly correlated (r = 0.9, p = 0.01).ConclusionsThe AD measurement has better reproducibility and is directly correlated with Hertel exophthalmometry. This method could be sufficient in routine practice.

Segmentation of orbital and periorbital lesions detected in orbital magnetic resonance imaging by deep learning method

PurposeMagnetic resonance imaging (MRI) has a special place in the evaluation of orbital and periorbital lesions. Segmentation is one of the deep learning methods. In this study, we aimed to perform segmentation in orbital and periorbital lesions.Material and methodsContrast-enhanced orbital MRIs performed between 2010 and 2019 were retrospectively screened, and 302 cross-sections of contrast-enhanced, fat-suppressed, T1-weighted, axial MRI images of 95 patients obtained using 3 T and 1.5 T devices were included in the study. The dataset was divided into 3: training, test, and validation. The number of training and validation data was increased 4 times by applying data augmentation (horizontal, vertical, and both). Pytorch UNet was used for training, with 100 epochs. The intersection over union (IOU) statistic (the Jaccard index) was selected as 50%, and the results were calculated.ResultsThe 77th epoch model provided the best results: true positives, 23; false positives, 4; and false negatives, 8. The pre-cision, sensitivity, and F1 score were determined as 0.85, 0.74, and 0.79, respectively.ConclusionsOur study proved to be successful in segmentation by deep learning method. It is one of the pioneering studies on this subject and will shed light on further segmentation studies to be performed in orbital MR images.

Using Marker-Controlled Watershed Transform to Detect Baker's Cyst in Magnetic Resonance Imaging Images: A Pilot Study.

Nowadays, magnetic resonance imaging (MRI) has a high ability to distinguish between soft tissues because of high spatial resolution. Image processing is extensively used to extract clinical data from imaging modalities. In the medical image processing field, the knee's cyst (especially Baker) segmentation is one of the novel research areas. There are different methods for image segmentation. In this paper, the mathematical operation of the watershed algorithm is utilized by MATLAB software based on marker-controlled watershed segmentation for the detection of Baker's cyst in the knee's joint MRI sagittal and axial T2-weighted images. The performance of this algorithm was investigated, and the results showed that in a short time Baker's cyst can be clearly extracted from original images in axial and sagittal planes. The marker-controlled watershed segmentation was able to detect Baker's cyst reliable and can save time and current cost, especially in the absence of specialists it can help us for the easier diagnosis of MRI pathologies.

Analysis of the paraspinal muscle morphology of the lumbar spine using a convolutional neural network (CNN)

PurposeThis single-center study aimed to develop a convolutional neural network to segment multiple consecutive axial magnetic resonance imaging (MRI) slices of the lumbar spinal muscles of patients with lower back pain and automatically classify fatty muscle degeneration.MethodsWe developed a fully connected deep convolutional neural network (CNN) with a pre-trained U-Net model trained on a dataset of 3,650 axial T2-weighted MRI images from 100 patients with lower back pain. We included all qualities of MRI; the exclusion criteria were fractures, tumors, infection, or spine implants. The training was performed using k-fold cross-validation (k = 10), and performance was evaluated using the dice similarity coefficient (DSC) and cross-sectional area error (CSA error). For clinical correlation, we used a simplified Goutallier classification (SGC) system with three classes.ResultsThe mean DSC was high for overall muscle (0.91) and muscle tissue segmentation (0.83) but showed deficiencies in fatty tissue segmentation (0.51). The CSA error was small for the overall muscle area of 8.42%, and fatty tissue segmentation showed a high mean CSA error of 40.74%. The SGC classification was correctly predicted in 75% of the patients.ConclusionOur fully connected CNN segmented overall muscle and muscle tissue with high precision and recall, as well as good DSC values. The mean predicted SGC values of all available patient axial slices showed promising results. With an overall Error of 25%, further development is needed for clinical implementation. Larger datasets and training of other model architectures are required to segment fatty tissue more accurately.

The Danger Zone for Iatrogenic Neurovascular Injury in All-Inside Lateral Meniscal Repair in Relation to the Popliteal Tendon: An MRI Study.

Background:Lateral meniscal repair can endanger the nearby neurovascular structure (peroneal nerve or popliteal artery). To our knowledge, there have been no studies to evaluate the danger zone of all-inside meniscal repair through the anteromedial (AM) and anterolateral (AL) portals in relation to the medial and lateral edges of the popliteal tendon (PT).Purpose:To establish the risk of neurovascular injury and the danger zone in repairing the lateral meniscus in relation to the medial and lateral edges of the PT.Study Design:Descriptive laboratory study.Methods:Using axial magnetic resonance imaging (MRI) studies at the level of the lateral meniscus, lines were drawn to simulate a straight, all-inside meniscal repair device, drawn from the AM and AL portals to both the medial and lateral edges of the PT. In cases in which the line passed through the neurovascular structure, a risk of iatrogenic neurovascular injury was deemed, and measurements were made to determine the danger zones of neurovascular injury in relation to the medial or lateral edges of the PT.Results:Axial MRI images of 240 adult patients were reviewed retrospectively. Repairing the body of the lateral meniscus through the AM portal had a greater risk of neurovascular injury than repairs made through the AL portal in relation to the medial edge of the PT (P = .006). The danger zone in repairing the lateral meniscus through the AM portal extended 1.82 ± 1.68 mm laterally from the lateral edge of the PT and 3.13 ± 2.45 mm medially from the medial edge of the PT. Through the AL portal, the danger zone extended 2.81 ± 1.94 mm laterally from the lateral edge of the PT and 1.39 ± 1.53 mm medially from the medial edge of the PT.Conclusion:Repairing the lateral meniscus through either the AM or the AL portals in relation to the PT can endanger the peroneal nerve or popliteal artery.Clinical Relevance:The surgeon can minimize the risk of iatrogenic neurovascular injury in lateral meniscal repair by avoiding using the all-inside meniscal device in the danger zone area as described in this study.