Quantitative comparison of cortical bone thickness using correspondence-based shape modeling in patients with cam femoroacetabular impingement

A Correspondence-Based Network Approach for Groupwise Analysis of Patient-Specific Spatiotemporal Data

Methods for statistically analyzing patient-specific data that vary both spatially and over time are currently either limited to summary statistics or require elaborate surface registration. We propose a new method, called correspondence-based network analysis, which leverages particle-based shape modeling to establish correspondence across a population and preserve patient-specific measurements and predictions through statistical analysis. Herein, we evaluated this method using three published datasets of the hip describing cortical bone thickness of the proximal femur, cartilage contact stress, and dynamic joint space between control and patient cohorts to evaluate activity- and group-based differences, as applicable, using traditional statistical parametric mapping (SPM) and our proposed spatially considerate correspondence-based network analysis approach. The network approach was insensitive to correspondence density, while the traditional application of SPM showed decreasing area of the region of significance with increasing correspondence density. In comparison to SPM, the network approach identified broader and more connected regions of significance for all three datasets. The correspondence-based network analysis approach identified differences between groups and activities without loss of subject and spatial specificity which could improve clinical interpretation of results.

Particle-Based Shape Modeling for Arbitrary Regions-of-Interest

Statistical Shape Modeling (SSM) is a quantitative method for analyzing morphological variations in anatomical structures. These analyses often necessitate building models on targeted anatomical regions of interest to focus on specific morphological features. We propose an extension to particle-based shape modeling (PSM), a widely used SSM framework, to allow shape modeling to arbitrary regions of interest. Existing methods to define regions of interest are computationally expensive and have topological limitations. To address these shortcomings, we use mesh fields to define free-form constraints, which allow for delimiting arbitrary regions of interest on shape surfaces. Furthermore, we add a quadratic penalty method to the model optimization to enable computationally efficient enforcement of any combination of cutting-plane and free-form constraints. We demonstrate the effectiveness of this method on a challenging synthetic dataset and two medical datasets.

Learning spatiotemporal statistical shape models for non-linear dynamic anatomies

Numerous clinical investigations require understanding changes in anatomical shape over time, such as in dynamic organ cycle characterization or longitudinal analyses (e.g., for disease progression). Spatiotemporal statistical shape modeling (SSM) allows for quantifying and evaluating dynamic shape variation with respect to a cohort or population of interest. Existing data-driven SSM approaches leverage information theory to capture population-level shape variations by learning correspondence-based (landmark) representations of shapes directly from data using entropy-based optimization schemes. These approaches assume sample independence and thus are unsuitable for sequential dynamic shape observations. Previous methods for adapting entropy-based SSM optimization schemes for the spatiotemporal case either utilize a cross-sectional design (ignoring within-subject correlation) or impose other limiting assumptions, such as the linearity of shape dynamics. Here, we present a principled approach to spatiotemporal SSM that relaxes these assumptions to correctly capture population-level shape variation over time. We propose to incorporate modeling the underlying time dependency into correspondence optimization via a regularized principal component polynomial regression. This approach is flexible enough to capture non-linear temporal dynamics while encoding population-specific spatial regularity. We demonstrate our method's efficacy on synthetic data and left atrium segmented from cardiac MRI scans. Our approach better captures the population modes of variation and a statistically significant time dependency than existing methods.

Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection

BACKGROUND

Anterior temporal lobe resection is an effective treatment for temporal lobe epilepsy. The post-surgical structural changes could influence the follow-up treatment. Capturing post-surgical changes necessitates a well-established cortical shape correspondence between pre- and post-surgical surfaces. Yet, most cortical surface registration methods are designed for normal neuroanatomy. Surgical changes can introduce wide ranging artifacts in correspondence, for which conventional surface registration methods may not work as intended.

METHODS

In this paper, we propose a novel particle method for one-to-one dense shape correspondence between pre- and post-surgical surfaces with temporal lobe resection. The proposed method can handle partial structural abnormality involving non-rigid changes. Unlike existing particle methods using implicit particle adjacency, we consider explicit particle adjacency to establish a smooth correspondence. Moreover, we propose hierarchical optimization of particles rather than full optimization of all particles at once to avoid trappings of locally optimal particle update.

RESULTS

We evaluate the proposed method on 25 pairs of T1-MRI with pre- and post-simulated resection on the anterior temporal lobe and 25 pairs of patients with actual resection. We show improved accuracy over several cortical regions in terms of ROI boundary Hausdorff distance with 4.29 mm and Dice similarity coefficients with average value 0.841, compared to existing surface registration methods on simulated data. In 25 patients with actual resection of the anterior temporal lobe, our method shows an improved shape correspondence in qualitative and quantitative evaluation on parcellation-off ratio with average value 0.061 and cortical thickness changes. We also show better smoothness of the correspondence without self-intersection, compared with point-wise matching methods which show various degrees of self-intersection.

CONCLUSION

The proposed method establishes a promising one-to-one dense shape correspondence for temporal lobe resection. The resulting correspondence is smooth without self-intersection. The proposed hierarchical optimization strategy could accelerate optimization and improve the optimization accuracy. According to the results on the paired surfaces with temporal lobe resection, the proposed method outperforms the compared methods and is more reliable to capture cortical thickness changes.

Statistical multi-level shape models for scalable modeling of multi-organ anatomies

Statistical shape modeling is an indispensable tool in the quantitative analysis of anatomies. Particle-based shape modeling (PSM) is a state-of-the-art approach that enables the learning of population-level shape representation from medical imaging data (e.g., CT, MRI) and the associated 3D models of anatomy generated from them. PSM optimizes the placement of a dense set of landmarks (i.e., correspondence points) on a given shape cohort. PSM supports multi-organ modeling as a particular case of the conventional single-organ framework via a global statistical model, where multi-structure anatomy is considered as a single structure. However, global multi-organ models are not scalable for many organs, induce anatomical inconsistencies, and result in entangled shape statistics where modes of shape variation reflect both within- and between-organ variations. Hence, there is a need for an efficient modeling approach that can capture the inter-organ relations (i.e., pose variations) of the complex anatomy while simultaneously optimizing the morphological changes of each organ and capturing the population-level statistics. This paper leverages the PSM approach and proposes a new approach for correspondence-point optimization of multiple organs that overcomes these limitations. The central idea of multilevel component analysis, is that the shape statistics consists of two mutually orthogonal subspaces: the within-organ subspace and the between-organ subspace. We formulate the correspondence optimization objective using this generative model. We evaluate the proposed method using synthetic shape data and clinical data for articulated joint structures of the spine, foot and ankle, and hip joint.

Statistical shape model of the talus bone morphology: A comparison between impinged and nonimpinged ankles

Diagnosis of ankle impingement is performed primarily by clinical examination, whereas medical imaging is used for severity staging and treatment guidance. The association of impingement symptoms with regional three-dimensional (3D) bone shape variaties visible in medical images has not been systematically explored, nor do we know the type and magnitude of this relation. In this cross-sectional case-control study, we hypothesized that 3D talus bone shape could be used to quantitatively formulate the discriminating shape variations between ankles with impingement from ankles without impingement, and we aimed to characterize and quantify these variations. We used statistical shape modeling (SSM) methods to determine the most prevalent modes of shape variations that discriminate between the impinged and nonimpinged ankles. Results of the compactness and parallel analysis test on the statistical shape model identify 8 prominent shape modes of variations (MoVs) representing approximately 78% of the total 3D variations in the population of shapes, among which two modes captured discriminating features between impinged and nonimpinged ankles (p value of 0.023 and 0.042). Visual inspection confirms that these two shape modes, capturing abnormalities in the anterior and posterior parts of talus, represent the two main bony risk factors in anterior and posterior ankle impingement. In conclusion, in this research using SSM we have identified shape MoVs that were found to correlate significantly with bony ankle impingement. We also illustrated potential guidance from SSMs for surgical planning.

From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach

Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology analysis. Deep learning frameworks have increased the feasibility of adopting SSM in medical practice by reducing the expert-driven manual and computational overhead in traditional SSM workflows. However, translating such frameworks to clinical practice requires calibrated uncertainty measures as neural networks can produce over-confident predictions that cannot be trusted in sensitive clinical decision-making. Existing techniques for predicting shape with aleatoric (data-dependent) uncertainty utilize a principal component analysis (PCA) based shape representation computed in isolation of the model training. This constraint restricts the learning task to solely estimating pre-defined shape descriptors from 3D images and imposes a linear relationship between this shape representation and the output (i.e., shape) space. In this paper, we propose a principled framework based on the variational information bottleneck theory to relax these assumptions while predicting probabilistic shapes of anatomy directly from images without supervised encoding of shape descriptors. Here, the latent representation is learned in the context of the learning task, resulting in a more scalable, flexible model that better captures data non-linearity. Additionally, this model is self-regularized and generalizes better given limited training data. Our experiments demonstrate that the proposed method provides an accuracy improvement and better calibrated aleatoric uncertainty estimates than state-of-the-art methods.

Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach

Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM). Such a tool enables characterizing patient organ cycles or disease progression in relation to a cohort of interest. Constructing shape models requires establishing a quantitative shape representation (e.g., corresponding landmarks). Particle-based shape modeling (PSM) is a data-driven SSM approach that captures population-level shape variations by optimizing landmark placement. However, it assumes cross-sectional study designs and hence has limited statistical power in representing shape changes over time. Existing methods for modeling spatiotemporal or longitudinal shape changes require predefined shape atlases and pre-built shape models that are typically constructed cross-sectionally. This paper proposes a data-driven approach inspired by the PSM method to learn population-level spatiotemporal shape changes directly from shape data. We introduce a novel SSM optimization scheme that produces landmarks that are in correspondence both across the population (inter-subject) and across time-series (intra-subject). We apply the proposed method to 4D cardiac data from atrial-fibrillation patients and demonstrate its efficacy in representing the dynamic change of the left atrium. Furthermore, we show that our method outperforms an image-based approach for spatiotemporal SSM with respect to a generative time-series model, the Linear Dynamical System (LDS). LDS fit using a spatiotemporal shape model optimized via our approach provides better generalization and specificity, indicating it accurately captures the underlying time-dependency.

Benchmarking off-the-shelf statistical shape modeling tools in clinical applications

Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications(e.g., implant design and lesion screening). Here, we systematically assess the outcome of widely used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape models from different tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. We also present a lesion screening method to objectively characterize subtle abnormal shape changes with respect to learned population-level statistics of controls. Results demonstrate that SSM tools display different levels of consistencies, where ShapeWorks and Deformetrica models are more consistent compared to models from SPHARM-PDM due to the groupwise approach of estimating surface correspondences. Furthermore, ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability compared to SPHARM-PDM models.

Tibiofemoral bony morphology features associated with ACL injury and sex utilizing three-dimensional statistical shape modeling

Statistical shape modeling was employed to assess three-dimensional (3D) bony morphology between distal femurs and proximal tibiae of anterior cruciate ligament (ACL) injured knees, the contralateral uninjured knees of ACL injured subjects, and knees with no history of injury. Surface models were created by segmenting bone from bilateral computed-tomography scans of 20 subjects of their ACL injured knees and non-injured contralateral knees, and 20 knees of control subjects with no history of a knee injury. Correspondence particles were placed on each surface, and a principal component analysis determined modes of variation in the positions of the correspondence particles describing anatomical variation. ANOVAs assessed the statistical differences of 3D bony morphological features with main effects of injury state and sex. ACL injured knees were determined to have a more lateral femoral mechanical axis and a greater angle between the long axis and condylar axis of the femur. A smaller anterior-posterior dimension of the lateral tibial plateau was also associated with ACL injured knees. Results of this study demonstrate that there are more bony morphological features predisposing individuals for ACL injury than previously established. These bony morphological parameters may cause greater internal and valgus torques increasing stresses in the ACL. No differences were determined between the ACL injured knees and their uninjured contralateral knees demonstrating that knees of ACL injured individuals are at similar risk for injury. Further understanding of the effect of bony morphology on the risk for ACL injury could improve individualized ACL injury treatment and prevention.

Tibiofemoral bony morphology impacts the knee kinematics after anterolateral capsule injury and lateral extraarticular tenodesis differently than intact state

Anterolateral capsule injury, often concomitant with anterior cruciate ligament (ACL) injuries, may result in high-grade rotatory instability. Lateral extraarticular tenodesis (LET) is sometimes added to ACL reconstruction to address this instability. However, LET is a non-anatomic procedure and concerns regarding increased tibiofemoral contact pressure and reduced internal rotation exist for some individuals which may be due to their tibiofemoral bony morphology. Therefore, the objective of this study was to analyze the effect of bony morphology on knee kinematic and contact pressure before and after anterolateral capsule injury and LET. A (1) 134-N anterior tibial load with 200-N axial compression and (2) a 7-Nm internal torque with a 200-N axial compression were applied to cadaveric knees (n = 8) using a 6 degree-of-freedom robotic testing system. Tibiofemoral bony morphology was captured with computed tomography scans and analyzed using 3D statistical shape modeling. Kinematics at each state were correlated with the results from the statistical shape model. Two femoral and three tibial modes of variation correlated with kinematic and contact pressure data before and after anterolateral capsule injury and LET. A decreased lateral tibial plateau elevation correlated with greater internal rotation and anterior tibial translation after anterolateral capsule deficiency and LET. Decreased notch width correlated with decreased contact area after anterolateral capsule deficiency and LET demonstrating it as a risk factor for ACL injury. The results of this study demonstrate that bony morphology if properly understood, could help improve the efficacy of LET procedures and that bony morphology has different effects after injury and repair.

Leveraging unsupervised image registration for discovery of landmark shape descriptor

In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often driven by the identification of a relatively small number of geometrically consistent features found across the samples of a population. These features can subsequently provide information about the population shape variation. Dense correspondence models can provide ease of computation and yield an interpretable low-dimensional shape descriptor when followed by dimensionality reduction. However, automatic methods for obtaining such correspondences usually require image segmentation followed by significant preprocessing, which is taxing in terms of both computation as well as human resources. In many cases, the segmentation and subsequent processing require manual guidance and anatomy specific domain expertise. This paper proposes a self-supervised deep learning approach for discovering landmarks from images that can directly be used as a shape descriptor for subsequent analysis. We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well. We also propose a regularization term that allows for robust optimization of the neural network and ensures that the landmarks uniformly span the image domain. The proposed method circumvents segmentation and preprocessing and directly produces a usable shape descriptor using just 2D or 3D images. In addition, we also propose two variants on the training loss function that allows for prior shape information to be integrated into the model. We apply this framework on several 2D and 3D datasets to obtain their shape descriptors. We analyze these shape descriptors in their efficacy of capturing shape information by performing different shape-driven applications depending on the data ranging from shape clustering to severity prediction to outcome diagnosis.

Thinking outside the glenohumeral box: Hierarchical shape variation of the periarticular anatomy of the scapula using statistical shape modeling

Variation in the shape of the glenoid and periarticular anatomy of the scapula has been associated with shoulder pathology. The goal of this study was to identify the modes of shape variation of periarticular scapular anatomy in relation to the glenoid in nonpathologic shoulders. Computed tomography scans of 31 cadaveric scapulae, verified to be free of pathology, were three-dimensionally reconstructed. Statistical shape modeling and principal component analysis identified the modes of shape variation across the population. Corresponding linear and angular measurements quantified the morphometric variance identified by the modes. Linear measures were normalized to the radius of the inferior glenoid to account for differences in the scaling of the bones. Five modes captured 89.7% of total shape variation of the glenoid and periarticular anatomy. Apart from size differences (mode 1: 33.0%), acromial anatomy accounted for the largest variation (mode 2: 32.0%). Further modes described variation in glenoid inclination (mode 3: 11.8%), coracoid orientation and size (mode 4: 9.0%), and variation in coracoacromial (CA) morphology (mode 5: 3.1%). The average scapula had a mean acromial tilt of 49 ± 7°, scapular spine angle of 61 ± 6°, the glenoid inclination of 84 ± 4°, coracoid deviation angle of 26 ± 4°, coracoid length of 3.7 ± 0.3 glenoid radii, and a CA base length of 5.6 ± 0.5 radii. In this study, the identified shape modes explain almost all of the variance in scapular anatomy. The acromion exhibited the highest variance of all periarticular anatomic structures of the scapula in relation to the glenoid, which may play a role in many shoulder pathologies.

Coracoacromial morphology: a contributor to recurrent traumatic anterior glenohumeral instability?

BACKGROUND

Although scapular morphology contributes to glenohumeral osteoarthritis and rotator cuff disease, its role in traumatic glenohumeral instability remains unknown. We hypothesized that coracoacromial and glenoid morphology would differ between healthy subjects and patients with recurrent traumatic anterior shoulder instability.

METHODS

Computed tomography scans of 31 cadaveric control scapulae and 54 scapulae of patients with recurrent traumatic anterior shoulder instability and Hill-Sachs lesions were 3-dimensionally reconstructed. Statistical shape modeling identified the modes of variation between the scapulae of both groups. Corresponding measurements quantified these modes in relation to the glenoid center (linear offset measures), defined by the best-fit circle of the inferior glenoid, or the glenoid center plane (angles), which bisects the glenoid longitudinally. Distances were normalized for glenoid size.

RESULTS

Compared with controls, the unstable coracoids were shorter (P = .004), with a more superior and medial offset of the tip (mean difference [MD], 7 and 3 mm, respectively; P < .001) and an origin closer to the 12-o'clock position (MD, 6°; P < .001). The unstable scapular spines originated closer to the 9-o'clock position (MD, 4°; P = .012), and the unstable acromions were more vertically oriented (MD, 6°; P < .001). The unstable glenoids had an increased height-width index (MD, 0.04; P = .021), had a flatter anterior-posterior radius of curvature (MD, 77 mm; P < .001), and were more anteriorly tilted (MD, 5°; P = .005).

CONCLUSIONS

Coracoacromial and glenoid anatomy differs between individuals with and without recurrent traumatic anterior shoulder instability. This pathologic anatomy is not addressed by current soft-tissue stabilization procedures and may contribute to instability recurrence.

Do Your Routine Radiographs to Diagnose Cam Femoroacetabular Impingement Visualize the Region of the Femoral Head-Neck Junction You Intended?

PURPOSE

To use computer models and image analysis to identify the position on the head-neck junction visualized in 10 radiographic views used to quantify cam morphology.

METHODS

We generated 97 surface models of the proximal femur from computed tomography scans of 59 control femurs and 38 femurs with cam morphology-a flattening or convexity at the femoral head-neck junction. Each model was transformed to a position that represents the anteroposterior, Meyer lateral, 45° Dunn, modified false-profile, Espié frog-leg, modified 45° Dunn, frog-leg lateral, cross-table, 90° Dunn, and false-profile views. The position on the head-neck junction visualized from each view was identified on the surfaces. This position was then quantified by a clock face generated on the plane of the head-neck junction, in which the 12-o'clock position indicated the superior head-neck junction and the 3-o'clock position indicated the anterior head-neck junction. The mean visualized clock-face position was calculated for all subjects. Analysis was repeated to account for variability in femoral version. A general linear model with repeated measures was used to compare each radiographic view and anteversion angle.

RESULTS

Each radiographic view provided visualization of the mean clock-face position as follows: anteroposterior view, 12:01; Meyer lateral view, 1:08; 45° Dunn view, 1:40; modified false-profile view, 2:01; Espié frog-leg view, 2:14; modified 45° Dunn view, 2:35; frog-leg lateral view, 2:45; cross-table view, 3:00; 90° Dunn view, 3:13; and false-profile view, 3:44. Each view visualized a different position on the clock face (all P < .001). Increasing simulated femoral anteversion by 10° changed the visualized position of the head-neck junction to a more clockwise position (range, 0:07 to 0:29; all P < .001), whereas decreasing anteversion by 10° visualized a more counterclockwise position (range, -0:23 to -0:08; all P < .001).

CONCLUSIONS

Ten common radiographic views used to identify cam morphology visualized different clock-face positions of the head-neck junction. Our data will help clinicians to understand the position of the head-neck junction visualized for each radiographic view and make educated decisions in the selection of radiographs acquired in the clinic.

CLINICAL RELEVANCE

Our findings will aid clinicians in choosing a set of radiographs to capture cam morphology in the assessment of patients with hip pain.

Statistical shape modeling of femur shape variability in female patients with hip dysplasia

Although increasing evidence suggests that abnormal femur geometry in developmental dysplasia of the hip (DDH) may contribute to intra-articular damage and the development of hip osteoarthritis, a comprehensive 3D description of femoral abnormalities in DDH remains incomplete. Statistical shape modeling (SSM) was used to quantify three-dimensional (3D) geometric variation among femurs in female patients with DDH and control subjects. SSM correspondence points (n = 8,192) were placed on each femur using a gradient descent energy function to derive mean DDH and control femoral shapes and principal component analysis (PCA) was then used to describe shape variation. PCA results were associated with common 2D radiographic measures of femur shape using general linear models. For patients with DDH, the first eight principal components (modes) captured 90.9% of the cumulative variance accounted for (VAF). Notably, mode 2 captured 23.6% VAF and described variation in femoral version, the neck-shaft angle, and femoral neck length, while mode 3 captured 16.4% VAF and described variation in femoral version, femoral head size, and femoral offset. SSM captured complex geometric deformities in DDH, which may not be fully described by 2D measures of the acetabulum and proximal femur alone. By determining the primary shape variations among femurs in cases of DDH, SSM may further understanding of pathologies on the femoral side of dysplastic hips, in context with more commonly recognized acetabular deformities. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Which Two-dimensional Radiographic Measurements of Cam Femoroacetabular Impingement Best Describe the Three-dimensional Shape of the Proximal Femur?

BACKGROUND

Many two-dimensional (2-D) radiographic views are used to help diagnose cam femoroacetabular impingement (FAI), but there is little consensus as to which view or combination of views is most effective at visualizing the magnitude and extent of the cam lesion (ie, severity). Previous studies have used a single image from a sequence of CT or MR images to serve as a reference standard with which to evaluate the ability of 2-D radiographic views and associated measurements to describe the severity of the cam lesion. However, single images from CT or MRI data may fail to capture the apex of the cam lesion. Thus, it may be more appropriate to use measurements of three-dimensional (3-D) surface reconstructions from CT or MRI data to serve as an anatomic reference standard when evaluating radiographic views and associated measurements used in the diagnosis of cam FAI.

QUESTIONS/PURPOSES

The purpose of this study was to use digitally reconstructed radiographs and 3-D statistical shape modeling to (1) determine the correlation between 2-D radiographic measurements of cam FAI and 3-D metrics of proximal femoral shape; and 2) identify the combination of radiographic measurements from plain film projections that were most effective at predicting the 3-D shape of the proximal femur.

METHODS

This study leveraged previously acquired CT images of the femur from a convenience sample of 37 patients (34 males; mean age, 27 years, range, 16-47 years; mean body mass index [BMI], 24.6 kg/m, range, 19.0-30.2 kg/m) diagnosed with cam FAI imaged between February 2005 and January 2016. Patients were diagnosed with cam FAI based on a culmination of clinical examinations, history of hip pain, and imaging findings. The control group consisted of 59 morphologically normal control participants (36 males; mean age, 29 years, range, 15-55 years; mean BMI, 24.4 kg/m, range, 16.3-38.6 kg/m) imaged between April 2008 and September 2014. Of these controls, 30 were cadaveric femurs and 29 were living participants. All controls were screened for evidence of femoral deformities using radiographs. In addition, living control participants had no history of hip pain or previous surgery to the hip or lower limbs. CT images were acquired for each participant and the surface of the proximal femur was segmented and reconstructed. Surfaces were input to our statistical shape modeling pipeline, which objectively calculated 3-D shape scores that described the overall shape of the entire proximal femur and of the region of the femur where the cam lesion is typically located. Digital reconstructions for eight plain film views (AP, Meyer lateral, 45° Dunn, modified 45° Dunn, frog-leg lateral, Espié frog-leg, 90° Dunn, and cross-table lateral) were generated from CT data. For each view, measurements of the α angle and head-neck offset were obtained by two researchers (intraobserver correlation coefficients of 0.80-0.94 for the α angle and 0.42-0.80 for the head-neck offset measurements). The relationships between radiographic measurements from each view and the 3-D shape scores (for the entire proximal femur and for the region specific to the cam lesion) were assessed with linear correlation. Additionally, partial least squares regression was used to determine which combination of views and measurements was the most effective at predicting 3-D shape scores.

RESULTS

Three-dimensional shape scores were most strongly correlated with α angle on the cross-table view when considering the entire proximal femur (r = -0.568; p < 0.001) and on the Meyer lateral view when considering the region of the cam lesion (r = -0.669; p < 0.001). Partial least squares regression demonstrated that measurements from the Meyer lateral and 90° Dunn radiographs produced the optimized regression model for predicting shape scores for the proximal femur (R = 0.405, root mean squared error of prediction [RMSEP] = 1.549) and the region of the cam lesion (R = 0.525, RMSEP = 1.150). Interestingly, views with larger differences in the α angle and head-neck offset between control and cam FAI groups did not have the strongest correlations with 3-D shape.

CONCLUSIONS

Considered together, radiographic measurements from the Meyer lateral and 90° Dunn views provided the most effective predictions of 3-D shape of the proximal femur and the region of the cam lesion as determined using shape modeling metrics.

CLINICAL RELEVANCE

Our results suggest that clinicians should consider using the Meyer lateral and 90° Dunn views to evaluate patients in whom cam FAI is suspected. However, the α angle and head-neck offset measurements from these and other plain film views could describe no more than half of the overall variation in the shape of the proximal femur and cam lesion. Thus, caution should be exercised when evaluating femoral head anatomy using the α angle and head-neck offset measurements from plain film radiographs. Given these findings, we believe there is merit in pursuing research that aims to develop the framework necessary to integrate statistical shape modeling into clinical evaluation, because this could aid in the diagnosis of cam FAI.

Cortical Bone Mapping: Measurement and Statistical Analysis of Localised Skeletal Changes

PURPOSE OF REVIEW

Cortical bone mapping (CBM) is a technique for measuring localised skeletal changes from computed tomography (CT) images. It can provide measurements with accuracy surpassing the underlying imaging resolution. CBM can detect changes in several properties of the cortex, with no prior assumptions about the likely location of said changes. This paper summarises the theory behind CBM, discusses its strengths and limitations, and reviews some studies in which it has been applied.

RECENT FINDINGS

CBM has revealed associations between fracture risk and cortical properties in specific regions of the proximal femur which present feasible therapeutic targets. Analyses of several pharmaceutical and exercise interventions quantify effects that are distinct both in location and in the nature of the micro-architectural changes. CBM has illuminated age-related changes in the proximal femur and has recently been applied to other bones, as well as to the assessment of cartilage. The CBM processing pipeline is designed primarily for large cohort studies. Its main impact thus far has not been in the realm of clinical practice, but rather to improve our fundamental understanding of localised bone structure and changes.

DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images

Statistical shape modeling is an important tool to characterize variation in anatomical morphology. Typical shapes of interest are measured using 3D imaging and a subsequent pipeline of registration, segmentation, and some extraction of shape features or projections onto some lower-dimensional shape space, which facilitates subsequent statistical analysis. Many methods for constructing compact shape representations have been proposed, but are often impractical due to the sequence of image preprocessing operations, which involve significant parameter tuning, manual delineation, and/or quality control by the users. We propose DeepSSM: a deep learning approach to extract a low-dimensional shape representation directly from 3D images, requiring virtually no parameter tuning or user assistance. DeepSSM uses a convolutional neural network (CNN) that simultaneously localizes the biological structure of interest, establishes correspondences, and projects these points onto a low-dimensional shape representation in the form of PCA loadings within a point distribution model. To overcome the challenge of the limited availability of training images with dense correspondences, we present a novel data augmentation procedure that uses existing correspondences on a relatively small set of processed images with shape statistics to create plausible training samples with known shape parameters. In this way, we leverage the limited CT/MRI scans (40-50) into thousands of images needed to train a deep neural net. After the training, the CNN automatically produces accurate low-dimensional shape representations for unseen images. We validate DeepSSM for three different applications pertaining to modeling pediatric cranial CT for characterization of metopic craniosynostosis, femur CT scans identifying morphologic deformities of the hip due to femoroacetabular impingement, and left atrium MRI scans for atrial fibrillation recurrence prediction.

Modified False-Profile Radiograph of the Hip Provides Better Visualization of the Anterosuperior Femoral Head-Neck Junction

PURPOSE

The purpose of this study was to quantify the amount of internal femur rotation required to visualize the 12 to 3 o'clock positions of the femoral head-neck junction as seen on the false-profile radiograph.

METHODS

Computed tomography (CT) images of the femur were retrospectively reviewed from control subjects and cam femoroacetabular impingement (FAI) patients. Using an automatically determined clockface, the positions between 12 and 3 o'clock were determined. The optimal femoral rotation angle to visualize each clockface position on the femoral head-neck junction was calculated based on the CT surface data.

RESULTS

Fifty-nine control subjects and 38 cam FAI patients were evaluated for this study. The mean (95% confidence interval) internal femur rotation needed to optimally visualize the clockface positions of the femoral head-neck junction on the modified false-profile radiograph were 0.9° (0.8°-1.0°) for 3:00, 10.3° (10.0°-10.6°) for 2:30, 21.6° (21.0°-22.1°) for 2:00, 34.3° (33.6°-35.1°) for 1:30, 49.6° (48.6°-50.4°) for 1:00, 68.4° (67.7°-69.0°) for 12:30, and 90.1° (89.9°-90.4°) for 12:00.

CONCLUSIONS

Internal femur rotation of 35° during the false-profile radiograph may better visualize the femoral head-neck junction in the anterosuperior (1 to 2 o'clock) region commonly associated with the cam lesion. From this view, rotation angles between 0° and 90° can be used to visualize other regions of the anterosuperior femoral head-neck junction.

CLINICAL RELEVANCE

The internal rotation of the affected femur for a modified false-profile radiograph may provide a new radiographic view that can be used to quantify anterosuperior femoral head-neck morphology.

Does Removal of Subchondral Cortical Bone Provide Sufficient Resection Depth for Treatment of Cam Femoroacetabular Impingement?

BACKGROUND

Residual impingement resulting from insufficient resection of bone during the index femoroplasty is the most-common reason for revision surgery in patients with cam-type femoroacetabular impingement (FAI). Development of surgical resection guidelines therefore could reduce the number of patients with persistent pain and reduced ROM after femoroplasty.

QUESTIONS/PURPOSES

We asked whether removal of subchondral cortical bone in the region of the lesion in patients with cam FAI could restore femoral anatomy to that of screened control subjects. To evaluate this, we analyzed shape models between: (1) native cam and screened control femurs to observe the location of the cam lesion and establish baseline shape differences between groups, and (2) cam femurs with simulated resections and screened control femurs to evaluate the sufficiency of subchondral cortical bone thickness to guide resection depth.

METHODS

Three-dimensional (3-D) reconstructions of the inner and outer cortical bone boundaries of the proximal femur were generated by segmenting CT images from 45 control subjects (29 males; 15 living subjects, 30 cadavers) with normal radiographic findings and 28 nonconsecutive patients (26 males) with a diagnosis of cam FAI based on radiographic measurements and clinical examinations. Correspondence particles were placed on each femur and statistical shape modeling (SSM) was used to create mean shapes for each cohort. The geometric difference between the mean shape of the patients with cam FAI and that of the screened controls was used to define a consistent region representing the cam lesion. Subchondral cortical bone in this region was removed from the 3-D reconstructions of each cam femur to create a simulated resection. SSM was repeated to determine if the resection produced femoral anatomy that better resembled that of control subjects. Correspondence particle locations were used to generate mean femur shapes and evaluate shape differences using principal component analysis.

RESULTS

In the region of the cam lesion, the median distance between the mean native cam and control femurs was 1.8 mm (range, 1.0-2.7 mm). This difference was reduced to 0.2 mm (range, -0.2 to 0.9 mm) after resection, with some areas of overresection anteriorly and underresection superiorly. In the region of resection for each subject, the distance from each correspondence particle to the mean control shape was greater for the cam femurs than the screened control femurs (1.8 mm, [range, 1.1-2.9 mm] and 0.0 mm [range, -0.2-0.1 mm], respectively; p < 0.031). After resection, the distance was not different between the resected cam and control femurs (0.3 mm; range, -0.2-1.0; p > 0.473).

CONCLUSIONS

Removal of subchondral cortical bone in the region of resection reduced the deviation between the mean resected cam and control femurs to within a millimeter, which resulted in no difference in shape between patients with cam FAI and control subjects. Collectively, our results support the use of the subchondral cortical-cancellous bone margin as a visual intraoperative guide to limit resection depth in the correction of cam FAI.

CLINICAL RELEVANCE

Use of the subchondral cortical-cancellous bone boundary may provide a method to guide the depth of resection during arthroscopic surgery, which can be observed intraoperatively without advanced tooling, or imaging.