Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients

Magnetic resonance imaging (MRI) is routinely employed to assess muscular response and presence of inflammatory reactions in patients treated with metal-on-metal hip arthroplasty, driving the decision for revision surgery. However, MRI is lacking contrast for bony structures and as a result orthopaedic surgical planning is mostly performed on computed tomography images. In this paper, we combine the complementary information of both modalities into a novel framework for the joint segmentation of healthy and pathological musculoskeletal structures as well as implants on all images. Our processing pipeline is fully automated and was designed to handle the highly anisotropic resolution of clinical MR images by means of super resolution reconstruction. The accuracy of the intra-subject multimodal registration was improved by employing a non-linear registration algorithm with hard constraints on the deformation of bony structures, while a multi-atlas segmentation propagation approach provided robustness to the large shape variability in the population. The suggested framework was evaluated in a leave-one-out cross-validation study on 20 hip sides. The proposed pipeline has potential for the extraction of clinically relevant imaging biomarkers for implant failure detection

Automating the multimodal analysis of musculoskeletal imaging in the presence of hip implants

In patients treated with hip arthroplasty, the muscular condition and presence of inflammatory reactions are assessed using magnetic resonance imaging (MRI). As MRI lacks contrast for bony structures, computed tomography (CT) is preferred for clinical evaluation of bone tissue and orthopaedic surgical planning. Combining the complementary information of MRI and CT could improve current clinical practice for diagnosis, monitoring and treatment planning. In particular, the different contrast of these modalities could help better quantify the presence of fatty infiltration to characterise muscular condition after hip replacement. In this thesis, I developed automated processing tools for the joint analysis of CT and MR images of patients with hip implants. In order to combine the multimodal information, a novel nonlinear registration algorithm was introduced, which imposes rigidity constraints on bony structures to ensure realistic deformation. I implemented and thoroughly validated a fully automated framework for the multimodal segmentation of healthy and pathological musculoskeletal structures, as well as implants. This framework combines the proposed registration algorithm with tailored image quality enhancement techniques and a multi-atlas-based segmentation approach, providing robustness against the large population anatomical variability and the presence of noise and artefacts in the images. The automation of muscle segmentation enabled the derivation of a measure of fatty infiltration, the Intramuscular Fat Fraction, useful to characterise the presence of muscle atrophy. The proposed imaging biomarker was shown to strongly correlate with the atrophy radiological score currently used in clinical practice. Finally, a preliminary work on multimodal metal artefact reduction, using an unsupervised deep learning strategy, showed promise for improving the postprocessing of CT and MR images heavily corrupted by metal artefact. This work represents a step forward towards the automation of image analysis in hip arthroplasty, supporting and quantitatively informing the decision-making process about patient’s management

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

Automated measurement of fat infiltration in the hip abductors from Dixon magnetic resonance imaging

PURPOSE: Intramuscular fat infiltration is a dynamic process, in response to exercise and muscle health, which can be quantified by estimating fat fraction (FF) from Dixon MRI. Healthy hip abductor muscles are a good indicator of a healthy hip and an active lifestyle as they have a fundamental role in walking. The automated measurement of the abductors' FF requires the challenging task of segmenting them. We aimed to design, develop and evaluate a multi-atlas based method for automated measurement of fat fraction in the main hip abductor muscles: gluteus maximus (GMAX), gluteus medius (GMED), gluteus minimus (GMIN) and tensor fasciae latae (TFL). METHOD: We collected and manually segmented Dixon MR images of 10 healthy individuals and 7 patients who underwent MRI for hip problems. Twelve of them were selected to build an atlas library used to implement the automated multi-atlas segmentation method. We compared the FF in the hip abductor muscles for the automated and manual segmentations for both healthy and patients groups. Measures of average and spread were reported for FF for both methods. We used the root mean square error (RMSE) to quantify the method accuracy. A linear regression model was used to explain the relationship between FF for automated and manual segmentations. RESULTS: The automated median (IQR) FF was 20.0(16.0-26.4) %, 14.3(10.9-16.5) %, 15.5(13.9-18.6) % and 16.2(13.5-25.6) % for GMAX, GMED, GMIN and TFL respectively, with a FF RMSE of 1.6%, 0.8%, 2.1%, 2.7%. A strong linear correlation (R2 = 0.93, p < .001, m = 0.99) was found between the FF from automated and manual segmentations. The mean FF was higher in patients than in healthy subjects. CONCLUSION: The automated measurement of FF of hip abductor muscles from Dixon MRI had good agreement with FF measurements from manually segmented images. The method was accurate for both healthy and patients groups

Registration of Total Hip Replacement Implants from Subjects Imaged with 1.5 and 3 Tesla MRI Scanners Using Mavric

Introduction: Total hip arthroplasty (THA) is a common treatment to correct arthritis and necrosis ailing the hip joint. An array of diseases can occur in response to the presence and wear of the implant. MRI is an imaging modality that can assess and diagnosis the diseases ailing the periprosthetic tissues, however images of a metal implants are susceptible to artifacts. Aim 1 of this study was to register the THAs imaged at 3T to 1.5T images; Aim 2 was to validate that metal susceptibility artifacts increase with field strength.Methods: 21 subjects with a total of 29 hip prosthetics that had coronal MAVRIC SL scans at 1.5T of their hips were recruited to be rescanned at 3T. MAVRIC SL is GE’s metal artifact reducing acquisition technology. The prosthetics imaged at 1.5T and 3T were then registered together. The acetabular and femoral component had to be registered independently since they are separate rigid bodies. The acetabular was registered by finding the registration transformation of a bone fixed to the component, then applying this transform to the 3T acetabular volume. The femoral components were registered by taking three anatomical landmarks and registering them together using a least-squares fitting of 3-D points algorithm. Lastly, the two 3T components were combined and then registered to the 1.5T prosthetic. The size of the volume of the of 1.5T and 3T images were compared.Results: Of the 29 hips scanned, 25 were viable for this study. The percent alignment of the 1.5T and 3T acetabular volumes had a mean = 90.03%, SD = 8.64%. For the femoral component the alignment had a mean = 67.89% and SD = 14.74. The total alignment had a mean = 77.64% and SD = 12.64%. The 3T artifact volumes were 7.93% and 5.49% smaller than the 1.5T volumes for the acetabular and femoral components respectively.Conclusion: While the acetabular component registration was successful, work still needs to be done for the femoral registration. The size of the artifact was larger at 1.5T than 3T, this was not expected and is a result of user error when tracing the two components

How to Build a Patient-Specific Hybrid Simulator for Orthopaedic Open Surgery: Benefits and Limits of Mixed-Reality Using the Microsoft HoloLens

Orthopaedic simulators are popular in innovative surgical training programs, where trainees gain procedural experience in a safe and controlled environment. Recent studies suggest that an ideal simulator should combine haptic, visual, and audio technology to create an immersive training environment. This article explores the potentialities of mixed-reality using the HoloLens to develop a hybrid training system for orthopaedic open surgery. Hip arthroplasty, one of the most common orthopaedic procedures, was chosen as a benchmark to evaluate the proposed system. Patient-specific anatomical 3D models were extracted from a patient computed tomography to implement the virtual content and to fabricate the physical components of the simulator. Rapid prototyping was used to create synthetic bones. The Vuforia SDK was utilized to register virtual and physical contents. The Unity3D game engine was employed to develop the software allowing interactions with the virtual content using head movements, gestures, and voice commands. Quantitative tests were performed to estimate the accuracy of the system by evaluating the perceived position of augmented reality targets. Mean and maximum errors matched the requirements of the target application. Qualitative tests were carried out to evaluate workload and usability of the HoloLens for our orthopaedic simulator, considering visual and audio perception and interaction and ergonomics issues. The perceived overall workload was low, and the self-assessed performance was considered satisfactory. Visual and audio perception and gesture and voice interactions obtained a positive feedback. Postural discomfort and visual fatigue obtained a nonnegative evaluation for a simulation session of 40 minutes. These results encourage using mixed-reality to implement a hybrid simulator for orthopaedic open surgery. An optimal design of the simulation tasks and equipment setup is required to minimize the user discomfort. Future works will include Face Validity, Content Validity, and Construct Validity to complete the assessment of the hip arthroplasty simulator

Image Processing Algorithms for Detection of Anomalies in Orthopedic Surgery Implants

Orthopedic implant procedures for hip implants are performed on 300,000 patients annually in the United States, with 22.3 million procedures worldwide. While most such operations are successfully performed to relieve pain and restore joint function for the duration of the patient\u27s life, advances in medicine have enabled patients to outlive the life of their implant, increasing the likelihood of implant failure. There is significant advantage to the patient, the surgeon, and the medical community in early detection of implant failures.The research work presented in this thesis demonstrates a non-invasive digital image processing technique for the automated detection of specific arthroplasty failures before requiring revision surgery. This thesis studies hip implant loosening as the primary cause of failure. A combination of digital image segmentation, representation and numerical description is employed and validated on 2-D X-ray images of hip implant phantoms to detect 3-D rotations of the implant, with the support of radial basis function neural networks to accomplish this task. A successful clinical implementation of the methods developed in this thesis can eliminate the need for revision surgery and prolong the life of the orthopedic implant

How to Build a Patient-Specific Hybrid Simulator for Orthopaedic Open Surgery: Benefits and Limits of Mixed-Reality Using the Microsoft HoloLens

Orthopaedic simulators are popular in innovative surgical training programs, where trainees gain procedural experience in a safe and controlled environment. Recent studies suggest that an ideal simulator should combine haptic, visual, and audio technology to create an immersive training environment. This article explores the potentialities of mixed-reality using the HoloLens to develop a hybrid training system for orthopaedic open surgery. Hip arthroplasty, one of the most common orthopaedic procedures, was chosen as a benchmark to evaluate the proposed system. Patient-specific anatomical 3D models were extracted from a patient computed tomography to implement the virtual content and to fabricate the physical components of the simulator. Rapid prototyping was used to create synthetic bones. The Vuforia SDK was utilized to register virtual and physical contents. The Unity3D game engine was employed to develop the software allowing interactions with the virtual content using head movements, gestures, and voice commands. Quantitative tests were performed to estimate the accuracy of the system by evaluating the perceived position of augmented reality targets. Mean and maximum errors matched the requirements of the target application. Qualitative tests were carried out to evaluate workload and usability of the HoloLens for our orthopaedic simulator, considering visual and audio perception and interaction and ergonomics issues. The perceived overall workload was low, and the self-assessed performance was considered satisfactory. Visual and audio perception and gesture and voice interactions obtained a positive feedback. Postural discomfort and visual fatigue obtained a nonnegative evaluation for a simulation session of 40 minutes. These results encourage using mixed-reality to implement a hybrid simulator for orthopaedic open surgery. An optimal design of the simulation tasks and equipment setup is required to minimize the user discomfort. Future works will include Face Validity, Content Validity, and Construct Validity to complete the assessment of the hip arthroplasty simulator