Three-dimensional Magnetic Resonance Imaging Research Articles

Location of the Anatomic Footprint Centers of the Anterior Cruciate Ligament Determined by Quadrant Method on Three-Dimensional Magnetic Resonance Imaging

Location of the Anatomic Footprint Centers of the Anterior Cruciate Ligament Determined by Quadrant Method on Three-Dimensional Magnetic Resonance Imaging

Automated Three-Dimensional Imaging and Pfirrmann Classification of Intervertebral Disc Using a Graphical Neural Network in Sagittal Magnetic Resonance Imaging of the Lumbar Spine.

This study aimed to develop a graph neural network (GNN) for automated three-dimensional (3D) magnetic resonance imaging (MRI) visualization and Pfirrmann grading of intervertebral discs (IVDs), and benchmark it against manual classifications. Lumbar IVD MRI data from 300 patients were retrospectively analyzed. Two clinicians assessed the manual segmentation and grading for inter-rater reliability using Cohen's kappa. The IVDs were then processed and classified using an automated convolutional neural network (CNN)-GNN pipeline, and their performance was evaluated using F1 scores. Manual Pfirrmann grading exhibited moderate agreement (κ = 0.455-0.565) among the clinicians, with higher exact match frequencies at lower lumbar levels. Single-grade discrepancies were prevalent except at L5/S1. Automated segmentation of IVDs using a pretrained U-Net model achieved an F1 score of 0.85, with a precision and recall of 0.83 and 0.88, respectively. Following 3D reconstruction of the automatically segmented IVD into a 3D point-cloud representation of the target intervertebral disc, the GNN model demonstrated moderate performance in Pfirrmann classification. The highest precision (0.81) and F1 score (0.71) were observed at L2/3, whereas the overall metrics indicated moderate performance (precision: 0.46, recall: 0.47, and F1 score: 0.46), with variability across spinal levels. The integration of CNN and GNN offers a new perspective for automating IVD analysis in MRI. Although the current performance highlights the need for further refinement, the moderate accuracy of the model, combined with its 3D visualization capabilities, establishes a promising foundation for more advanced grading systems.

Association of open skill exercise and long-chain polyunsaturated fatty acid intake with brain volume changes among older community-dwelling Japanese individuals

Considering that a multifactorial lifestyle approach may prove more effective than a single factor approach to improve or maintain brain health, we evaluated the association of exercise (open skill exercise [OSE] or closed skill exercise [CSE]) combined with long-chain polyunsaturated fatty acid (LCPUFAs) (docosahexaenoic acid [C22:6n-3, DHA], eicosapentaenoic acid [C20:5n-3, EPA], and arachidonic acid [C20:4n-6, ARA]) intake with brain atrophy among older Japanese individuals (n = 795, aged 60–88 years) without a self-reported history of dementia based on the datasets of a two-year longitudinal study. Brain volumes were measured using three-dimensional T1-weighted brain magnetic resonance imaging for follow-up periods of two years. The associations between multivariate-adjusted changes in brain volumes and OSE or CSE frequency (≥ once/month and < once/month) along with LCPUFA intake (≥ median and < median) at the baseline were assessed using a general linear model. Subgroup analysis was performed by restricting DHA and EPA intakes (n = 263; median, 323 mg/d), which represented levels similar to those in countries with low fish consumption. Higher OSE frequencies, ARA intakes, and their combination were inversely associated with decreases in total gray matter and frontal cortex volumes. In subgroup analysis, a combination of higher OSE frequencies and DHA intakes was also associated with a smaller decrease in total gray matter volume. Overall, our findings suggest that regular OSE engagement and appropriate LCPUFA intake may contribute to preventing brain volume decreases in older individuals.

Effect of Dialysis on Structural Brain Connectivity in Patients with End-Stage Renal Disease.

Patients with end-stage renal disease (ESRD) are known to have reduced structural and functional brain connectivity in the brain regions associated with cognitive function. However, the effect of dialysis on brain connectivity remains unclear. This study aimed to evaluate the effects of dialysis on structural brain connectivity in patients with ESRD. This prospective study included 20 patients with ESRD in the pre-dialysis stage and 35 healthy controls. The patients underwent T2-weighted and three-dimensional T1-weighted magnetic resonance imaging before and 3 months after dialysis initiation. Moreover, the cortical thickness was calculated. We applied graph theoretical analysis to calculate the structural covariance network based on cortical thickness. We compared the cortical thickness and structural covariance network of patients with ESRD in the pre-dialysis stage with those of healthy controls and with those of patients with ESRD in the post-dialysis stage. The mean cortical thickness in both hemispheres was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (2.296 vs. 2.354, p = 0.030; 2.282 vs. 2.362, p = 0.004, respectively) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (2.333 vs. 2.296, p = 0.001; 2.322 vs. 2.282, p = 0.002, respectively). Analysis of the structural covariance network revealed that the assortative coefficient was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (-0.062 vs. -0.031, p = 0.029) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (-0.002 vs. -0.062, p = 0.042). We observed differences in the cortical thickness and structural covariance networks before and after dialysis in patients with ESRD. This indicates that dialysis affects structural brain connectivity, contributing to the understanding of the pathophysiological mechanism of cognitive function alterations resulting from dialysis in patients with ESRD.

Submucosal cystic adenomyosis: a report of five cases and review of the literature

AimTo investigate the clinical characteristics, diagnosis, and clinical treatment of submucosal cystic adenomyosis.MethodsThe clinical data of five cases of patients with submucosal cystic adenomyosis in our hospital from January 2020 to June 2023 were retrospectively analyzed.ResultsThe average age of the patients was 37.8 ± 4.5 years old, three of them experienced prolonged menstruation and heavy menstrual bleeding. All patients had a history of abnormal uterine bleeding and mild to moderate dysmenorrhea, with a VAS score of 2.8 ± 1.6. The average Carbohydrate antigen 125 (CA125) value was 29.9 ± 23.6U/ml. Two out of the five patients (40%) had CA125 values above the upper limit of normal. The nodules had a diameter of 3.2 ± 1.3 cm and a cavity size of 1.3 ± 0.7 cm. Color ultrasound revealed hypo or iso or anechoic echoic cysts, and blood flow signals were detected. The magnetic resonance imaging (MRI) findings varied among each patient. All the patients underwent hysteroscopy and resection of uterine cavity-occupying lesions, and no recurrence was observed.ConclusionsThe clinical features of submucosal cystic adenomyosis include abnormal uterine bleeding and menstrual changes, and the degree of dysmenorrhea is generally not severe. The diagnostic utility of CA125 in submucosal cystic adenomyosis may be limited. The three-dimensional ultrasound and MRI are valuable preoperative examination methods currently. Hysteroscopy can not only diagnose submucosal cystic adenomyosis, but also treat it, and preserve the fertility function of the patient.

Deep learning–constrained compressed sensing reconstruction improves high-resolution three-dimensional (3D) T2-weighted turbo spin echo magnetic resonance imaging (MRI) of the lumbar spine

Deep learning–constrained compressed sensing reconstruction improves high-resolution three-dimensional (3D) T2-weighted turbo spin echo magnetic resonance imaging (MRI) of the lumbar spine

A comparison of machine learning methods for recovering noisy and missing 4D flow MRI data.

Experimental blood flow measurement techniques are invaluable for a better understanding of cardiovascular disease formation, progression, and treatment. One of the emerging methods is time-resolved three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI), which enables noninvasive time-dependent velocity measurements within large vessels. However, several limitations hinder the usability of 4D flow MRI and other experimental methods for quantitative hemodynamics analysis. These mainly include measurement noise, corrupt or missing data, low spatiotemporal resolution, and other artifacts. Traditional filtering is routinely applied for denoising experimental blood flow data without any detailed discussion on why it is preferred over other methods. In this study, filtering is compared to different singular value decomposition (SVD)-based machine learning and autoencoder-type deep learning methods for denoising and filling in missing data (imputation). An artificially corrupted and voxelized computational fluid dynamics (CFD) simulation as well as in vitro 4D flow MRI data are used to test the methods. SVD-based algorithms achieve excellent results for the idealized case but severely struggle when applied to in vitro data. The autoencoders are shown to be versatile and applicable to all investigated cases. For denoising, the in vitro 4D flow MRI data, the denoising autoencoder (DAE), and the Noise2Noise (N2N) autoencoder produced better reconstructions than filtering both qualitatively and quantitatively. Deep learning methods such as N2N can result in noise-free velocity fields even though they did not use clean data during training. This work presents one of the first comprehensive assessments and comparisons of various classical and modern machine-learning methods for enhancing corrupt cardiovascular flow data in diseased arteries for both synthetic and experimental test cases.

Examining The Relationship Between Lateral Ventricle Volumetric Index and Linear Indexes in Childhood and Adolescence: A Retrospective MRI Study

Objective: Indexes obtained from two-dimensional and three-dimensional measurements are used to evaluate the brain's lateral ventricle (LV) structure. However, there were limited studies on how well the Evans index (EI) and the Bicaudate index (BI), which estimate LV volume, represent the LV volumetric index (LVVI) in the childhood and adolescence period. This study investigated the relationship between LVVI and linear indexes (EI and BI) in the pediatric period regarding age and sex factors. Method: This study was performed retrospectively in 588 individuals (267 [45.4%] females) aged 1-18 years with normal brain magnetic resonance images (MRI) between 2012 and 2021. LVVI was obtained by segmenting three-dimensional T1-weighted MRIs with volBrain1.0. LVVI was obtained by dividing the LV absolute volume by the total intracranial volume. We made linear measurements for EI and BI with the 3D Slicer. In this study, we compared the data obtained from individuals in 16 different age ranges between 1-18 years old with SPSS (ver.28). Results: In our study, we found the mean value of LVVI to be 0.656±0.372%. The mean values of EI and BI were 0.242±0.025 and 0.070±0.022, respectively. In the pediatric population, we found that mean values of EI and BI were significantly higher in males than in females (p

Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer's disease classification.

Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict AD-specific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)]. Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs. Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images. Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D.

Accuracy of 3D real-time MRI temperature mapping in gel phantoms during microwave heating

BackgroundInterventional magnetic resonance imaging (MRI) can provide a comprehensive setting for microwave ablation of tumors with real-time monitoring of the energy delivery using MRI-based temperature mapping. The purpose of this study was to quantify the accuracy of three-dimensional (3D) real-time MRI temperature mapping during microwave heating in vitro by comparing MRI thermometry data to reference data measured by fiber-optical thermometry.MethodsNine phantom experiments were evaluated in agar-based gel phantoms using an in-room MR-conditional microwave system and MRI thermometry. MRI measurements were performed for 700 s (25 slices; temporal resolution 2 s). The temperature was monitored with two fiber-optical temperature sensors approximately 5 mm and 10 mm distant from the microwave antenna. Temperature curves of the sensors were compared to MRI temperature data of single-voxel regions of interest (ROIs) at the sensor tips; the accuracy of MRI thermometry was assessed as the root-mean-squared (RMS)-averaged temperature difference. Eighteen neighboring voxels around the original ROI were also evaluated and the voxel with the smallest temperature difference was additionally selected for further evaluation.ResultsThe maximum temperature changes measured by the fiber-optical sensors ranged from 7.3 K to 50.7 K. The median RMS-averaged temperature differences in the originally selected voxels ranged from 1.4 K to 3.4 K. When evaluating the minimum-difference voxel from the neighborhood, the temperature differences ranged from 0.5 K to 0.9 K. The microwave antenna and the MRI-conditional in-room microwave generator did not induce relevant radiofrequency artifacts.ConclusionAccurate 3D real-time MRI temperature mapping during microwave heating with very low RMS-averaged temperature errors below 1 K is feasible in gel phantoms.Relevance statementAccurate MRI-based volumetric real-time monitoring of temperature distribution and thermal dose is highly relevant in clinical MRI-based interventions and can be expected to improve local tumor control, as well as procedural safety by extending the limits of thermal (e.g., microwave) ablation of tumors in the liver and in other organs.Key PointsInterventional MRI can provide a comprehensive setting for the microwave ablation of tumors.MRI can monitor the microwave ablation using real-time MRI-based temperature mapping.3D real-time MRI temperature mapping during microwave heating is feasible.Measured temperature errors were below 1 °C in gel phantoms.The active in-room microwave generator did not induce any relevant radiofrequency artifacts.Graphical

Intra- and interrater measurement reliability of lateral ankle ligament attachment locations using three-dimensional magnetic resonance imaging

BackgroundWe aimed to evaluate the intra- and interrater measurement reliability of the lateral ankle ligament attachment locations using three-dimensional magnetic resonance imaging. MethodsWe analysed 54 participants with a mean age of 43 years who underwent three-dimensional ankle magnetic resonance imaging and had normal lateral ligaments. Bony landmarks of the distal fibula, talus, and calcaneus were identified in the reconstructed images. The centers of the anterior talofibular ligament and calcaneofibular ligament attachments were also identified. The distances between the landmarks and attachments were measured. Two raters performed the measurements twice, and intra- and interrater intraclass correlation coefficients were calculated. ResultsThe intrarater intraclass correlation coefficient values were between 0.71 and 0.96 for the anterior talofibular ligament attachment measurements and between 0.77 and 0.95 for the calcaneofibular ligament attachments. The interrater intraclass correlation coefficient was higher than 0.7, except for the distance between the anterior talofibular ligament superior bundle and fibular obscure tubercle. The fibular attachment of a single-bundle anterior talofibular ligament was located 13.3 mm from the inferior tip and 43% along the anterior edge of the distal fibula. The superior and inferior bundles of the double-bundle ligament were located at 43% and 23%, respectively. The calcaneofibular ligament fibular attachment was 5.5 mm from the inferior tip, at 16% along the anterior edge of the distal fibula. ConclusionThe measurements of anterior talofibular ligament and calcaneofibular ligament attachment locations identified on three-dimensional magnetic resonance imaging were sufficiently reliable. This measurement method provides in vivo anatomical data on the lateral ankle ligament anatomy.

Comparison of Left Ventricular Function Derived from Subject-Specific Inverse Finite Element Modeling Based on 3D ECHO and Magnetic Resonance Images.

Three-dimensional echocardiography (3D ECHO) and magnetic resonance (MR) imaging are frequently used in patients and animals to evaluate heart functions. Inverse finite element (FE) modeling is increasingly applied to MR images to quantify left ventricular (LV) function and estimate myocardial contractility and other cardiac biomarkers. It remains unclear, however, as to whether myocardial contractility derived from the inverse FE model based on 3D ECHO images is comparable to that derived from MR images. To address this issue, we developed a subject-specific inverse FE model based on 3D ECHO and MR images acquired from seven healthy swine models to investigate if there are differences in myocardial contractility and LV geometrical features derived using these two imaging modalities. We showed that end-systolic and end-diastolic volumes derived from 3D ECHO images are comparable to those derived from MR images (R2=0.805 and 0.969, respectively). As a result, ejection fraction from 3D ECHO and MR images are linearly correlated (R2=0.977) with the limit of agreement (LOA) ranging from -17.95% to 45.89%. Using an inverse FE modeling to fit pressure and volume waveforms in subject-specific LV geometry reconstructed from 3D ECHO and MR images, we found that myocardial contractility derived from these two imaging modalities are linearly correlated with an R2 value of 0.989, a gradient of 0.895, and LOA ranging from -6.11% to 36.66%. This finding supports using 3D ECHO images in image-based inverse FE modeling to estimate myocardial contractility.

AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.

TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females) with glioblastoma multiforme were downloaded from The Cancer Imaging Archive (TCIA) to test the tumor segmentation performance of this software. Regions of interest (ROIs) corresponding to contrast-enhancing lesions, necrotic portions, and non-enhancing T2 high signal intensity components were segmented for each tumor. TumorPrism3D demonstrated high accuracy in segmenting all three tumor components in cases of glioblastoma multiforme. They achieved a better Dice similarity coefficient (DSC) ranging from 0.83 to 0.91 than 3DSlicer with a DSC ranging from 0.80 to 0.84 for the accuracy of segmented tumors. Comparative analysis with the widely used 3DSlicer software revealed TumorPrism3D to be approximately 37.4% faster in the segmentation process from initial contour drawing to final segmentation mask determination. The semi-automated nature of TumorPrism3D facilitates reproducible tumor segmentation at a rapid pace, offering the potential for quantitative analysis of tumor characteristics and artificial intelligence-assisted segmentation in brain MR imaging.

The deltoid ligament complex is reliably visualised as four fascicles in two layers with 3D volumetric MRI in the ankles of 20 asymptomatic volunteers.

The anatomy of the deltoid ligament is complex. There is agreement on the presence of superficial and deep layers but the number and frequency of fascicles remains controversial. Identifying injuries to specific components of the deltoid ligament may inform decision-making on their management. The anatomy was reviewed to establish the number and dimension of fascicles visible with three-dimensional (3D) volumetric magnetic resonance images (MRI). Twenty ankles from asymptomatic healthy volunteers were imaged with 3D volumetric MRI. The presence of individual fascicles was recorded and measured in 3D. The median age of participants was 26 years (range: 20-37) of which 13 (65%) were female. All 20 ankles had a deltoid ligament formed of four fascicles in two layers: three fascicles in the superficial layer; tibionavicular (mean dimensions 22.5 × 10.0 × 2.4 mm), tibiospring (16.6 × 6.7 × 1.9 mm) and tibiocalcaneal (23.8 × 4.6 × 1.8 mm) and a deep layer consisting of the tibiotalar fascicle, which could be divided into two parts: anterior tibiotalar (mean dimensions 10 × 5.6 × 4.1 mm) and the significantly larger posterior tibiotalar (14.2 × 13.8 × 17.5 mm, p < 0.01). There were no additional fascicles observed. The deltoid ligament complex was consistently visualised as four fascicles (tibionavicular, tibiospring, tibiocalcaneal, tibiotalar) in two layers (superficial and deep) in all 20 ankles. The posterior part of the tibiotalar fascicle was the thickest of all the fascicles in the deltoid ligament. It is, therefore, possible to accurately identify the components of the deltoid ligament, and 3T MRI can be used to assess fascicle-specific injury, which will guide treatment and rehabilitation. Level III.

Three-dimensional ultrasound versus magnetic resonance imaging in the diagnosis of placenta accreta spectrum

Background Placenta accrete spectrum (PAS), is an increasing obstetric issue. The prevalence of PAS has fundamentally increased because of the global increase in cesarean section (CS) rates over the past few decades. To diagnose PAS in utero. Aim This study aimed to assess and compare the diagnostic accuracy of two imaging modalities: magnetic resonance imaging (MRI) and three-dimensional (3D) ultrasound (US). Patients and methods This prospective observational study included 60 pregnant women between the ages of 21 and 43 who were between 26- and 38 weeks’ gestation and met the clinical criteria of having had placenta previa central and prior uterine surgery (such as cesarean section, myomectomy, or curettage). 3D US and MRI were performed on each patient. Results 3D US can significantly diagnose accurately compared with clinical diagnosis (P value &lt;0.001) with 100% sensitivity, 72.7% specificity, 86.4% positive predictive value (PPV), 100% negative predictive value (NPV), and 90% accuracy, area under the curve=0.864. MRI can significantly diagnose accurate compared with Clinical diagnosis (P value &lt;0.001) with 97.4% sensitivity, 63.6% specificity, 82.2% PPV, 93.3% NPV, and 85% accuracy, area under the curve= 0.805. Conclusions MRI and 3D US were observed to be able to diagnose PAS prenatally with no difference between them and the clinical diagnosis (intraoperative findings). However, 3D US exhibited increased sensitivity, specificity, PPV, NPV, and accuracy compared with MRI.

The association of the medial meniscus covering ratio with knee cartilage thickness at 15 medial compartment subregions as found by three-dimensional analysis using MRI.

The correlation of cartilage thickness measured by three-dimensional (3D) magnetic resonance imaging (MRI) and the medial meniscal coverage ratio (MMCR), which presented pathology of the medial meniscus extrusion (MME) in 3D MRI, has not yet been elucidated. The study's purpose was to retrospectively verify whether the average cartilage thickness calculated by the automatic MRI 3D analysis system for each subregion was correlated with MMCR. A total of 60 patients underwent medial meniscus repair or high tibial osteotomy to treat their medial knee osteoarthritis. Cartilage thickness and MMCR were automatically calculated using 3D MRI software. The MMCR was defined as the ratio of the area covered by the meniscus within the medial tibial cartilage area to the total medial tibial cartilage area. The association between MMCR and the average cartilage thickness at 15 subregions in the medial femoral condyle (MFC) and medial tibial plateau (MTP) was evaluated using Spearman's rank correlation coefficient. Kellgren-Lawrence grade exhibited a negative correlation with MMCR and a positive correlation with MME width. Cartilage thickness in the MTP had a moderately positive correlation with MMCR at four subregions and a weakly positive correlation at another subregion. Cartilage thickness in the MFC showed a moderately positive correlation with MMCR at five subregions and a weakly positive correlation at one subregion. Cartilage thickness calculated by automatic MRI 3D analysis system had a positive correlation with MMCR for all subregions of the anterior and middle subregions in the MFC and for five regions of nine subregions of the anterior and middle subregions in the MTP. Level II, cross-sectional study (diagnosis).

Learning carotid vessel wall segmentation in black-blood MRI using sparsely sampled cross-sections from 3D data.

Atherosclerosis of the carotid artery is a major risk factor for stroke. Quantitative assessment of the carotid vessel wall can be based on cross-sections of three-dimensional (3D) black-blood magnetic resonance imaging (MRI). To increase reproducibility, a reliable automatic segmentation in these cross-sections is essential. We propose an automatic segmentation of the carotid artery in cross-sections perpendicular to the centerline to make the segmentation invariant to the image plane orientation and allow a correct assessment of the vessel wall thickness (VWT). We trained a residual U-Net on eight sparsely sampled cross-sections per carotid artery and evaluated if the model can segment areas that are not represented in the training data. We used 218 MRI datasets of 121 subjects that show hypertension and plaque in the ICA or CCA measuring in ultrasound. The model achieves a high mean Dice coefficient of 0.948/0.859 for the vessel's lumen/wall, a low mean Hausdorff distance of , and a low mean average contour distance of on the test set. The model reaches similar results for regions of the carotid artery that are not incorporated in the training set and on MRI of young, healthy subjects. The model also achieves a low median Hausdorff distance of on the 2021 Carotid Artery Vessel Wall Segmentation Challenge test set. The proposed method can reduce the effort for carotid artery vessel wall assessment. Together with human supervision, it can be used for clinical applications, as it allows a reliable measurement of the VWT for different patient demographics and MRI acquisition settings.

ESMAC Best Paper Award 2023: Increased knee flexion in participants with cerebral palsy results in altered stresses at the distal femoral growth plate compared to a typically developing cohort

IntroductionFemoral deformities are highly prevalent in children with cerebral palsy (CP) and can have a severe impact on patients’ gait abilities. While the mechanical stress regime within the distal femoral growth plate remains underexplored, understanding it is crucial given bone's adaptive response to mechanical stimuli. We quantified stresses at the distal femoral growth plate to deepen our understanding of the relationship between healthy and pathological gait patterns, internal loading, and femoral growth patterns. MethodsThis study included three-dimensional motion capture data and magnetic resonance images of 13 typically developing children and twelve participants with cerebral palsy. Employing a multi-scale mechanobiological approach, integrating musculoskeletal simulations and subject-specific finite element analysis, we investigated the orientation of the distal femoral growth plate and the stresses within it. Limbs of participants with CP were grouped depending on their knee flexion kinematics during stance phase as this potentially changes the stresses induced by knee and patellofemoral joint contact forces. ResultsDespite similar growth plate orientation across groups, significant differences were observed in the shape and distribution of growth values. Higher growth rates were noted in the anterior compartment in CP limbs with high knee flexion while CP limbs with normal knee flexion showed high similarity to the group of healthy participants. DiscussionResults indicate that the knee flexion angle during the stance phase is of high relevance for typical bone growth at the distal femur. The evaluated growth rates reveal plausible results, as long-term promoted growth in the anterior compartment leads to anterior bending of the femur which was confirmed for the group with high knee flexion through analyses of the femoral geometry. The framework for these multi-scale simulations has been made accessible on GitHub, empowering peers to conduct similar mechanobiological studies. Advancing our understanding of femoral bone development could ultimately support clinical decision-making.

Assessment of the sacroiliac joint in patients with axial spondyloarthritis via three-dimensional ultrashort echo time magnetic resonance imaging

Assessment of the sacroiliac joint in patients with axial spondyloarthritis via three-dimensional ultrashort echo time magnetic resonance imaging

Lower synaptic density and its association with cognitive dysfunction in patients with obsessive-compulsive disorder

BackgroundUnderstanding synaptic alteration in obsessive-compulsive disorder (OCD) is crucial for elucidating its pathological mechanisms, but in vivo research on this topic remains limited.AimsThis study aimed to identify the synaptic density...