Integrated Neuromusculoskeletal Modeling within a Finite Element Framework to Investigate Mechanisms and Treatment of Neurodegenerative Conditions

Neurodegenerative and neurodevelopmental disorders are a group of conditions that stem from irregularities in the nervous system that lead to complications in function and movement. The goal of this work is to develop computational tools that: (1) measure the accuracy of surgical interventions in neurodegenerative and neurodevelopmental conditions, and (2) integrate neural and musculoskeletal frameworks to provide a platform to better investigate neurodegenerative and neurodevelopmental disorders. Parkinson’s disease (PD) is a neurodegenerative condition projected to affect over 1.2 million people by 2030 in the US. It is caused by atypical firing patterns in the basal ganglia region of the brain that leads to primary motor symptoms of tremor, slowness of movement, and rigidity. A potential treatment for PD is deep brain stimulation (DBS). DBS involves implanting electrodes into central brain structures to regulate the pathological signaling. Electrode placement accuracy is a key metric that helps to determine patient outcomes postoperatively. An automated measurement system was developed to quantify electrode placement accuracy in robot-assisted asleep DBS procedures (Chapter 2). This measurement system allows for precise metrics without human bias in large cohorts of patients. This measurement system was later modified to measure screw placement accuracy in spinal fusion procedures for the treatment of degenerative musculoskeletal conditions (Chapter 3). DBS is an effective treatment for PD, but it is not a cure for the cause of the disease itself. To cure neurodegenerative and neurodevelopmental diseases, the underlying disease mechanisms must be better understood. A major limitation in studying neural conditions is the infeasibility of performing in vivo experiments, particularly in humans due to ethical considerations. Computational modeling, specifically fully predictive neuromusculoskeletal (NMS) models, can help to accumulate additional knowledge about neural pathways that cannot be determined experimentally. NMS models typically include complexity in either the neuromuscular or musculoskeletal system, but not both, making it difficult or infeasible to investigate the relationship between neural signaling and musculoskeletal function. To overcome this, a fully predictive NMS model was developed by integrating NEURON software within Abaqus, a finite element (FE) environment (Chapter 4). The neural model consisted of a pool of motor neurons innervating the soleus muscle in a FE human ankle model. Software integration was verified against previously published data, and the neuronal network was verified for motor unit recruitment and rate coding, which are the two principles required for in vivo muscle generation. To demonstrate the applicability of the model to study neurodegenerative and neurodevelopmental diseases, a fully predictive mouse hindlimb NMS model was developed using the integrated framework to investigate Rett syndrome (RS) (Chapter 5). RS is a neurodevelopmental disorder caused by a mutation of the Mecp2 gene with hallmark motor symptoms of a loss of purposeful hand movement, changes in muscle tone, and a loss of speech. Recent experimental analysis has found that the axon initial segment (AIS) in mice that model RS has torsional morphology compared to wildtype littermate controls. The effects these neural morphological changes have on joint motion will be studied using the mouse NMS model. This work encompasses a range of research that uses computational models to study the underlying mechanisms and design targeted treatment options for neurodegenerative and neurodevelopmental disorders. The outcomes of this work have quantified the accuracy at which surgical interventions for these conditions can be performed and have resulted in a neuromusculoskeletal model that can be applied to understand how neural morphology, and associated changes due to these disorders, affects musculoskeletal function

A brief review of surface meshing in medical images for biomedical computing and visualization

A visual representation of the interior of a body is important for clinical analysis and medical intervention. The technique, process and art of creating this visual representation are called medical imaging. The images produced from medical imaging need to be analyses by using Finite Element Method (FEM) especially for intraoperative registration and biomechanical modeling of the tissues. This medical model ranges from the smallest vascular to bones and the complex brain. In order to use FEM, the images need to go through surface meshing generator. Although numerous mesh generation methods have been described to date, there is a few which can deal with medical data input. In this paper, a briefing review of surface meshing that can deal in medical images is presented especially in biomedical computing and visualization. Some automatic mesh generators software used in medical imaging is also discussed such as ScanIP, MIMICS, TETGEN, NetGen, BioMesh3D,CUBITMesh and Gmsh

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echogradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deeplearning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93–0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1 mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models

Developments in PET-MRI for Radiotherapy Planning Applications

The hybridization of magnetic resonance imaging (MRI) and positron emission tomography (PET) provides the benefit of soft-tissue contrast and specific molecular information in a simultaneous acquisition. The applications of PET-MRI in radiotherapy are only starting to be realised. However, quantitative accuracy of PET relies on accurate attenuation correction (AC) of, not only the patient anatomy but also MRI hardware and current methods, which are prone to artefacts caused by dense materials. Quantitative accuracy of PET also relies on full characterization of patient motion during the scan. The simultaneity of PET-MRI makes it especially suited for motion correction. However, quality assurance (QA) procedures for such corrections are lacking. Therefore, a dynamic phantom that is PET and MR compatible is required. Additionally, respiratory motion characterization is needed for conformal radiotherapy of lung. 4D-CT can provide 3D motion characterization but suffers from poor soft-tissue contrast. In this thesis, I examine these problems, and present solutions in the form of improved MR-hardware AC techniques, a PET/MRI/CT-compatible tumour respiratory motion phantom for QA measurements, and a retrospective 4D-PET-MRI technique to characterise respiratory motion. Chapter 2 presents two techniques to improve upon current AC methods that use a standard helical CT scan for MRI hardware in PET-MRI. One technique uses a dual-energy computed tomography (DECT) scan to construct virtual monoenergetic image volumes and the other uses a tomotherapy linear accelerator to create CT images at megavoltage energies (1.0 MV) of the RF coil. The DECT-based technique reduced artefacts in the images translating to improved μ-maps. The MVCT-based technique provided further improvements in artefact reduction, resulting in artefact free μ-maps. This led to more AC of the breast coil. In chapter 3, I present a PET-MR-CT motion phantom for QA of motion-correction protocols. This phantom is used to evaluate a clinically available real-time dynamic MR images and a respiratory-triggered PET-MRI protocol. The results show the protocol to perform well under motion conditions. Additionally, the phantom provided a good model for performing QA of respiratory-triggered PET-MRI. Chapter 4 presents a 4D-PET/MRI technique, using MR sequences and PET acquisition methods currently available on hybrid PET/MRI systems. This technique is validated using the motion phantom presented in chapter 3 with three motion profiles. I conclude that our 4D-PET-MRI technique provides information to characterise tumour respiratory motion while using a clinically available pulse sequence and PET acquisition method

Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans

Abstract. This paper presents a new method for automatic localiza-tion and identification of vertebrae in arbitrary field-of-view CT scans. No assumptions are made about which section of the spine is visible or to which extent. Thus, our approach is more general than previous work while being computationally efficient. Our algorithm is based on re-gression forests and probabilistic graphical models. The discriminative, regression part aims at roughly detecting the visible part of the spine. Ac-curate localization and identification of individual vertebrae is achieved through a generative model capturing spinal shape and appearance. The system is evaluated quantitatively on 200 CT scans, the largest dataset reported for this purpose. We obtain an overall median localization error of less than 6mm, with an identification rate of 81%.

Statistical Shape and Intensity Modeling of the Shoulder

Anatomical variability in the shoulder is inherently present and can influence healthy and pathologic biomechanics and ultimately clinical decision-making. Characterizing variation in bony morphology and material properties in the population can support treatment and specifically the design, via shape and sizing, of shoulder implants. Total Shoulder Arthroplasty (TSA) is the treatment of choice for glenohumeral osteoarthritis as well as bone fracture. Complications and poor outcomes in TSA are generally influenced by the inability of the implant to replicate the natural joint biomechanics and by the bone quality around the fixation features. For this reason, knowledge of bony morphology and mechanical properties can support optimal implant design and sizing, and thus improve TSA results. Statistical shape and intensity modeling is a powerful tool to represent the shape and mechanical properties variation in a training set. Accordingly, the objectives of this thesis were: 1) to develop a statistical shape model (SSM) of the proximal humeral cortical and cancellous bone; 2) to develop an SSM and a statistical intensity model (SIM) of the scapular bone. A training set of 85 humeri and 53 scapulae were reconstructed from CT scans and registered to common templates. Principal Component Analysis (PCA) was applied to the registered geometries to quantify morphological and bone properties variation in the population. For both the humerus and the scapula SSM, the first mode of variation accounted for most of the variation and described scaling. Subsequent modes described changes in the scapular plate, acromion process and scapular notch for the scapula, and in the neck angle, head inclination, greater and lesser tubercles for the humerus. Variation in cortical thickness of the humeral diaphysis was largely independent of size and statistically significant differences with ethnicity were noted. Asian subjects showed higher humeral cortical thickness with respect to Caucasians, regardless of gender. The first mode of variation in the scapular SIM described scaling in material properties distribution, with higher bone density located centrally and anteriorly in the glenoid region. The bone property maps developed for the scapular training set realistically captured inter-subject variability and they represent a valuable tool to assess fixation features and screw location and trajectories for TSA glenoid component. The SSMs and SIM developed in this thesis represent a useful infrastructure to support population-based evaluations and assess possible anatomical differences with gender and ethnicity, SSM and SIM can also provide anatomical relationship in support of implant design and sizing