The relationship between the duration and the retraction and atrophy grades in traumatic isolated full-thickness supraspinatus tears in young patients.

BACKGROUND: The study aimed to determine the grade of retraction and atrophy according to the time elapsed in traumatic isolated full-thickness supraspinatus (SS) tears in young patients. METHODS: One thousand twenty-six patients, who underwent arthroscopic shoulder surgery, were retrospectively reviewed. Pre-operative magnetic resonance imaging (MRI) of 69 patients aged 18 to 40 years with isolated traumatic full-thickness SS lesions remaining after exclusion criteria were evaluated for tendon retraction and atrophy grades. SS retraction was determined from a T2-weighted oblique coronal MRI slice, and the atrophy grade was determined from the T1-weighted oblique sagittal MRI slice. The patients were divided into four groups 0-1 month, 1-3 months, 3-6 months, and 6-12 months according to the time between trauma and MRI. The relationship of tendon retraction and muscle atrophy with elapsed time was evaluated, in addition, comparisons between groups were made. RESULTS: Thirty-one (45%) of the patients were female and their mean age was 30 ± 7.3 (18-40) years. The mean age of men was 30.5 ± 6.9 (18-39) years (p = 0.880). The time between rupture and MRI was moderately correlated with retraction and strongly correlated with atrophy grades (r = 0.599, 0.751, respectively). It was observed that there was a statistically significant difference between the 1st (0-1 month) and 2nd (1-3 months) groups (p = 0.003, 0.001, respectively), and between the 2nd and 3rd (3-6 months) groups (p = 0.032, 0.002, respectively), but there was no significant difference between the 3rd and 4th (6-12 months) groups (p = 0.118, 0.057, respectively). In addition, there was a moderate correlation between tendon retraction and atrophy grades (r = 0.668). Power (1- b) in post hoc analysis was calculated as 0.826. CONCLUSIONS: The current study, supported by arthroscopy, showed that there is a moderate and strong positive correlation between the time elapsed after trauma and the level of retraction and degree of atrophy in traumatic full-thickness SS tears, and demonstrated the importance of early surgical intervention in young patients.

Current Management of Neuroendocrine Tumour Liver Metastases.

PURPOSE OF REVIEW: This article aims to illustrate the current state of investigations and management of liver metastases in patients with Neuroendocrine Neoplasms. Neuroendocrine tumours (NETs) are rising in incidence globally and have become the second most prevalent gastrointestinal malignancy in UK and USA. Frequently, patients have metastatic disease at time of presentation. The liver is the most common site of metastases for gastro-enteropancreatic NETs. Characterisation of liver metastases with imaging is important to ensure disease is not under-staged. RECENT FINDINGS: Magnetic resonance imaging and positron emission tomography are now becoming standard of care for imaging liver metastases. There is an increasing armamentarium of therapies available for management of NETs and loco-regional therapy for liver metastases. The data supporting surgical and loco-regional therapy is reviewed with focus on role of liver transplantation. It is important to use appropriate imaging and classification of NET liver metastases. It is key that decisions regarding approach to treatment is undertaken in a multidisciplinary team and that individualised approaches are considered for management of patients with metastatic NETs.

MRI reporting radiographers - Has there been a progression or regression in numbers and scope of practice?

INTRODUCTION: The aim of this study was to determine whether there had been a progression or regression in the numbers and scope of practice of reporting radiographers in Magnetic Resonance Imaging (MRI) in the United Kingdom (UK), using data from a previous study conducted by the author in 2019/20 as a comparison. METHODS: Data was gathered via a survey sent to multiple users and groups of MRI Radiographers in the UK. The survey consisted of a questionnaire, using both numerical and free text answers to enable the responders to elaborate on their scope of practice whilst also allowing numerical data to be easily analysed. The responses were analysed for patterns and comparison was made with the data from the previous survey. The questionnaire was based on three main themes of those in training, those trained and those in practice. RESULTS: A total of 62 sites had radiographers in training (n =24) or trained (n = 125) in MRI reporting compared to 46 sites in the previous survey. The majority of responders were from England (n = 56/62) with only a few sites in Scotland (n = 4) and Wales (n = 2). There were 24 radiographers in training, a reduction compared to the previous survey and an increase in the number of radiographers trained (n = 80 to n = 125) and in practice (n = 57 to n = 84). Scope of practice had also increased with the addition of MRCPs, orbits and pituitary. CONCLUSION: This survey provides evidence that the number and scope of practice of MRI reporting radiographers within the UK has progressed when compared to a previous survey from 2019/20. The numbers however are still low and there remain significant geographical variations. The continued predominance of single handed practice is a concern and the reasons behind this and the slow expansion of skill mix reporting in this modality needs further investigation. IMPLICATIONS FOR PRACTICE: This study provides evidence of the number and scope of practice of the radiographer reporting of MRI examinations in the UK. It also demonstrates that there has been an increase in scope and number of radiographers reporting MRI scans, this will provide evidence and proof of concept for departments looking to initiate or increase this practice.

Neurodevelopmental outcome in preterm infants with intraventricular hemorrhages: the potential of quantitative brainstem MRI.

OBJECTIVES: This retrospective study aimed to identify quantitative magnetic resonance imaging markers in the brainstem of preterm neonates with intraventricular hemorrhages. It delves into the intricate associations between quantitative brainstem magnetic resonance imaging metrics and neurodevelopmental outcomes in preterm infants with intraventricular hemorrhage, aiming to elucidate potential relationships and their clinical implications. MATERIALS AND METHODS: Neuroimaging was performed on preterm neonates with intraventricular hemorrhage using a multi-dynamic multi-echo sequence to determine T1 relaxation time, T2 relaxation time, and proton density in specific brainstem regions. Neonatal outcome scores were collected using the Bayley Scales of Infant and Toddler Development. Statistical analysis aimed to explore potential correlations between magnetic resonance imaging metrics and neurodevelopmental outcomes. RESULTS: Sixty preterm neonates (mean gestational age at birth 26.26 ± 2.69 wk; n = 24 [40%] females) were included. The T2 relaxation time of the midbrain exhibited significant positive correlations with cognitive (r = 0.538, P < 0.0001, Pearson's correlation), motor (r = 0.530, P < 0.0001), and language (r = 0.449, P = 0.0008) composite scores at 1 yr of age. CONCLUSION: Quantitative magnetic resonance imaging can provide valuable insights into neurodevelopmental outcomes after intraventricular hemorrhage, potentially aiding in identifying at-risk neonates. Multi-dynamic multi-echo sequence sequences hold promise as an adjunct to conventional sequences, enhancing the sensitivity of neonatal magnetic resonance neuroimaging and supporting clinical decision-making for these vulnerable patients.

Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach.

Background: This study evaluates the performance of a vision transformer (ViT) model, ViT-b16, in classifying ischemic stroke cases from Moroccan MRI scans and compares it to the Visual Geometry Group 16 (VGG-16) model used in a prior study. Methods: A dataset of 342 MRI scans, categorized into 'Normal' and 'Stroke' classes, underwent preprocessing using TensorFlow's tf.data API. Results: The ViT-b16 model was trained and evaluated, yielding an impressive accuracy of 97.59%, surpassing the VGG-16 model's 90% accuracy. Conclusions: This research highlights the ViT-b16 model's superior classification capabilities for ischemic stroke diagnosis, contributing to the field of medical image analysis. By showcasing the efficacy of advanced deep learning architectures, particularly in the context of Moroccan MRI scans, this study underscores the potential for real-world clinical applications. Ultimately, our findings emphasize the importance of further exploration into AI-based diagnostic tools for improving healthcare outcomes.

CDPNet: a radiomic feature learning method with epigenetic application to estimating MGMT promoter methylation status in glioblastoma.

Radiomics has been widely recognized for its effectiveness in decoding tumor phenotypes through the extraction of quantitative imaging features. However, the robustness of radiomic methods to estimate clinically relevant biomarkers non-invasively remains largely untested. In this study, we propose Cascaded Data Processing Network (CDPNet), a radiomic feature learning method to predict tumor molecular status from medical images. We apply CDPNet to an epigenetic case, specifically targeting the estimation of O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation from Magnetic Resonance Imaging (MRI) scans of glioblastoma patients. CDPNet has three components: 1) Principal Component Analysis (PCA), 2) Fisher Linear Discriminant (FLD), and 3) a combination of hashing and blockwise histograms. The outlined architectural framework capitalizes on PCA to reconstruct input image patches, followed by FLD to extract discriminative filter banks, and finally using binary hashing and blockwise histogram module for indexing, pooling, and feature generation. To validate the effectiveness of CDPNet, we conducted an exhaustive evaluation on a comprehensive retrospective cohort comprising 484 IDH-wildtype glioblastoma patients with pre-operative multi-parametric MRI scans (T1, T1-Gd, T2, and T2-FLAIR). The prediction of MGMT promoter methylation status was cast as a binary classification problem. The developed model underwent rigorous training via 10-fold cross-validation on a discovery cohort of 446 patients. Subsequently, the model's performance was evaluated on a distinct and previously unseen replication cohort of 38 patients. Our method achieved an accuracy of 70.11% and an area under the curve of 0.71 (95% CI: 0.65 - 0.74).

Multispace Maxillofacial Space Infections Involving Orbit: Report of a Rare Case.

Peri-apical infection is a prevalent form of odontogenic infection, typically following the path of least resistance through cancellous bone towards the cortical plate. This infection begins as cellulitis and may progress to an abscess, sometimes involving various anatomically potential spaces. While frontal space infection is a rare complication of odontogenic infection spread, it can occur in conjunction with buccal space, sub-mental space, and orbital space involvement. In this report, we present the case of a 62-year-old man with a history of tooth pain and diabetes mellitus for the past decade. The patient exhibited symptoms of swelling, multiple draining sinuses, pain, proptosis, and fever. Prompt action was taken to drain the multiple abscesses, and the causative teeth were subsequently extracted. This case illustrates one of the severe complications associated with odontogenic infection, which has the potential to be life-threatening.

Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.

PURPOSE: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-based models. The correlation between geometric and dosimetric measures was also investigated to establish whether dosimetric assessment is required for clinical validation. METHOD: CT and MRI-based deep learning autosegmentation models were trained using edited and unedited clinical contours. Autosegmentations were dosimetrically compared to gold standard contours for a test cohort. D1%, D5%, D50%, and maximum dose were used as clinically relevant dosimetric measures. The statistical significance of dosimetric differences between the gold standard and autocontours was established using paired Student's t-tests. Clinically significant cases were identified via dosimetric headroom to the OAR tolerance. Pearson's Correlations were used to investigate the relationship between geometric measures and absolute percentage dose changes for each autosegmentation model. RESULTS: Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the CT DL-AC models or between the MRI DL-AC for any investigated brain OARs. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than other OARs, for all autosegmentation models. A weak correlation was consistently observed between the outcomes of dosimetric and geometric evaluations. CONCLUSIONS: Editing contours before training the DL-AC model had no significant impact on dosimetry. The geometric test metrics were inadequate to estimate the impact of contour inaccuracies on dose. Accordingly, dosimetric analysis is needed to evaluate the clinical applicability of DL-AC models in the brain.

Use of Compressed Sensing Accelerated, Low-Velocity Encoded, Isotropic Resolution, Phase Contrast Magnetic Resonance Angiography for SEEG Electrode Implantation.

OBJECTIVE: We assessed the feasibility of using compressed sensing accelerated, low-velocity encoded, isotropic resolution phase contrast (CLIP) magnetic resonance angiography (MRA) for avascular trajectory planning of stereoelectroencephalography. METHODS: Ten healthy subjects (1 woman and 9 men; age, 33.6 ± 9.0 years) and 20 consecutive patients (12 female patients; age, 22 ± 13.6 years) were enrolled in the present study. The healthy subjects underwent CLIP-MRA, and 3 other phase contrast MRA protocols with conventional parallel imaging (PI) acceleration, including low resolution with twofold PI (PI2), high resolution (HR) with fivefold PI (PI5), and HR-PI2. The patients underwent CLIP-MRA and computed tomography angiography (CTA). The image qualities were evaluated. The numbers and locations of trajectory-vessel conflict detected using CLIP-MRA were noted. RESULTS: With similar scan durations, CLIP-MRA achieved higher spatial resolution compared with low resolution with PI2 and detected significantly more branches compared with HR-PI5. With the same spatial resolution, the signal/noise and contrast/noise ratios of CLIP-MRA were higher than those with HR-PI2 with a shorter scan duration. For the 12 adult patients (10 female patients; 28.8 ± 12.7 years), CLIP-MRA had better signal/noise and contrast/noise ratios than CTA. The trajectory had required modification for 14 of the 20 patients (70%), with a proportion of trajectory modification of 10.7% (23 of 215 electrodes). The middle meningeal artery, cortical vessel, and skull vessel were the main vessels with conflict (n = 11, n = 7, and n = 5, respectively). CONCLUSIONS: In the present study, CLIP-MRA provided a clear cortical vascular display noninvasively without intravascular contrast and radiation. The middle meningeal artery and diploic and emissary veins were the main conflict vessels and could be clearly displayed using CLIP-MRA but not CTA.

Using Geometric Morphometric Analysis of Magnetic Resonance Imaging to Assess the Anatomy of the Eustachian Tube in Children with and without Otitis Media.

Otitis media (OM) is among the most common of childhood illnesses. It has long been hypothesized that children under age two are predisposed to OM due to differences in the anatomy of the Eustachian tube (ET), including the angle of the ET. OM in later childhood is less common but does occur, begging the question, are there shape differences in the ET that persist underlying later occurrences of OM? To answer this question, a novel method, which applied geometric and morphometric shape analysis to landmarks obtained from MRI data, was used. MRI scans were performed on 16 children (5 control, 3 cOME, and 8 rAOM) between 2011 and 2015. Sixteen landmarks representing the shape of the ET, cranial base, and palate were analyzed. The results of a Procrustes ANOVA indicate that the shape of the ET varies significantly (p < 0.01) between the OM and control groups. The shape differences between the OM group and the control are a medial and low attachment site of the tensor veli palatini (TVP) muscle, a posterior and high torus tubarius, and an anteriorly projected palate. These results support previous findings that a relatively horizontal ET is associated with a predisposition for OM. This study used a novel approach to examine anatomical differences in children with and without OM. First, the data set is unique in that it includes MRI scans of children with a confirmed OM diagnosis. Second, the use of MRI scans in craniofacial anatomy OM research is novel and allows for the collection of soft tissue landmarks and the visualization of soft tissue structures. Third, geometric morphometric shape analysis is a statistical method that captures shape differences, offering a more universal picture of nuanced changes within the entire set of landmarks, in contrast to more traditional linear and angular measurements used in prior OM studies examining craniofacial anatomy.

Safety of Magnetic Resonance Imaging in Pregnancy.

Magnetic resonance imaging is being increasingly used to diagnose and follow up a variety of medical conditions in pregnancy, both for maternal and fetal indications. However, limited data regarding its safe use in pregnancy may be a source of anxiety and avoidance for both patients and their healthcare providers. In this review, we critically discuss the main safety concerns of Magnetic Resonance Imaging (MRI) in pregnancy including energy deposition, acoustic noise, and use of contrast agents, supported by data from animal and human studies. Use of maternal sedatives and concerns related to occupational exposure in pregnant personnel are also addressed. Exposure to gadolinium-based contrast agents and sedation for MRI during pregnancy should be avoided whenever feasible.

Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy.

Objective.Deep-learning auto-contouring (DL-AC) promises standardisation of organ-at-risk (OAR) contouring, enhancing quality and improving efficiency in radiotherapy. No commercial models exist for OAR contouring based on brain magnetic resonance imaging (MRI). We trained and evaluated computed tomography (CT) and MRI OAR autosegmentation models in RayStation. To ascertain clinical usability, we investigated the geometric impact of contour editing before training on model quality.Approach.Retrospective glioma cases were randomly selected for training (n= 32, 47) and validation (n= 9, 10) for MRI and CT, respectively. Clinical contours were edited using international consensus (gold standard) based on MRI and CT. MRI models were trained (i) using the original clinical contours based on planning CT and rigidly registered T1-weighted gadolinium-enhanced MRI (MRIu), (ii) as (i), further edited based on CT anatomy, to meet international consensus guidelines (MRIeCT), and (iii) as (i), further edited based on MRI anatomy (MRIeMRI). CT models were trained using: (iv) original clinical contours (CTu) and (v) clinical contours edited based on CT anatomy (CTeCT). Auto-contours were geometrically compared to gold standard validation contours (CTeCT or MRIeMRI) using Dice Similarity Coefficient, sensitivity, and mean distance to agreement. Models' performances were compared using paired Student's t-testing.Main results.The edited autosegmentation models successfully generated more segmentations than the unedited models. Paired t-testing showed editing pituitary, orbits, optic nerves, lenses, and optic chiasm on MRI before training significantly improved at least one geometry metric. MRI-based DL-AC performed worse than CT-based in delineating the lacrimal gland, whereas the CT-based performed worse in delineating the optic chiasm. No significant differences were found between the CTeCT and CTu except for optic chiasm.Significance.T1w-MRI DL-AC could segment all brain OARs except the lacrimal glands, which cannot be easily visualized on T1w-MRI. Editing contours on MRI before model training improved geometric performance. MRI DL-AC in RT may improve consistency, quality and efficiency but requires careful editing of training contours.

The experience of interval scans for adults living with primary malignant brain tumors.

PURPOSE: People with primary malignant brain tumors (PMBT) undergo anti-tumor treatment and are followed up with MRI interval scans. There are potential burdens and benefits to interval scanning, yet high-quality evidence to suggest whether scans are beneficial or alter outcomes of importance for patients is lacking. We aimed to gain an in-depth understanding of how adults living with PMBTs experience and cope with interval scanning. METHODS: Twelve patients diagnosed with WHO grade III or IV PMBT from two sites in the UK took part. Using a semi-structured interview guide, they were asked about their experiences of interval scans. A constructivist grounded theory approach was used to analyze data. RESULTS: Although most participants found interval scans uncomfortable, they accepted that scans were something that they had to do and were using various coping methods to get through the MRI scan. All participants said that the wait between their scan and results was the most difficult part. Despite the difficulties they experienced, all participants said that they would rather have interval scans than wait for a change in their symptoms. Most of the time, scans provided relief, gave participants some certainty in an uncertain situation, and a short-term sense of control over their lives. CONCLUSION: The present study shows that interval scanning is important and highly valued by patients living with PMBT. Although interval scans are anxiety provoking, they appear to help people living with PMBT cope with the uncertainty of their condition.

Segmentation and classification of brain tumors using fuzzy 3D highlighting and machine learning.

PURPOSE: Brain tumors are among the most lethal forms of cancer, so early diagnosis is crucial. As a result of machine learning algorithms, radiologists can now make accurate diagnoses of tumors without resorting to invasive procedures. There are, however, a number of obstacles to overcome. To begin, classifying brain tumors presents a significant difficulty in developing the most effective deep learning framework. Furthermore, physically dividing the brain tumor is a time-consuming and challenging process that requires the expertise of medical professionals. METHODS: Here, we have discussed the use of a fuzzy 3D highlighting method for the segmentation of brain tumors and the selection of suspect tumor areas based on the geometric characteristics of MRI scans. After features were extracted from the brain tumor section, the images were classified using two machine learning methods: a support vector machine technique optimized with the grasshopper optimization algorithm (GOA-SVM), and a deep neural network technique based on features selected with the genetic algorithm (GA-DNN). This classifies brain tumors into benign and malignant. Implemented on the MATLAB platform, the proposed method is evaluated for effectiveness using performance metrics like sensitivity, accuracy, specificity, and Youden index. RESULTS: From these results, it is clear that the proposed strategy is significantly superior to the alternatives. The average classification accuracy was determined to be 97.53%, 97.65%, for GA-DNN and GOA-SVM, respectively. CONCLUSION: These findings may be a quick and important step to detect the presence of lesions at the same time as cancerous tumors in neurology diagnosis.

A three-sequence dynamic contrast enhanced abbreviated MRI protocol to evaluate response to breast cancer neoadjuvant chemotherapy.

PURPOSE: To develop an ABP-MRI to evaluate response to NAC for invasive breast carcinoma. STUDY TYPE: A single-center, cross-sectional study. SUBJECTS: A consecutive series of 210 women with invasive breast carcinoma who underwent breast MRI after NAC between 2016 and 2020. FIELD STRENGTH/SEQUENCE: 1.5 T / Dynamic contrast-enhanced. ASSESSMENT: MRI scans were independently reevaluated, with access to dynamic contrast-enhanced without contrast and to the first, second, and third post-contrast time (ABP-MRI 1-3). STATISTICAL TESTS: The diagnostic performance of the ABP-MRIs and the Full protocol (FP-MRI) were analyzed. The Wilcoxon non-parametric test (p-value <0.050) was used to compare the capability in measuring the most extensive residual lesion. RESULTS: The median age was 47 (24-80) years. ABP-MRI 1 showed higher specificity (84.6%; 77/91) but a higher probability of false-negatives (16.8%) and lower sensitivity (83.2%; 99/119) than ABP-MRI 2,3 and the FP-MRI, which were identical in specificity (81.3%; 74/91), probability of false-negatives (8.4%), and sensitivity (91.6%; 109/119). ABP-MRI 2 showed a mean underestimation of only 0.03 cm in the measurement of the longest axis of the residual lesion (p = 0.008) with an average reduction in the acquisition time of 75%, compared with the FP-MRI. CONCLUSION: ABP-MRI 2 showed diagnostic performance equivalent to the FP-MRI with a 75% reduction in the acquisition time.

Refined Automatic Brain Tumor Classification Using Hybrid Convolutional Neural Networks for MRI Scans.

Refined hybrid convolutional neural networks are proposed in this work for classifying brain tumor classes based on MRI scans. A dataset of 2880 T1-weighted contrast-enhanced MRI brain scans are used. The dataset contains three main classes of brain tumors: gliomas, meningiomas, and pituitary tumors, as well as a class of no tumors. Firstly, two pre-trained, fine-tuned convolutional neural networks, GoogleNet and AlexNet, were used for classification process, with validation and classification accuracy being 91.5% and 90.21%, respectively. Then, to improving the performance of the fine-tuning AlexNet, two hybrid networks (AlexNet-SVM and AlexNet-KNN) were applied. These hybrid networks achieved 96.9% and 98.6% validation and accuracy, respectively. Thus, the hybrid network AlexNet-KNN was shown to be able to apply the classification process of the present data with high accuracy. After exporting these networks, a selected dataset was employed for testing process, yielding accuracies of 88%, 85%, 95%, and 97% for the fine-tuned GoogleNet, the fine-tuned AlexNet, AlexNet-SVM, and AlexNet-KNN, respectively. The proposed system would help for automatic detection and classification of the brain tumor from the MRI scans and safe the time for the clinical diagnosis.

MRI assessment of femoral head docking following closed reduction of developmental dysplasia of the hip.

AIMS: Eccentric reductions may become concentric through femoral head 'docking' (FHD) following closed reduction (CR) for developmental dysplasia of the hip (DDH). However, changes regarding position and morphology through FHD are not well understood. We aimed to assess these changes using serial MRI. METHODS: We reviewed 103 patients with DDH successfully treated by CR and spica casting in a single institution between January 2016 and December 2020. MRI was routinely performed immediately after CR and at the end of each cast. Using MRI, we described the labrum-acetabular cartilage complex (LACC) morphology, and measured the femoral head to triradiate cartilage distance (FTD) on the midcoronal section. A total of 13 hips with initial complete reduction (i.e. FTD < 1 mm) and ten hips with incomplete MRI follow-up were excluded. A total of 86 patients (92 hips) with a FTD > 1 mm were included in the analysis. RESULTS: At the end of the first cast period, 73 hips (79.3%) had a FTD < 1 mm. Multiple regression analysis showed that FTD (p = 0.011) and immobilization duration (p = 0.028) were associated with complete reduction. At the end of the second cast period, all 92 hips achieved complete reduction. The LACC on initial MRI was inverted in 69 hips (75.0%), partly inverted in 16 hips (17.4%), and everted in seven hips (7.6%). The LACC became everted-congruent in 45 hips (48.9%) and 92 hips (100%) at the end of the first and second cast period, respectively. However, a residual inverted labrum was present in 50/85 hips (58.8%) with an initial inverted or partly inverted LACC. CONCLUSION: An eccentric reduction can become concentric after complete reduction and LACC remodelling following CR for DDH. Varying immobilization durations were required for achieving complete reduction. A residual inverted labrum was present in more than half of all hips after LACC remodelling.Cite this article: Bone Joint J 2023;105-B(2):140-147.

Comparing Smartphone Virtual Reality Exposure Preparation to Care as Usual in Children Aged 6 to 14 Years Undergoing Magnetic Resonance Imaging: Protocol for a Multicenter, Observer-Blinded, Randomized Controlled Trial.

BACKGROUND: A magnetic resonance imaging (MRI) procedure can cause preprocedural and periprocedural anxiety in children. Psychosocial interventions are used to prepare children for the procedure to alleviate anxiety, but these interventions are time-consuming and costly, limiting their clinical use. Virtual reality (VR) is a promising way to overcome these limitations in the preparation of children before an MRI scan. OBJECTIVE: The objective of this study is (1) to develop a VR smartphone intervention to prepare children at home for an MRI procedure; and (2) to examine the effect of the VR intervention in a randomized controlled trial, in which the VR intervention will be compared to care as usual (CAU). CAU involves an information letter about an MRI examination. The primary outcome is the child's procedural anxiety during the MRI procedure. Secondary outcomes include preprocedural anxiety and parental anxiety. We hypothesize that the VR preparation will result in a higher reduction of the periprocedural anxiety of both parents and children as compared to CAU. METHODS: The VR intervention provides a highly realistic and child-friendly representation of an MRI environment. In this randomized controlled trial, 128 children (aged 6 to 14 years) undergoing an MRI scan will be randomly allocated to the VR intervention or CAU. Children in the VR intervention will receive a log-in code for the VR app and are sent cardboard VR glasses. RESULTS: The VR smartphone preparation app was developed in 2020. The recruitment of participants is expected to be completed in December 2022. Data will be analyzed, and scientific papers will be submitted for publication in 2023. CONCLUSIONS: The VR smartphone app is expected to significantly reduce pre- and periprocedural anxiety in pediatric patients undergoing an MRI scan. The VR app offers a realistic and child-friendly experience that can contribute to modern care. A smartphone version of the VR app has the advantage that children, and potentially their parents, can get habituated to the VR environment and noises in their own home environment and can do this VR MRI preparation as often and as long as needed. TRIAL REGISTRATION: ISRCTN Registry ISRCTN20976625; https://www.isrctn.com/ISRCTN20976625. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41080.

The reliability and clinical utility of a simple MRI based classification tool for acute scaphoid injuries: the OxSMART.

AIMS: The evidence demonstrating the superiority of early MRI has led to increased use of MRI in clinical pathways for acute wrist trauma. The aim of this study was to describe the radiological characteristics and the inter-observer reliability of a new MRI based classification system for scaphoid injuries in a consecutive series of patients. METHODS: We identified 80 consecutive patients with acute scaphoid injuries at one centre who had presented within four weeks of injury. The radiographs and MRI scans were assessed by four observers, two radiologists, and two hand surgeons, using both pre-existing classifications and a new MRI based classification tool, the Oxford Scaphoid MRI Assessment Rating Tool (OxSMART). The OxSMART was used to categorize scaphoid injuries into three grades: contusion (grade 1); unicortical fracture (grade 2); and complete bicortical fracture (grade 3). RESULTS: In total there were 13 grade 1 injuries, 11 grade 2 injuries, and 56 grade 3 injuries in the 80 consecutive patients. The inter-observer reliability of the OxSMART was substantial (Kappa = 0.711). The inter-observer reliability of detecting an obvious fracture was moderate for radiographs (Kappa = 0.436) and MRI (Kappa = 0.543). Only 52% (29 of 56) of the grade 3 injuries were detected on plain radiographs. There were two complications of delayed union, both of which occurred in patients with grade 3 injuries, who were promptly treated with cast immobilization. There were no complications in the patients with grade 1 and 2 injuries and the majority of these patients were treated with early mobilization as pain allowed. CONCLUSION: This MRI based classification tool, the OxSMART, is reliable and clinically useful in managing patients with acute scaphoid injuries.Cite this article: Bone Jt Open 2022;3(11):913-920.

An MRI Scans-Based Alzheimer's Disease Detection via Convolutional Neural Network and Transfer Learning.

Alzheimer's disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85−3.88%, 2.43−2.66%, and 1.8−40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively.