Three Dimensional Nonlinear Statistical Modeling Framework for Morphological Analysis

This dissertation describes a novel three-dimensional (3D) morphometric analysis framework for building statistical shape models and identifying shape differences between populations. This research generalizes the use of anatomical atlases on more complex anatomy as in case of irregular, flat bones, and bones with deformity and irregular bone growth. The foundations for this framework are: 1) Anatomical atlases which allow the creation of homologues anatomical models across populations; 2) Statistical representation for output models in a compact form to capture both local and global shape variation across populations; 3) Shape Analysis using automated 3D landmarking and surface matching. The proposed framework has various applications in clinical, forensic and physical anthropology fields. Extensive research has been published in peer-reviewed image processing, forensic anthropology, physical anthropology, biomedical engineering, and clinical orthopedics conferences and journals. The forthcoming discussion of existing methods for morphometric analysis, including manual and semi-automatic methods, addresses the need for automation of morphometric analysis and statistical atlases. Explanations of these existing methods for the construction of statistical shape models, including benefits and limitations of each method, provide evidence of the necessity for such a novel algorithm. A novel approach was taken to achieve accurate point correspondence in case of irregular and deformed anatomy. This was achieved using a scale space approach to detect prominent scale invariant features. These features were then matched and registered using a novel multi-scale method, utilizing both coordinate data as well as shape descriptors, followed by an overall surface deformation using a new constrained free-form deformation. Applications of output statistical atlases are discussed, including forensic applications for the skull sexing, as well as physical anthropology applications, such as asymmetry in clavicles. Clinical applications in pelvis reconstruction and studying of lumbar kinematics and studying thickness of bone and soft tissue are also discussed

Generalizable Methods for Modeling Lumbar Spine Kinematics

A more complete understanding of lumbar spine kinematics could improve diagnoses and treatment of low back pathologies and may advance the development of biomechanical models. Kinematics describes motion of the five lumbar vertebrae without consideration for the forces that cause the motion. Despite considerable attention from researchers and clinicians, lumbar spine kinematics are not fully understood because the anatomy is not accessible for direct observation and the complex governing biomechanics produce small magnitude, coupled intervertebral movements. The overall goal of this project was to develop a descriptive model of intervertebral lumbar spine kinematics that is applicable to a generalizable subject population with diverse anthropometry. To accomplish this, a method was developed for measuring three-dimensional vertebral configuration using positional magnetic resonance imaging (MRI). The method makes use of automated vertebral registration to address time limitations in current data processing techniques and improves the ability to power experimental investigations. Finally, a geometric model of lumbar vertebral kinematics was developed using principal component regression applied to in vivo vertebral measurement data across the range of flexion and extension joint motion. This principal component-based approach offers unique advantages for predicting and interpreting performance of complex systems such as lumbar joint biomechanics because no assumptions are made regarding the governing mechanisms. This provides an opportunity to infer mechanistic characteristics about intervertebral joint kinematics and to use in vivo data to validate musculoskeletal models

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.Comment: Paper under revie

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

3D Shape Reconstruction of Knee Bones from Low Radiation X-ray Images Using Deep Learning

Understanding the bone kinematics of the human knee during dynamic motions is necessary to evaluate the pathological conditions, design knee prosthesis, orthosis and surgical treatments such as knee arthroplasty. Also, knee bone kinematics is essential to assess the biofidelity of the computational models. Kinematics of the human knee has been reported in the literature either using in vitro or in vivo methodologies. In vivo methodology is widely preferred due to biomechanical accuracies. However, it is challenging to obtain the kinematic data in vivo due to limitations in existing methods. One of the several existing methods used in such application is using X-ray fluoroscopy imaging, which allows for the non-invasive quantification of bone kinematics. In the fluoroscopy imaging method, due to procedural simplicity and low radiation exposure, single-plane fluoroscopy (SF) is the preferred tool to study the in vivo kinematics of the knee joint. Evaluation of the three-dimensional (3D) kinematics from the SF imagery is possible only if prior knowledge of the shape of the knee bones is available. The standard technique for acquiring the knee shape is to either segment Magnetic Resonance (MR) images, which is expensive to procure, or Computed Tomography (CT) images, which exposes the subjects to a heavy dose of ionizing radiation. Additionally, both the segmentation procedures are time-consuming and labour-intensive. An alternative technique that is rarely used is to reconstruct the knee shape from the SF images. It is less expensive than MR imaging, exposes the subjects to relatively lower radiation than CT imaging, and since the kinematic study and the shape reconstruction could be carried out using the same device, it could save a considerable amount of time for the researchers and the subjects. However, due to low exposure levels, SF images are often characterized by a low signal-to-noise ratio, making it difficult to extract the required information to reconstruct the shape accurately. In comparison to conventional X-ray images, SF images are of lower quality and have less detail. Additionally, existing methods for reconstructing the shape of the knee remain generally inconvenient since they need a highly controlled system: images must be captured from a calibrated device, care must be taken while positioning the subject's knee in the X-ray field to ensure image consistency, and user intervention and expert knowledge is required for 3D reconstruction. In an attempt to simplify the existing process, this thesis proposes a new methodology to reconstruct the 3D shape of the knee bones from multiple uncalibrated SF images using deep learning. During the image acquisition using the SF, the subjects in this approach can freely rotate their leg (in a fully extended, knee-locked position), resulting in several images captured in arbitrary poses. Relevant features are extracted from these images using a novel feature extraction technique before feeding it to a custom-built Convolutional Neural Network (CNN). The network, without further optimization, directly outputs a meshed 3D surface model of the subject's knee joint. The whole procedure could be completed in a few minutes. The robust feature extraction technique can effectively extract relevant information from a range of image qualities. When tested on eight unseen sets of SF images with known true geometry, the network reconstructed knee shape models with a shape error (RMSE) of 1.91± 0.30 mm for the femur, 2.3± 0.36 mm for the tibia and 3.3± 0.53 mm for the patella. The error was calculated after rigidly aligning (scale, rotation, and translation) each of the reconstructed shape models with the corresponding known true geometry (obtained through MRI segmentation). Based on a previous study that examined the influence of reconstructed shape accuracy on the precision of the evaluation of tibiofemoral kinematics, the shape accuracy of the proposed methodology might be adequate to precisely track the bone kinematics, although further investigation is required

Pelvic kinematics as confounding factor for cam hip impingement

The purpose of this thesis was to explore a range of biomechanical factors linked to the development of symptoms and potentially early onset hip OA in people with cam hip impingement. This was achieved through shape analysis on 3D bone models (segmented from medical images), and motion analysis performed during walking and squatting. Following ethical approval, kinematic and morphological variables were obtained from 19 pre-operative hip impingement patients and 18 healthy controls, and these were compared between groups. Patients demonstrated reduced neck-shaft-angles (-6.0°, p<.01) and increased anterior pelvic tilt during gait (+3.2°, p=.04) which are thought to predispose to impingement by decreasing the proximity between the cam and acetabular rim and making abutment more likely. The transverse pelvic plane is used to measure pelvic tilt during motion analysis, it is therefore interesting that the angle between the transverse and anterior pelvic plane is increased (+4.6°, p=.03) in patients, emphasising that the interplay between shape and function is a priority for further research. Avoidance of hip extension (-5.9°, p<.01) was also observed, which could be a compensatory mechanism to prevent further damages to the hip. Furthermore, large cams are thought to act as a mechanical constraint and limit rotation movement allowed within the acetabulum, as demonstrated by reduced peak hip internal rotation (during squat, -8.5°, p=.03). Controls were regrouped based on morphology to allow comparison between asymptomatic (CAM-; n=11) and symptomatic (CAM+, n=16) cams. Symptomatic cams have an increased width (+41.4°, p<.01), and start more superiorly (-29.4°, p<.01). Increased sagittal pelvic mobility (e.g. during a squat; -11.2° for CAM+, p<.01) is thought to be protective against hip impingement symptoms, as during high flexion angles the pelvic tilts backwards reducing the risk of abutment. These findings highlight the need to establish thresholds taking confounding factors into account.Open Acces

Determination and Comparison of In Vivo Forces and Torques in Normal and Degenerative Lumbar Spines

In vivo motions of normal and degenerative lumbar spine patients performing extension/flexion were obtained using video fluoroscopy. 3-D models of each patient’s vertebrae were registered to the 2-D fluoroscopy images using a process developed at Rocky Mountain Musculoskeletal Research Laboratory. Temporal equations representing the motions were input into a math model and the forces at the contact point between vertebral levels and the body torques between the vertebrae were the output. The vertical forces in the normal and degenerative patients were similar and ranged from 0.35-0.42 times the body weight of the patient. The maximum torques were higher in the degenerative patient than in the normal patient. The maximum torques between L4 and L5 were 11.1 N*m in the degenerative patient and 9.72 N*m in the normal patient. At L3/L4, the maximum torque was 10.3 N*m in the degenerative and 9.03 N*m in the normal patient. The maximum torques in the degenerative patient were also higher than in the normal patient at the L2/L3 and L1/L2 levels. Left untreated these higher torques could cause deterioration of other levels as the spine tries to compensate for existing degenerative levels. This model will lead to a better understanding of the lumbar spine and could aid in treating lower back pain and in the design of spinal prostheses

Assessment of Normal Knee Kinematics Using High-Speed Stereo-Radiography System

The measurement of dynamic joint kinematics in vivo is important in order to understand the effects of joint injuries and diseases as well as for evaluating the treatment effectiveness. Quantification of knee motion is essential for assessment of joint function for diagnosis of pathology, such as tracking and progression of osteoarthritis and evaluation of outcome following conservative or surgical treatment. Total knee arthroplasty (TKA) is an invasive treatment for arthritic pain and functional disability and it is used for deformed joint replacement with implants in order to restore joint alignment. It is important to describe knee kinematics in healthy individuals for comparison in diagnosis of pathology and understanding treatment to restore normal function. However measuring the in vivo dynamic biomechanics in 6 degrees of freedom with an accuracy that is acceptable has been shown to be technically challenging. Skin marker based methods, commonly used in human movement analysis, are still prone to large errors produced by soft tissue artifacts. Thus, great deal of research has been done to obtain more accurate data of the knee joint by using other measuring techniques like dual plane fluoroscopy. The goal of this thesis is to use high-speed stereo radiography (HSSR) system for measuring joint kinematics in healthy older adults performing common movements of daily living such as straight walking and during higher demand activities of pivoting and step descending in order to establish a useful baseline for the envelope of healthy knee motion for subsequent comparison with patients with TKA. Prior to data collection, validation and calibration techniques as well as dose estimations were mandatory for the successful accomplishment of this study

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

A Computational Model to Predict \u3cem\u3eIn Vivo\u3c/em\u3e Kinetics in Implanted and Non-Implanted Shoulders

The purpose of this study was to develop and implement a computational model designed to input in vivo kinematic and predict in vivo forces and torques for the shoulder, elbow, and wrist in normal, rotator cuff-deficient (RCD), reverse shoulder arthroplasty (RSA) and total shoulder arthroplasty (TSA) shoulder subjects. Twenty subjects, divided evenly amongst the four shoulder types, performed a box-lift activity while under fluoroscopic surveillance. Three dimensional (3D) in vivo kinematics was determined for the subjects using implant models and bone models created from CT (computed tomography) scans in a 2D-to-3D registration process. The kinematics were used as input for an inverse dynamics mathematical model, and the subject-specific kinetics were derived. Average resultant shoulder forces were 78.3N (range: 70.4N to 117N, SD: 5.213), 102N (range: 90.2N to 180.2N, SD: 12.339), 94.9N (range: 84.9N to 149N, SD: 10.02), and 92.5N (range: 87.984N to 95.370N, SD: 1.848), for normal, RCD, RSA, and TSA subjects, respectively. Average resultant shoulder torques were 23.6Nm (range: 8.32Nm to 73.7Nm, SD: 11.227), 29.6Nm (range: 22.892Nm to 71.377Nm, SD: 7.581), 27.2Nm (range: 19.961Nm to 59.352Nm, SD: 6.664), 20.3Nm (range: 11.700Nm to 31.409Nm, SD: 6.496), for normal, RCD, RSA, and TSA shoulders, respectively. This study revealed that RCD subjects exhibited a decreased ROM (range of motion) of the humeral head with respect to the glenoid, as compared to the other groups. This study also showed that subjects having a rotator cuff-deficient shoulder and/or a replaced shoulder tend to use compensatory motions to perform the task of lifting a box, and, as a result, they experience greater forces at the glenohumeral joint. Paradoxically, the RCD subjects experienced the highest joint forces and torques among the different shoulder types