3D Magnetic Resonance Imaging Research Articles

Morphological analysis of three-dimensional MR images of patellofemoral joints in asymptomatic subjects

The existing methods for analyzing patellofemoral (PF) osteoarthritis (OA) are limited. Our purpose was to clarify the frequency, localization, and morphological progression of PFOA by observing three-dimensional (3D) magnetic resonance (MR) images from a cohort population. The subjects were 561 patients aged 30–79 years from the Kanagawa Knee Study who had not visited a hospital for more than three consecutive months for knee symptoms. MR images of the PF joints, separated into the medial and lateral types, were presented in order of the highest to lowest patella cartilage area ratios. Cartilage defects in the patella were detected in 37 subjects (6.6%). Medial lesions (4.6%) were significantly more frequent than lateral lesions (2.0%) (p < 0.01). For both medial and lateral lesions, the patellar cartilage defects were divided into confined and unconfined types. The 3D MR images of the PF joint showed that the patellar cartilage defect occurred along each ridge of the femoral trochlea. The 3D MR images revealed a 6.6% prevalence of patellar cartilage defects, higher in the medial than lateral regions. The 3D MR images can easily determine PF morphology and cartilage defect location, making them useful in understanding the pathophysiology and etiology of PFOA.

Effects of MRI scanner manufacturers in classification tasks with deep learning models

Deep learning has become a leading subset of machine learning and has been successfully employed in diverse areas, ranging from natural language processing to medical image analysis. In medical imaging, researchers have progressively turned towards multi-center neuroimaging studies to address complex questions in neuroscience, leveraging larger sample sizes and aiming to enhance the accuracy of deep learning models. However, variations in image pixel/voxel characteristics can arise between centers due to factors including differences in magnetic resonance imaging scanners. Such variations create challenges, particularly inconsistent performance in machine learning-based approaches, often referred to as domain shift, where the trained models fail to achieve satisfactory or improved results when confronted with dissimilar test data. This study analyzes the performance of multiple disease classification tasks using multi-center MRI data obtained from three widely used scanner manufacturers (GE, Philips, and Siemens) across several deep learning-based networks. Furthermore, we investigate the efficacy of mitigating scanner vendor effects using ComBat-based harmonization techniques when applied to multi-center datasets of 3D structural MR images. Our experimental results reveal a substantial decline in classification performance when models trained on one type of scanner manufacturer are tested with data from different manufacturers. Moreover, despite applying ComBat-based harmonization, the harmonized images do not demonstrate any noticeable performance enhancement for disease classification tasks.

Transfer learning from T1‐weighted to T2‐weighted Magnetic resonance sequences for brain image segmentation

AbstractMagnetic resonance (MR) imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body. The segmentation of MR images plays a crucial role in medical image analysis, as it enables accurate diagnosis, treatment planning, and monitoring of various diseases and conditions. Due to the lack of sufficient medical images, it is challenging to achieve an accurate segmentation, especially with the application of deep learning networks. The aim of this work is to study transfer learning from T1‐weighted (T1‐w) to T2‐weighted (T2‐w) MR sequences to enhance bone segmentation with minimal required computation resources. With the use of an excitation‐based convolutional neural networks, four transfer learning mechanisms are proposed: transfer learning without fine tuning, open fine tuning, conservative fine tuning, and hybrid transfer learning. Moreover, a multi‐parametric segmentation model is proposed using T2‐w MR as an intensity‐based augmentation technique. The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources. The segmentation results are evaluated using 14 clinical 3D brain MR and CT images. The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393 ± 0.0007. Although T2‐w‐based augmentation has no significant impact on the performance of T1‐w MR segmentation, it helps in improving T2‐w MR segmentation and developing a multi‐sequences segmentation model.

Reconstruction of particle positions and orientations from 3D MRI images of non-spherical particle packings

Packed bed reactors of non-spherical particles are widely used in chemical industry with the aim to obtain a high active surface area and achieve a homogeneous flow. Despite this, little is known about the arrangement of particles within the bed and the influence of this arrangement on the fluid flow distribution. Magnetic Resonance Imaging (MRI) is a non-invasive tomographic imaging technique that allows 3D visualisation of the packing and flow structure. However, the individual particle information is not obtained using MRI. In this work we investigate different particle detection methods to retrieve the particle position and orientation from MRI images. Results show the successful reconstruction of random packing structures of various non-spherical particle shapes: ellipsoid, spherocylinder, cylinder and cube. The applicability of each method in relation to the particle shape, as well as strengths and drawbacks of each particle detection method are discussed. This paper shows the ability to reconstruct real packed beds of non-spherical particle shapes from MRI images, which opens several research opportunities in the field of chemical engineering.

Assessment of Daily Target Motion to Optimize ITV Margins in Online Adaptive Plan for Liver and Pancreatic SBRT on MR-LINAC

Accurate measurement of daily target motion on online plan adaptation is essential for re-optimizing the internal target volume (ITV) margins. In this study, by analyzing 2D cine MR images acquired in a 1.5T MR-Linac, we assessed daily target motion and its inter-fractional variations, which is then used to customize ITV margins for MR-based online adaptive plan of liver and pancreatic stereotactic body radiotherapy (SBRT). Twenty-threepatients, enrolled in this study, underwent MR-guided online adaptive SBRT on 1.5 T MR-Linac: liver (n = 12 without compression belt (CB), n = 2 with CB) and pancreas (n = 9 with CB). Target motion in a 2D coronal and sagittal MR images (> 200 cine images to cover multiple breathing cycles), as well as a set of 4D-CT images during the simulation, was analyzed to determine patient specific ITV margins for a reference plan. On every fraction of online adaptive SBRT, the 2D MR images were exported to commercially available software along with a planning MR images: a T1-wighted MR image set for pancreas and or a navigated T2-wighted MR image set for liver. Daily target motion was then measured by overlaying the orthogonal cine image set on the T1- or T2-wighted image set. The ITV margin was adjusted when required prior to online plan optimization RESULTS: Thisstudy successfully assessed the daily target motion and inter-fractional variations in 23 liver or pancreatic cancer patients. We found a discrepancy of daily target motion of orthogonal 2D cine MR images from that of 4D-CT, required to adjust the ITV margin for online plan adaptation. In liver SBRT without CB, 15 out of 45 fractions from 12 patients (33.3 %) went through adjusted ITV margin. The tumor motion was large up to a 2.2 cm along the inferior direction in a patient without CB and thus the patient was treated on a conventional Linac with the active breathing control (ABC) unit for breath-hold. On the other hand, no adjustments were necessary for 2 liver patients with CB. In 9 patients with pancreatic cancers, we found the inter-fractional ITV variations in 3 out of 45 fractions (6.7 %) adjusted the ITV margin. The daily target motion in the pancreatic patients with CB was more stable and relatively smaller than that of the liver patients. Theorthogonal 2D cine MR images are evidently essential for assessing daily target motion to evaluate the inter-fractional variation and achieve daily ITV-customized liver and pancreatic SBRT in the 1.5 T MR-Linac. The customized ITV approach will improve the treatment outcome of the MR-guided online adaptive therapy.

Intra Fraction Organs at Risk Movements in Adaptive Radiotherapy of Upper-Abdominal Stereotactic Body Radiotherapy on 0.35 T MR-Linac

The recent development of magnetic resonance guided radiotherapy (MRgRT) has made possible adaptive radiotherapy (ART) especially for abdominal stereotactic body radiotherapy (SBRT). Online ART process allows to adapt the treatment at each fraction by considering the mobility of the organs at risk (OAR) and the target. These volumes are daily delineated and a new treatment plan is reoptimized. This process is multidisciplinary involving therapists, physicians and physicists. Time is a key element because of the presence of the patient on the treatment couch. In spite of having a well-trained team, the fraction duration is quite long, usually equal or higher than 45 min. Consequently, the elapsed time between the acquisition of the MR images used for the ART process and the end of the treatment delivery can be substantial. In this context, we decided to investigate the intra fraction OAR mobility by analyzing and comparing two images of the MR-Linac: one acquired at the beginning of the fraction and used for the ART process and another one immediately acquired at the end of the treatment delivery. The objectives of this study are to investigate the OAR mobility during the fraction and evaluate the possible impact on the dose distribution. Twenty patients treated in 5 fractions for upper-abdominal SBRT (liver, adrenal, pancreas, adenopathy) on the 0.35 T MR-Linac of our institution have been prospectively included in this study between May 2021 and August 2021. For each fraction an additional 3D MR image has been acquired immediately at the end of the treatment delivery. The OARs (colon, small bowel and duodenum) included in the ART process have been delineated on the post-fraction images. After having registered both images of each fraction, OAR volumes and their dose distributions have been compared. A high level of mobility of several OARs has been observed. For instance, a relative mean volume variation (increase or diminution) of 85%, 60% and 24% have been calculated, respectively, for the small bowel, the colon and duodenum. These level of volume variations strongly impacted the OAR dose distributions. For instance, the variation (increase or diminution) of maximum dose for colon, small bowel and duodenum was, respectively, about 4.3 Gy, 3.4 Gy and 2.8 Gy. These modifications led to exceed dose constraints in 2 fractions for colon, 4 fractions for small bowel and 3 fractions for duodenum. Nevertheless, by accumulating all the fractions, the dose constraints were always satisfied. OARs volume modifications during ART process can be significant and lead to important dose variations. At the scale of the treatment, these dose variations respect the dose constraints. In the near future, the correlation of these volumetric variations with the duration of the fraction will be investigated.

Hippocampus segmentation after brain tumor resection via postoperative region synthesis

PurposeAccurately segmenting the hippocampus is an essential step in brain tumor radiotherapy planning. Some patients undergo brain tumor resection beforehand, which can significantly alter the postoperative regions’ appearances and intensity of the 3D MR images. However, there are limited tumor resection patient images for deep neural networks to be effective.MethodsWe propose a novel automatic hippocampus segmentation framework via postoperative image synthesis. The variational generative adversarial network consists of intensity alignment and a weight-map-guided feature fusion module, which transfers the postoperative regions to the preoperative images. In addition, to further boost the performance of hippocampus segmentation, We design a joint training strategy to optimize the image synthesis network and the segmentation task simultaneously.ResultsComprehensive experiments demonstrate that our proposed method on the dataset with 48 nasopharyngeal carcinoma patients and 67 brain tumor patients observes consistent improvements over state-of-the-art methods.ConclusionThe proposed postoperative image synthesis method act as a novel and powerful scheme to generate additional training data. Compared with existing deep learning methods, it achieves better accuracy for hippocampus segmentation of brain tumor patients who have undergone brain tumor resection. It can be used as an automatic contouring tool for hippocampus delineation in hippocampus-sparing radiotherapy.

Are T1-Weighted Three-Dimensional Magnetic Resonance Images Inferior to T2-Weighted Images for Diagnosing Lumbar Foraminal Stenosis in the Fifth Lumbar Nerve Root? A Prospective, Comparative Study in Identical Patients.

Imaging analysis of foraminal stenosis in the fifth lumbar (L5) nerve root remains to be a challenge because of the anatomical complexity of the lumbosacral transition. T2-weighted three-dimensional (3D) magnetic resonance images (MRI) have been dominantly used for diagnosis of lumbar foraminal stenosis, while the reliability of T1-weighted images (WI) has also been proven. In this study, we aim to compare the reliability and reproducibility of T1- and T2-weighted 3D MRI in diagnosing lumbar foraminal stenosis (LFS) of the L5 nerve root. In this study, 39 patients with unilateral L5 radiculopathy (20 had L4-L5 intracanal stenosis; 19 had L5-S foraminal stenosis) were enrolled, prospectively. T1- and T2-weighted 3D lumbar MRI were obtained from each patient. T1WI and T2WI were blinded and then separately reviewed twice by four examiners randomly. The examiners were instructed to answer the side of LFS or absence of LFS. The correct answer rate, sensitivity, specificity, and area under the curve were analyzed and compared between T1WI and T2WI. Also, intra- and interobserver agreements were calculated using kappa (κ)-statistics and compared in the same manner. The average correct answer rate, sensitivity, specificity, and area under the curve of the T1WI/T2WI were 84.6%/80.1%, 82.9%/80.3%, 86.3%/81.3%, and 0.846/0.801, respectively. The intraobserver κ-values of the four examiners ranged from 0.692 to 0.916 (average: 0.762) and from 0.669 to 0.801 (average: 0.720) for T1WI and T2WI, respectively. The interobserver κ-values calculated in a round-robin manner (24 combinations in total) ranged from 0.544 to 0.790 (average: 0.657) and from 0.524 to 0.828 (average: 0.652), respectively. As per our findings, T1- and T2-weighted 3D MRI were determined to have nearly equivalent reliability and reproducibility in terms of diagnosing LFS of the L5 nerve root.

EVOLVE: Learning volume-adaptive phases for fast 3D magnetic resonance scan and image reconstruction

Compared with 2D Magnetic Resonance Imaging (MRI), 3D MRI is more powerful for generating high resolution images and visualizing small anatomical structures. However, 3D MRI acquisition is much more time-consuming due to the significantly larger number of phase encoding steps, which is directly proportional to the acquisition time. This paper proposes to select a volume-adaptive small subset of phases to accelerate 3D MRI scans and accurately reconstruct 3D images from the corresponding undersampled 3D k-space data. To avoid the delays caused by computationally expensive yet high-performance volume-adaptive phase selection, we propose a strategy of selecting multiple phases based on sampled slices from the volume during idle time within the repetition time (TR). To enhance the performance of phase selection, we propose a novel three-directional cross-attention phase selection network. Additionally, to improve the reconstruction performance, we introduce a three-directional slice-wise volume reconstruction. To the best of our knowledge, the proposed method, which we called EVOLVE (learning volume-adaptive phases), is the first work that learns volume-adaptive phases for fast 3D MRI. The extensive experimental results on a large-scale 3D MRI dataset at various acceleration factors demonstrate the substantial performance improvement in terms of image reconstruction achieved by using the EVOLVE method for phase selection compared to traditional learning free 3D MRI phase selection methods.

Multi-object 3D segmentation of brain structures using a geometric deformable model with a priori knowledge

ABSTRACT Brain structure segmentation in 3D Magnetic Resonance Images is crucial for understanding neurodegenerative disorders. Manual segmentation is error-prone, necessitating robust automated techniques. In this paper, we introduce a novel and robust approach for the simultaneous segmentation of multiple brain structures in MRI images. Our method involves the concurrent evolution of 3D surfaces toward predefined anatomical targets, employing an efficient multi-object generalized fast marching method (MOGFMM) for simultaneous object detection. Additionally, we propose an effective evolution function that integrates prior knowledge from anatomical and probabilistic atlases, as well as spatial relationships among the segmented structures. Each deformable surface corresponds to a specific structure. To validate our approach, we conducted experiments on a dataset of real brain images (IBSR) and compared the results with several state-of-the-art methods. The obtained results were promising, demonstrating the effectiveness and superiority of our developed method.

Ultrasound Frame-to-Volume Registration via Deep Learning for Interventional Guidance.

Fusing intra-operative 2D ultrasound (US) frames with preoperative 3D magnetic resonance (MR) images for guiding interventions has become the clinical gold standard in image-guided prostate cancer biopsy. However, developing an automatic image registration system for this application is challenging because of the modality gap between US/MR and the dimensionality gap between 2D/3D data. To overcome these challenges, we propose a novel US frame-to-volume registration pipeline to bridge dimensionality gap between 2D US frames and 3D US volume. The developed pipeline is implemented using deep neural networks, which is fully automatic without requiring external tracking devices. The framework consists of three major components, including (1) a frame-to-frame registration network that estimates the current frame's 3D spatial position based on previous video context, (2) a frame-to-slice correction network adjusting the estimated frame position using the 3D US volumetric information, and (3) a similarity filtering mechanism selecting the frame with the highest image similarity with the query frame. We validated our method on a clinical dataset with 618 subjects and tested its potential on real-time 2D-US to 3D-MR fusion navigation tasks. The proposed Frame-to-Volume Registration (FVReg) achieved an average target navigation error of 1.93 mm at 5 to 14 frames per second. Our source code is publicly available at https://github.com/DIAL-RPI/Frame-to-Volume-Registration.

Articulatory and Phonatory Precision When Singing Staccato at Different Speeds

Vocal tract adjustments are important for resonatory modification of the voice and also with respect to interactions concerning the voice source production. It is not clear, however, how fast, separated notes (staccato) influence vocal tract adjustments. Twelve professional singers (four sopranos, three mezzo-sopranos, three tenors, and two baritones/basses) were recorded with dynamic real-time 2D-magnetic resonance imaging (MRI) with 25fps singing a scale in legato and different staccato speeds (60, 120, 180, and 240bps). From the MRI material, the lip opening, jaw opening, jaw protrusion, tongue position, pharynx width, and larynx position were measured. Furthermore, the fundamental frequency was analyzed from the simultaneously recorded audio signal after noise cancellation. The data show only very small differences between the legato and the staccato tasks and no great variations for different staccato speeds. During the pauses in between the staccato notes, minimal vocal tract adjustments were only detectable for the slowest staccato task. There are no great differences in the vocal tract shapes for legato singing in contrast to staccato phonation and no great differences for different staccato speeds.

3D Ultrasound and MRI in Assessing Resection Margins during Tongue Cancer Surgery: A Research Protocol for a Clinical Diagnostic Accuracy Study.

Surgery is the primary treatment for tongue cancer. The goal is a complete resection of the tumor with an adequate margin of healthy tissue around the tumor.Inadequate margins lead to a high risk of local cancer recurrence and the need for adjuvant therapies. Ex vivo imaging of the resected surgical specimen has been suggested for margin assessment and improved surgical results. Therefore, we have developed a novel three-dimensional (3D) ultrasound imaging technique to improve the assessment of resection margins during surgery. In this research protocol, we describe a study comparing the accuracy of 3D ultrasound, magnetic resonance imaging (MRI), and clinical examination of the surgical specimen to assess the resection margins during cancer surgery. Tumor segmentation and margin measurement will be performed using 3D ultrasound and MRI of the ex vivo specimen. We will determine the accuracy of each method by comparing the margin measurements and the proportion of correctly classified margins (positive, close, and free) obtained by each technique with respect to the gold standard histopathology.

Aortic Wall Thickness as a Surrogate for Subclinical Atherosclerosis in Familial and Nonfamilial Hypercholesterolemia: Quantitative 3D Magnetic Resonance Imaging Study and Interrelations with Computed Tomography Calcium Scores, and Carotid Ultrasonography.

We aimed to compare the extent of subclinical atherosclerosis in the ascending and descending aortas by measuring wall area and thickness using 3D cardiovascular magnetic resonance imaging (aAWAI and dAWAI) in patients with asymptomatic familial hypercholesterolemia (FH) and nonfamilial hypercholesterolemia (NFH). We also aimed to establish the interrelations of CMR parameters with other subclinical atherosclerosis measurements, such as calcium scores, obtained using computed tomography in coronary arteries (CCS) and ascending and descending aorta (TCSasc and TCSdsc), as well as the carotid intima-media thicknesses (cIMT) using ultrasonography. A total of 60 patients with FH (29 men and 31 women), with a mean age of 52.3 ± 9.6 years, were analyzed. A subclinical atherosclerosis assessment was also performed on a group consisting of 30 age- and gender-matched patients with NFH, with a mean age of 52.5 ± 7.9 years. We found the ascending and descending aortic wall areas and thicknesses in the FH group to be significantly increased than those of the NFH group. A multivariate logistic regression analysis showed that a positive FH mutation value was a strong predictor of high aAWAI and dAWAI independent of the LDL cholesterol level. Correlations across CMR atherosclerotic parameters, calcium scores, and cIMT in the FH and NFH groups, were significant but low. Most of the atherosclerosis tests with high results belonged to the FH group. We found that patients with documented heterozygous FH had a higher atherosclerosis burden in the aorta compared to patients with severe hypercholesterolemia without FH gene mutation. Atherosclerosis is not severe in asymptomatic patients with FH, but is more pronounced and also more diffuse than in patients with NFH. The etiology of hypercholesterolemia, and not just cholesterol levels, plays a significant role in determining the degree of subclinical atherosclerosis.

Diagnostic performance of a 3D double-echo steady-state sequence at 3 T using radial reformats for detecting and grading rotator cuff tears: a pilot diagnostic accuracy study with magnetic resonance imaging and arthroscopic correlation.

In diagnosing rotator cuff tears (RCTs), magnetic resonance imaging (MRI) is the imaging modality of choice, and its accuracy is improving constantly. To evaluate the diagnostic performance of a high-resolution 3D double-echo steady-state (DESS) sequence with radial and paracoronal 3-T MRI regarding the grading of RCTs in correlation with conventional 2D MRI and arthroscopic findings. We retrospectively compared arthroscopic findings of RCTs with preoperative MRI, including conventional 2D sequences and radial and paracoronal DESS images in 20 patients. Two observers evaluated supraspinatus (SSP), infraspinatus (ISP), and subscapularis (SSC) tendon tears using a grading system. For statistical analysis, arthroscopy was used as the reference standard. Inter-observer agreement for detecting and grading SSP, ISP, and SSC tendon tears on radial and paracoronal sliced 3D DESS MRI was excellent (intraclass-correlation [ICC] = 0.92-0.98; all P < 0.001). Regarding the detection of SSP lesions, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 93.8%, 50%, 88.2%, and 66.7% for both radial and paracoronal DESS imaging. A sensitivity of 100%, specificity of 61.1%, PPV of 22.2%, and NPV of 100% were noted for detecting ISP tendon tears using radially reformatted DESS images. Regarding detecting SSC tendon tears using radially reformatted DESS images, sensitivity, specificity, PPV, and NPV were 100%, 81.3%, 50%, and 100%, respectively. The results with standard 2D MRI were similar. The DESS technique with radially reformatted images provided excellent sensitivity and high inter-observer agreement in detecting RCTs. It showed a moderate to high correlation between MRI and arthroscopy for diagnosing SSP and SSC tendon tears.

Analysis of cam location characteristics in FAI syndrome patients from 3D MR images demonstrates sex-specific differences.

Cam femoroacetabular impingement (FAI) syndrome is associated with hip osteoarthritis (OA) development. Hip shape features, derived from statistical shape modeling (SSM), are predictive for OA incidence, progression, and arthroplasty. Currently, no three-dimensional (3D) SSM studies have investigated whether there are cam shape differences between male and female patients, which may be of potential clinical relevance for FAI syndrome assessments. This study analyzed sex-specific cam location and shape in FAI syndrome patients from clinical magnetic resonance examinations (M:F 56:41, age: 16-63 years) using 3D focused shape modeling-based segmentation (CamMorph) and partial least squares regression to obtain shape features (latent variables [LVs]) of cam morphology. Two-way analysis of variance tests were used to assess cam LV data for sex and cam volume severity differences. There was no significant interaction between sex and cam volume severity for the LV data. A sex main effect was significant for LV 1 (cam size) and LV 2 (cam location) with medium to large effect sizes (p < 0.001, d > 0.75). Mean results revealed males presented with a superior-focused cam, whereas females presented with an anterior-focused cam. When stratified by cam volume, cam morphologies were located superiorly in male and anteriorly in female FAI syndrome patients with negligible, mild, or moderate cam volumes. Both male and female FAI syndrome patients with major cam volumes had a global cam distribution. In conclusion, sex-specific cam location differences are present in FAI syndrome patients with negligible, mild, and moderate cam volumes, whereas major cam volumes were globally distributed in both male and female patients.

The Reliability of Measurements for Tibial Torsion: A Comparison of CT, MRI, Biplanar Radiography, and 3D Reconstructions With and Without Standardized Measurement Training

Background: Accurate and reliable assessment of tibial torsion is important for the identification and treatment of tibial rotation malalignment; however, the ideal rotational measurement modality and technique are controversial. This study compares rotational measurements between computed tomography (CT), magnetic resonance imaging (MRI), biplanar erect radiograph (BER) reconstructions, and three-dimensional (3D) reconstructed CT before and after standardized training of observers to evaluate the reliability of each assessment method.Methods: Eight adult cadaveric specimens underwent CT, MRI, and BER imaging. Tibial torsion was measured by three independent observers (one resident and two experienced orthopaedic surgeons) both before and after standardized measurement instruction. Reliability for inter-observer measurement was evaluated using the Intraclass Correlation Coefficient (ICC). Measurement values for CT, MRI, and BER reconstructions were compared to 3D CT reconstructions analyzed using custom software to align and measure tibial torsion (used as the reference standard).Results: Before training, there was poor inter-observer reliability for CT (ICC=0.492, p=0.014) and moderate interobserver reliability for MRI (ICC=0.633, p=0.002). There was no inter-method reliability between 3D CT and MRI for two of the three surgeons and moderate to good reliability between 3D CT and CT. After training, the interobserver reliability for CT improved to 0.536 and the inter-observer reliability for MRI improved to 0.701. The BER measurements (no observer involvement) had moderate reliability compared to the 3D CT reconstructions (ICC=0.69, p=0.026). Measurement error was 4 degrees for CT pre-training and 7 degrees post-training, and 7 degrees for MRI pre-training and 8 degrees post-training.Conclusions: A standardized training regimen for MRI measurements improved both inter-observer and intraobserver reliability. Inter-method reliability between CT, MRI, and BER compared to reference 3D CT reconstructions demonstrated that all imaging modalities are a valid means to measure tibial rotation but differ in reliability from moderate to good. When assessing tibial torsional deformities, it is important to consider these variations from true rotation and feel comfortable using them for pre-operative planning purposes.Level of Evidence: N/A for a cadaveric imaging study Key Concepts•A standardized training regimen for MRI measurements of tibial torsion significantly improved inter-observer and intra-observer reliability.•CT, MRI, and BER measurements are valid imaging modalities compared to 3D CT reference standards for measuring tibial rotation.•Established manual measurements of tibial torsion from MRI may be improved with adequate training.•Measurement error of up to 8 degrees can be seen from true tibial torsion during the imaging assessment.

Definition and evaluation of a finite element model of the human heel for diabetic foot ulcer prevention under shearing loads.

Diabetic foot ulcers are triggered by mechanical loadings applied to the surface of the plantar skin. Strain is considered to play a crucial role in relation to ulcer etiology and can be assessed by Finite Element (FE) modeling. A difficulty in the generation of these models is the choice of the soft tissue material properties. In the literature, many studies attempt to model the behavior of the heel soft tissues by implementing constitutive laws that can differ significantly in terms of mechanical response. Moreover, current FE models lack of proper evaluation techniques that could estimate their ability to simulate realistic strains. In this article, we propose and evaluate a FE model of the human heel for diabetic foot ulcer prevention. Soft tissue constitutive laws are defined through the fitting of experimental stretch-stress curves published in the literature. The model is then evaluated through Digital Volume Correlation (DVC) based on non-rigid 3D Magnetic Resonance Image Registration. The results from FE analysis and DVC show similar strain locations in the fat pad and strain intensities according to the type of applied loads. For additional comparisons, different sets of constitutive models published in the literature are applied into the proposed FE mesh and simulated with the same boundary conditions. In this case, the results in terms of strains show great diversity in locations and intensities, suggesting that more research should be developed to gain insight into the mechanical properties of these tissues.

Hybrid Contextual Semantic Network for Accurate Segmentation and Detection of Small-Size Stroke Lesions From MRI.

Stroke is a cerebrovascular disease with high mortality and disability rates. The occurrence of the stroke typically produces lesions of different sizes, with the accurate segmentation and detection of small-size stroke lesions being closely related to the prognosis of patients. However, the large lesions are usually correctly identified, the small-size lesions are usually ignored. This paper provides a hybrid contextual semantic network (HCSNet) that can accurately and simultaneously segment and detect small-size stroke lesions from magnetic resonance images. HCSNet inherits the advantages of the encoder-decoder architecture and applies a novel hybrid contextual semantic module that generates high-quality contextual semantic features from the spatial and channel contextual semantic features through the skip connection layer. Moreover, a mixing-loss function is proposed to optimize HCSNet for unbalanced small-size lesions. HCSNet is trained and evaluated on 2D magnetic resonance images produced from the Anatomical Tracings of Lesions After Stroke challenge (ATLAS R2.0). Extensive experiments demonstrate that HCSNet outperforms several other state-of-the-art methods in its ability to segment and detect small-size stroke lesions. Visualization and ablation experiments reveal that the hybrid semantic module improves the segmentation and detection performance of HCSNet.

Three-Dimensional Volumetric Magnetic Resonance Imaging Analysis of Syringomyelia Evolution After Posterior Fossa Decompression for Chiari Malformation Type1

This study aims to introduce a method of three-dimensional (3D) volume magnetic resonance imaging (MRI) measurements to evaluate the evolution of syringomyelia (SM) in adults with Chiari malformation type 1 (CM-1) after posterior fossa decompression (PFD), to provide reproducible and accurate measurements. Radiologic and clinical data were obtained for 28 patients. MRI 3D syrinx volumes were evaluated by 2 observers using Philips Healthcare Tumor Tracking tool. The mean SM preoperative volume was 9.1 ± 15.3 cm3 versus 4.6 ± 8.7 cm3 postoperatively, corresponding to a volume variation reduced by -50% ± 33.4%. A total of 21 patients (75%) showed >25% reduction in SM volume postoperatively and 25 patients (89.2%) showed good to excellent postoperative clinical outcomes. Very high interobserver and intraobserver agreement was noted for 3D volumetric analysis with Cohen weighted κ coefficients (0.899-0.991). A negative correlation was found between the number of involved metameres and the postoperative SM volume reduction (Pearson correlation coefficient= 0.378; P= 0.047). Patients with better clinical outcomes had fewer syrinx septae (F= 8.830; P= 0.001). Similarly, better syrinx volume reduction was associated with fewer septae (F= 1.554; P= 0.233). A positive relationship was observed between clinical outcomes and postoperative syrinx volume reduction (F= 1.554; P= 0.233), with data similar to the reported literature. This study shows that 3D volume measurement of the syrinx cavity on MRI is a reproducible and accurate method to assess the postoperative evolution of SM in CM-1 after PFD. Most patients (75%) showed a reduction of SM volume, with a mean volume decrease of 50%. The reliability is yet to be validated in further larger studies.