Purpose This study introduces a novel surface-topographic scanning system capable of automatically generating a suite of objective measurements to characterize torso shape. Research Question: what is the reliability of the proposed system for measurement of trunk alignment parameters in patients with adolescent idiopathic scoliosis (AIS) and controls? Methods Forty-six adolescents (26 with AIS and 20 controls) were recruited for a prospective reliability study. A series of angular, volumetric, and area measures were computed from topographic scans in each of three clinically relevant poses using a fully automated processing pipeline. Intraclass correlation coefficients (ICC(2,1)) were computed within (intra-) and between (inter-) raters. Measurements were also performed on a torso phantom. Results Topographic measurements computed on a phantom were highly accurate (mean RMS error 1.7%) compared with CT. For human subjects, intra- and inter-rater reliability were both high (average ICC > 0.90) with intrinsic (pose-independent) measurements having near-perfect reliability (average ICC > 0.98). Conclusion The proposed system is a suitable tool for topographic analysis of AIS; topographic measurements offer an objective description of torso shape that may complement other imaging modalities. Further research is needed to compare topographic findings with gold standard imaging of spinal alignment, e.g., standing radiography. Conclusion: clinical parameters can be reliably measured in a fully automated system, paving the way for objective analysis of symmetry, body shape pre/post-surgery, and tracking of pathology without ionizing radiation.
Axial twisting of the spine has been previously shown to be affected by scoliosis with decreased motion and asymmetric twisting. Existing methods for evaluating twisting may be cumbersome, unreliable, or require radiation exposure. In this study, we present an automated surface topographic measurement tool to evaluate global axial rotation of the spine, along with two measurements: twisting range of motion (TROM) and twisting asymmetry index (TASI). The aim of this study is to evaluate the impact of scoliosis on axial range of motion. Adolescent idiopathic scoliosis (AIS) patients and asymptomatic controls were scanned in a topographic scanner while twisting maximally to the left and right. TROM was significantly lower for AIS patients compared to control patients (69.1° vs. 78.5°, p = 0.020). TASI was significantly higher for AIS patients compared to control patients (29.6 vs. 19.8, p = 0.023). After stratifying by scoliosis severity, both TROM and TASI were significantly different only between control and severe scoliosis patients (Cobb angle > 40°). AIS patients were then divided by their major curve region (thoracic, thoracolumbar, or lumbar). ANOVA and post hoc tests showed that only TROM is significantly different between thoracic AIS patients and control patients. Thus, we demonstrate that surface topographic scanning can be used to evaluate twisting in AIS patients.
The proliferation of 3D scanning technology has driven a need for methods to interpret geometric data, particularly for human subjects. In this paper we propose an elegant fusion of regression (bottom-up) and generative (top-down) methods to fit a parametric template model to raw scan meshes.Our first major contribution is an intrinsic convolutional mesh U-net architecture that predicts pointwise correspondence to a template surface. Soft-correspondence is formulated as coordinates in a newly-constructed Cartesian space. Modeling correspondence as Euclidean proximity enables efficient optimization, both for network training and for the next step of the algorithm.Our second contribution is a generative optimization algorithm that uses the U-net correspondence predictions to guide a parametric Iterative Closest Point registration. By employing pre-trained human surface parametric models we maximally leverage domain-specific prior knowledge.The pairing of a mesh-convolutional network with generative model fitting enables us to predict correspondence for real human surface scans including occlusions, partialities, and varying genus (e.g. from self-contact). We evaluate the proposed method on the FAUST correspondence challenge where we achieve 20% (33%) improvement over state of the art methods for inter-(intra-) subject correspondence.
Spine shape can be reconstructed from stereoradiography, but often requires specialized infrastructure or fails to account for subject posture. In this paper a protocol is presented for stereo reconstructions that integrates surface recordings with radiography and naturally accounts for variations in patient posture. Low cost depth cameras are added to an existing radiographic system to capture patient pose. A statistical model of human body shape is learned from public datasets and registered to depth scans, providing 3D correspondence across images for stereo reconstruction of radiographic landmarks. A radiographic phantom was used to validate these methods in vitro with RMS 3D landmark reconstruction error of 2.0 mm. Surfaces were automatically and reliably registered, with SD 12 mm translation disparity and SD .5° rotation. The proposed method is suitable for 3D radiographic reconstructions and may be beneficial in compensating for involuntary patient motion.
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