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Background and ObjectivesThe erector spinae plane (ESP) and retrolaminar blocks are ultrasound-guided techniques for thoracoabdominal wall analgesia involving injection into the musculofascial plane between the paraspinal back muscles and underlying thoracic vertebrae. The ESP block targets the tips of the transverse processes, whereas the retrolaminar block targets the laminae. We investigated if there were differences in injectate spread between the 2 techniques that would have implications for their clinical effect.MethodsThe blocks were performed in 3 fresh cadavers. The ESP and retrolaminar blocks were performed on opposite sides of each cadaver at the T5 vertebral level. Twenty milliliters of a radiocontrast dye mixture was injected in each block, and injectate spread was assessed by magnetic resonance imaging and anatomical dissection.ResultsBoth blocks exhibited spread to the epidural and neural foraminal spaces over 2 to 5 levels. The ESP block produced additional spread to intercostal spaces over 5 to 9 levels and was associated with a greater extent of craniocaudal spread along the paraspinal muscles.ConclusionsThe clinical effect of ESP and retrolaminar blocks can be explained by epidural and neural foraminal spread of local anesthetic. The ESP block produces additional intercostal spread, which may contribute to more extensive analgesia. The implications of these cadaveric observations require confirmation in clinical studies.

BACKGROUND AND OBJECTIVESThe knee joint is the most common site of osteoarthritis. While joint replacement is considered an ultimate solution, radiofrequency denervation may be contemplated in some cases. Radiofrequency ablation requires precise localization of the articular branches innervating the joint capsule. The objective of this cadaveric study was to determine the source, course, relationships, and frequency of articular branches innervating the anterior knee joint capsule. METHODSFifteen knees were meticulously dissected. The number and origin of the articular branches were recorded, and their distribution defined by quadrants. Their relationships to anatomical landmarks were identified. RESULTSThe articular branches terminated in 1 of the 4 quadrants with minimal overlap. In all specimens, the superolateral quadrant was innervated by the nerve to vastus lateralis, nerve to vastus intermedius, superior lateral genicular and common fibular nerves; inferolateral by the inferior lateral genicular and recurrent fibular nerves; superomedial by the nerve to vastus medialis, nerve to vastus intermedius and superior medial genicular nerve; and inferomedial by the inferior medial genicular nerve. In 3 specimens, the inferomedial quadrant also received innervation from the infrapatellar branch of saphenous nerve. All articular branches except the nerves to vastus lateralis and medialis course at the periosteal level. CONCLUSIONSThe frequency map of the articular branches provides an anatomical basis for the development of new clinical protocols for knee radiofrequency denervation and perioperative pain management.

Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle's algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.

The various speech sounds of a language are obtained by varying the shape and position of the articulators surrounding the vocal tract. Analyzing their variations is crucial for understanding speech production, diagnosing speech disorders and planning therapy. Identifying key anatomical landmarks of these structures on medical images is a pre-requisite for any quantitative analysis and the rising amount of data generated in the field calls for an automatic solution. The challenge lies in the high inter- and intra-speaker variability, the mutual interaction between the articulators and the moderate quality of the images. This study addresses this issue for the first time and tackles it by means of Deep Learning. It proposes a dedicated network architecture named Flat-net and its performance are evaluated and compared with eleven state-of-the-art methods from the literature. The dataset contains midsagittal anatomical Magnetic Resonance Images for 9 speakers sustaining 62 articulations with 21 annotated anatomical landmarks per image. Results show that the Flat-net approach outperforms the former methods, leading to an overall Root Mean Square Error of 3.6 pixels/0.36 cm obtained in a leave-one-out procedure over the speakers. The implementation codes are also shared publicly on GitHub.

The identification of femoral landmarks is a common procedure in multiple academic fields. Femoral bone coordinate systems are used particularly in orthopedics and biomechanics, and are defined by landmarks, axes and planes. A fully automatic detection overcomes the drawbacks of a labor-intensive manual identification. In this paper, a new automatic atlas- and a priori knowledge-based approach that processes femoral surface models, called the A&A method, was evaluated. The A&A method is divided in two stages. Firstly, a single atlas-based registration maps landmarks and areas from a template surface to the subject. In the second stage, landmarks, axes and planes that are used to construct several femoral bone coordinate systems are refined using a priori knowledge. Three common femoral coordinate systems are defined by the landmarks detected. The A&A method proved to be very robust against a variation of the spatial alignment of the surface models. The results of the A&A method and a manual identification were compared. No significant rotational differences existed for the bone coordinate system recommended by the International Society of Biomechanics. Minor significant differences of maximally 0.5° were observed for the two other coordinate systems. This might be clinically irrelevant, depending on the context of use and should, therefore, be evaluated by the potential user regarding the specific application. The entire source code of the A&A method and the data used in the study is open source and can be accessed via https://github.com/RWTHmediTEC/FemoralCoordinateSystem .

Background Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuously been made but they are insufficient for clinical applications due to low reliability of specific landmarks. In this study, we aimed to develop a novel framework for locating cephalometric landmarks with confidence regions using Bayesian Convolutional Neural Networks (BCNN). Methods We have trained our model with the dataset from the ISBI 2015 grand challenge in dental X-ray image analysis. The overall algorithm consisted of a region of interest (ROI) extraction of landmarks and landmarks estimation considering uncertainty. Prediction data produced from the Bayesian model has been dealt with post-processing methods with respect to pixel probabilities and uncertainties. Results Our framework showed a mean landmark error (LE) of 1.53 ± 1.74 mm and achieved a successful detection rate (SDR) of 82.11, 92.28 and 95.95%, respectively, in the 2, 3, and 4 mm range. Especially, the most erroneous point in preceding studies, Gonion, reduced nearly halves of its error compared to the others. Additionally, our results demonstrated significantly higher performance in identifying anatomical abnormalities. By providing confidence regions (95%) that consider uncertainty, our framework can provide clinical convenience and contribute to making better decisions. Conclusion Our framework provides cephalometric landmarks and their confidence regions, which could be used as a computer-aided diagnosis tool and education.

Patients with abnormal relative position of the upper and lower jaws (the main part of the facial bones) require orthognathic surgery to improve the occlusal relationship and facial appearance. However, in addition to the retraction and protrusion of the maxillomandibular advancement, these patients may also develop asymmetry. This study aims to use a semi-supervised learning method to demonstrate the maxillary and mandible retraction, protrudation and asymmetry of patients before orthognathic surgery through automatic segmentation of 3D cone beam computed tomography (CBCT) images and landmark detection, so as to provide help for the preoperative planning of orthognathic surgery. Among them, the dice of the semi-supervised algorithm adopted in this study reached 93.41 and 96.89% in maxillary and mandibular segmentation tasks, and the average error of landmark detection tasks reached 1.908 ± 1.166 mm, both of which were superior to the full-supervised algorithm with the same data volume annotation. Therefore, we propose that the method can be applied in a clinical setting to assist surgeons in preoperative planning for orthognathic surgery.

Objective A retrospective analysis was conducted to evaluate the application of the intraoperative fluoroscopic positioning and anatomical landmark positioning methods in medial patellofemoral ligament (MPFL) reconstruction for recurrent patellar dislocation. The aim was to summarize the positioning accuracy and clinical efficacy of each method, to serve as a reference for femoral positioning. Method We conducted a retrospective analysis of a cohort comprising 75 patients who underwent treatment for recurrent patellar dislocation at our institution between January 2014 and September 2020.Based on the different positioning methodologies utilized for identifying the MPFL femoral footprint, the included patients were systematically allocated to either the fluoroscopy group or the palpation group.Preoperative evaluations and assessments at the latest follow-up encompassed the International Knee Documentation Committee (IKDC) score, Lysholm score, and Kujala score for both groups.We utilized immediate postoperative CT scans for our evaluations. A total of 48 knee 3D-CT scans were acquired using Mimics Medical 21.0 for both groups. From these scans, we constructed a standard lateral Schottle point on a 3D-CT image. To assess the relative positions between the actual and standard location points in both groups, we established a coordinate system based on a simplified, constructed standard point baseline (as illustrated in Chart e). Subsequently, the relative positions of the actual points were evaluated. Result All 75 patients were followed up for a period ranging from 36 to 96 months( mean: 62.27 ± 21.36 months). Significant improvements were observed in the IKDC score, Lysholm score, and Kujala score from preoperative to the latest follow-up ( p  < 0.05) (Table 2), indicating statistical significance.Furthermore, the latest follow-up revealed no significant differences in knee function scores between the two groups ( P  > 0.05) (Table 3). Similarly, the latest evaluation showed no significant differences in knee function scores between patients undergoing MPFLR and MPFLR + TTO In their respective groups ( P  > 0.05) (Table 4).CT-3D reconstruction was conducted on 48 postoperative patients (24 in the fluoroscopy group and 24 in the palpation group). Evaluation of the positioning revealed that most cases in the palpation group were located in quadrants 1 and 3, whereas those in the fluoroscopy group were primarily distributed across quadrants 1, 3, and 4 ( p  < 0.05), indicating statistical significance.In the palpation group, the isometric distance was 3.90 ± 2.17 mm, with an isometric rate of 75%. In the fluoroscopy group, the isometric distance was 7.55 ± 3.94 mm, with an isometric rate of 29.2%.The femoral tunnel isometric rate was significantly higher in the palpation group, at 75%, compared to 29.2% in the fluoroscopy group. among the two positioning methods, there was no statistical difference in the positioning of the femoral footprint at the anterior and posterior ends of the standard point, but there was a statistical difference at the proximal and distal ends ( P  < 0.05). Conclusion Clinical outcomes significantly improved and were similar in both groups. Nevertheless, the palpation of femoral anatomical landmarks exhibited superior convenience and efficiency for experienced sports medicine practitioners, and additionally, it frequently achieved a more isometric femoral footprint than fluoroscopic positioning in certain scenarios.

Cephalometric analysis is critically important and common procedure prior to orthodontic treatment and orthognathic surgery. Recently, deep learning approaches have been proposed for automatic 3D cephalometric analysis based on landmarking from CBCT scans. However, these approaches have relied on uniform datasets from a single center or imaging device but without considering patient ethnicity. In addition, previous works have considered a limited number of clinically relevant cephalometric landmarks and the approaches were computationally infeasible, both impairing integration into clinical workflow. Here our aim is to analyze the clinical applicability of a light-weight deep learning neural network for fast localization of 46 clinically significant cephalometric landmarks with multi-center, multi-ethnic, and multi-device data consisting of 309 CBCT scans from Finnish and Thai patients. The localization performance of our approach resulted in the mean distance of 1.99 ± 1.55 mm for the Finnish cohort and 1.96 ± 1.25 mm for the Thai cohort. This performance turned out to be clinically significant i.e., ≤ 2 mm with 61.7% and 64.3% of the landmarks with Finnish and Thai cohorts, respectively. Furthermore, the estimated landmarks were used to measure cephalometric characteristics successfully i.e., with ≤ 2 mm or ≤ 2° error, on 85.9% of the Finnish and 74.4% of the Thai cases. Between the two patient cohorts, 33 of the landmarks and all cephalometric characteristics had no statistically significant difference (p < 0.05) measured by the Mann-Whitney U test with Benjamini–Hochberg correction. Moreover, our method is found to be computationally light, i.e., providing the predictions with the mean duration of 0.77 s and 2.27 s with single machine GPU and CPU computing, respectively. Our findings advocate for the inclusion of this method into clinical settings based on its technical feasibility and robustness across varied clinical datasets.

Automatic dense 3D surface registration is a powerful technique for comprehensive 3D shape analysis that has found a successful application in human craniofacial morphology research, particularly within the mandibular and cranial vault regions. However, a notable gap exists when exploring the frontal aspect of the human skull, largely due to the intricate and unique nature of its cranial anatomy. To better examine this region, this study introduces a simplified single-surface craniofacial bone mask comprising of 6707 quasi-landmarks, which can aid in the classification and quantification of variation over human facial bone surfaces. Automatic craniofacial bone phenotyping was conducted on a dataset of 31 skull scans obtained through cone-beam computed tomography (CBCT) imaging. The MeshMonk framework facilitated the non-rigid alignment of the constructed craniofacial bone mask with each individual target mesh. To gauge the accuracy and reliability of this automated process, 20 anatomical facial landmarks were manually placed three times by three independent observers on the same set of images. Intra- and inter-observer error assessments were performed using root mean square (RMS) distances, revealing consistently low scores. Subsequently, the corresponding automatic landmarks were computed and juxtaposed with the manually placed landmarks. The average Euclidean distance between these two landmark sets was 1.5 mm, while centroid sizes exhibited noteworthy similarity. Intraclass coefficients (ICC) demonstrated a high level of concordance (> 0.988), with automatic landmarking showing significantly lower errors and variation. These results underscore the utility of this newly developed single-surface craniofacial bone mask, in conjunction with the MeshMonk framework, as a highly accurate and reliable method for automated phenotyping of the facial region of human skulls from CBCT and CT imagery. This craniofacial template bone mask expansion of the MeshMonk toolbox not only enhances our capacity to study craniofacial bone variation but also holds significant potential for shedding light on the genetic, developmental, and evolutionary underpinnings of the overall human craniofacial structure.