Reconstruction of Patient-Specific Bone Models from X-Ray Radiography

The availability of a patient‐specific bone model has become an increasingly invaluable addition to orthopedic case evaluation and planning [1]. Utilized within a wide range of specialized visualization and analysis tools, such models provide unprecedented wealth of bone shape information previously unattainable using traditional radiographic imaging [2]. In this work, a novel bone reconstruction method from two or more x‐ray images is described. This method is superior to previous attempts in terms of accuracy and repeatability. The new technique accurately models the radiological scene in a way that eliminates the need for expensive multi‐planar radiographic imaging systems. It is also flexible enough to allow for both short and long film imaging using standard radiological protocols, which makes the technology easily utilized in standard clinical setups

Automatic knee joint space measurement from plain radiographs

Abstract. Knee osteoarthritis is a common joint disease and one of the leading causes of disability. The disease is characterized by loss of articular cartilage and bone remodeling. Tissue deformations eventually lead to joint space narrowing which can be detected from plain radiographs. Joint space narrowing is typically measured by an experienced radiologist manually, which can be time consuming and error prone process. The aim of this study was to develop and evaluate a fully automatic joint space width measurement method for bilateral knee radiographs. The knee joint was localized from the x-ray images using template matching and the joint space was delineated using active shape model (ASM). Two different automatic joint space measurement methods were tested and the results were validated against manual measurements performed by an experienced researcher. The first joint space width measurements were done by binarizing the joint space and measuring the local thickness of the binary mask using disk fitting. The second method classified bone pixels to tibia and femur. Classification was based on the ASM delineation. Nearest neighbors between femur and tibia were then used to find the joint space width. An automatic method for tibial region of interest (ROI) selection was also implemented. The algorithms used in this thesis were also made publicly available online. The automatically obtained joint space widths were in line with manual measurements. Higher accuracy was obtained using the disk fitting algorithm. Automatic Tibial ROI selection was accurate, although the orientation of the joint was ignored in this study. An open source software with a simple graphical user interface and visualization tools was also developed. Computationally efficient and easily explainable methods were utilized in order to improve accessibility and transparency of computer assisted diagnosis of knee osteoarthritis.Tiivistelmä. Polvinivelrikko on eräs yleisimpiä niveltauteja sekä yksi merkittävimmistä liikuntavammojen aiheuttajista. Nivelrikolle ominaisia piirteitä ovat nivelruston vaurioituminen ja muutokset nivelrustonalaisessa luussa. Kudosten muutokset ja vauriot johtavat lopulta niveltilan kaventumiseen, mikä voidaan havaita röntgenkuvista. Tavallisesti kokenut radiologi tekee niveltilan mittaukset manuaalisesti, mikä vaatii usein paljon aikaa ja on lisäksi virhealtis prosessi. Tämän tutkielman tavoitteena oli kehittää täysin automaattinen niveltilan mittausmenetelmä bilateraalisille polven röntgenkuville. Polvinivel paikallistettiin röntgenkuvista muotoon perustuvalla hahmontunnistuksella ja nivelväli rajattiin käyttämällä aktiivista muodon sovitusta (active shape model, ASM). Nivelvälin mittaukseen käytettiin kahta eri menetelmää, joita verrattiin kokeneen tutkijan tekemiin manuaalisiin mittauksiin. Ensimmäinen nivelvälin mittausmenetelmä sovitti ympyränmuotoisia maskeja niveltilasta tehtyyn binäärimaskiin. Toinen mittausmenetelmä luokitteli luuhun kuuluvat pikselit sääri- ja reisiluuhun. Luokittelu perustui aikaisemmin tehtyyn automaattiseen nivelvälin rajaukseen. Nivelvälin mittaukseen käytettiin lähimpiä naapuripikseleitä sääri- ja reisiluusta. Työssä kehitettiin myös menetelmä automaattiseen sääriluun mielenkiintoalueiden (region of interest, ROI) valintaan. Käytetyt algoritmit ovat julkisesti saatavilla verkossa. Automaattiset nivelväli mittaukset vastasivat manuaalisia mittauksia hyvin. Parempi tarkkuus saatiin käyttämällä ympyrän sovitusta hyödyntävää algoritmia nivelvälin mittaukseen. Sääriluun mielenkiintoalueet onnistuttiin määrittämään automaattisesti, tosin nivelen orientaatiota ei huomioitu tässä työssä. Lisäksi kehitettiin avoimen lähdekoodin ohjelmisto yksinkertaisella graafisella käyttöliittymällä ja visualisointityökaluilla. Työssä käytettiin laskennallisesti tehokkaita ja helposti selitettäviä menetelmiä, mikä edesauttaa tietokoneavusteisen menetelmien käyttöä polvinivelrikon tutkimuksessa

3D reconstruction of ribcage geometry from biplanar radiographs using a statistical parametric model approach

Rib cage 3D reconstruction is an important prerequisite for thoracic spine modelling, particularly for studies of the deformed thorax in adolescent idiopathic scoliosis. This study proposes a new method for rib cage 3D reconstruction from biplanar radiographs, using a statistical parametric model approach. Simplified parametric models were defined at the hierarchical levels of rib cage surface, rib midline and rib surface, and applied on a database of 86 trunks. The resulting parameter database served to statistical models learning which were used to quickly provide a first estimate of the reconstruction from identifications on both radiographs. This solution was then refined by manual adjustments in order to improve the matching between model and image. Accuracy was assessed by comparison with 29 rib cages from CT scans in terms of geometrical parameter differences and in terms of line-to-line error distance between the rib midlines. Intra and inter-observer reproducibility were determined regarding 20 scoliotic patients. The first estimate (mean reconstruction time of 2’30) was sufficient to extract the main rib cage global parameters with a 95% confidence interval lower than 7%, 8%, 2% and 4° for rib cage volume, antero-posterior and lateral maximal diameters and maximal rib hump, respectively. The mean error distance was 5.4 mm (max 35mm) down to 3.6 mm (max 24 mm) after the manual adjustment step (+3’30). The proposed method will improve developments of rib cage finite element modeling and evaluation of clinical outcomes.This work was funded by Paris Tech BiomecAM chair on subject specific muscular skeletal modeling, and we express our acknowledgments to the chair founders: Cotrel foundation, Société générale, Protéor Company and COVEA consortium. We extend your acknowledgements to Alina Badina for medical imaging data, Alexandre Journé for his advices, and Thomas Joubert for his technical support

Image analysis for extracapsular hip fracture surgery

PhD ThesisDuring the implant insertion phase of extracapsular hip fracture surgery, a surgeon visually inspects digital radiographs to infer the best position for the implant. The inference is made by “eye-balling”. This clearly leaves room for trial and error which is not ideal for the patient. This thesis presents an image analysis approach to estimating the ideal positioning for the implant using a variant of the deformable templates model known as the Constrained Local Model (CLM). The Model is a synthesis of shape and local appearance models learned from a set of annotated landmarks and their corresponding local patches extracted from digital femur x-rays. The CLM in this work highlights both Principal Component Analysis (PCA) and Probabilistic PCA as regularisation components; the PPCA variant being a novel adaptation of the CLM framework that accounts for landmark annotation error which the PCA version does not account for. Our CLM implementation is used to articulate 2 clinical metrics namely: the Tip-Apex Distance and Parker’s Ratio (routinely used by clinicians to assess the positioning of the surgical implant during hip fracture surgery) within the image analysis framework. With our model, we were able to automatically localise signi cant landmarks on the femur, which were subsequently used to measure Parker’s Ratio directly from digital radiographs and determine an optimal placement for the surgical implant in 87% of the instances; thereby, achieving fully automatic measurement of Parker’s Ratio as opposed to manual measurements currently performed in the surgical theatre during hip fracture surgery

Development and Implementation of a Computational Surgical Planning Model for Pre-Operative Planning and Post-Operative Assessment and Analysis of Total Hip Arthroplasty

Total hip arthroplasty (THA) is most often used to treat osteoarthritis of the hip joint. Due to lack of a better alternative, newer designs are evaluated experimentally using mechanical simulators and cadavers. These evaluation techniques, though necessary, are costly and time-consuming, limiting testing on a broader population. Due to the advancement in technology, the current focus has been to develop patient-specific solutions. The hip joint can be approximated as encompassing a bone socket geometry, and therefore the shapes of the implant are well constrained. The variability of performance after the surgery is mostly driven by surgical procedures. It is believed that placing the acetabular component within the “safe zone” will commonly lead to successful surgical outcomes [1]. Unfortunately, recent research has revealed problems with the safe zone concept, and there is a need for a better tool which can aid surgeons in planning for surgery.With the advancement of computational power, more recent focus has been applied to the development of simulation tools that can predict implant performances. In this endeavor, a virtual hip simulator is being developed at the University of Tennessee Knoxville to provide designers and surgeons alike instant feedback about the performance of the hip implants. The mathematical framework behind this tool has been developed.In this dissertation, the primary focus is to further expand the capabilities of the existing hip model and develop the front-end that can replicate a total hip arthroplasty surgery procedure pre-operatively, intra-operatively, and post-operatively. This new computer-assisted orthopaedic surgical tool will allow surgeons to simulate surgery, then predict, compare, and optimize post-operative THA outcomes based on component placement, sizing choices, reaming and cutting locations, and surgical methods. This more advanced mathematical model can also reveal more information pre-operatively, allowing a surgeon to gain ample information before surgery, especially with difficult and revision cases. Moreover, this tool could also help during the implant development design process as designers can instantly simulate the performance of their new designs, under various surgical, simulated in vivo conditions

A 3D computer assisted Orthopedic Surgery Planning approach based on planar radiography

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)The main goal of this work consisted in develop a system to perform the 3D reconstruction of bone models from radiographic images. This system can be then integrated with a commercial software that performs pre-operative planning of orthopedic surgeries. The benefit of performing this 3D reconstruction from planar radiography is that this modality has some advantages over other modalities that perform this reconstruction directly, like CT and MRI. To develop the system it was used radiographic images of the femur obtained from medical image databases online. It was also used a generic model of the femur available in the online repository BEL. This generic model completes the information missing in the radiographic images. It was developed two methods to perform the 3D reconstruction through the deformation of the generic model, one uses triangulation of extracted edge points and the other don't. The first method was not successful, the final model had very low thickness, possibly because the triangulation process was not performed correctly. With the second method it was obtained a 3D bone model of the femur aligned with the radiographic images of the patient and with the same size as the patient's bone. However, the obtained model still needs some adjustment to coincide fully with reality. To perform this is necessary to enhance the deformation step of the model so that it will have the same shape as the patient's bone. The second method is more advantageous because it doesn't need the parameters of the x-ray imaging system. However, it's necessary to enhance the step deformation of this method so that the final model matches patient's anatomy.O principal objetivo deste trabalho consistiu em desenvolver um sistema capaz de realizar a reconstrução 3D de modelos ósseos a partir de imagens radiográficas. Este sistema pode posteriormente ser integrado num produto comercial que realiza o planeamento pré-operativo de cirurgias ortopédicas. O benefício de realizar esta reconstrução 3D a partir de radiografias está relacionado com o facto desta modalidade ter vantagens em relação às outras modalidades que fazem esta reconstrução diretamente, como as modalidades CT e MRI. Para desenvolver este sistema foram usadas imagens radiográficas do fémur obtidas através de bases de dados online de imagens médicas. Também foi usado um modelo genérico do fémur disponível no repositório online BEL. Este modelo genérico completa a informação que está em falta nas imagens radiográficas. Foram desenvolvidos dois métodos, que realizam a reconstrução 3D através da deformação do modelo genérico sendo que num é feita a triangulação de pontos dos contornos e noutro não. O primeiro método não foi bem sucedido, visto que o modelo final tinha uma espessura muito pequena, possivelmente devido ao facto do processo de triangulação não ter sido executado corretamente. Com o segundo método foi obtido um modelo 3D do fémur alinhado com as imagens radiográficas do paciente e com o mesmo tamanho do osso do paciente. No entanto, o modelo obtido carece ainda de alguma afinação de modo a coincidir na íntegra com a realidade. Para fazer isto é necessário melhorar o passo de deformação do modelo, para que este fique com a mesma forma do osso do paciente. O segundo método é mais vantajoso porque não necessita dos parâmetros dos sistema de raios- X. No entanto, é necessário melhorar o passo de deformação deste método para que o modelo final coincida com a anatomia do paciente

AN AUTOMATED, DEEP LEARNING APPROACH TO SYSTEMATICALLY & SEQUENTIALLY DERIVE THREE-DIMENSIONAL KNEE KINEMATICS DIRECTLY FROM TWO-DIMENSIONAL FLUOROSCOPIC VIDEO

Total knee arthroplasty (TKA), also known as total knee replacement, is a surgical procedure to replace damaged parts of the knee joint with artificial components. It aims to relieve pain and improve knee function. TKA can improve knee kinematics and reduce pain, but it may also cause altered joint mechanics and complications. Proper patient selection, implant design, and surgical technique are important for successful outcomes. Kinematics analysis plays a vital role in TKA by evaluating knee joint movement and mechanics. It helps assess surgery success, guides implant and technique selection, informs implant design improvements, detects problems early, and improves patient outcomes. However, evaluating the kinematics of patients using conventional approaches presents significant challenges. The reliance on 3D CAD models limits applicability, as not all patients have access to such models. Moreover, the manual and time-consuming nature of the process makes it impractical for timely evaluations. Furthermore, the evaluation is confined to laboratory settings, limiting its feasibility in various locations. This study aims to address these limitations by introducing a new methodology for analyzing in vivo 3D kinematics using an automated deep learning approach. The proposed methodology involves several steps, starting with image segmentation of the femur and tibia using a robust deep learning approach. Subsequently, 3D reconstruction of the implants is performed, followed by automated registration. Finally, efficient knee kinematics modeling is conducted. The final kinematics results showed potential for reducing workload and increasing efficiency. The algorithms demonstrated high speed and accuracy, which could enable real-time TKA kinematics analysis in the operating room or clinical settings. Unlike previous studies that relied on sponsorships and limited patient samples, this algorithm allows the analysis of any patient, anywhere, and at any time, accommodating larger subject populations and complete fluoroscopic sequences. Although further improvements can be made, the study showcases the potential of machine learning to expand access to TKA analysis tools and advance biomedical engineering applications

Statistical Modeling to Investigate Anatomy and Function of the Knee

The natural knee is a hinge joint with significant functional requirements during activities of daily living; as a result, acute and chronic injuries can occur. Pathologies are influenced by joint anatomy and may include patellar maltracking, cartilage degeneration (e.g. osteoarthritis), or acute injuries such as meniscal or ligamentous tears. Population variability makes broadly applicable conclusions about etiology of these conditions from small-scale investigations challenging. The work presented in this dissertation is a demonstration of statistical modeling approaches to evaluate population variability in anatomy of the knee and function of its tibiofemoral (TF) and patellofemoral (PF) joints. Three-dimensional (3D) computational models of the bone and cartilage in the knee were characterized using a principal component analysis (PCA) algorithm to understand the primary sources of variability in shape and motion and make predictions from sparse data. Statistical models were used to investigate relationships between natural knee anatomy and kinematics and make predictions of both shape and function from sparse data. A whole-joint characterization study identified key correlations between shape and function of the TF and PF joints, successfully recreating results from multiple studies and introducing new relationships under one unified approach. Results from this study were used in a subsequent investigation to build a statistical model of two-dimensional (2D) shape and alignment measures and 6 degree-of-freedom (DOF) kinematics to identify the key measures capable of predicting PF joint motion. The ability to reconstruct the 3D implanted patellar bone of a subject with a total knee replacement (TKR) was evaluated by a statistical shape model of the patella and simulated 2D edge profiles in a custom optimization algorithm. Lastly, a validated predictive algorithm was employed to assess the accuracy of subject-specific knee articular cartilage predictions from bony geometry. The utility of statistical modeling is elucidated by the population-based evaluations of the musculoskeletal system described in this work and could continue to inform characteristics related to pathological conditions and large-scale computational evaluations of implant performance