Development of a patient-specific anatomical foot model from structured light scan data

The use of anatomically accurate finite element (FE) models of the human foot in research studies has increased rapidly in recent years. Uses for FE foot models include advancing knowledge of orthotic design, shoe design, ankle–foot orthoses, pathomechanics, locomotion, plantar pressure, tissue mechanics, plantar fasciitis, joint stress and surgical interventions. Similar applications but for clinical use on a per-patient basis would also be on the rise if it were not for the high costs associated with developing patient-specific anatomical foot models. High costs arise primarily from the expense and challenges of acquiring anatomical data via magnetic resonance imaging (MRI) or computed tomography (CT) and reconstructing the three-dimensional models. The proposed solution morphs detailed anatomy from skin surface geometry and anatomical landmarks of a generic foot model (developed from CT or MRI) to surface geometry and anatomical landmarks acquired from an inexpensive structured light scan of a foot. The method yields a patient-specific anatomical foot model at a fraction of the cost of standard methods. Average error for bone surfaces was 2.53 mm for the six experiments completed. Highest accuracy occurred in the mid-foot and lowest in the forefoot due to the small, irregular bones of the toes. The method must be validated in the intended application to determine if the resulting errors are acceptable.     

Assessment of scapular morphology and bone quality with statistical models

The design of total shoulder arthroplasty implants are guided by anatomy. The objective of this study was to develop statistical models to quantify shape and material property variation in the scapula. Material-mapped models were reconstructed from CT scans for a training set of subjects. Statistical shape (SSM) and intensity (SIM) models were created; SSM modes described scaling, changes in the medial border and acromial process, and elongation of the scapular blade. SIM modes captured bone quality changes in the anterior and inferior glenoid. Bone quality was independent of scapular morphology. Variation described by the statistical representations can inform implant design and sizing.     

Three-dimensional printer-generated patient-specific phantom for artificial in vivo dosimetry in radiotherapy quality assurance

Pretreatment intensity-modulated radiotherapy quality assurance is performed using simple rectangular or cylindrical phantoms; thus, the dosimetric errors caused by complex patient-specific anatomy are absent in the evaluation objects. In this study, we construct a system for generating patient-specific three-dimensional (3D)-printed phantoms for radiotherapy dosimetry. An anthropomorphic head phantom containing the bone and hollow of the paranasal sinus is scanned by computed tomography (CT). Based on surface rendering data, a patient-specific phantom is formed using a fused-deposition-modeling-based 3D printer, with a polylactic acid filament as the printing material. Radiophotoluminescence glass dosimeters can be inserted in the 3D-printed phantom. The phantom shape, CT value, and absorbed doses are compared between the actual and 3D-printed phantoms. The shape difference between the actual and printed phantoms is less than 1 mm except in the bottom surface region. The average CT value of the infill region in the 3D-printed phantom is −6 ± 18 Hounsfield units (HU) and that of the vertical shell region is 126 ± 18 HU. When the same plans were irradiated, the dose differences were generally less than 2%. These results demonstrate the feasibility of the 3D-printed phantom for artificial in vivo dosimetry in radiotherapy quality assurance.     

Patient-specific in vitro models for hemodynamic analysis of congenital heart disease – Additive manufacturing approach

Non-invasive hemodynamic assessment of total cavopulmonary connection (TCPC) is challenging due to the complex anatomy. Additive manufacturing (AM) is a suitable alternative for creating patient-specific in vitro models for flow measurements using four-dimensional (4D) Flow MRI. These in vitro systems have the potential to serve as validation for computational fluid dynamics (CFD), simulating different physiological conditions. This study investigated three different AM technologies, stereolithography (SLA), selective laser sintering (SLS) and fused deposition modeling (FDM), to determine differences in hemodynamics when measuring flow using 4D Flow MRI. The models were created using patient-specific MRI data from an extracardiac TCPC. These models were connected to a perfusion pump circulating water at three different flow rates. Data was processed for visualization and quantification of velocity, flow distribution, vorticity and kinetic energy. These results were compared between each model. In addition, the flow distribution obtained in vitro was compared to in vivo. The results showed significant difference in velocities measured at the outlets of the models that required internal support material when printing. Furthermore, an ultrasound flow sensor was used to validate flow measurements at the inlets and outlets of the in vitro models. These results were highly correlated to those measured with 4D Flow MRI. This study showed that commercially available AM technologies can be used to create patient-specific vascular models for in vitro hemodynamic studies at reasonable costs. However, technologies that do not require internal supports during manufacturing allow smoother internal surfaces, which makes them better suited for flow analyses.     

Automated three-dimensional finite element modelling of bone: a new method

Three-dimensional finite element stress analysis of bone is a key to understanding bone remodelling, assessing fracture risk, and designing prostheses; however, the cost and complexity of predicting the stress field in bone with accuracy has precluded the routine use of this method. A new, automated method of generating patient-specific three-dimensional finite element models of bone is presented — it uses digital computed tomographic (CT) scan data to derive the geometry of the bone and to estimate its inhomogeneous material properties. Cubic elements of a user-specified size are automatically defined and then individually assigned the CT scan-derived material properties. The method is demonstrated by predicting the stress, strain, and strain energy in a human proximal femur in vivo. Three-dimensional loading conditions corresponding to the stance phase of gait were taken from the literature and applied to the model. Maximum principal compressive stresses of 8–23 MPa were computed for the medial femoral neck. Automated generation of additional finite element models with larger numbers of elements was used to verify convergence in strain energy.     

Significance of preoperative planning for prophylactic augmentation of osteoporotic hip: A computational modeling study

A potential effective treatment for prevention of osteoporotic hip fractures is augmentation of the mechanical properties of the femur by injecting it with bone cement. This therapy, however, is only in research stage and can benefit substantially from computational simulations to optimize the pattern of cement injection. Some studies have considered a patient-specific planning paradigm for Osteoporotic Hip Augmentation (OHA). Despite their biomechanical advantages, customized plans require advanced surgical systems for implementation. Other studies, therefore, have suggested a more generalized injection strategy. The goal of this study is to investigate as to whether the additional computational overhead of the patient-specific planning can significantly improve the bone strength as compared to the generalized injection strategies attempted in the literature. For this purpose, numerical models were developed from high resolution CT images (n = 4). Through finite element analysis and hydrodynamic simulations, we compared the biomechanical efficiency of the customized cement-based augmentation along with three generalized injection strategies developed previously. Two series of simulations were studied, one with homogeneous and one with inhomogeneous material properties for the osteoporotic bone. The customized cement-based augmentation inhomogeneous models showed that injection of only 10 ml of bone cement can significantly increase the yield load (79.6%, P < 0.01) and yield energy (199%, P < 0.01) of an osteoporotic femur. This increase is significantly higher than those of the generalized injections proposed previously (23.8% on average). Our findings suggest that OHA can significantly benefit from a patient-specific plan that determines the pattern and volume of the injected cement.     

Pair-wise vs group-wise registration in statistical shape model construction: representation of physiological and pathological variability of bony surface morphology

Statistical shape models (SSM) of bony surfaces have been widely proposed in orthopedics, especially for anatomical bone modeling, joint kinematic analysis, staging of morphological abnormality, and pre- and intra-operative shape reconstruction. In the SSM computation, reference shape selection, shape registration and point correspondence computation are fundamental aspects determining the quality (generality, specificity and compactness) of the SSM. Such procedures can be made critical by the presence of large morphological dissimilarities within the surfaces, not only because of anthropometrical variability but also mainly due to pathological abnormalities. In this work, we proposed a SW pipeline for SSM construction based on pair-wise (PW) shape registration, which requires the a-priori selection of the reference shape, and on a custom iterative point correspondence algorithm. We addressed large morphological deformations in five different bony surface sets, namely proximal femur, distal femur, patella, proximal fibula and proximal tibia, extracted from a retrospective patient dataset. The technique was compared to a method from the literature, based on group-wise (GW) shape registration. As a main finding, the proposed technique provided generalization and specificity median errors, for all the five bony regions, lower than 2?mm. The comparative analysis provided basically similar results. Particularly, for the distal femur that was the shape affected by the largest pathological deformations, the differences in generalization, specificity and compactness were lower than 0.5?mm, 0.5?mm, and 1%, respectively. We can argue the proposed pipeline, along with the robust correspondence algorithm, is able to compute high-quality SSM of bony shapes, even affected by large morphological variability.     

Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the Osteoarthritis Initiative database

We hypothesize that variability in knee subchondral bone surface geometry will differentiate between patients at risk and those not at risk for developing osteoarthritis (OA) and suggest that statistical shape modeling (SSM) methods form the basis for developing a diagnostic tool for predicting the onset of OA. Using a subset of clinical knee MRI data from the osteoarthritis initiative (OAI), the objectives of this study were to (1) utilize SSM to compactly and efficiently describe variability in knee subchondral bone surface geometry and (2) determine the efficacy of SSM and rigid body transformations to distinguish between patients who are not expected to develop osteoarthritis (i.e. Control group) and those with clinical risk factors for OA (i.e. Incidence group). Quantitative differences in femur and tibia surface geometry were demonstrated between groups, although differences in knee joint alignment measures were not statistically significant, suggesting that variability in individual bone geometry may play a greater role in determining joint space geometry and mechanics. SSM provides a means of explicitly describing complete articular surface geometry and allows the complex spatial variation in joint surface geometry and joint congruence between healthy subjects and those with clinical risk of developing or existing signs of OA to be statistically demonstrated.     

MRI vs CT-based 2D-3D auto-registration accuracy for quantifying shoulder motion using biplane video-radiography

Biplane 2D-3D registration approaches have been used for measuring 3D, in vivo glenohumeral (GH) joint kinematics. Computed tomography (CT) has become the gold standard for reconstructing 3D bone models, as it provides high geometric accuracy and similar tissue contrast to video-radiography. Alternatively, magnetic resonance imaging (MRI) would not expose subjects to radiation and provides the ability to add cartilage and other soft tissues to the models. However, the accuracy of MRI-based 2D-3D registration for quantifying glenohumeral kinematics is unknown. We developed an automatic 2D-3D registration program that works with both CT- and MRI-based image volumes for quantifying joint motions. The purpose of this study was to use the proposed 2D-3D auto-registration algorithm to describe the humerus and scapula tracking accuracy of CT- and MRI-based registration relative to radiostereometric analysis (RSA) during dynamic biplanar video-radiography. The GH kinematic accuracy (RMS error) was 0.6–1.0 mm and 0.6–2.2° for the CT-based registration and 1.4–2.2 mm and 1.2–2.6° for MRI-based registration. Higher kinematic accuracy of CT-based registration was expected as MRI provides lower spatial resolution and bone contrast as compared to CT and suffers from spatial distortions. However, the MRI-based registration is within an acceptable accuracy for many clinical research questions.     

Statistical shape modelling reveals large and distinct subchondral bony differences in osteoarthritic knees

Knee osteoarthritis (OA) results in changes such as joint-space narrowing and osteophyte formation. Radiographic classification systems group patients by the presence or absence of these gross anatomical features but are poorly correlated to function. Statistical-shape modelling (SSM) can detect subtle differences in 3D-bone geometry, providing an opportunity for accurate predictive models. The aim of this study was to describe and quantify the main modes of shape variation which distinguish end-stage OA from asymptomatic knees. Seventy-six patients with OA and 77 control participants received a CT of their knee. 3D models of the joint were created by manual segmentation. A template mesh was fitted to all meshes and rigidly aligned resulting in a set of correspondent meshes. Principal Component Analysis (PCA) was performed to create the SSM. Logistic regression was performed on the PCA weights to distinguish morphological features of the two groups. The first 7 modes of the SSM captured >90% shape variation with 6 modes best distinguishing between OA and asymptomatic knees. OA knees displayed sub-chondral bone expansion particularly in the condyles and posterior medial tibial plateau of up to 10 mm. The model classified the two groups with 95% accuracy, 96% sensitivity, 94% specificity, and 97% AUC. There were distinct features which differentiated OA from asymptomatic knees. Further research will elucidate how magnitude and location of shape changes in the knee influence clinical and functional outcomes.     

Linking statistical shape models and simulated function in the healthy adult human heart

Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.     

中国数字化可视人体鞍上池的横断面解剖与CT、MRI对照研究

目的:探讨鞍上池在中国数字化可视人体(Chinese visible human,CVH)与CT、MRI上的横断面解剖形态学表现。方法:选择做64层螺旋CT和MRI头部检查的健康志愿者各60例,获得5mm层厚横断面图像。从第2例中国数字化可视人体数据集中选取与CT、MRI相对应层面的头部薄层连续横断面标本图像,对照观察鞍上池在CVH、MRI与CT图像上的正常解剖形态、毗邻及内部结构。结果:CVH图像上,鞍上池表现为六角形和五角形两种形状。CVH薄层横断面图像能连续、清晰地显示鞍上池的正常形态、毗邻及内部结构。60例CT及MRI图像上,鞍上池全部显示,但解剖结构均不及CVH清晰。鞍上池在CT、MRI横断面图像上形状变化更大,以六角形最多,五角形次之,四角形最少,相应毗邻及内部结构也有所不同。六角形鞍上池在CVH、CT、MRI上有良好的对应关系,五角形鞍上池部分相匹配,CVH图像上无四角形鞍上池。结论:通过与CT、MRI进行对照研究,中国数字化可视人体能为颅脑疾病的影像识别和诊断提供断层解剖学依据。     

Personalized Computer Simulation of Diastolic Function in Heart Failure

The search for a parameter representing left ventricular relaxation from non-invasive and invasive diagnostic tools has been extensive, since heart failure (HF) with preserved ejection fraction (HF-pEF) is a global health problem. We explore here the feasibility using patient-specific cardiac computer modeling to capture diastolic parameters in patients suffering from different degrees of systolic HF. Fifty eight patients with idiopathic dilated cardiomyopathy have undergone thorough clinical evaluation, including cardiac magnetic resonance imaging (MRI), heart catheterization, echocardiography, and cardiac biomarker assessment. A previously-introduced framework for creating multi-scale patient-specific cardiac models has been applied on all these patients. Novel parameters, such as global stiffness factor and maximum left ventricular active stress, representing cardiac active and passive tissue properties have been computed for all patients. Invasive pressure measurements from heart catheterization were then used to evaluate ventricular relaxation using the time constant of isovolumic relaxation Tau (s). Parameters from heart catheterization and the multi-scale model have been evaluated and compared to patient clinical presentation. The model parameter global stiffness factor, representing diastolic passive tissue properties, is correlated signif-icantly across the patient population with s. This study shows that multi-modal cardiac models can successfully capture diastolic (dys) function, a prerequisite for future clinical trials on HF-pEF.     

Development and validation of a generic 3D model of the distal femur

The development and validation of a virtual generic 3D model of the distal femur using computer graphical methods is presented. The synthesis of the generic model requires the following steps: acquisition of bony 3D morphology using standard computed tomography (CT) imaging; alignment of 3D models reconstructed from CT images with a common coordinate system; computer graphical sectioning of the models; extraction of bone contours from the image sections; combining and averaging of extracted contours; and 3D reconstruction of the averaged contours.

The generic models reconstructed from the averaged contours of six cadaver femora were validated by comparing their surface geometry on a point to point basis with that of the CT reconstructed reference models. The mean errors ranged from 0.99 to 2.5 mm and were in agreement with the qualitative assessment of the models.     

Imaging Non-Specific Wrist Pain: Interobserver Agreement and Diagnostic Accuracy of SPECT/CT,MRI, CT,Bone Scan and Plain Radiographs

Purpose

Chronic hand and wrist pain is a common clinical issue for orthopaedic surgeons and rheumatologists. The purpose of this study was 1. To analyze the interobserver agreement of SPECT/CT, MRI, CT, bone scan and plain radiographs in patients with non-specific pain of the hand and wrist, and 2. to assess the diagnostic accuracy of these imaging methods in this selected patient population.

Materials and Methods

Thirty-two consecutive patients with non-specific pain of the hand or wrist were evaluated retrospectively. All patients had been imaged by plain radiographs, planar early-phase imaging (bone scan), late-phase imaging (SPECT/CT including bone scan and CT), and MRI. Two experienced and two inexperienced readers analyzed the images with a standardized read-out protocol. Reading criteria were lesion detection and localisation, type and etiology of the underlying pathology. Diagnostic accuracy and interobserver agreement were determined for all readers and imaging modalities.

Results

The most accurate modality for experienced readers was SPECT/CT (accuracy 77%), followed by MRI (56%). The best performing, though little accurate modality for inexperienced readers was also SPECT/CT (44%), followed by MRI and bone scan (38% each). The interobserver agreement of experienced readers was generally high in SPECT/CT concerning lesion detection (kappa 0.93, MRI 0.72), localisation (kappa 0.91, MRI 0.75) and etiology (kappa 0.85, MRI 0.74), while MRI yielded better results on typification of lesions (kappa 0.75, SPECT/CT 0.69). There was poor agreement between experienced and inexperienced readers in SPECT/CT and MRI.

Conclusions

SPECT/CT proved to be the most helpful imaging modality in patients with non-specific wrist pain. The method was found reliable, providing high interobserver agreement, being outperformed by MRI only concerning the typification of lesions. We believe it is beneficial to integrate SPECT/CT into the diagnostic imaging algorithm of chronic wrist pain.     

Detection of Rib Metastases in Patients with Lung Cancer: A Comparative Study of MRI,CT and Bone Scintigraphy

We retrospectively investigated the imaging findings of bone scintigraphy, chest CT and chest MRI in 55 cases of lung cancer. The sensitivity, specificity and accuracy of the detection of rib metastases were compared between imaging modalities on both a per-lesion and a per-patient basis. On a per-lesion basis, MRI sensitivity and accuracy were significantly higher than that of bone scintigraphy and CT (P<0.05). The sensitivities, specificities, and accuracy levels between CT and bone scintigraphy did not differ on either a per-lesion or per-patient basis (P>0.05). MRI appears to be superior for the detection of ribs metastases in lung cancer.     

Are bone erosions detected by magnetic resonance imaging and ultrasonography true erosions? A comparison with computed tomography in rheumatoid arthritis metacarpophalangeal joints

The objective of the study was, with multidetector computed tomography (CT) as the reference method, to determine whether bone erosions in rheumatoid arthritis (RA) metacarpophalangeal (MCP) joints detected with magnetic resonance imaging (MRI) and ultrasonography (US), but not with radiography, represent true erosive changes. We included 17 RA patients with at least one, previously detected, radiographically invisible MCP joint MRI erosion, and four healthy control individuals. They all underwent CT, MRI, US and radiography of the 2nd to 5th MCP joints of one hand on the same day. Each imaging modality was evaluated for the presence of bone erosions in each MCP joint quadrant. In total, 336 quadrants were examined. The sensitivity, specificity and accuracy, respectively, for detecting bone erosions (with CT as the reference method) were 19%, 100% and 81% for radiography; 68%, 96% and 89% for MRI; and 42%, 91% and 80% for US. When the 16 quadrants with radiographic erosions were excluded from the analysis, similar values for MRI (65%, 96% and 90%) and US (30%, 92% and 80%) were obtained. CT and MRI detected at least one erosion in all patients but none in control individuals. US detected at least one erosion in 15 patients, however, erosion-like changes were seen on US in all control individuals. Nine patients had no erosions on radiography. In conclusion, with CT as the reference method, MRI and US exhibited high specificities (96% and 91%, respectively) in detecting bone erosions in RA MCP joints, even in the radiographically non-erosive joints (96% and 92%). The moderate sensitivities indicate that even more erosions than are seen on MRI and, particularly, US are present. Radiography exhibited high specificity (100%) but low sensitivity (19%). The present study strongly indicates that bone erosions, detected with MRI and US in RA patients, represent a loss of calcified tissue with cortical destruction, and therefore can be considered true bone erosions.     

A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm

Geometric features of the aorta are linked to patient risk of rupture in the clinical decision to electively repair an ascending aortic aneurysm (AsAA). Previous approaches have focused on relationship between intuitive geometric features (e.g., diameter and curvature) and wall stress. This work investigates the feasibility of a machine learning approach to establish the linkages between shape features and FEA-predicted AsAA rupture risk, and it may serve as a faster surrogate for FEA associated with long simulation time and numerical convergence issues. This method consists of four main steps: (1) constructing a statistical shape model (SSM) from clinical 3D CT images of AsAA patients; (2) generating a dataset of representative aneurysm shapes and obtaining FEA-predicted risk scores defined as systolic pressure divided by rupture pressure (rupture is determined by a threshold criterion); (3) establishing relationship between shape features and risk by using classifiers and regressors; and (4) evaluating such relationship in cross-validation. The results show that SSM parameters can be used as strong shape features to make predictions of risk scores consistent with FEA, which lead to an average risk classification accuracy of 95.58% by using support vector machine and an average regression error of 0.0332 by using support vector regression, while intuitive geometric features have relatively weak performance. Compared to FEA, this machine learning approach is magnitudes faster. In our future studies, material properties and inhomogeneous thickness will be incorporated into the models and learning algorithms, which may lead to a practical system for clinical applications.     

A patient-specific computer tomography-based finite element methodology to calculate the six dimensional stiffness matrix of human vertebral bodies

In most finite element (FE) studies of vertebral bodies, axial compression is the loading mode of choice to investigate structural properties, but this might not adequately reflect the various loads to which the spine is subjected during daily activities or the increased fracture risk associated with shearing or bending loads. This work aims at proposing a patient-specific computer tomography (CT)-based methodology, using the currently most advanced, clinically applicable finite element approach to perform a structural investigation of the vertebral body by calculation of its full six dimensional (6D) stiffness matrix. FE models were created from voxel images after smoothing of the peripheral voxels and extrusion of a cortical shell, with material laws describing heterogeneous, anisotropic elasticity for trabecular bone, isotropic elasticity for the cortex based on experimental data. Validated against experimental axial stiffness, these models were loaded in the six canonical modes and their 6D stiffness matrix calculated. Results show that, on average, the major vertebral rigidities correlated well or excellently with the axial rigidity but that weaker correlations were observed for the minor coupling rigidities and for the image-based density measurements. This suggests that axial rigidity is representative of the overall stiffness of the vertebral body and that finite element analysis brings more insight in vertebral fragility than densitometric approaches. Finally, this extended patient-specific FE methodology provides a more complete quantification of structural properties for clinical studies at the spine.