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Background Purpose Methods Results Conclusions One big challenge in the noninvasive high‐intensity focused ultrasound (HIFU) surgery is that the location and shape of its focus is unpredictable at the preoperative stage due to the complexity of sound wave propagation. The Acoustic Radiation Force Impulse (ARFI) imaging is a potential solution to this problem, but artifacts resulting from shear wave propagation remain to be solved.In this study, we proposed avoiding those artefacts by applying the ARFI technique at a high imaging frame rate within a very short time before the shear waves start to propagate.Using single transmission with a convex imaging probe, two ultrafast imaging modalities (the diverging wave and the wide beam), were developed in the ARFI framework, and their reliabilities were validated on a nylon string phantom by the centroid tracking method borrowed from ultrasound localization microscopy (ULM). The proposed ARFI method was tested on a clinically equivalent HIFU system under different acoustic radiation intensities by in‐vitro, ex‐vivo and in‐vivo experiments. In three experimental scenarios, we delivered short HIFU stimulation pulses at varying acoustic powers to induce tissue motion within the focal region. At each experimental site, both diverging wave and wide‐beam imaging techniques were employed for motion estimation. Based on the focus prediction derived from the motion estimation, HIFU ablation treatment was performed. The treated samples were then incised to examine the damaged areas. Additionally, ultrasound B‐mode images were acquired before and after the procedure and saved for analysis.Quantitative analysis showed that the ARFI with wide beam imaging was able to predict the HIFU focus preoperatively, only with 1 to 3 mm of errors in focal central location, and less than 23% of percentage errors in focal area in most cases. However, the diverging wave imaging failed to predict the HIFU focus due to its low signal‐to‐noise ratio.In conclusion, the inherent shear wave artefacts in ARFI for predicting the HIFU focus can be successfully avoided by carefully designing the imaging strategy and its working sequence. This ARFI technique was validated through a series of experiments on a clinically equivalent HIFU system, which demonstrated its capability in assisting surgical planning. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we investigate the effectiveness of shape completion neural networks as clinical aids in maxillofacial surgery planning. We present a pipeline to apply shape completion networks to automatically reconstruct complete eumorphic 3D meshes starting from a partial input mesh, easily obtained from CT data routinely acquired for surgery planning. Most of the existing works introduced solutions to aid the design of implants for cranioplasty, i.e. all the defects are located in the neurocranium. In this work, we focus on reconstructing defects localized on both neurocranium and splanchnocranium. To this end, we introduce a new dataset, specifically designed for this task, derived from publicly available CT scans and subjected to a comprehensive pre-processing procedure. All the scans in the dataset have been manually cleaned and aligned to a common reference system. In addition, we devised a pre-processing stage to automatically extract point clouds from the scans and enrich them with virtual defects. We experimentally compare several state-of-the-art point cloud completion networks and identify the two most promising models. Finally, expert surgeons evaluated the best-performing network on a clinical case. Our results show how casting the creation of personalized implants as a problem of shape completion is a promising approach for automatizing this complex task.

In this paper, we investigate the effectiveness of shape completion neural networks as clinical aids in maxillofacial surgery planning. We present a pipeline to apply shape completion networks to automatically reconstruct complete eumorphic 3D meshes starting from a partial input mesh, easily obtained from CT data routinely acquired for surgery planning. Most of the existing works introduced solutions to aid the design of implants for cranioplasty, i.e. all the defects are located in the neurocranium. In this work, we focus on reconstructing defects localized on both neurocranium and splanchnocranium. To this end, we introduce a new dataset, specifically designed for this task, derived from publicly available CT scans and subjected to a comprehensive pre-processing procedure. All the scans in the dataset have been manually cleaned and aligned to a common reference system. In addition, we devised a pre-processing stage to automatically extract point clouds from the scans and enrich them with virtual defects. We experimentally compare several state-of-the-art point cloud completion networks and identify the two most promising models. Finally, expert surgeons evaluated the best-performing network on a clinical case. Our results show how casting the creation of personalized implants as a problem of shape completion is a promising approach for automatizing this complex task. [ABSTRACT FROM AUTHOR]

The design of patient-specific implants for cranioplasty surgery is time-consuming and challenging. Hence, the 2021 AutoImplant II challenge, consisting of the SkullBreak and SkullFix datasets, was organized to foster research on computer vision techniques pursuing automation of the cranial implant design task. Data-driven methods working on Computed Tomography (CT) emerged as a promising procedure for the realization of such a task. The best performing approaches turned out to rely on ensembles of Convolutional Neural Networks (CNN) architectures that either process each CT slice separately or the entire voxelized volume through computationally demanding three-dimensional convolutions. More recently, few methods were designed to deal with different data representations, for instance point clouds, to perform skull completion. Similarly, we investigate a novel solution for implant generation that deploys a conditioned occupancy network. Starting from the partial point cloud, we directly reconstruct the completed voxel grid by evaluating the learned occupancy function in the given space resolution. Our approach can generate high-quality implants achieving qualitative and quantitative results comparable to state-of-the-art methods on the SkullBreak and SkullFix datasets while requiring significantly less computational resources. The model trained on the SkullBreak dataset successfully generalize to real craniotomies provided in the MUG500+ dataset.

Introduction: The number and invasion depth of endometriotic bowel lesions, total length of bowel affected by endometriosis, lesion‐to‐anal verge distance, and extent of pouch of Douglas obliteration are important factors in preoperatively determining risk and complexity of endometriosis surgery. The intra‐ and interobserver reproducibility of transvaginal ultrasound in the evaluation of many of these parameters has not yet been investigated. Our study aimed to assess the intra‐ and interobserver reproducibility of transvaginal ultrasound between an experienced and less experienced examiner for all of these parameters. Material and methods: This prospective observational cross‐sectional study was conducted between July 2019 and November 2020. Fifty consecutive premenopausal women who underwent transvaginal ultrasound examination in our clinic for the first time, were examined by the same two operators during the same attendance. Outcomes of interest were the inter‐rater reproducibility of transvaginal ultrasound for detecting the presence, number, depth and size of bowel endometriotic nodules, lesion‐to‐anal‐verge distance, total length of bowel affected, and pouch of Douglas obliteration. The intraobserver reproducibility was assessed for the continuous parameters. Cohen's kappa (κ) statistic, Cohen's weighted kappa (κ), proportions of agreement, intraclass correlation coefficient (ICC) and Bland–Altman limits of agreement were used to assess the reproducibility of the parameters. Results: The inter‐rater agreement and reliability were very good for identifying bowel endometriosis, the number and invasion depth of bowel nodules, determining whether the maximum nodule length was <3 cm, and lesion‐to‐anal‐verge distance <8 cm (proportion of agreement 0.92, 0.94, 0.97, 0.94, 0.96; κ 0.92, 0.91, 0.92, 0.82, 0.89). The inter‐rater agreement and reliability were good for assessing pouch of Douglas obliteration (proportion of agreement 0.86, κ 0.80). The intra‐rater reliability for the mean nodule diameter (ICC 0.93 and 0.97) and total length of bowel affected (ICC 0.94 and 0.91) were excellent for operators A and B, respectively. The inter‐rater reliability for the mean nodule diameter was good (ICC 0.80), and moderate for the total length of bowel affected (ICC 0.70). The Bland–Altman limits of agreement demonstrated clinically acceptable ranges for these two parameters. Conclusions: This study demonstrated a high intra‐ and inter‐rater reproducibility of transvaginal ultrasound in the diagnosis of bowel endometriosis and measurement of its various components. [ABSTRACT FROM AUTHOR]

Background: High‐intensity focused ultrasound (HIFU) is currently used for the treatment of various diseases, but it still lacks a reliable technique in the preoperative stage to accurately place its "energy blade" onto diseased targets. Acoustic radiation force imaging (ARFI) was recently introduced to tackle this issue, but its applicability and limitations were not clear. Purpose: The aim of this study was to evaluate the performance of ARFI method in prediction of HIFU focal location at the preoperative stage. Methods: A point spread function (PSF) localization method, which was borrowed from the ultrasound super resolution field, was used to validate the core autocorrelation‐based motion estimation algorithm in the ARFI procedure. Accuracy of the ARFI method for estimating the HIFU focus were tested with in vitro and ex vivo experiments with a clinically equivalent HIFU system. Comparisons were made between the estimated focal locations and those of the damaged area after the testing objects were cut open. Results: Results showed that the PSF localization was able to serve as a validating method for motion detection only when the tissue displacement was large. With the ARFI method, location of the HIFU focus could be accurately predicted by a 2D motion map preoperatively, and the axial spatial errors were less than 0.5 mm. However, the derived 2D motion maps can only be valuable when the acoustic stimulation in ARFI were strong enough, which was probably due to the fact that the HIFU focal locations were at large depths and the ultrasound imaging signal had low signal to noise ratio. Conclusion: The ARFI method was indeed an accurate technique for preoperatively predicting HIFU focus in vitro and ex vivo. If clinical applications were to be considered, particularly in deep tissues, efforts might need to be made to improve ability of the ultrasound motion estimation technique. [ABSTRACT FROM AUTHOR]

This paper presents a multi-material dual "contouring" method used to convert a digital 3D voxel-based atlas of basal ganglia to a deformable discrete multi-surface model that supports surgical navigation for an intraoperative MRI-compatible surgical robot, featuring fast intraoperative deformation computation. It is vital that the final surface model maintain shared boundaries where appropriate so that even as the deep-brain model deforms to reflect intraoperative changes encoded in ioMRI, the subthalamic nucleus stays in contact with the substantia nigra, for example, while still providing a significantly sparser representation than the original volumetric atlas consisting of hundreds of millions of voxels. The dual contouring (DC) algorithm is a grid-based process used to generate surface meshes from volumetric data. The DC method enables the insertion of vertices anywhere inside the grid cube, as opposed to the marching cubes (MC) algorithm, which can insert vertices only on the grid edges. This multi-material DC method is then applied to initialize, by duality, a deformable multi-surface simplex model, which can be used for multi-surface atlas-based segmentation. We demonstrate our proposed method on synthetic and deep-brain atlas data, and a comparison of our method's results with those of traditional DC demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

3D printing has been one of the recent buzzwords, along with Machine learning and AI. The combination of these three provides a great deal of improvisation in health education and healthcare management techniques. This paper studies various implementations of 3D printing solutions. Shortly, AI combined with 3D printing would revolutionize the healthcare industry in most areas, not just limited to human implants, pharmaceuticals, tissue engineering/regenerative medicine, education, and other evidence-based decision support systems. 3D printing is a manufacturing method in which objects are made by fusion or depositing materials such as plastic, metals, ceramics, powder, liquids, or even living cells in layers to produce a desired 3D-Object. [ABSTRACT FROM AUTHOR]

BackgroundThe recognition of anatomical variants is essential in preoperative planning for lung cancer surgery. Although three-dimensional (3-D) reconstruction provided an intuitive demonstration of the anatomical structure, the recognition process remains fully manual. To render a semiautomated approach for surgery planning, we developed an artificial intelligence (AI)–based chest CT semantic segmentation algorithm that recognizes pulmonary vessels on lobular or segmental levels. Hereby, we present a retrospective validation of the algorithm comparing surgeons’ performance.MethodsThe semantic segmentation algorithm to be validated was trained on non-contrast CT scans from a single center. A retrospective pilot study was performed. An independent validation dataset was constituted by an arbitrary selection from patients who underwent lobectomy or segmentectomy in three institutions during Apr. 2020 to Jun. 2021. The golden standard of anatomical variants of each enrolled case was obtained via expert surgeons’ judgments based on chest CT, 3-D reconstruction, and surgical observation. The performance of the algorithm is compared against the performance of two junior thoracic surgery attendings based on chest CT.ResultsA total of 27 cases were included in this study. The overall case-wise accuracy of the AI model was 82.8% in pulmonary vessels compared to 78.8% and 77.0% for the two surgeons, respectively. Segmental artery accuracy was 79.7%, 73.6%, and 72.7%; lobular vein accuracy was 96.3%, 96.3%, and 92.6% by the AI model and two surgeons, respectively. No statistical significance was found. In subgroup analysis, the anatomic structure-wise analysis of the AI algorithm showed a significant difference in accuracies between different lobes (p = 0.012). Higher AI accuracy in the right-upper lobe (RUL) and left-lower lobe (LLL) arteries was shown. A trend of better performance in non-contrast CT was also detected. Most recognition errors by the algorithm were the misclassification of LA1+2 and LA3. Radiological parameters did not exhibit a significant impact on the performance of both AI and surgeons.ConclusionThe semantic segmentation algorithm achieves the recognition of the segmental pulmonary artery and the lobular pulmonary vein. The performance of the model approximates that of junior thoracic surgery attendings. Our work provides a novel semiautomated surgery planning approach that is potentially beneficial to lung cancer patients.

Endovascular interventional radiology (IR) is a minimally invasive procedure for the treatment of vascular diseases. This procedure requires physicians to be highly skilled at manipulating interventional devices under the guidance of two-dimensional X-ray imaging. By offering a non-error-sensitive and radiation-free environment, a virtual reality-based simulator provides a promising alternative for surgical skills training and surgery planning. Building a realistic and interactive simulator is a challenging task. To achieve better realism, this paper proposes a novel method of simulating the heartbeat for both standard and patient-specific anatomical data. A time-dependent offset field approach is proposed to efficiently and stably simulate the interactive behavior between the dynamic heart mesh and surgical devices. For medical imaging simulation, we propose a GPU-based linear depth subtraction method to approximate fluoroscopic images based on the attenuation of the X-ray. On this basis, a topology-based flow map method is proposed to simulate the propagation of the contrast medium in angiography. Experimental results show that the proposed algorithm can simulate heartbeat stably for meshes with varying geometrical shapes and complexities. In efficiency, the dynamic heart mesh can interact with surgical devices stably at 60 frames/s. Under the simulated fluoroscopic imaging effect, the injected contrast medium can realistically visualize both dynamic and static vessels. In a face validity by medical students and clinicians, the category of effectiveness score 8.35 out of 10 on average, demonstrating that our simulator is useful in surgical skills training and surgery planning.

Retrieving intravesical foreign bodies warrants open cystotomy; therefore, preoperative evaluation of the material, size, shape, and location is essential for surgical planning. A 79-year-old man presented with dysuria and admitted inserting a jump rope into his urethra. Reconstructed three-dimensional computed tomography showed an entangled jump rope; therefore an endoscopic surgery was deemed unsuitable. Instead, the rope was removed through a small open cystotomy. He had no complications. Intravesical foreign bodies are not rare, and they should be considered as a differential diagnosis in patients with lower urinary tract symptoms. Three-dimensional reconstruction computed tomography contributes to surgical planning.

New developments in terms of additive manufacturing, computational tools and mathematical simulation techniques have favored the development of successful methodologies for the restoration or restitution of bone structures in the human body. Likewise, achievements in Materials Science have allowed the development of biocompatible composites capable of achieving mechanical characteristics and biological similarities comparable to those of natural bone. Without considering the advantages and disadvantages of some biomaterials with respect to others, this research aims to evaluate the surgical planning, the design process, the impact resistance and the critical deflection of a customized cranial implant manufactured from polymethylmethacrylate (PMMA). With the support of finite element methods (FEM), the level of neurocranial protection offered by the implant is assessed.

This paper presents a multi-material dual “contouring” method used to convert a digital 3D voxel-based atlas of basal ganglia to a deformable discrete multi-surface model that supports surgical navigation for an intraoperative MRI-compatible surgical robot, featuring fast intraoperative deformation computation. It is vital that the final surface model maintain shared boundaries where appropriate so that even as the deep-brain model deforms to reflect intraoperative changes encoded in ioMRI, the subthalamic nucleus stays in contact with the substantia nigra, for example, while still providing a significantly sparser representation than the original volumetric atlas consisting of hundreds of millions of voxels. The dual contouring (DC) algorithm is a grid-based process used to generate surface meshes from volumetric data. The DC method enables the insertion of vertices anywhere inside the grid cube, as opposed to the marching cubes (MC) algorithm, which can insert vertices only on the grid edges. This multi-material DC method is then applied to initialize, by duality, a deformable multi-surface simplex model, which can be used for multi-surface atlas-based segmentation. We demonstrate our proposed method on synthetic and deep-brain atlas data, and a comparison of our method’s results with those of traditional DC demonstrates its effectiveness.

Periosteal chondrosarcoma is an extremely rare malignant cartilage-forming tumour that originates from the periosteum and occurs on the surface of bone. Often, it is difficult to distinguish periosteal chondrosarcoma from other tumours, and reports in the literature are scarce. This study aims to investigate the characteristics of periosteal chondrosarcoma, focusing particularly on medullary invasion. Among 33 periosteal cartilaginous tumours, seven patients with pathologically proven periosteal chondrosarcoma were identified retrospectively. The average tumour size was 5.4 cm in the long axis; two tumours were smaller than 3.0 cm. Six tumours were resected with a wide margin, and the remaining tumour had a marginal margin. Histology revealed that six tumours (85.7%) had invaded the medullary cavity; three of these did not show invasion into the medullary cavity on MRI evaluation. Neither local recurrence nor metastasis was observed among these patients. The frequency of invasion of the medullary cavity was higher than that reported previously. The recommended treatment for periosteal chondrosarcoma is resection with an adequate margin. Therefore, surgeons should consider the possibility of medullary invasion when attempting to achieve a histologically negative margin, even if the tumour does not show invasion into the medullary cavity on MRI. [ABSTRACT FROM AUTHOR]

PurposeFor complex cases, preoperative surgical planning is a standard procedure to ensure patient safety and keep the surgery time to a minimum. Based on the available information, such as MRI or CT images, and prior anatomical knowledge the surgeons create their own mental 3D model of the organ of interest. This is challenging, requires years of training and an inherent uncertainty remains even for experienced surgeons.GoalVirtual reality (VR) is by nature excellent in showing spatial relationships through its stereoscopic displays. Therefore, it is well suited to be used to support the understanding of individual anatomy of patient-specific 3D organ models generated from MRI or CT data. Utilizing this potential, we developed a VR surgical planning tool that provides a 3D view of the medical data for better spatial understanding and natural interaction with the data in 3D space. Following a user-centered design process, in this first user study, we focus on usability, usefulness, and target audience feedback. Thereby, we also investigate the individual impact the tool and the 3D presentation of the organ have on the understanding of the 3D structures for the surgical team.MethodsWe employed the VR prototype for surgical planning using a standard VR setup to two real cases of patients with liver tumors who were scheduled for surgery at a University Hospital for Visceral Surgery. Surgeons (N = 4) used the VR prototype before the surgery to plan the procedure in addition to their regular planning process. We used semi-structured interviews before and after the surgery to explore the benefits and pitfalls of VR surgical planning.ResultsThe participants used on average 14.3 min (SD = 3.59) to plan the cases in VR. The reported usability was good. Results from the interviews and observations suggest that planning in VR can be very beneficial for surgeons. They reported an improved spatial understanding of the individual anatomical structures and better identification of anatomical variants. Additionally, as the surgeons mentioned an improved recall of the information and better identification of surgical relevant structures, the VR tool has the potential to improve the surgery and patient safety.

Purpose: Rhinoplasty is one of the most common and challenging plastic surgery procedures. Facial analysis is a crucial step in planning. Utilizing three-dimensional (3D) model of a patient's face is an emerging way of performing facial analysis. This paper evaluates the agreement and reliability of facial measurements taken using a web app, located at digitized-rhinoplasty.com, that utilizes 3D models of the patient's face. Methods: Eleven measurements were calculated on 16 human subjects. Three methods of measurements were performed: direct measurements on human subjects' faces, measurements on 2D photographs, and measurements on 3D models of face scans. The Bland–Altman plot is used for testing the agreement between the web app and the well-known Blender 3D modeling software. Intra-rater and inter-rater reliability was calculated and compared for 2D and 3D methods using the intraclass correlation coefficient (ICC) method. The statistical analysis methods were checked for the normality and homoscedasticity assumptions. Results: The results indicate that the web app and Blender software show agreement within 95% confidence limits. The web app performs well in intra-rater and inter-rater reliability statistical analysis. The web app's reliability scores are consistently better than facial analysis software which was found highly reliable in a previous study. We also compare the methods of measurements in terms of time, ease of use, and cost. Conclusion: The utilization of 3D computer modeling for facial analysis has its advantages and started to become more common due to recent advances in technology. The web app utilizes 3D face scans for pre-operative planning and post-operative evaluation of facial surgeries. The web app performs well in agreement and inter-/intra-reliability analysis and performs consistently better than software that works utilizing 2D photographs. The web app provides accurate, repeatable, affordable, and fast facial measurements for facial analysis when compared to direct and 2D methods. [ABSTRACT FROM AUTHOR]

Cranio-maxillofacial surgery often alters the aesthetics of the face which can be a heavy burden for patients to decide whether or not to undergo surgery. Today, physicians can predict the post-operative face using surgery planning tools to support the patient’s decision-making. While these planning tools allow a simulation of the post-operative face, the facial texture must usually be captured by another 3D texture scan and subsequently mapped on the simulated face. This approach often results in face predictions that do not appear realistic or lively looking and are therefore ill-suited to guide the patient’s decision-making. Instead, we propose a method using a generative adversarial network to modify a facial image according to a 3D soft-tissue estimation of the post-operative face. To circumvent the lack of available data pairs between pre- and post-operative measurements we propose a semi-supervised training strategy using cycle losses that only requires paired open-source data of images and 3D surfaces of the face’s shape. After training on “in-the-wild” images we show that our model can realistically manipulate local regions of a face in a 2D image based on a modified 3D shape. We then test our model on four clinical examples where we predict the post-operative face according to a 3D soft-tissue prediction of surgery outcome, which was simulated by a surgery planning tool. As a result, we aim to demonstrate the potential of our approach to predict realistic post-operative images of faces without the need of paired clinical data, physical models, or 3D texture scans. [ABSTRACT FROM AUTHOR]

Biomechanical functionality of artificial hips strongly correlates with quality of life of patients after total hip arthroplasty. As the numbers of total hip arthroplasty are growing due to increasing life expectancy, biomechanical research is of utmost importance to improve the implants used and the operative procedures applied. Multibody simulation is used to predict forces and moments inside the human body. Generic scaling is usually performed to adapt the human models used in multibody simulation to individual patients. However, since the shape and size of the bones can vary considerably, this type of scaling often is not sufficient. In this work various CT datasets were used to quantify differences of individual femoral shapes, especially with regard to important biomechanical hip parameters, such as the CCD angle or the femoral offset. Our results prove that multibody simulations should be modeled more patientspecific to be able to calculate articular forces and moments more precisely, and thus, to improve surgical planning.

Orthognathic surgery is used to treat misaligned jaws in adults by repositioning them. Intervention planning has to take into account different clinical, anatomical, functional, and aesthetic parameters to determine the optimal position. Current planning systems usually present 3D surface models obtained from a CT or Cone-Beam scan of the patient. The surgeon then interactively positions the upper and lower jaw. Thus, the surgeon can manually optimize anatomical aspects but has to consider functional and aesthetic effects simultaneously, which may be error-prone. We are developing a computer-assisted planning system which generates an optimized position for both jaws based on different analyses (cephalometric, plaster model, photostat) of the head, using a gradient descent algorithm. For this purpose, landmarks are interactively identified on a 3D surface representation. The system is developed as a plugin for MITK utilizing a knowledge base realized in the sematic web standard RDFS, which is queried with SPARQL requests. In a preliminary evaluation with five different cases we compare the automatically generated planning proposal with the planning results of a maxillofacial expert (ground truth). Good general agreement is observed, although more research for the identification and development of 3D cephalometric analyses is needed.

Orthopaedic trauma surgery is a complex surgical speciality in which anatomy, physiology and physics are mixed. Proper diagnosing and based on that planning and performing surgery is of crucial matter. This article presents usefulness of 3D reconstruction in diagnostics and surgical planning. It focuses on utility of computed tomography reconstruction in trauma surgery. Moreover, two cases in which this technique was used is described. Complex 3D reconstruction proved its usefulness and in future it may become a modality of choice for planning complex trauma procedures in which standard implants and approaches are insufficient.

Congenital transsphenoidal encephalocele (CTE) surgical correction is a challenging procedure. Although rare, this anomaly, characterized with neural herniation elements, including the pituitary gland or optic pathway through the sphenoid bone with anatomical alteration, can be presented in many different ways and should be individually analyzed. Significant advances in medical technology and the 3D models may simulate the complex anatomical relations of the human body. Nowadays, medical education relies on the availability of standardized materials that can reliably emulate human anatomy. Therefore, realistic anatomical models have become an alternative for cadavers or animal specimens. In this technical note, the authors present a new technique to create personalized models that combine 3D printing, molding, and casting to create an anatomically and tactilely realistic model based on magnetic resonance and computerized tomography images. Produced from different silicon types, the model recreated the anatomic alterations precisely, allowing a multidisciplinary team to determine the adequate surgical approach for this patient. We describe a case of congenital transsphenoidal encephalocele of a 3-year-old boy, whose surgical correction was planned using a hybrid model. The technical description of the model is given in detail. This new hybrid model allowed a detailed discussion of the surgical approach aspects by having tissues of different consistencies and resistances and a very high prediction rate. This approach may allow a reduction in surgery time and possible complications after operative procedures. [ABSTRACT FROM AUTHOR]

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Orthognathic surgery (OGS) is a very complex surgical procedure aiming to correct a wide range of skeletal and dental irregularities, including jaws and teeth misalignments. It requires a precise pre-surgical planning and high surgical skills that are traditionally acquired through years of hands-on training in the operating room or in laboratory-based surgical practices using cadavers or models. Although modern engineering technologies have led to the development or computer-aided surgical procedures and systems, surgical training in OGS still relies on the traditional physical hands-on approach. This paper presents the results of an investigation carried out with the aim to evaluate the use of haptics and virtual reality technologies as an OGS training tool. Three case studies corresponding to cephalometry training, osteotomy training and surgery planning training were conducted. Participants comprised novices and experts in the area of OGS. Surgical skills, performance and confidence of trainees, in addition to reducing execution times and errors associated with the traditional OGS process, indicate that the haptic-enabled virtual reality approach is an effective training tool. [ABSTRACT FROM AUTHOR]

Copyright of Arthroskopie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Surgery planning for liver tumour is carried out using contrast enhanced computed tomography (CECT) images to determine the optimal resection strategy and to assess the volume of liver and tumour. Current surgery planning tools interpret even the functioning liver cells present within the tumour boundary as tumour. Plain CT images provide inadequate information for treatment planning. This work attempts to address two shortcomings of existing surgery planning tools: (i) to delineate functioning liver cells from the non-functioning tumourous tissues within the tumour boundary and (ii) to provide 3D visualization and actual tumour volume from the plain CT images., Methods: All slices of plain CT images containing liver are enhanced by means of fuzzy histogram equalization in Non-Subsampled Contourlet Transform (NSCT) domain prior to 3D reconstruction to clearly delineate liver, non-functioning tumourous tissues and functioning liver cells within the tumour boundary. The 3D analysis from plain and CECT images was carried out on five types of liver lesions viz. HCC, metastasis, hemangioma, cyst, and abscess along with normal liver., Results: The study resulted in clear delineation of functional liver tissues from non-functioning tumourous tissues within the tumour boundary from CECT as well as plain CT images. The volume of liver calculated using the proposed approach is found comparable with that obtained using Myrian-XP, a currently followed surgery planning tool in clinical practice., Conclusions: The obtained results from plain CT images will undoubtedly provide valuable diagnostic assistance and surgery planning even for the subset of patients for whom CECT acquisition is not advisable., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

This is the phase 1 of a multicenter clinical trial (NCT03738488), which aims to assess the efficacy and efficiency of surgery planning with 3D models of renal cell carcinoma (RCC) with venous tumor thrombus extension (VTE) compared to the standard images (CT). The objective of this phase is to obtain a 3D printed model of RCC with VTE that is feasible, accurate, reproducible, suitable for surgical simulation, and affordable. A specific protocol was developed to obtain the computed tomography (CT) image: early arterial and nephrogenic phase. ITK-snap® and VirSSPA Software® were used to segment the areas of interest. The resulting 3D mesh was processed with MeshMixer® and Cura®. Ten models from seven different cases were segmented and printed using different 3D printers and materials. We evaluated the material, scale, wall thickness, anatomy printed, 3D conformation, accuracy compared to the CT, suitability to perform the surgery, material, cost, and time (segmentation + design + fabrication + finishing). The four selected models were printed with a BQ Witbox FDM printer in polyurethane filament with a 0.8 mm wall thickness and 100% scale. All the relevant anatomical structures could be correctly identified, the 3D conformation was maintained with good accuracy compared to the CT and the surgery could be performed on them. Mean design time, model cost and printing time were 8.3 h, 33.4 €, and 38.5 h respectively. Various feasible 3D models of RCC with VTE were obtained after a few attempts. The final models were proved to be reproducible, accurate compared to the CT, and suitable for surgery simulation. The printing process was standardized making it possible to manufacture affordable 3D printed models. [ABSTRACT FROM AUTHOR]

There is a paucity of high‐quality evidence and guidelines on the prediction of skin defect reconstruction, and the type of repair rests on the operating surgeon's experience and skill. Mismatches between planning and execution can have negative consequences on resources, staff, and patient counseling. To investigate the factors that predict complex reconstruction in facial skin cancer surgery, we performed a retrospective study collecting information on the several parameters that may affect the method of reconstruction. A total of 325 cases were included. Only the factors tumor size (Z = 2.54; P <.05) and predicted repair plan (Z = 2.73; P <.01) were found to be significant predictors of complex repairs. When broken down by site and size, only the nose, scalp, and temple demonstrated a significant correlation between tumor size and the need for complex repairs. Clinical judgment at the time of initial examination is by far the greatest predictor of complex repairs. This occurs even when clinicians have heterogeneous surgical skills, and most are not technically versed in complex repairs. Increasing tumor size was also found to be an independent risk factor albeit only at certain body sites such as the temple, scalp, and nose. [ABSTRACT FROM AUTHOR]

Purpose: Rhinoplasty is one of the most common and challenging plastic surgery procedures. The results of the operation have a significant impact on the facial appearance. The planning is critical for successful rhinoplasty surgery. In this paper, we present a web application designed for preoperative rhinoplasty surgery planning. Methods: The application uses the three-dimensional (3D) model of a patient's face and facilitates marking of an extensive number of facial features and auto-calculation of facial measurements to develop a numerical plan of the surgery. The web application includes definitions, illustrations, and formulas to describe the features and measurements. In addition to the existing measurements, the user can calculate the distance between any two points, the angle between any three points, and the ratio of any two distances. We conducted a survey among experienced rhinoplasty surgeons to get feedback about the web application and to understand their attitude toward utilizing 3D models for preoperative planning. Results: The web application can be accessed and used through any web browser at digitized-rhinoplasty.com. The web application was utilized in our tests and also by the survey participants. The users successfully marked the facial features on the 3D models and reviewed the auto-calculated measurements. The survey results show that the experienced surgeons who tried the web application found it useful for preoperative planning and they also think that utilizing 3D models is beneficial. Conclusions: The web application introduced in this paper helps analyzing the patient's face in details utilizing 3D models and provides numeric outputs to be used in the rhinoplasty operation planning. The experienced rhinoplasty surgeons that participated to our survey agree that the web app would be a beneficial tool for rhinoplasty surgeons. We aim to further improve the web application with more functionality to help surgeons for preoperative planning of rhinoplasty. [ABSTRACT FROM AUTHOR]

Purpose: Radio-frequency ablations play an important role in the therapy of malignant liver lesions. The navigation of a needle to the lesion poses a challenge for both the trainees and intervening physicians. Methods: This publication presents a new GPU-based, accurate method for the simulation of radio-frequency ablations for lesions at the needle tip in general and for an existing visuo-haptic 4D VR simulator. The method is implemented real time capable with Nvidia CUDA. Results: It performs better than a literature method concerning the theoretical characteristic of monotonic convergence of the bioheat PDE and a in vitro gold standard with significant improvements ( p < 0.05 ) in terms of Pearson correlations. It shows no failure modes or theoretically inconsistent individual simulation results after the initial phase of 10 s. On the Nvidia 1080 Ti GPU, it achieves a very high frame rendering performance of > 480 Hz. Conclusion: Our method provides a more robust and safer real-time ablation planning and intraoperative guidance technique, especially avoiding the overestimation of the ablated tissue death zone, which is risky for the patient in terms of tumor recurrence. Future in vitro measurements and optimization shall further improve the conservative estimate. [ABSTRACT FROM AUTHOR]

Conventional Orthognathic surgery (OGS) planning involves cephalometric analyses and dental casts to be mounted on an articulator. Dental segments are subsequently identified, cut and repositioned to allow the fabrication of intraoral wafers that guide the positioning of the osteotomy bone segments. This conventional planning introduces many inaccuracies that affect the post-surgery outcomes. Although computer technologies have advanced computational tools for OGS planning, they have failed in providing a practical solution. Many focuses only on some specific stages of the planning process, and their ability to transfer preoperative planning data to the operating room is limited. This paper proposes a new integrated haptic-enabled virtual reality (VR) system for OGS planning. The system incorporates CAD tools and haptics to facilitate a complete planning process and is able to automatically generate preoperative plans. A clinical pre-diagnosis is also provided automatically by the system based on the patient's digital data. A functional evaluation based on a real patient case study demonstrates that the proposed virtual OGS planning method is feasible and more effective than the traditional approach at increasing the intuitiveness and reducing errors and planning times. [ABSTRACT FROM AUTHOR]

Hintergrund: Die Entwicklung einer posttraumatischen Gonarthrose ist häufig Folge von Gelenkfrakturen oder Kapsel‑/Bandverletzungen sowie von direkten Knorpel- oder Meniskusschäden. Auch extraartikuläre Frakturen mit Achs- bzw. Rotationsfehlstellungen prädisponieren für eine beschleunigte Arthroseentwicklung. Die Patienten sind zumeist jünger, und es bestehen häufig komplexe Ausgangsbefunde, die das operative Ergebnis wesentlich beeinflussen.Material und Methode: Die Besonderheiten im Hinblick auf Diagnostik und Therapie werden in diesem Beitrag wiedergegeben. Zudem wird ein Überblick über operative Techniken, Implantatwahl und zu erwartende Ergebnisse gegeben.Ergebnis und Diskussion: Neben dem präoperativen Infektausschluss stellen die Analyse der Fehlstellung, der Bandstabilität sowie das Ausmaß der knöchernen Defekte wesentliche Faktoren der Operationsplanung dar. Es muss entschieden werden, ob und wann Osteosynthesematerialien entfernt werden müssen, ob eine ein- oder zweizeitige Achskorrektur erfolgen muss und welche Art der Prothese benötigt wird. Zudem spielen das Weichteilmanagement und die Wahl des Zugangs eine wesentliche Rolle.Schlussfolgerung: Die Behandlung der posttraumatischen Gonarthrose ist deutlich komplexer und komplikationsträchtiger verglichen mit der Primärendoprothetik. Es werden besondere Anforderungen an Vorhaltung und Erfahrung mit verschiedenen Primär- und Revisionssystemen gestellt. Zudem müssen notwendige Operationsschritte wie Umstellungsosteotomien, Rekonstruktion des Streckapparates, lokale oder freie Lappenplastiken (interdisziplinär) vorhanden sein. [ABSTRACT FROM AUTHOR]