Simulation Software to Plan the Treatment of Acetabular Fractures: The Patient-Specific Biomechanical Model.

OBJECTIVES: The objective of this study was to assess the impact of using simulation software for preoperative planning: a patient-specific biomechanical model (PSBM) in acetabular surgery. The secondary objectives were to assess operating time, intraoperative bleeding, and peroperative complications. DESIGN: This is a prospective control study. SETTING: Level 1 trauma center. PATIENTS/PARTICIPANTS: Between January 2019 and December 2022, patients with operative acetabular fracture treated by the first author were prospectively enrolled. INTERVENTION: Patients were divided into 2 groups according to the use or not of PSBM for preoperative planning. When PSBM was used, data were extracted from the preoperative high-resolution computed tomography scans to build a biomechanical model implemented in a custom software [simulation (SIM group)]. When computed tomography scans were not performed in our hospital, PSBM was not feasible (non-SIM group). MAIN OUTCOME MEASUREMENTS: Radiological results, surgery duration, blood loss, and peroperative complications were recorded. RESULTS: Sixty-six patients were included; 26 in the PSBM group and 40 in the standard group. The 2 groups were comparable regarding fracture patterns and epidemiological data. After simulation, in the SIM group, a poor reduction (>3 mm) was found in 2 of 26 patients (7.7%) versus 11 of 40 patients (27.5%) in the non-SIM group, P = 0.048. The mean operative time was shorter after simulation (110 minutes vs. 155 minutes, P = 0.01), and the mean blood loss was reduced (420 vs. 670 mL, P = 0.01). CONCLUSIONS: By reducing the peroperative trials for reduction, PSBM allows better reduction in a shorter operative time and with less blood loss. LEVEL OF EVIDENCE: Level II: prospective study.

Measurement and Analysis of Lobar Lung Deformation After a Change of Patient Position During Video-Assisted Thoracoscopic Surgery.

Video-assisted thoracoscopic surgery (VATS) is a minimally invasive surgical technique for the diagnosis and treatment of early-stage lung cancer. During VATS, large lung deformation occurs as a result of a change of patient position and a pneumothorax (lung deflation), which hinders the intraoperative localization of pulmonary nodules. Modeling lung deformation during VATS for surgical navigation is desirable, but the mechanisms causing such deformation are yet not well-understood. In this study, we estimate, quantify and analyze the lung deformation occurring after a change of patient position during VATS. We used deformable image registration to estimate the lung deformation between a preoperative CT (in supine position) and an intraoperative CBCT (in lateral decubitus position) of six VATS clinical cases. We accounted for sliding motion between lobes and against the thoracic wall and obtained consistently low average target registration errors (under 1 mm). We observed large lung displacement (up to 40 mm); considerable sliding motion between lobes and against the thoracic wall (up to 30 mm); and localized volume changes indicating deformation. These findings demonstrate the complexity of the change of patient position phenomenon, which should necessarily be taken into account to model lung deformation for intraoperative guidance during VATS.

Virtual preoperative planning of acetabular fractures using patient-specific biomechanical simulation: A case-control study.

INTRODUCTION: The first patient-specific biomechanical model for planning the surgical reduction of acetabular fractures was developed in our institution and validated retrospectively. There are no prior studies showing its effectiveness in terms of reduction quality, operative duration and intraoperative bleeding. Therefore, we performed a case control study aiming to: 1) evaluate the effect of preoperative simulation by patient-specific biomechanical simulator on the operating time and intraoperative bleeding; 2) evaluate the effect of preoperative simulation by patient-specific biomechanical simulator on the quality of reduction. METHOD: All patients operated on between January 2019 and June 2019 after planning by biomechanical simulation were included in this case-control study. Each patient included was matched to 2 controls from our database (2015-2018) according to age and fracture-type. DICOM data were extracted from the preoperative high-resolution scanners to build a three-dimensional model of the fracture by semi-automatic segmentation. A biomechanical model was built to virtually simulate the different stages of surgical reduction. Surgery was then performed according to simulation data. Surgical duration, blood loss, radiological findings and intraoperative complications were recorded, analysed and compared. RESULTS: Thirty patients were included, 10 in the simulation group and 20 in the control group. The two groups were comparable in terms of age, time from accident to surgery, fracture-type and surgical approach. The mean operative time was significantly reduced in the simulation group: 113min±33 (60-180) versus 196min±32 (60-260) (p=0.01). Mean blood loss was significantly reduced in the simulation group: 505mL±189 (100-750) versus 745mL±130 (200-850) (p<0.01). However, no significant difference was found in the radiological results according to Matta's criteria, although an anatomical reduction was obtained for 9 patients in the simulation group (90%) versus 12 patients in the control group (60%) (p=0.26). A postoperative neurological complication was recorded in the control group (sensory deficit of the lateral cutaneous nerve of thigh). CONCLUSION: This study confirms the promising results of preoperative planning in acetabular trauma surgery based on patient-specific biomechanical simulation as well as its feasibility in routine clinical practice. By providing a better understanding of the fracture and its behavior, a reduction in intraoperative bleeding and in operative duration is achieved. LEVEL OF EVIDENCE: III; case-control study.

Planning acetabular fracture reduction using a patient-specific biomechanical model: a prospective and comparative clinical study.

PURPOSE: A simple, patient-specific biomechanical model (PSBM) is proposed in which the main surgical tools and actions can be simulated, which enables clinicians to evaluate different strategies for an optimal surgical planning. A prospective and comparative clinical study was performed to assess early clinical and radiological results. METHODS: From January 2019 to July 2019, a PSBM was created for every operated acetabular fracture (simulation group). DICOM data were extracted from the pre-operative high-resolution CT scans to build a 3D model of the fracture using segmentation methods. A PSBM was implemented in a custom software allowing a biomechanical simulation of the surgery in terms of reduction sequences. From July 2019 to December 2019, every patient with an operated for acetabular fracture without PSBM was included in the standard group. Surgery duration, blood loss, radiological results and per-operative complications were recorded and compared between the two groups. RESULTS: Twenty-two patients were included, 10 in the simulation group and 12 in the standard group. The two groups were comparable regarding age, time to surgery, fracture pattern distribution and surgical approaches. The mean operative time was significantly lower in the simulation group: 113 min ± 33 (60-180) versus 184 ± 58 (90-260), p = 0.04. The mean blood loss was significantly lower in the simulation group, p = 0.01. No statistical significant differences were found regarding radiological results (p = 0.16). No per-operative complications were recorded. CONCLUSION: This study confirms that pre-operative planning in acetabular surgery based on a PSBM results in a shorter operative time and a reduction of blood loss during surgery. This study also confirms the feasibility of PSBM planning in daily clinical routine. LEVEL OF EVIDENCE: II: prospective study.

A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery.

The resection of small, low-dense or deep lung nodules during video-assisted thoracoscopic surgery (VATS) is surgically challenging. Nodule localization methods in clinical practice typically rely on the preoperative placement of markers, which may lead to clinical complications. We propose a markerless lung nodule localization framework for VATS based on a hybrid method combining intraoperative cone-beam CT (CBCT) imaging, free-form deformation image registration, and a poroelastic lung model with allowance for air evacuation. The difficult problem of estimating intraoperative lung deformations is decomposed into two more tractable sub-problems: (i) estimating the deformation due the change of patient pose from preoperative CT (supine) to intraoperative CBCT (lateral decubitus); and (ii) estimating the pneumothorax deformation, i.e. a collapse of the lung within the thoracic cage. We were able to demonstrate the feasibility of our localization framework with a retrospective validation study on 5 VATS clinical cases. Average initial errors in the range of 22 to 38 mm were reduced to the range of 4 to 14 mm, corresponding to an error correction in the range of 63 to 85%. To our knowledge, this is the first markerless lung deformation compensation method dedicated to VATS and validated on actual clinical data.

Automatic segmentation of brain tumor resections in intraoperative ultrasound images using U-Net.

To compensate for the intraoperative brain tissue deformation, computer-assisted intervention methods have been used to register preoperative magnetic resonance images with intraoperative images. In order to model the deformation due to tissue resection, the resection cavity needs to be segmented in intraoperative images. We present an automatic method to segment the resection cavity in intraoperative ultrasound (iUS) images. We trained and evaluated two-dimensional (2-D) and three-dimensional (3-D) U-Net networks on two datasets of 37 and 13 cases that contain images acquired from different ultrasound systems. The best overall performing method was the 3-D network, which resulted in a 0.72 mean and 0.88 median Dice score over the whole dataset. The 2-D network also had good results with less computation time, with a median Dice score over 0.8. We also evaluated the sensitivity of network performance to training and testing with images from different ultrasound systems and image field of view. In this application, we found specialized networks to be more accurate for processing similar images than a general network trained with all the data. Overall, promising results were obtained for both datasets using specialized networks. This motivates further studies with additional clinical data, to enable training and validation of a clinically viable deep-learning model for automated delineation of the tumor resection cavity in iUS images.

Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge.

In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.

Computer assisted surgery in preoperative planning of acetabular fracture surgery: state of the art.

INTRODUCTION: The development of imaging modalities and computer technology provides a new approach in acetabular surgery. AREAS COVERED: This review describes the role of computer-assisted surgery (CAS) in understanding of the fracture patterns, in the virtual preoperative planning of the surgery and in the use of custom-made plates in acetabular fractures with or without 3D printing technologies. A Pubmed internet research of the English literature of the last 20 years was carried out about studies concerning computer-assisted surgery in acetabular fractures. The several steps for CAS in acetabular fracture surgery are presented and commented by the main author regarding to his personal experience. EXPERT COMMENTARY: Computer-assisted surgery in acetabular fractures is still initial experiences with promising results. Patient-specific biomechanical models considering soft tissues should be developed to allow a more realistic planning.

Brain-shift compensation using intraoperative ultrasound and constraint-based biomechanical simulation.

PURPOSE: During brain tumor surgery, planning and guidance are based on preoperative images which do not account for brain-shift. However, this deformation is a major source of error in image-guided neurosurgery and affects the accuracy of the procedure. In this paper, we present a constraint-based biomechanical simulation method to compensate for craniotomy-induced brain-shift that integrates the deformations of the blood vessels and cortical surface, using a single intraoperative ultrasound acquisition. METHODS: Prior to surgery, a patient-specific biomechanical model is built from preoperative images, accounting for the vascular tree in the tumor region and brain soft tissues. Intraoperatively, a navigated ultrasound acquisition is performed directly in contact with the organ. Doppler and B-mode images are recorded simultaneously, enabling the extraction of the blood vessels and probe footprint, respectively. A constraint-based simulation is then executed to register the pre- and intraoperative vascular trees as well as the cortical surface with the probe footprint. Finally, preoperative images are updated to provide the surgeon with images corresponding to the current brain shape for navigation. RESULTS: The robustness of our method is first assessed using sparse and noisy synthetic data. In addition, quantitative results for five clinical cases are provided, first using landmarks set on blood vessels, then based on anatomical structures delineated in medical images. The average distances between paired vessels landmarks ranged from 3.51 to 7.32 (in mm) before compensation. With our method, on average 67% of the brain-shift is corrected (range [1.26; 2.33]) against 57% using one of the closest existing works (range [1.71; 2.84]). Finally, our method is proven to be fully compatible with a surgical workflow in terms of execution times and user interactions. CONCLUSION: In this paper, a new constraint-based biomechanical simulation method is proposed to compensate for craniotomy-induced brain-shift. While being efficient to correct this deformation, the method is fully integrable in a clinical process.

Orbital and maxillofacial computer aided surgery: patient-specific finite element models to predict surgical outcomes.

This paper addresses an important issue raised for the clinical relevance of Computer-Assisted Surgical applications, namely the methodology used to automatically build patient-specific finite element (FE) models of anatomical structures. From this perspective, a method is proposed, based on a technique called the mesh-matching method, followed by a process that corrects mesh irregularities. The mesh-matching algorithm generates patient-specific volume meshes from an existing generic model. The mesh regularization process is based on the Jacobian matrix transform related to the FE reference element and the current element. This method for generating patient-specific FE models is first applied to computer-assisted maxillofacial surgery, and more precisely, to the FE elastic modelling of patient facial soft tissues. For each patient, the planned bone osteotomies (mandible, maxilla, chin) are used as boundary conditions to deform the FE face model, in order to predict the aesthetic outcome of the surgery. Seven FE patient-specific models were successfully generated by our method. For one patient, the prediction of the FE model is qualitatively compared with the patient's post-operative appearance, measured from a computer tomography scan. Then, our methodology is applied to computer-assisted orbital surgery. It is, therefore, evaluated for the generation of 11 patient-specific FE poroelastic models of the orbital soft tissues. These models are used to predict the consequences of the surgical decompression of the orbit. More precisely, an average law is extrapolated from the simulations carried out for each patient model. This law links the size of the osteotomy (i.e. the surgical gesture) and the backward displacement of the eyeball (the consequence of the surgical gesture).

Biomechanical models to simulate consequences of maxillofacial surgery.

This paper presents the biomechanical finite element models that have been developed in the framework of the computer-assisted maxillofacial surgery. After a brief overview of the continuous elastic modelling method, two models are introduced and their use for computer-assisted applications discussed. The first model deals with orthognathic surgery and aims at predicting the facial consequences of maxillary and mandibular osteotomies. For this, a generic three-dimensional model of the face is automatically adapted to the morphology of the patient by the mean of elastic registration. Qualitative simulations of the consequences of an osteotomy of the mandible can thus be provided. The second model addresses the Sleep Apnoea Syndrome. Its aim is to develop a complete modelling of the interaction between airflow and upper airways walls during breathing. Dynamical simulations of the interaction during a respiratory cycle are computed and compared with observed phenomena.

Patient specific finite element model of the face soft tissues for computer-assisted maxillofacial surgery.

This paper addresses the prediction of face soft tissue deformations resulting from bone repositioning in maxillofacial surgery. A generic 3D Finite Element model of the face soft tissues was developed. Face muscles are defined in the mesh as embedded structures, with different mechanical properties (transverse isotropy, stiffness depending on muscle contraction). Simulations of face deformations under muscle actions can thus be performed. In the context of maxillofacial surgery, this generic soft-tissue model is automatically conformed to patient morphology by elastic registration, using skin and skull surfaces segmented from a CT scan. Some elements of the patient mesh could be geometrically distorted during the registration, which disables Finite Element analysis. Irregular elements are thus detected and automatically regularized. This semi-automatic patient model generation is robust, fast and easy to use. Therefore it seems compatible with clinical use. Six patient models were successfully built, and simulations of soft tissue deformations resulting from bone displacements performed on two patient models. Both the adequation of the models to the patient morphologies and the simulations of post-operative aspects were qualitatively validated by five surgeons. Their conclusions are that the models fit the morphologies of the patients, and that the predicted soft tissue modifications are coherent with what they would expect.

Shaping by stiffening: a modeling study for lips.

On the basis of simulations carried out with a finite element biomechanical model of the face, the influence of the muscle stress stiffening effect was studied for the protrusion/rounding of the lips produced with the Orbicularis Oris (OO). It is shown that the stress stiffening effect influences lip shape. When stress stiffening is modeled, the variation in the crucial geometrical characteristics of the lips shows a clear saturation effect as the OO activation level increases. Similarly, for a sufficient amount of OO activation, a saturation effect is observed when stiffening increases. In both cases, differences in lip shaping associated with the absence or presence of stiffening have consequences for the spectral characteristics of the speech signal obtained for the French vowel /u/. These results are interpreted in terms of their consequences for the motor control strategies underlying the protrusion/rounding gesture in speech production.

Simulation of dynamic orofacial movements using a constitutive law varying with muscle activation.

This paper presents a biomechanical model of the face to simulate orofacial movements in speech and non-verbal communication. A 3D finite element model, based on medical images of a subject, is presented. A hyperelastic Mooney-Rivlin constitutive law accounts for the non-linear behaviour of facial tissue. Muscle fibres are represented by piece-wise uniaxial tensile element that generate force. The stress stiffening effect, an increase in the stiffness of the muscles when activated, is modelled by varying the constitutive law of the tissue with the level of activation of the muscle. A large number of facial movements occurring during speech and facial mimics are simulated. Results show that our modelling approach provides a realistic account of facial mimics. The differences between dynamic vs. quasi-static simulations are also discussed, proving that dynamic trajectories better fit experimental data.