Position Of Anatomical Landmarks Research Articles

A Deep Learning Model for Markerless Pose Estimation Based on Keypoint Augmentation: What Factors Influence Errors in Biomechanical Applications?

In biomechanics, movement is typically recorded by tracking the trajectories of anatomical landmarks previously marked using passive instrumentation, which entails several inconveniences. To overcome these disadvantages, researchers are exploring different markerless methods, such as pose estimation networks, to capture movement with equivalent accuracy to marker-based photogrammetry. However, pose estimation models usually only provide joint centers, which are incomplete data for calculating joint angles in all anatomical axes. Recently, marker augmentation models based on deep learning have emerged. These models transform pose estimation data into complete anatomical data. Building on this concept, this study presents three marker augmentation models of varying complexity that were compared to a photogrammetry system. The errors in anatomical landmark positions and the derived joint angles were calculated, and a statistical analysis of the errors was performed to identify the factors that most influence their magnitude. The proposed Transformer model improved upon the errors reported in the literature, yielding position errors of less than 1.5 cm for anatomical landmarks and 4.4 degrees for all seven movements evaluated. Anthropometric data did not influence the errors, while anatomical landmarks and movement influenced position errors, and model, rotation axis, and movement influenced joint angle errors.

AI-Driven Segmentation and Automated Analysis of the Whole Sagittal Spine from X-ray Images for Spinopelvic Parameter Evaluation.

Spinal-pelvic parameters are utilized in orthopedics for assessing patients' curvature and body alignment in diagnosing, treating, and planning surgeries for spinal and pelvic disorders. Segmenting and autodetecting the whole spine from lateral radiographs is challenging. Recent efforts have employed deep learning techniques to automate the segmentation and analysis of whole-spine lateral radiographs. This study aims to develop an artificial intelligence (AI)-based deep learning approach for the automated segmentation, alignment, and measurement of spinal-pelvic parameters through whole-spine lateral radiographs. We conducted the study on 932 annotated images from various spinal pathologies. Using a deep learning (DL) model, anatomical landmarks of the cervical, thoracic, lumbar vertebrae, sacrum, and femoral head were automatically distinguished. The algorithm was designed to measure 13 radiographic alignment and spinal-pelvic parameters from the whole-spine lateral radiographs. Training data comprised 748 digital radiographic (DR) X-ray images, while 90 X-ray images were used for validation. Another set of 90 X-ray images served as the test set. Inter-rater reliability between orthopedic spine specialists, orthopedic residents, and the DL model was evaluated using the intraclass correlation coefficient (ICC). The segmentation accuracy for anatomical landmarks was within an acceptable range (median error: 1.7-4.1 mm). The inter-rater reliability between the proposed DL model and individual experts was fair to good for measurements of spinal curvature characteristics (all ICC values > 0.62). The developed DL model in this study demonstrated good levels of inter-rater reliability for predicting anatomical landmark positions and measuring radiographic alignment and spinal-pelvic parameters. Automated segmentation and analysis of whole-spine lateral radiographs using deep learning offers a promising tool to enhance accuracy and efficiency in orthopedic diagnostics and treatments.

Evaluating the biomechanics of an in-between posture to create a multi-posture office environment.

Prolonged sitting during work is common and has been shown to cause health issues. However, changing working postures has been reported to reduce musculoskeletal issues and impact other health issues; thus, there is a need for an office environment with multiple choices of working postures. The purpose of this study was to evaluate changes in body position, body loading, and blood perfusion while in a seated, standing, and new office seating position, termed the in-between position. Ground reaction forces, joint angles, pelvic tilt, openness angle (angle between the pelvis plane and thorax), and blood perfusion were evaluated for three positions. A motion capture system with markers was used to capture the position of anatomical landmarks. A six-axis force plate was used to collect the ground reaction forces, and a laser doppler perfusion monitor was used to obtain the blood perfusion. Data showed that the in-between position articulated the hips, which provided a hip and lumbar position closer to a standing posture than a seated posture. The average vertical ground reaction force in the in-between position was larger than the seated position but significantly smaller than during standing (p < 0.0001). There were no significant differences in anterior/posterior ground reaction forces between the seated and the in-between positions (p = 0.4934). Lastly, blood perfusion increased during the dynamic transitions between positions indicating changes in blood flow. The in-between position provides benefits of both standing (larger pelvic tilt and increased lumbar lordosis) and sitting (reduction in ground reaction forces).

The method for assessment of local permutations in the glomerular patterns of the rat olfactory bulb by aligning interindividual odor maps.

The comparison of odor functional maps in rodents demonstrates a high degree of inter-individual variability in glomerular activity patterns. There are substantial methodological difficulties in the interindividual assessment of local permutations in the glomerular patterns, since the position of anatomical extracranial landmarks, as well as the size, shape and angular orientation of olfactory bulbs can vary significantly. A new method for defining anatomical coordinates of active glomeruli in the rat olfactory bulb has been developed. The method compares the interindividual odor functional maps and calculates probabilistic maps of glomerular activity with adjustment. This adjustment involves rotation, scaling and shift of the functional map relative to its expected position in probabilistic map, computed according to the anatomical coordinates. The calculation of the probabilistic map of the odorant-specific response compensates for potential anatoamical errors due to individual variability in olfactory bulb dimensions and angular orientation. We show its efficiency on real data from a large animal sample recorded by two-photon calcium imaging in dorsal surface of the rat olfactory bulb. The proposed method with probabilistic map calculation enables the spatial consistency of the effects of individual odorants in different rats to be assessed and allow stereotypical positions of odor-specific clusters in the glomerular layer of the olfactory bulb to be identified.

Reliability of the pelvis and femur anatomical landmarks and geometry with the EOS system before and after total hip arthroplasty

Bi-plane X-ray provides 3D measurements of the lower limb based on the identification of anatomical landmarks in sagittal and frontal X-rays. In clinical practice, such measurements involve multiple operators and sessions. This study aimed at evaluating the reliability of anatomical landmarks identification and geometric parameters of the pelvis and femur measured with bi-plane X-rays before and after total hip arthroplasty (THA). Twenty-eight patients undergoing primary THA were selected retrospectively. Two operators performed three reconstructions for each patient before and after THA. Intraclass correlation (ICC) and smallest detectable change (SDC) were computed for intra-operator, inter-operator, and test–retest conditions. Most anatomical landmark positions had good to excellent SDC (< 5 mm) apart from the centre of the sacral slope, greater trochanter, and anterior superior iliac spines (up to 7.1, 16.9, and 21.5 mm respectively). Geometric parameters had moderate to excellent SDC, apart from femoral and stem torsion, pelvic incidence, and APP inclination with poor SDC (9–12°). The sagittal view had significantly higher measurement errors than the frontal view. Test–retest and inter-operator conditions had no significant differences suggesting a low influence of patient posture. Osteoarthritis and the presence of implants did not seem to influence reliability and measurement error. This study could be used as a reference when assessing lower limb structure with bi-plane X-rays.

Adaptive Inertial Sensor-Based Step Length Estimation Model

Pedestrian dead reckoning (PDR) using inertial sensors has paved the way for developing several approaches to step length estimation. In particular, emerging step length estimation models are readily available to be utilized on smartphones, yet they are seldom formulated considering the kinematics of the human body during walking in combination with measured step lengths. We present a new step length estimation model based on the acceleration magnitude and step frequency inputs herein. Spatial positions of anatomical landmarks on the human body during walking, tracked by an optical measurement system, were utilized in the derivation process. We evaluated the performance of the proposed model using our publicly available dataset that includes measurements collected for two types of walking modes, i.e., walking on a treadmill and rectangular-shaped test polygon. The proposed model achieved an overall mean absolute error (MAE) of 5.64 cm on the treadmill and an overall mean walked distance error of 4.55% on the test polygon, outperforming all the models selected for the comparison. The proposed model was also least affected by walking speed and is unaffected by smartphone orientation. Due to its promising results and favorable characteristics, it could present an appealing alternative for step length estimation in PDR-based approaches.

Redo laparoscopic pyeloplasty for recurrent ureteropelvic junction obstruction: Propensity score matched analyses of a high-volume center.

PurposeReview the experience of redo laparoscopic pyeloplasty (RLP) in patients with recurrent ureteropelvic junction obstruction (UPJO) in comparison to primary laparoscopic pyeloplasty (PLP) and redo open pyeloplasty (ROP), and determine the feasibility and effectiveness of RLP for recurrent UPJO in children.MethodsWe retrospectively reviewed the clinical data of patients treated with transperitoneal PLP, RLP, and ROP for UPJO from December 2015 to December 2022. The Propensity score matching (PSM) was used to balance confounding variables. RLP patients were 1:4 matched with PLP and 1:3 matched with ROP. The primary outcomes were failure and post-operative complications. Complications were classified according to the Clavien-Dindo grading system.ResultsThe study included ten patients who underwent RLP, 43 patients who underwent ROP, and 412 patients who underwent PLP. The follow-up time ranged from 6 to 36 months in the RLP group, 12 to 60 months in the PLP group, and 24 to 54 months in the ROP group. In the RLP group, no failure but three post-operative complications (Clavien grade II) were observed during the follow-up. Compared with the PLP group, the older age, higher weight, larger pre-operative anteroposterior diameter (APD) and APD/cortical thickness (P/C ratio), longer operation time, and post-operative length of stay (LOS) in the RLP group (P < 0.05). After PSM, longer operation time and post-operative LOS were observed in the RLP group (P < 0.05). Compared with the ROP group, the older age, higher weight, and longer post-operative LOS in the RLP group (P < 0.05). After PSM, longer post-operative LOS was observed in the ROP group (P < 0.05). The failure and complication rates were comparable between RLP and PLP or RLP and ROP (P > 0.05).ConclusionsOur result demonstrated that RLP performed as well as PLP except for a longer operation time. Compared with ROP, RLP has the advantages of a clearer surgical view, sufficient exposure, clearer anatomical landmark position, and minor trauma with a comparable clinical outcome. On experienced hands, RLP for recurrent UPJO after is a safe and effective procedure and should be considered an excellent alternative to the more commonly recommended ROP in select patients.

YÜZ BÖLÜMLERİNİN ÖZELLİKLERİ İLE ORTODONTİK MALOKLUZYONLAR ARASINDA HERHANGİ BİR İLİŞKİ VAR MI? BÖLÜM I: FRONTAL GÖRÜNÜM

Background: The purpose of this study was to evaluate frontal facial features of individuals and analyze their relevance to orthodontic problems. Methods: Frontal photographs were taken and cephalometric data of 250 patient were collected. Photographs were used to evaluate facial dimensions, general symmetry, forehead, trichion anatomical landmark position, nostril visibility, nasal root breaking point, glabella position, cantus positions, eyeball symmetry, sclera visibility, nose symmetry and shape, nose widths, mouth width, lip vermillion areas and chin. Correlation between photographic data and malocclusion groups was statistically evaluated. Results: Forehead shape, nostril visibility, mouth width and chin symmetry differences were statistically significant due to type of skeletal malocclusion. Conversely, there are significant correlations between forehead shape, nose symmetry, upper lip vermillion, and chin symmetry and dental malocclusions. Chin asymmetry is mostly seen with skeletal Class III malocclusion, while nasal asymmetry with dental Class III malocclusion. Asymmetry is mostly seen in lower third of the face. In dental Class III malocclusion patients, forehead was mostly "wide". In front view, skeletal Class I and II individuals have increased nostril visibility and Class III decreased nostril visibility. Conclusions: The photographic analyzes add perspective to orthodontics. Skeletal class III patients are prone to asymmetries especially in lower third of the face.

Presurgical Assessment of Anatomical Landmark Position Using Two Views of Cone Beam Computed Tomography: A Comparative Radiological Study.

Cone beam computerized computed tomography (CBCT) has been widely indicated in dental implant procedure. The first step that the doctors should perform in local hospitals is to use orthopantomography for surgical planning to avoid and limit the risk of complications. As a result, determining the magnification amount of orthopantomography to achieve a precise diagnosis is clinically important. This study investigated the difference in measurement of the mental foramen (MF) position before dental surgery using 2 views of CBCT. Cone beam computed tomography scan was performed for 100 patients who required implant placement. In the panoramic and three-dimensional views of CBCT, the vertical distance between the margin of the mandible and the lower border of MF, and the horizontal distance between the mandibular symphysis and the mesial anterior border of MF were calculated. The differences between the 2 views were compared using Wilcoxon-rank U test with P value ≤ 0.05 considered statistically significant. The findings of this study showed a substantial statistical difference in the horizontal distance for the dentate patients in panoramic and three-dimensional views (22.7 ± 3.04 versus 21.1 ± 1.6), (22.5 ± 2.4 versus 20.9 ± 1.5) and left side (22.4 ± 2.8 versus 21.2 ± 1.6), (22.4 ± 2.8 versus 20.6 ± 1.4) of both genders. Concerning the vertical distance, a significant difference was also observed in the dentate (12.1 ± 2.1 versus 11.1 ± 1.4), (10.6 ± 1.4 versus 9.6 ± 1.3) and left side of the jaw (11.6 ± 1.95 versus 10.9 ± 1.2), (10.5 ± 1.2 versus 9.96 ± 1.4) for both genders in both views. In the edentulous and right side of the jaw, however no statistical difference was observed between male and female patients in terms of horizontal and vertical measurements.

Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks

In video-assisted thoracoscopic surgeries, successful procedures of nodule resection are highly dependent on the precise estimation of lung deformation between the inflated lung in the computed tomography (CT) images during preoperative planning and the deflated lung in the treatment views during surgery. Lungs in the pneumothorax state during surgery have a large volume change from normal lungs, making it difficult to build a mechanical model.The purpose of this study is to develop a deformation estimation method of 3D surface of a deflated lung from a few partial observations. To estimate deformations for a largely deformed lung, a kernel regression-based solution was introduced. The proposed method used a few landmarks to capture the partial deformation between the 3D surface mesh obtained from preoperative CT and the intraoperative anatomical positions. The deformation for each vertex of the entire mesh model was estimated per-vertex as a relative position from the landmarks. The landmarks were placed in the anatomical position of the lung’s outer contour. The method was applied on nine datasets of the left lungs of live beagle dogs. Contrast-enhanced CT images of the lungs were acquired.The proposed method achieved a local positional error of vertices of 2.74 mm, Hausdorff distance of 6.11 mm, and Dice similarity coefficient of 0.94. Moreover, the proposed method achieved the estimation lung deformations from a small number of training cases and a small observation area.This study contributes to data-driven modeling of pneumothorax deformation of the lung.

Characterising the Mould Rectification Process for Designing Scoliosis Braces: Towards Automated Digital Design of 3D-Printed Braces

The plaster-casting method to create a scoliosis brace consists of mould generation and rectification to obtain the desired orthosis geometry. Alternative methods entail the use of 3D scanning and CAD/CAM. However, both manual and digital design entirely rely on the orthotist expertise. Characterisation of the rectification process is needed to ensure that digital designs are as efficient as plaster-cast designs. Three-dimensional scans of five patients, pre-, and post-rectification plaster moulds were obtained using a Structure Mark II scanner. Anatomical landmark positions, transverse section centroids, and 3D surface deviation analyses were performed to characterise the rectification process. The rectification process was characterised using two parameters. First, trends in the external contours of the rectified moulds were found, resulting in lateral tilt angles of 81 ± 3.8° and 83.3 ± 2.6° on the convex and concave side, respectively. Second, a rectification ratio at the iliac crest (0.23 ± 0.04 and 0.11 ± 0.02 on the convex and concave side, respectively) was devised, based on the pelvis width to estimate the volume to be removed. This study demonstrates that steps of the manual rectification process can be characterised. Results from this study can be fed into software to perform automatic digital rectification.

A neural network to predict the knee adduction moment in patients with osteoarthritis using anatomical landmarks obtainable from 2D video analysis

The knee adduction moment (KAM) can inform treatment of medial knee osteoarthritis; however, measuring the KAM requires an expensive gait analysis laboratory. We evaluated the feasibility of predicting the peak KAM during natural and modified walking patterns using the positions of anatomical landmarks that could be identified from video analysis. Using inverse dynamics, we calculated the KAM for 86 individuals (64 with knee osteoarthritis, 22 without) walking naturally and with foot progression angle modifications. We trained a neural network to predict the peak KAM using the 3-dimensional positions of 13 anatomical landmarks measured with motion capture (3D neural network). We also trained models to predict the peak KAM using 2-dimensional subsets of the dataset to simulate 2-dimensional video analysis (frontal and sagittal plane neural networks). Model performance was evaluated on a held-out, 8-person test set that included steps from all trials. The 3D neural network predicted the peak KAM for all test steps with r2( Murray etal., 2012) 2=0.78. This model predicted individuals' average peak KAM during natural walking with r2( Murray etal., 2012) 2=0.86 and classified which 15° foot progression angle modifications reduced the peak KAM with accuracy=0.85. The frontal plane neural network predicted peak KAM with similar accuracy (r2( Murray etal., 2012) 2=0.85) to the 3D neural network, but the sagittal plane neural network did not (r2( Murray etal., 2012) 2=0.14). Using the positions of anatomical landmarks from motion capture, a neural network accurately predicted the peak KAM during natural and modified walking. This study demonstrates the feasibility of measuring the peak KAM using positions obtainable from 2D video analysis.

Evaluation of anatomical landmark position differences for head computed tomography: A reliability study among technologists

IntroductionIn computed tomography (CT) imaging protocols, lack of practice standards and variability in head positioning may all yield substantial inter-study image variance in the clinical setting which may limit the diagnostic and comparative value of subsequent scans. We aimed to evaluate repeatability of multiplanar reformatting of head CT based on the tuberculum sella (TS) to internal occipital protuberance (IOP) reference line and reduce variance. MethodsReference lines that correspond to the TS-IOP plane on high-resolution CT scans were reviewed by technologists manually to calculate Yaw (z-rotation, rotation along the superoinferior direction), Pitch (x-rotation, rotation along the left-right direction), and Roll (y-rotation, rotation along the anteroposterior direction) angles in this pre-post design intervention study. The Yaw, Pitch, and Roll angles deviating from the reference TS-IOP in the head CT images before and after technologist training were measured with the technologists’ actual graphical prescriptions, and their differences were calculated with t-tests. The intra-rater agreement was calculated using the intraclass correlation coefficient (ICC). ResultsMean pitch, yaw, and roll before technologist training was 6.7° ± 5.4°, 0.9° ± 1.5°, and 1.1° ± 1.2° and after training were 3.2° ± 2.6°, 0.6° ± 1.1°, and 0.6° ± 1.1°, respectively. Technologist training resulted in a significant decrease in pitch (p < 0.001) and roll (p = 0.001) inter-subject variability with respect to the TS-IOP line, however no significant difference for the yaw correction (p = 0.065) was noted. Intra-rater agreement regarding the reproducibility of TS-IOP reformation was excellent (ICC>0.950). ConclusionTS-IOP reference line corrected for direct roll, yaw, and pitch can be readily achieved by trained technologists. Implications for practiceAdoption of the TS-IOP reference line should facilitate intra- and intermodality comparisons, leading to more reproducible and readily interpretable CT images.

Custom-Made Horizontal and Vertical Maxillary Augmentation with Smartbone® On Demand™: A Seven-Year Follow-Up Case

The presence of non-sufficient bone height and width requires an increase in the amount of bone available to insert an implant. Different materials are described in the literature, and the “custom-made bone graft approach” is a modern option which currently requires a preoperative stage of studying the bone defect and designing the implant. SmartBone® (SB®) mimics the characteristics of healthy human bone. Thanks to the strong performance, high workability, resistance and shape retention of SB®, it is possible to obtain SmartBone® on DemandTM, a bone graft uniquely shaped exactly to patient specifications, produced by following the data precisely and contoured to the bone defect site. The aim of this study was to determine the success over 7 years following a customized SmartBone® on DemandTM, a xeno-hybrid bone graft and installation of implants in a maxillary horizontal and vertical atrophy. This case study presents the diagnosis for a 60-year-old male patient requesting the rehabilitation of his edentulous maxilla with dental implants. Preoperative evaluation included the study of photographs, a radiological examination and 3D reconstruction to assess the missing bone, implant size, positioning of implants and anatomical landmarks. Rehabilitation included the insertion of a custom-made xeno-hybrid bone block into the maxilla in order to restore the anatomy prior to the implants’ placement. The newly developed bone substitute SB® is a safe and effective material, and its custom-made variant SmartBone® on DemandTM has been shown to be a valid alternative to traditional autologous bone grafting techniques in terms of accuracy, absence of infection/rejection and overall clinical outcome.

Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network.

The objective investigation of the dynamic properties of vocal fold vibrations demands the recording and further quantitative analysis of laryngeal high-speed video (HSV). Quantification of the vocal fold vibration patterns requires as a first step the segmentation of the glottal area within each video frame from which the vibrating edges of the vocal folds are usually derived. Consequently, the outcome of any further vibration analysis depends on the quality of this initial segmentation process. In this work we propose for the first time a procedure to fully automatically segment not only the time-varying glottal area but also the vocal fold tissue directly from laryngeal high-speed video (HSV) using a deep Convolutional Neural Network (CNN) approach. Eighteen different Convolutional Neural Network (CNN) network configurations were trained and evaluated on totally 13,000 high-speed video (HSV) frames obtained from 56 healthy and 74 pathologic subjects. The segmentation quality of the best performing Convolutional Neural Network (CNN) model, which uses Long Short-Term Memory (LSTM) cells to take also the temporal context into account, was intensely investigated on 15 test video sequences comprising 100 consecutive images each. As performance measures the Dice Coefficient (DC) as well as the precisions of four anatomical landmark positions were used. Over all test data a mean Dice Coefficient (DC) of 0.85 was obtained for the glottis and 0.91 and 0.90 for the right and left vocal fold (VF) respectively. The grand average precision of the identified landmarks amounts 2.2 pixels and is in the same range as comparable manual expert segmentations which can be regarded as Gold Standard. The method proposed here requires no user interaction and overcomes the limitations of current semiautomatic or computational expensive approaches. Thus, it allows also for the analysis of long high-speed video (HSV)-sequences and holds the promise to facilitate the objective analysis of vocal fold vibrations in clinical routine. The here used dataset including the ground truth will be provided freely for all scientific groups to allow a quantitative benchmarking of segmentation approaches in future.

Lumbar posture assessment with fabric strain sensors

Astronauts are at risk for low back pain and injury during extravehicular activity because of the deconditioning of the lumbar region and biomechanical demands associated with wearing a spacesuit. To understand and mitigate injury risks, it is necessary to study the lumbar kinematics of astronauts inside their spacesuit. To expand on previous efforts, the purpose of this study was to develop and test a generalizable method to assess complex lumbar motion using 10 fabric strain sensors placed on the torso. Anatomical landmark positions and corresponding sensor measurements were collected from 12 male study participants performing 16 static lumbar postures. A multilayer principal component and regression-based model was constructed to estimate lumbar joint angles from the sensor measurements. Good lumbar joint angle estimation was observed (<9° mean error) from flexion and lateral bending joint angles, and lower accuracy (13.7° mean error) was observed from axial rotation joint angles. With continued development, this method can become a useful technique for measuring suited lumbar motion and could potentially be extrapolated to civilian work applications.

The prospects for application of computational anatomy in forensic anthropology for sex determination

The purpose of this study is to assess the relevance of computational anatomy for the sex determination in forensic anthropology. A novel groupwise registration algorithm is used, based on keypoint extraction, able to register several hundred full body images in a common space. Experiments were conducted on 83 CT scanners of living individuals from the public VISCERAL database. In our experiments, we first verified that the well-known criteria for sex discrimination on the hip-bone were well preserved in mean images. In a second experiment, we have tested semi-automatic positioning of anatomical landmarks to measure the relevance of groupwise registration for future research. We applied the Probabilistic Sex Diagnosis tool on the predicted landmarks. This resulted in 62% of correct sex determinations, 37% of undetermined cases, and 1% of errors. The main limiting factors are the population sample size and the lack of precision for the initial manual positioning of the landmarks in the mean image. We also give insights on future works for robust and fully automatic sex determination.

Evaluation of MRI RF electromagnetic field induced heating near leads of cochlear implants

A typical cochlear implant system under magnetic resonance imaging (MRI) procedures may couple with the radio frequency (RF) electromagnetic field (EMF) and results in an intensified electric field at the lead tip. As a result, the RF energy deposited in human tissues around the lead tip may induce heating and cause tissue damage concerns. The purpose of this work is to evaluate the RF-EMF-induced heating for cochlear implant system in 1.5 T MRI coil and highlight the factors that have significant effects on the heating. The potential factors involved in the RF-EMF-induced heating including the lead type, lead trajectory, human model and MRI landmark. In this paper, the RF-EMF-induced heating for three types of leads is evaluated in two virtual human models. A total of 24 lead trajectories and 23 anatomical landmark positions are studied using the transfer function method. The average temperature rise for all the studied cases in the human models is 0.79 °C, and the maximum value is 2.80 °C for a maximum whole-body average specific absorption rate of 2 W kg−1 in an RF body coil. It is found that the lead trajectory and MRI landmark are two primary influencing factors. The maximum temperature rises for different lead trajectories can vary from 0.82 to 2.80 °C. A difference in heating of 2.80 °C is observed when the landmark changes from −100 to 700 mm. This work demonstrates that it is necessary to take these factors into account when evaluating the RF-EMF-induced heating for implanted medical devices.

Three-dimensional assessment of squats and drop jumps using the Microsoft Xbox One Kinect: Reliability and validity

ABSTRACTThe Microsoft Xbox One Kinect™ (Kinect V2) contains a depth camera that can be used to manually identify anatomical landmark positions in three-dimensions independent of the standard skeletal tracking, and therefore has potential for low-cost, time-efficient three-dimensional movement analysis (3DMA). This study examined inter-session reliability and concurrent validity of the Kinect V2 for the assessment of coronal and sagittal plane kinematics for the trunk, hip and knee during single leg squats (SLS) and drop vertical jumps (DVJ). Thirty young, healthy participants (age = 23 ± 5yrs, male/female = 15/15) performed a SLS and DVJ protocol that was recorded concurrently by the Kinect V2 and 3DMA during two sessions, one week apart. The Kinect V2 demonstrated good to excellent reliability for all SLS and DVJ variables (ICC ≥ 0.73). Concurrent validity ranged from poor to excellent (ICC = 0.02 to 0.98) during the SLS task, although trunk, hip and knee flexion and two-dimensional measures of knee abduction and frontal plane projection angle all demonstrated good to excellent validity (ICC ≥ 0.80). Concurrent validity for the DVJ task was typically worse, with only two variables exceeding ICC = 0.75 (trunk and hip flexion). These findings indicate that the Kinect V2 may have potential for large-scale screening for ACL injury risk, however future prospective research is required.

Fast Multiple Landmark Localisation Using a Patch-based Iterative Network.

We propose a new Patch-based Iterative Network (PIN) for fast and accurate landmark localisation in 3D medical volumes. PIN utilises a Convolutional Neural Network (CNN) to learn the spatial relationship between an image patch and anatomical landmark positions. During inference, patches are repeatedly passed to the CNN until the estimated landmark position converges to the true landmark location. PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume. Our approach adopts a multitask learning framework that combines regression and classification to improve localisation accuracy. We extend PIN to localise multiple landmarks by using principal component analysis, which models the global anatomical relationships between landmarks. We have evaluated PIN using 72 3D ultrasound images from fetal screening examinations. PIN achieves quantitatively an average landmark localisation error of 5.59mm and a runtime of 0.44s to predict 10 landmarks per volume. Qualitatively, anatomical 2D standard scan planes derived from the predicted landmark locations are visually similar to the clinical ground truth.