Computer-Aided Orthopaedic Surgery: State-of-the-Art and Future Perspectives.

Introduced more than two decades ago, computer-aided orthopaedic surgery (CAOS) has emerged as a new and independent area, due to the importance of treatment of musculoskeletal diseases in orthopaedics and traumatology, increasing availability of different imaging modalities and advances in analytics and navigation tools. The aim of this chapter is to present the basic elements of CAOS devices and to review state-of-the-art examples of different imaging modalities used to create the virtual representations, of different position tracking devices for navigation systems, of different surgical robots, of different methods for registration and referencing, and of CAOS modules that have been realized for different surgical procedures. Future perspectives will be outlined. It is expected that the recent advancement on smart instrumentation, medical robotics, artificial intelligence, machine learning, and deep learning techniques, in combination with big data analytics, may lead to smart CAOS systems and intelligent orthopaedics in the near future.

3X-Knee: A Novel Technology for 3D Preoperative Planning and Postoperative Evaluation of TKA Based on 2D X-Rays.

This chapter introduces a solution called "3X-knee" that can robustly derive 3D models of the lower extremity from 2D long leg standing X-ray radiographs for preoperative planning and postoperative treatment evaluation of total knee arthroplasty (TKA). There are three core components in 3X-knee technology: (1) a knee joint immobilization apparatus, (2) an X-ray image calibration phantom, and (3) a statistical shape model-based 2D-3D reconstruction algorithm. These three components are integrated in a systematic way in 3X-knee to derive 3D models of the complete lower extremity from 2D long leg standing X-ray radiographs acquired in weight-bearing position. More specifically, the knee joint immobilization apparatus will be used to rigidly fix the X-ray calibration phantom with respect to the underlying anatomy during the image acquisition. The calibration phantom then serves two purposes. For one side, the phantom will allow one to calibrate the projection parameters of any acquired X-ray image. For the other side, the phantom also allowsone to track positions of multiple X-ray images of the underlying anatomy without using any additional positional tracker, which is a prerequisite condition for the third component to compute patient-specific 3D models from 2D X-ray images and the associated statistical shape models. Validation studies conducted on both simulated X-ray images and on patients' X-ray data demonstrate the efficacy of the present solution.

Biomechanical Optimization-Based Planning of Periacetabular Osteotomy.

Modern computerized planning tools for periacetabular osteotomy (PAO) use either morphology-based or biomechanics-based methods. The latter rely on estimation of peak contact pressures and contact areas using either patient-specific or constant thickness cartilage models. We performed a finite element analysis investigating the optimal reorientation of the acetabulum in PAO surgery based on simulated joint contact pressures and contact areas using patient-specific cartilage model. Furthermore we investigated the influences of using patient-specific cartilage model or constant thickness cartilage model on the biomechanical simulation results. Ten specimens with hip dysplasia were used in this study. Image data were available from CT arthrography studies. Bone models were reconstructed. Mesh models for the patient-specific cartilage were defined and subsequently loaded under previously reported boundary and loading conditions. Peak contact pressures and contact areas were estimated in the original position. Afterward we used validated preoperative planning software to change the acetabular inclination by an increment of 5° and measured the lateral center-edge angle (LCE) at each reorientation position. The position with the largest contact area and the lowest peak contact pressure was defined as the optimal position. In order to investigate the influence of using patient-specific cartilage model or constant thickness cartilage model on the biomechanical simulation results, the same procedure was repeated with the same bone models but with a cartilage mesh of constant thickness. Comparison of the peak contact pressures and the contact areas between these two different cartilage models showed that good correlation between these two cartilage models for peak contact pressures (r = 0.634 ∈[0.6, 0.8], p < 0.001) and contact areas (r = 0.872 > 0.8, p < 0.001). For both cartilage models, the largest contact areas and the lowest peak pressures were found at the same position. Our study is the first study comparing peak contact pressures and contact areas between patient-specific and constant thickness cartilage models during PAO planning. Good correlation for these two models was detected. Computer-assisted planning with FE modeling using constant thickness cartilage models might be a promising PAO planning tool when a conventional CT is available.

Computer-Assisted Planning, Simulation, and Navigation System for Periacetabular Osteotomy.

Periacetabular osteotomy (PAO) is an effective approach for surgical treatment of hip dysplasia in young adults. However, achieving an optimal acetabular reorientation during PAO is the most critical and challenging step. Routinely, the correct positioning of the acetabular fragment largely depends on the surgeon's experience and is done under fluoroscopy to provide the surgeon with continuous live x-ray guidance. Our developed system starts with a fully automatic detection of the acetabular rim, which allows for quantifying the acetabular 3D morphology with parameters such as acetabular orientation, femoral head extrusion index (EI), lateral center-edge (LCE) angle, and total and regional femoral head coverage (FHC) ratio for computer-assisted diagnosis, planning, and simulation of PAO. Intraoperative navigation is conducted to implement the preoperative plan. Two validation studies were conducted on four sawbone models to evaluate the efficacy of the system intraoperatively and postoperatively. By comparing the preoperatively planned situation with the intraoperatively achieved situation, average errors of 0.6° ± 0.3°, 0.3° ± 0.2°, and 1.1° ± 1.1° were found, respectively, along three motion directions (flexion/extension, abduction/adduction, and external rotation/internal rotation). In addition, by comparing the preoperatively planned situation with the postoperative results, average errors of 0.9° ± 0.3° and 0.9° ± 0.7° were found for inclination and anteversion, respectively.

Image guidance in orthopaedics and traumatology: A historical perspective.

In this note we summarize the history of computer aided surgery in orthopaedics and traumatology from the end of the nineteenth century to currently observable future trends. We concentrate on the two major components of such systems, pre-operative planning and intra-operative execution. The evolution of the necessary technological components, the numerous platforms and components offered commercially as well as their clinical use are surveyed.

Radiographic reconstruction of lower-extremity bone fragments: a first trial.

PURPOSE: The correct rotational alignment of the proximal and the distal bone fragments is an essential step in a long-bone deformity correction process. In order to plan the deformity correction, plain radiographs are conventionally used. But as three-dimensional information of the complex situation is not available, the correct amount of rotation can only be approximated. Thus, the objective of this study was to develop a system to assess the rotational relationship between the proximal and distal fragments of a long bone (tibia or femur) based on a set of two calibrated X-ray radiographs. METHODS: In order to robustly determine the rotational relationship of proximal and distal bone fragments, a statistical shape model-based 2D/3D reconstruction approach was employed. The resulting fragment models were used to determine the angle between its anatomical axes and the rotation around its particular axes. Two different studies were performed to evaluate the accuracy of the proposed system. RESULTS: The accuracy of the complete system was evaluated in terms of major bone axis and in-plane rotational difference. The angle between the anatomical fragment axes could be measured with an average error of 0.33[Formula: see text] ± 0.27[Formula: see text], while an average in-plane rotational error of 2.27[Formula: see text] ± 1.76[Formula: see text]  and 2.67[Formula: see text]  ± 1.80[Formula: see text]  was found for the proximal and distal fragments, respectively. The overall mean surface reconstruction error was 0.81  ± 0.59 mm when the present technique was applied to the tibia and 1.12 ± 0.87 mm when it was applied to the femur. CONCLUSIONS: A new approach for estimating the rotational parameters of long-bone fragments has been proposed. This approach is based on two conventional radiographs and 2D/3D reconstruction technology. It is generally applicable to the alignment of any long-bone fragments and could provide an important means for achieving accurate rotational alignment.

A complete-pelvis segmentation framework for image-free total hip arthroplasty (THA): methodology and clinical study.

BACKGROUND: Complete-pelvis segmentation in antero-posterior pelvic radiographs is required to create a patient-specific three-dimensional pelvis model for surgical planning and postoperative assessment in image-free navigation of total hip arthroplasty. METHODS: A fast and robust framework for accurately segmenting the complete pelvis is presented, consisting of two consecutive modules. In the first module, a three-stage method was developed to delineate the left hemi-pelvis based on statistical appearance and shape models. To handle complex pelvic structures, anatomy-specific information processing techniques were employed. As the input to the second module, the delineated left hemi-pelvis was then reflected about an estimated symmetry line of the radiograph to initialize the right hemi-pelvis segmentation. The right hemi-pelvis was segmented by the same three-stage method, RESULTS: Two experiments conducted on respectively 143 and 40 AP radiographs demonstrated a mean segmentation accuracy of 1.61±0.68 mm. A clinical study to investigate the postoperative assessment of acetabular cup orientations based on the proposed framework revealed an average accuracy of 1.2°±0.9° and 1.6°±1.4° for anteversion and inclination, respectively. Delineation of each radiograph costs less than one minute. CONCLUSIONS: Despite further validation needed, the preliminary results implied the underlying clinical applicability of the proposed framework for image-free THA.

Computer-Assisted Orthopedic Surgery: Current State and Future Perspective.

Introduced about two decades ago, computer-assisted orthopedic surgery (CAOS) has emerged as a new and independent area, due to the importance of treatment of musculoskeletal diseases in orthopedics and traumatology, increasing availability of different imaging modalities, and advances in analytics and navigation tools. The aim of this paper is to present the basic elements of CAOS devices and to review state-of-the-art examples of different imaging modalities used to create the virtual representations, of different position tracking devices for navigation systems, of different surgical robots, of different methods for registration and referencing, and of CAOS modules that have been realized for different surgical procedures. Future perspectives will also be outlined.

X-ray image calibration and its application to clinical orthopedics.

X-ray imaging is one of the most commonly used medical imaging modality. Albeit X-ray radiographs provide important clinical information for diagnosis, planning and post-operative follow-up, the challenging interpretation due to its 2D projection characteristics and the unknown magnification factor constrain the full benefit of X-ray imaging. In order to overcome these drawbacks, we proposed here an easy-to-use X-ray calibration object and developed an optimization method to robustly find correspondences between the 3D fiducials of the calibration object and their 2D projections. In this work we present all the details of this outlined concept. Moreover, we demonstrate the potential of using such a method to precisely extract information from calibrated X-ray radiographs for two different orthopedic applications: post-operative acetabular cup implant orientation measurement and 3D vertebral body displacement measurement during preoperative traction tests. In the first application, we have achieved a clinically acceptable accuracy of below 1° for both anteversion and inclination angles, where in the second application an average displacement of 8.06±3.71 mm was measured. The results of both applications indicate the importance of using X-ray calibration in the clinical routine.

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.

PURPOSE: Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. METHODS: Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. RESULTS: A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was 0.96 mm ± 0.35 mm, requiring <5 s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. CONCLUSIONS: A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.

A survey of MRI-based medical image analysis for brain tumor studies.

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

An integrated system for 3D hip joint reconstruction from 2D X-rays: a preliminary validation study.

The acquisition of conventional X-ray radiographs remains the standard imaging procedure for the diagnosis of hip-related problems. However, recent studies demonstrated the benefit of using three-dimensional (3D) surface models in the clinical routine. 3D surface models of the hip joint are useful for assessing the dynamic range of motion in order to identify possible pathologies such as femoroacetabular impingement. In this paper, we present an integrated system which consists of X-ray radiograph calibration and subsequent 2D/3D hip joint reconstruction for diagnosis and planning of hip-related problems. A mobile phantom with two different sizes of fiducials was developed for X-ray radiograph calibration, which can be robustly detected within the images. On the basis of the calibrated X-ray images, a 3D reconstruction method of the acetabulum was developed and applied together with existing techniques to reconstruct a 3D surface model of the hip joint. X-ray radiographs of dry cadaveric hip bones and one cadaveric specimen with soft tissue were used to prove the robustness of the developed fiducial detection algorithm. Computed tomography scans of the cadaveric bones were used to validate the accuracy of the integrated system. The fiducial detection sensitivity was in the same range for both sizes of fiducials. While the detection sensitivity was 97.96% for the large fiducials, it was 97.62% for the small fiducials. The acetabulum and the proximal femur were reconstructed with a mean surface distance error of 1.06 and 1.01 mm, respectively. The results for fiducial detection sensitivity and 3D surface reconstruction demonstrated the capability of the integrated system for 3D hip joint reconstruction from 2D calibrated X-ray radiographs.

Comparison of partial least squares regression and principal component regression for pelvic shape prediction.

This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures were used in a cross-validation study to predict the pelvic shape. Results obtained from applying these two different regression techniques were compared to the population mean model. In almost all the prediction experiments, both regression techniques unanimously generated better results than the population mean model, while the difference on prediction accuracy between these two regression methods is not statistically significant (α=0.01).

Prediction of cranio-maxillofacial surgical planning using an inverse soft tissue modelling approach.

In cranio-maxillofacial surgery, the determination of a proper surgical plan is an important step to attain a desired aesthetic facial profile and a complete denture closure. In the present paper, we propose an efficient modeling approach to predict the surgical planning on the basis of the desired facial appearance and optimal occlusion. To evaluate the proposed planning approach, the predicted osteotomy plan of six clinical cases that underwent CMF surgery were compared to the real clinical plan. Thereafter, simulated soft-tissue outcomes were compared using the predicted and real clinical plan. This preliminary retrospective comparison of both osteotomy planning and facial outlook shows a good agreement and thereby demonstrates the potential application of the proposed approach in cranio-maxillofacial surgical planning prediction.

Fully automatic X-ray image segmentation via joint estimation of image displacements.

We propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. Our algorithm works by estimating the displacements from image patches to the (unknown) landmark positions and then integrating them via voting. The fundamental contribution is that, we jointly estimate the displacements from all patches to multiple landmarks together, by considering not only the training data but also geometric constraints on the test image. The various constraints constitute a convex objective function that can be solved efficiently. Validated on three challenging datasets, our method achieves high accuracy in landmark detection, and, combined with statistical shape model, gives a better performance in shape segmentation compared to the state-of-the-art methods.

Automatic scan planning for magnetic resonance imaging of the knee joint.

Automatic scan planning for magnetic resonance imaging of the knee aims at defining an oriented bounding box around the knee joint from sparse scout images in order to choose the optimal field of view for the diagnostic images and limit acquisition time. We propose a fast and fully automatic method to perform this task based on the standard clinical scout imaging protocol. The method is based on sequential Chamfer matching of 2D scout feature images with a three-dimensional mean model of femur and tibia. Subsequently, the joint plane separating femur and tibia, which contains both menisci, can be automatically detected using an information-augmented active shape model on the diagnostic images. This can assist the clinicians in quickly defining slices with standardized and reproducible orientation, thus increasing diagnostic accuracy and also comparability of serial examinations. The method has been evaluated on 42 knee MR images. It has the potential to be incorporated into existing systems because it does not change the current acquisition protocol.

Compensation of sound speed deviations in 3-D B-mode ultrasound for intraoperative determination of the anterior pelvic plane.

An accurate determination of the pelvic orientation is inevitable for the correct cup prosthesis placement of navigated total hip arthroplasties. Conventionally, this step is accomplished by percutaneous palpation of anatomic landmarks. Sterility issues and an increased landmark localization error for obese patients lead to the application of B-mode ultrasound imaging in the field of computer-assisted orthopedic surgery. Many approaches have been proposed in the literature to replace the percutaneous digitization by 3-D B-mode ultrasound imaging. However, the correct depth localization of the pelvic landmarks could be significantly affected by the acoustic properties of the penetrated tissues. Imprecise depth estimation could lead to a miscalculation of the pelvic orientation and subsequently to a misalignment of the acetabular cup implant. But so far, no solution has been presented, which compensates for acoustic property differences for correct depth estimation. In this paper, we present a novel approach to determine pelvic orientation from ultrasound images by applying a hierarchical registration scheme based on patch statistical shape models to compensate for differences in speed of sound. The method was validated based on plastic bones and a cadaveric specimen.

Determination of pelvic orientation from sparse ultrasound data for THA operated in the lateral position.

BACKGROUND: B-mode ultrasound imaging has the potential to replace conventional percutaneous digitization of bony landmarks in navigated total hip arthroplasties (THAs). For THAs operated in the lateral position, only one side of the pelvis is freely accessible. This paper presents a new ultrasound to statistical shape model (SSM) registration method for operating the pelvis in the lateral position, based on sparse ultrasound images, which does not interfere with the clinical routine in terms of redraping and repositioning the patient. METHODS: It is proposed to use the mid-sagittal plane of the pelvis to symmetrically reflect data derived from sparse B-mode ultrasound images of the ipsilateral patient side to the contralateral side. A SSM of the pelvis is registered to the bilaterally available data without additional information on the pubic tubercle region. RESULTS: The feasibility and robustness of the proposed method were evaluated by comprehensive in silico studies and laboratory experiments with plastic and dry cadaveric pelvises, showing a mean error of 3.48 ± 1.10° for anteversion angle and 1.26 ± 1.62° for inclination angle. CONCLUSION: Experimental results demonstrate that the symmetry property of the pelvis could be used for registration in the lateral position without accessing the error-prone pubic tubercle landmarks.

Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization.

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Scaled, patient-specific 3D vertebral model reconstruction based on 2D lateral fluoroscopy.

BACKGROUNDS: Accurate three-dimensional (3D) models of lumbar vertebrae are required for image-based 3D kinematics analysis. MRI or CT datasets are frequently used to derive 3D models but have the disadvantages that they are expensive, time-consuming or involving ionizing radiation (e.g., CT acquisition). An alternative method using 2D lateral fluoroscopy was developed. MATERIALS AND METHODS: A technique was developed to reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image and a statistical shape model of the lumbar vertebrae. Four cadaveric lumbar spine segments and two statistical shape models were used for testing. Reconstruction accuracy was determined by comparison of the surface models reconstructed from the single lateral fluoroscopic images to the ground truth data from 3D CT segmentation. For each case, two different surface-based registration techniques were used to recover the unknown scale factor, and the rigid transformation between the reconstructed surface model and the ground truth model before the differences between the two discrete surface models were computed. RESULTS: Successful reconstruction of scaled surface models was achieved for all test lumbar vertebrae based on single lateral fluoroscopic images. The mean reconstruction error was between 0.7 and 1.6 mm. CONCLUSIONS: A scaled, patient-specific surface model of the lumbar vertebra from a single lateral fluoroscopic image can be synthesized using the present approach. This new method for patient-specific 3D modeling has potential applications in spine kinematics analysis, surgical planning, and navigation.