Deep-learning based 3D reconstruction of lower limb bones from biplanar radiographs for preoperative osteotomy planning.

PURPOSE: Three-dimensional (3D) preoperative planning has become the gold standard for orthopedic surgeries, primarily relying on CT-reconstructed 3D models. However, in contrast to standing radiographs, a CT scan is not part of the standard protocol but is usually acquired for preoperative planning purposes only. Additionally, it is costly, exposes the patients to high doses of radiation and is acquired in a non-weight-bearing position. METHODS: In this study, we develop a deep-learning based pipeline to facilitate 3D preoperative planning for high tibial osteotomies, based on 3D models reconstructed from low-dose biplanar standing EOS radiographs. Using digitally reconstructed radiographs, we train networks to localize the clinically required landmarks, separate the two legs in the sagittal radiograph and finally reconstruct the 3D bone model. Finally, we evaluate the accuracy of the reconstructed 3D models for the particular application case of preoperative planning, with the aim of eliminating the need for a CT scan in specific cases, such as high tibial osteotomies. RESULTS: The mean Dice coefficients for the tibial reconstructions were 0.92 and 0.89 for the right and left tibia, respectively. The reconstructed models were successfully used for clinical-grade preoperative planning in a real patient series of 52 cases. The mean differences to ground truth values for mechanical axis and tibial slope were 0.52° and 4.33°, respectively. CONCLUSIONS: We contribute a novel framework for the 2D-3D reconstruction of bone models from biplanar standing EOS radiographs and successfully use them in automated clinical-grade preoperative planning of high tibial osteotomies. However, achieving precise reconstruction and automated measurement of tibial slope remains a significant challenge.

Development and evaluation of robot-assisted ultrasound navigation system for pedicle screw placement: An ex-vivo animal validation.

PURPOSE: Spinal instrumentation with pedicle screw placement (PSP) is an important surgical technique for spinal diseases. Accurate screw trajectory is a prerequisite for PSP. Ultrasound (US) imaging with robot-assisted system forms a non-radiative alternative to provide precise screw trajectory. This study reports on the development and assessment of US navigation for this application. METHODS: A robot-assisted US reconstruction was proposed and an automatic CT-to-US registration algorithm was investigated, allowing the registration of screw trajectories. Experiments were conducted on ex-vivo lamb spines to evaluate system performance. RESULTS: In total, 72 screw trajectories are measured, displaying an average position accuracy of 2.80 ± 1.14 mm and orientation accuracy of 1.38 ± 0.61°. CONCLUSION: The experimental results demonstrate the feasibility of proposed US system. This work, although restricted to laboratory settings, encourages further exploration of the potential of this technology in clinical practice.

Automatic breach detection during spine pedicle drilling based on vibroacoustic sensing.

Pedicle drilling is a complex and critical spinal surgery task. Detecting breach or penetration of the surgical tool to the cortical wall during pilot-hole drilling is essential to avoid damage to vital anatomical structures adjacent to the pedicle, such as the spinal cord, blood vessels, and nerves. Currently, the guidance of pedicle drilling is done using image-guided methods that are radiation intensive and limited to the preoperative information. This work proposes a new radiation-free breach detection algorithm leveraging a non-visual sensor setup in combination with deep learning approach. Multiple vibroacoustic sensors, such as a contact microphone, a free-field microphone, a tri-axial accelerometer, a uni-axial accelerometer, and an optical tracking system were integrated into the setup. Data were collected on four cadaveric human spines, ranging from L5 to T10. An experienced spine surgeon drilled the pedicles relying on optical navigation. A new automatic labeling method based on the tracking data was introduced. Labeled data was subsequently fed to the network in mel-spectrograms, classifying the data into breach and non-breach. Different sensor types, sensor positioning, and their combinations were evaluated. The best results in breach recall for individual sensors could be achieved using contact microphones attached to the dorsal skin (85.8%) and uni-axial accelerometers clamped to the spinous process of the drilled vertebra (81.0%). The best-performing data fusion model combined the latter two sensors with a breach recall of 98%. The proposed method shows the great potential of non-visual sensor fusion for avoiding screw misplacement and accidental bone breaches during pedicle drilling and could be extended to further surgical applications.

Robot-assisted ultrasound reconstruction for spine surgery: from bench-top to pre-clinical study.

PURPOSE: Robot-assisted ultrasound (rUS) systems have already been used to provide non-radiative three-dimensional (3D) reconstructions that form the basis for guiding spine surgical procedures. Despite promising studies on this technology, there are few studies that offer insight into the robustness and generality of the approach by verifying performance in various testing scenarios. Therefore, this study aims at providing an assessment of a rUS system, with technical details from experiments starting at the bench-top to the pre-clinical study. METHODS: A semi-automatic control strategy was proposed to ensure continuous and smooth robotic scanning. Next, a U-Net-based segmentation approach was developed to automatically process the anatomic features and derive a high-quality 3D US reconstruction. Experiments were conducted on synthetic phantoms and human cadavers to validate the proposed approach. RESULTS: Average deviations of scanning force were found to be 2.84±0.45 N on synthetic phantoms and to be 5.64±1.10 N on human cadavers. The anatomic features could be reliably reconstructed at mean accuracy of 1.28±0.87 mm for the synthetic phantoms and of 1.74±0.89 mm for the human cadavers. CONCLUSION: The results and experiments demonstrate the feasibility of the proposed system in a pre-clinical setting. This work is complementary to previous work, encouraging further exploration of the potential of this technology in in vivo studies.

An automated optimization pipeline for clinical-grade computer-assisted planning of high tibial osteotomies under consideration of weight-bearing.

3D preoperative planning for high tibial osteotomies (HTO) has increasingly replaced 2D planning but is complex, time-consuming and therefore expensive. Several interdependent clinical objectives and constraints have to be considered, which often requires multiple rounds of revisions between surgeons and biomedical engineers. We therefore developed an automated preoperative planning pipeline, which takes imaging data as an input to generate a ready-to-use, patient-specific planning solution. Deep-learning based segmentation and landmark localization was used to enable the fully automated 3D lower limb deformity assessment. A 2D-3D registration algorithm allowed the transformation of the 3D bone models into the weight-bearing state. Finally, an optimization framework was implemented to generate ready-to use preoperative plannings in a fully automated fashion, using a genetic algorithm to solve the multi-objective optimization (MOO) problem based on several clinical requirements and constraints. The entire pipeline was evaluated on a large clinical dataset of 53 patient cases who previously underwent a medial opening-wedge HTO. The pipeline was used to automatically generate preoperative solutions for these patients. Five experts blindly compared the automatically generated solutions to the previously generated manual plannings. The overall mean rating for the algorithm-generated solutions was better than for the manual solutions. In 90% of all comparisons, they were considered to be equally good or better than the manual solution. The combined use of deep learning approaches, registration methods and MOO can reliably produce ready-to-use preoperative solutions that significantly reduce human workload and related health costs.

Malunion deformity of the forearm: Three-dimensional length variation of interosseous membrane and bone collision.

It remains unclear to what extent the interosseous membrane (IOM) is affected through the whole range of motion (ROM) in posttraumatic deformities of the forearm. The purpose of this study is to describe the ligament- and bone-related factors involved in rotational deficit of the forearm. Through three-dimensional (3D) kinematic simulations on one cadaveric forearm, angular deformities of 5° in four directions (flexion, extension, valgus, varus) were produced at two locations of the radius and the ulna (proximal and distal third). The occurrence of bone collision in pronation and the linear length variation of six parts of the IOM through the whole ROM were compared between the 32 types of forearm deformities. Similar patterns could be observed among four groups: 12 types of deformity presented increased bone collision in pronation, 8 presented an improvement of bone collision with an increase of the mean linear lengthening of the IOM in neutral rotation, 6 had an increased linear lengthening of the IOM in supination with nearly unchanged bone collision in pronation and 6 types presented nearly unchanged bone collision in pronation with a shortening of the mean linear length of IOM in supination or neutral rotation. This kinematic analysis provides a better understanding of the ligament- and bone-related factors expected to cause rotational deficit in forearm deformity and may help to refine the surgical indications of patient-specific corrective osteotomy.

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review.

Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques.

Deep learning representations to support COVID-19 diagnosis on CT slices. / Representaciones basadas en aprendizaje profundo como apoyo del diagnóstico de la COVID-19 en cortes de tomografía computarizada

INTRODUCTION: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist's expertise, which may result in subjective evaluations. OBJECTIVE: To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples. MATERIALS AND METHODS: Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic's dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers. RESULTS: The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively. CONCLUSION: Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings.

Introducción. La enfermedad por coronavirus (COVID-19) es actualmente el principal problema de salud pública en el mundo. En este contexto, el análisis automático de tomografías computarizadas (TC) surge como una herramienta diagnóstica complementaria que permite caracterizar hallazgos radiológicos, y categorizar y hacer el seguimiento de pacientes con COVID-19. Sin embargo, este análisis depende de la experiencia de los radiólogos, por lo que las valoraciones pueden ser subjetivas. Objetivo. Explorar representaciones de aprendizaje profundo entrenadas con cortes de TC torácica para diferenciar automáticamente entre los casos de COVID-19 y personas no infectadas. Materiales y métodos. Se usaron dos conjuntos de datos de TC: de SARS-CoV-2 CT (conjunto 1) y de la clínica FOSCAL (conjunto 2). Los modelos de aprendizaje supervisados y previamente entrenados en imágenes naturales, se ajustaron usando aprendizaje por transferencia. La clasificación se llevó a cabo mediante aprendizaje de extremo a extremo y clasificadores tales como los árboles de decisiones y las máquinas de soporte vectorial, alimentados por la representación profunda previamente aprendida. Resultados. El enfoque de extremo a extremo alcanzó una exactitud promedio de 92,33 % (89,70 % de precisión) para el conjunto 1 y de 96,99 % (96,62 % de precisión) para el conjunto-2. La máquina de soporte vectorial alcanzó una exactitud promedio de 91,40 % (precisión del 95,77 %) para el conjunto-1 y del 96,00 % (precisión del 94,74 %) para el conjunto 2. Conclusión. Las representaciones profundas lograron resultados sobresalientes al caracterizar patrones radiológicos usados en la detección de casos de COVID-19 a partir de estudios de TC y demostraron ser una potencial herramienta de apoyo del diagnóstico.

Deep learning representations to support COVID-19 diagnosis on CT slices / Representaciones basadas en aprendizaje profundo como apoyo del diagnóstico de la COVID-19 en cortes de tomografía computarizada

Introduction: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist's expertise, which may result in subjective evaluations. Objective: To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples. Materials and methods: Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic's dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers. Results: The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively. Conclusion: Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings.

Introducción. La enfermedad por coronavirus (COVID-19) es actualmente el principal problema de salud pública en el mundo. En este contexto, el análisis automático de tomografías computarizadas (TC) surge como una herramienta diagnóstica complementaria que permite caracterizar hallazgos radiológicos, y categorizar y hacer el seguimiento de pacientes con COVID-19. Sin embargo, este análisis depende de la experiencia de los radiólogos, por lo que las valoraciones pueden ser subjetivas. Objetivo. Explorar representaciones de aprendizaje profundo entrenadas con cortes de TC torácica para diferenciar automáticamente entre los casos de COVID-19 y personas no infectadas. Materiales y métodos. Se usaron dos conjuntos de datos de TC: de SARS-CoV-2 CT (conjunto 1) y de la clínica FOSCAL (conjunto 2). Los modelos de aprendizaje supervisados y previamente entrenados en imágenes naturales, se ajustaron usando aprendizaje por transferencia. La clasificación se llevó a cabo mediante aprendizaje de extremo a extremo y clasificadores tales como los árboles de decisiones y las máquinas de soporte vectorial, alimentados por la representación profunda previamente aprendida. Resultados. El enfoque de extremo a extremo alcanzó una exactitud promedio de 92,33 % (89,70 % de precisión) para el conjunto 1 y de 96,99 % (96,62 % de precisión) para el conjunto-2. La máquina de soporte vectorial alcanzó una exactitud promedio de 91,40 % (precisión del 95,77 %) para el conjunto-1 y del 96,00 % (precisión del 94,74 %) para el conjunto 2. Conclusión. Las representaciones profundas lograron resultados sobresalientes al caracterizar patrones radiológicos usados en la detección de casos de COVID-19 a partir de estudios de TC y demostraron ser una potencial herramienta de apoyo del diagnóstico.

Automatic modelling of human musculoskeletal ligaments - Framework overview and model quality evaluation.

BACKGROUND: Accurate segmentation of connective soft tissues in medical images is very challenging, hampering the generation of geometric models for bio-mechanical computations. Alternatively, one could predict ligament insertion sites and then approximate the shapes, based on anatomical knowledge and morphological studies. OBJECTIVE: In this work, we describe an integrated framework for automatic modelling of human musculoskeletal ligaments. METHOD: We combine statistical shape modelling with geometric algorithms to automatically identify insertion sites, based on which geometric surface/volume meshes are created. As clinical use case, the framework has been applied to generate models of the forearm interosseous membrane. Ligament insertion sites in the statistical model were defined according to anatomical predictions following a published approach. RESULTS: For evaluation we compared the generated sites, as well as the ligament shapes, to data obtained from a cadaveric study, involving five forearms with 15 ligaments. Our framework permitted the creation of models approximating ligaments' shapes with good fidelity. However, we found that the statistical model trained with the state-of-the-art prediction of the insertion sites was not always reliable. Average mean square errors as well as Hausdorff distances of the meshes could increase by an order of magnitude, as compared to employing known insertion locations of the cadaveric study. Using those, an average mean square error of 0.59 mm and an average Hausdorff distance of less than 7 mm resulted, for all ligaments. CONCLUSIONS: The presented approach for automatic generation of ligament shapes from insertion points appears to be feasible but the detection of the insertion sites with a SSM is too inaccurate, thus making a patient-specific approach necessary.

Three-dimensional preoperative planning in the weight-bearing state: validation and clinical evaluation.

OBJECTIVES: 3D preoperative planning of lower limb osteotomies has become increasingly important in light of modern surgical technologies. However, 3D models are usually reconstructed from Computed Tomography data acquired in a non-weight-bearing posture and thus neglecting the positional variations introduced by weight-bearing. We developed a registration and planning pipeline that allows for 3D preoperative planning and subsequent 3D assessment of anatomical deformities in weight-bearing conditions. METHODS: An intensity-based algorithm was used to register CT scans with long-leg standing radiographs and subsequently transform patient-specific 3D models into a weight-bearing state. 3D measurement methods for the mechanical axis as well as the joint line convergence angle were developed. The pipeline was validated using a leg phantom. Furthermore, we evaluated our methods clinically by applying it to the radiological data from 59 patients. RESULTS: The registration accuracy was evaluated in 3D and showed a maximum translational and rotational error of 1.1 mm (mediolateral direction) and 1.2° (superior-inferior axis). Clinical evaluation proved feasibility on real patient data and resulted in significant differences for 3D measurements when the effects of weight-bearing were considered. Mean differences were 2.1 ± 1.7° and 2.0 ± 1.6° for the mechanical axis and the joint line convergence angle, respectively. 37.3 and 40.7% of the patients had differences of 2° or more in the mechanical axis or joint line convergence angle between weight-bearing and non-weight-bearing states. CONCLUSIONS: Our presented approach provides a clinically feasible approach to preoperatively fuse 2D weight-bearing and 3D non-weight-bearing data in order to optimize the surgical correction.

Similar scapular morphology in patients with dynamic and static posterior shoulder instability.

BACKGROUND: There is evidence that specific variants of scapular morphology are associated with dynamic and static posterior shoulder instability. To this date, observations regarding glenoid and/or acromial variants were analyzed independently, with two-dimensional imaging or without comparison with a healthy control group. Therefore, the purpose of this study was to analyze and describe the three-dimensional (3D) shape of the scapula in healthy and in shoulders with static or dynamic posterior instability using 3D surface models and 3D measurement methods. METHODS: In this study, 30 patients with unidirectional posterior instability and 20 patients with static posterior humeral head subluxation (static posterior instability, Walch B1) were analyzed. Both cohorts were compared with a control group of 40 patients with stable, centered shoulders and without any clinical symptoms. 3D surface models were obtained through segmentation of computed tomography images and 3D measurements were performed for glenoid (version and inclination) and acromion (tilt, coverage, height). RESULTS: Overall, the scapulae of patients with dynamic and static instability differed only marginally among themselves. Compared with the control group, the glenoid was 2.5° (P = .032), respectively, 5.7° (P = .001) more retroverted and 2.9° (P = .025), respectively, 3.7° (P = .014) more downward tilted in dynamic, respectively, static instability. The acromial roof of dynamic instability was significantly higher and on average 6.2° (P = .007) less posterior covering with an increased posterior acromial height of +4.8mm (P = .001). The acromial roof of static instability was on average 4.8° (P = .041) more externally rotated (axial tilt), 7.3° (P = .004) flatter (sagittal tilt), 8.3° (P = .001) less posterior covered with an increased posterior acromial height of +5.8 mm (0.001). CONCLUSION: The scapula of shoulders with dynamic and static posterior instability is characterized by an increased glenoid retroversion and an acromion that is shorter posterolaterally, higher, and more horizontal in the sagittal plane. All these deviations from the normal scapula values were more pronounced in static posterior instability.

Automated Pipeline to Generate Anatomically Accurate Patient-Specific Biomechanical Models of Healthy and Pathological FSUs.

State-of-the-art preoperative biomechanical analysis for the planning of spinal surgery not only requires the generation of three-dimensional patient-specific models but also the accurate biomechanical representation of vertebral joints. The benefits offered by computational models suitable for such purposes are still outweighed by the time and effort required for their generation, thus compromising their applicability in a clinical environment. In this work, we aim to ease the integration of computerized methods into patient-specific planning of spinal surgery. We present the first pipeline combining deep learning and finite element methods that allows a completely automated model generation of functional spine units (FSUs) of the lumbar spine for patient-specific FE simulations (FEBio). The pipeline consists of three steps: (a) multiclass segmentation of cropped 3D CT images containing lumbar vertebrae using the DenseVNet network, (b) automatic landmark-based mesh fitting of statistical shape models onto 3D semantic segmented meshes of the vertebral models, and (c) automatic generation of patient-specific FE models of lumbar segments for the simulation of flexion-extension, lateral bending, and axial rotation movements. The automatic segmentation of FSUs was evaluated against the gold standard (manual segmentation) using 10-fold cross-validation. The obtained Dice coefficient was 93.7% on average, with a mean surface distance of 0.88 mm and a mean Hausdorff distance of 11.16 mm (N = 150). Automatic generation of finite element models to simulate the range of motion (ROM) was successfully performed for five healthy and five pathological FSUs. The results of the simulations were evaluated against the literature and showed comparable ROMs in both healthy and pathological cases, including the alteration of ROM typically observed in severely degenerated FSUs. The major intent of this work is to automate the creation of anatomically accurate patient-specific models by a single pipeline allowing functional modeling of spinal motion in healthy and pathological FSUs. Our approach reduces manual efforts to a minimum and the execution of the entire pipeline including simulations takes approximately 2 h. The automation, time-efficiency and robustness level of the pipeline represents a first step toward its clinical integration.

Surgical Process Modeling for Open Spinal Surgeries.

Modern operating rooms are becoming increasingly advanced thanks to the emerging medical technologies and cutting-edge surgical techniques. Current surgeries are transitioning into complex processes that involve information and actions from multiple resources. When designing context-aware medical technologies for a given intervention, it is of utmost importance to have a deep understanding of the underlying surgical process. This is essential to develop technologies that can correctly address the clinical needs and can adapt to the existing workflow. Surgical Process Modeling (SPM) is a relatively recent discipline that focuses on achieving a profound understanding of the surgical workflow and providing a model that explains the elements of a given surgery as well as their sequence and hierarchy, both in quantitative and qualitative manner. To date, a significant body of work has been dedicated to the development of comprehensive SPMs for minimally invasive baroscopic and endoscopic surgeries, while such models are missing for open spinal surgeries. In this paper, we provide SPMs common open spinal interventions in orthopedics. Direct video observations of surgeries conducted in our institution were used to derive temporal and transitional information about the surgical activities. This information was later used to develop detailed SPMs that modeled different primary surgical steps and highlighted the frequency of transitions between the surgical activities made within each step. Given the recent emersion of advanced techniques that are tailored to open spinal surgeries (e.g., artificial intelligence methods for intraoperative guidance and navigation), we believe that the SPMs provided in this study can serve as the basis for further advancement of next-generation algorithms dedicated to open spinal interventions that require a profound understanding of the surgical workflow (e.g., automatic surgical activity recognition and surgical skill evaluation). Furthermore, the models provided in this study can potentially benefit the clinical community through standardization of the surgery, which is essential for surgical training.

Accuracy and Early Clinical Outcome After 3-Dimensional Correction of Distal Radius Intra-Articular Malunions Using Patient-Specific Instruments.

PURPOSE: To investigate the residual articular incongruity on computed tomography image data and the early clinical outcome of 3-dimensional planned and navigated intra-articular osteotomies of the distal radius. METHODS: We conducted a retrospective analysis of intra-articular osteotomies executed between 2008 and 2016. We identified 37 patients (aged 26-73 years) and performed a combined intra-articular and extra-articular osteotomy on 20 patients. A preoperative 3-dimensional plan with the superimposed bone of the contralateral healthy side was performed in each case to analyze and execute the osteotomy by intraoperative navigation. The residual articular incongruity was assessed by quantification of the maximal stepoff in the coronal or sagittal computed tomography scans. Clinical outcome, including range of motion, grip strength, and return to work, was assessed after a minimum follow-up of 12 months and compared with preoperative measurements. RESULTS: On average, the preoperative intra-articular stepoff was 2.5 mm (±0.6 mm; range, 1.4-4.2 mm) and was significantly reduced to 0.8 mm (±0.2 mm) after surgery. After surgery, 30 patients had a stepoff less than 1 mm; in 7, a stepoff of 1.1 to 1.4 mm was measured. After 1 year, 22 had no pain, 9 had slight pain during heavy work, and 5 had moderate pain with no improvement compared with their preoperative status, although wrist strength and range of motion improved in all 37 patients. One patient underwent a secondary radioscapholunate arthrodeses owing to persistent pain despite a congruent joint with a small residual intra-articular stepoff (0.6 mm). CONCLUSIONS: Intra-articular osteotomies of the distal radius treated by 3-dimensional preoperative planning and patient-specific guides are an accurate technique to reduce articular incongruity to an average stepoff of 0.8 mm (range, 0.3-1.4 mm). The early clinical outcomes demonstrated overall reduction in pain and improvement of range of motion and grip strength in 36 of 37 patients. TYPE OF STUDY/LEVEL OF EVIDENCE: Therapeutic IV.

Three-Dimensional Automated Assessment of the Distal Radioulnar Joint Morphology According to Sigmoid Notch Surface Orientation.

PURPOSE: To develop reproducible 3-dimensional measurements for quantification of the distal radioulnar joint (DRUJ) morphology. We hypothesized that automated 3-dimensional measurement of the ulnar variance (UV) and the sigmoid notch (SN) angle would be comparable to those of the reference standard while overcoming some drawbacks of conventional 2-dimensional measurements. METHODS: Radiological data of healthy forearm bones (radiographs and computed tomography) of 53 adult subjects were included in the study. Automated measurements were developed for assessment of the SN morphology based on 3-dimensional landmarks, incorporating subject-specific estimation of cartilage surface orientation. A common anatomical reference was defined among the different imaging modalities and a comparison of the SN angle and UV measurements was performed in radiographs, computed tomography scans, and 3-dimensional models. Finally, the 3-dimensional UV measurements were evaluated in an experimental setup using 3-dimensional printed bone models. RESULTS: The automated 3-dimensional measurements of SN subtypes showed a notably larger notch radius (18.9 mm) for negative SN angles compared with positive SN angles in subjects (16.9 mm). Similar UV measurements were obtained in healthy DRUJ morphologies, with a high correlation between radiographs and 3-dimensional measurements for the SN angle (0.77) and UV (0.85). In the experimental setup with pathological radial inclinations, UV was on average 1.13 mm larger in the radiographs compared with the 3-dimensional measurements, and 1.30 mm larger in the cases with pathological palmar tilts. Furthermore, UV radiograph measurements on the modified palmar tilt deviated from the 3-dimensional measurements. CONCLUSIONS: The developed 3-dimensional automated measurements were able to quantify morphological differences among sigmoid notch subtypes and were comparable to those of the reference standard. CLINICAL RELEVANCE: The developed methods do not depend on the forearm position or orientation of the distal radius and can be used for 3-dimensional quantification of DRUJ pathologies in 3-dimensional surgical planning.

Digitalization of the IOM: A comprehensive cadaveric study for obtaining three-dimensional models and morphological properties of the forearm's interosseous membrane.

State-of-the-art of preoperative planning for forearm orthopaedic surgeries is currently limited to simple bone procedures. The increasing interest of clinicians for more comprehensive analysis of complex pathologies often requires dynamic models, able to include the soft tissue influence into the preoperative process. Previous studies have shown that the interosseous membrane (IOM) influences forearm motion and stability, but due to the lack of morphological and biomechanical data, existing simulation models of the IOM are either too simple or clinically unreliable. This work aims to address this problematic by generating 3D morphological and tensile properties of the individual IOM structures. First, micro- and standard-CT acquisitions were performed on five fresh-frozen annotated cadaveric forearms for the generation of 3D models of the radius, ulna and each of the individual ligaments of the IOM. Afterwards, novel 3D methods were developed for the measurement of common morphological features, which were validated against established optical ex-vivo measurements. Finally, we investigated the individual tensile properties of each IOM ligament. The generated 3D morphological features can provide the basis for the future development of functional planning simulation of the forearm.

Evaluation of CT-MR image registration methodologies for 3D preoperative planning of forearm surgeries.

Computerized surgical planning for forearm procedures that considers both soft and bony tissue, requires alignment of preoperatively acquired computed tomography (CT) and magnetic resonance (MR) images by image registration. Normalized mutual information (NMI) registration techniques have been researched to improve efficiency and to eliminate the user dependency associated with manual alignment. While successfully applied in various medical fields, the application of NMI registration to images of the forearm, for which the relative pose of the radius and ulna likely differs between CT and MR acquisitions, is yet to be described. To enable the alignment of CT and MR forearm data, we propose an NMI-based registration pipeline, which allows manual steering of the registration algorithm to the desired image subregion and is, thus, applicable to the forearm. Successive automated registration is proposed to enable planning incorporating multiple target anatomical structures such as the radius and ulna. With respect to gold-standard manual registration, the proposed registration methodology achieved mean accuracies of 0.08 ± 0.09 mm (0.01-0.41 mm range) in comparison with 0.28 ± 0.23 mm (0.03-0.99 mm range) associated with a landmark-based registration when tested on 40 patient data sets. Application of the proposed registration pipeline required less than 10 minutes on average compared with 20 minutes required by the landmark-based registration. The clinical feasibility and relevance of the method were tested on two different clinical applications, a forearm tumor resection and radioulnar joint instability analysis, obtaining accurate and robust CT-MR image alignment for both cases.

An automatic genetic algorithm framework for the optimization of three-dimensional surgical plans of forearm corrective osteotomies.

Three-dimensional (3D) computer-assisted corrective osteotomy has become the state-of-the-art for surgical treatment of complex bone deformities. Despite available technologies, the automatic generation of clinically acceptable, ready-to-use preoperative planning solutions is currently not possible for such pathologies. Multiple contradicting and mutually dependent objectives have to be considered, as well as clinical and technical constraints, which generally require iterative manual adjustments. This leads to unnecessary surgeon efforts and unbearable clinical costs, hindering also the quality of patient treatment due to the reduced number of solutions that can be investigated in a clinically acceptable timeframe. In this paper, we propose an optimization framework for the generation of ready-to-use preoperative planning solutions in a fully automatic fashion. An automatic diagnostic assessment using patient-specific 3D models is performed for 3D malunion quantification and definition of the optimization parameters' range. Afterward, clinical objectives are translated into the optimization module, and controlled through tailored fitness functions based on a weighted and multi-staged optimization approach. The optimization is based on a genetic algorithm capable of solving multi-objective optimization problems with non-linear constraints. The framework outputs a complete preoperative planning solution including position and orientation of the osteotomy plane, transformation to achieve the bone reduction, and position and orientation of the fixation plate and screws. A qualitative validation was performed on 36 consecutive cases of radius osteotomy where solutions generated by the optimization algorithm (OA) were compared against the gold standard solutions generated by experienced surgeons (Gold Standard; GS). Solutions were blinded and presented to 6 readers (4 surgeons, 2 planning engineers), who voted OA solutions to be better in 55% of the time. The quantitative evaluation was based on different error measurements, showing average improvements with respect to the GS from 20% for the reduction alignment and up to 106% for the position of the fixation screws. Notably, our algorithm was able to generate feasible clinical solutions which were not possible to obtain with the current state-of-the-art method.

Correction to: Improving accuracy of opening-wedge osteotomies of distal radius using a patient-specific ramp-guide technique.

Following publication of the original article [1], the author pointed out that the references were numbered incorrectly. This error was introduced during the production process. The original article has been corrected.