Patient-specific modelling in orthopedics: from image to surgery

In orthopedic surgery, to decide upon intervention and how it can be optimized, surgeons usually rely on subjective analysis of medical images of the patient, obtained from computed tomography, magnetic resonance imaging, ultrasound or other techniques. Recent advancements in computational performance, image analysis and in silico modeling techniques have started to revolutionize clinical practice through the development of quantitative tools, including patient#specific models aiming at improving clinical diagnosis and surgical treatment. Anatomical and surgical landmarks as well as features extraction can be automated allowing for the creation of general or patient-specific models based on statistical shape models. Preoperative virtual planning and rapid prototyping tools allow the implementation of customized surgical solutions in real clinical environments. In the present chapter we discuss the applications of some of these techniques in orthopedics and present new computer-aided tools that can take us from image analysis to customized surgical treatment

Patient-Specific Implants in Musculoskeletal (Orthopedic) Surgery

Most of the treatments in medicine are patient specific, aren’t they? So why should we bother with individualizing implants if we adapt our therapy to patients anyway? Looking at the neighboring field of oncologic treatment, you would not question the fact that individualization of tumor therapy with personalized antibodies has led to the thriving of this field in terms of success in patient survival and positive responses to alternatives for conventional treatments. Regarding the latest cutting-edge developments in orthopedic surgery and biotechnology, including new imaging techniques and 3D-printing of bone substitutes as well as implants, we do have an armamentarium available to stimulate the race for innovation in medicine. This Special Issue of Journal of Personalized Medicine will gather all relevant new and developed techniques already in clinical practice. Examples include the developments in revision arthroplasty and tumor (pelvic replacement) surgery to recreate individual defects, individualized implants for primary arthroplasty to establish physiological joint kinematics, and personalized implants in fracture treatment, to name but a few

Automated Planning with Multivariate Shape Descriptors for Fibular Transfer in Mandibular Reconstruction

Objective: This paper introduces methods to automate preoperative planning of fibular segmentation and placement for mandibular reconstruction with fibular flaps. Methods: Preoperative virtual planning for this type of surgery has been performed by manual adjustment of many parameters, or based upon a single feature of the reconstruction. We propose a novel planning procedure formulated as a non-convex minimization problem of an objective function using the multilateral shape descriptors. Results: A retrospective study was designed and 120 reconstruction plans were reproduced using computed tomography images with oral surgeons. The proposed automated planning model was quantitatively compared with both the existing model and the surgeons’ plans. Conclusion: The results show that the developed framework attains stable automated planning that agrees with the surgeons’ decisions. Significance: This method addresses trade-off problems between symmetric reconstruction and restoration of the native contour of the mandible

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 $$0.96\,\hbox {mm} \pm 0.35\,\hbox {mm}$$ 0.96 mm ± 0.35 mm , requiring $$<\!5$$ < 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

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

1st EFORT European Consensus: Medical & Scientific Research Requirements for the Clinical Introduction of Artificial Joint Arthroplasty Devices

Innovations in Orthopaedics and Traumatology have contributed to the achievement of a high-quality level of care in musculoskeletal disorders and injuries over the past decades. The applications of new implants as well as diagnostic and therapeutic techniques in addition to implementation of clinical research, have significantly improved patient outcomes, reduced complication rates and length of hospital stay in many areas. However, the regulatory framework is extensive, and there is a lack of understanding and clarity in daily practice what the meaning of clinical &amp; pre‐clinical evidence as required by the MDR is. Thus, understanding and clarity are of utmost importance for introduction of new implants and implant-related instrumentation in combination with surgical technique to ensure a safe use of implants and treatment of patients. Therefore EFORT launched IPSI, The Implant and Patient Safety Initiative, which starting from an inaugural workshop in 2021 issued a set of recommendations, notably through a subsequent Delphi Process involving the National Member Societies of EFORT, European Specialty Societies as well as International Experts. These recommendations provide surgeons, researchers, implant manufacturers as well as patients and health authorities with a consensus of the development, implementation, and dissemination of innovation in the field of arthroplasty. The intended key outcomes of this 1st EFORT European Consensus on “Medical &amp; Scientific Research Requirements for the Clinical Introduction of Artificial Joint Arthroplasty Devices”are consented, practical pathways to maintain innovation and optimisation of orthopaedic products and workflows within the boundaries of MDR 2017/745. Open Access practical guidelines based on adequate, state of the art pre-clinical and clinical evaluation methodologies for the introduction of joint replacements and implant-related instrumentation shall provide hands-on orientation for orthopaedic surgeons, research institutes and laboratories, orthopaedic device manufacturers, Notified Bodies but also for National Institutes and authorities, patient representatives and further stakeholders. We would like to acknowledge and thank the Scientific Committee members, all International Expert Delegates, the Delegates from European National &amp; Specialty Societies and the Editorial Team for their outstanding contributions and support during this EFORT European Consensus

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 $$0.96\,\hbox {mm} \pm 0.35\,\hbox {mm}$$ 0.96 mm ± 0.35 mm , requiring $$<\!5$$ < 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

Individualised Modelling for Preoperative Planning of Total Knee Replacement Surgery

Total knee replacement (TKR) surgery is routinely prescribed for patients with severe knee osteoarthritis to alleviate the pain and restore the kinematics. Although this procedure was proven to be successful in reducing the joint pain, the number of failures and the low patients’ satisfaction suggest that while the number of reoperations is small, the surgery frequently fail to restore the function in full. The main cause are surgical techniques which inadequately address the problem of balancing the knee soft tissues. The preoperative planning technique allows to manufacture subject-specific cutting guides that improves the placement of the prosthesis, however the knee soft tissue is ignored. The objective of this dissertation was to create an optimized preplanning procedure to compute the soft tissue balance along with the placement of the prosthesis to ensure mechanical stability. The dissertation comprises the development of CT based static and quasi-static knee models able to estimate the postoperative length of the collateral lateral ligaments using a dataset of seven TKR patients; In addition, a subject-specific dynamic musculoskeletal model of the lower limb was created using in vivo knee contact forces to perform the same analysis during walking. The models were evaluated by their ability to predict the postoperative elongation using a threshold based on the 10 % of the preoperative length, through which the model detected whether an elongation was acceptable. The results showed that the subject-specific static model is the best solution to be included in the optimized, subject-specific, preoperative planning framework; full order musculoskeletal model allowed to estimate the postoperative length of the ligaments during walking, and at least in principle while performing any other activity. Unlike the current methodology used in clinic this optimized preoperative planning framework might help the surgeon to understand how the position of the TKR affects the knee soft tissue

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf