The main objective of this study was to develop a 3D X-ray reconstruction system of the spine and rib cage for an accurate 3D clinical assessment of spinal deformities. The system currently used at Sainte-Justine Hospital in Montreal is based on an implicit calibration technique based on a direct linear transform (DLT), using a sufficiently large rigid object incorporated in the positioning apparatus to locate any anatomical structure to be reconstructed within its bounds. During the time lapse between the two successive X-ray acquisitions required for the 3D reconstruction, involuntary patient motion introduce errors due to the incorrect epipolar geometry inferred from the stationary object. An approach using a new calibration jacket and explicit calibration algorithm is proposed in this paper. This approach yields accurate results and compensates for involuntary motion occurring between X-ray exposures.

This paper presents a novel 3-D reconstruction method of the scoliotic spine using prior vertebra models with image-based information taken from biplanar X-ray images. We first propose a global modeling approach by exploiting the 3-D scoliotic curve reconstructed from a coronal and sagittal X-ray image in order to generate an approximate statistical model from a 3-D database of scoliotic patients based on a transformation algorithm which incorporates intuitive geometrical properties. The personalized 3-D reconstruction of the spine is then achieved with a novel segmentation method which takes into account the variable appearance of scoliotic vertebrae (rotation, wedging) from standard quality images in order to segment and isolate individual vertebrae on the radiographic planes. More specifically, it uses prior 3-D models regulated from 2-D image level set functionals to identify and match corresponding bone structures on the biplanar X-rays. An iterative optimization procedure integrating similarity measures such as deformable vertebral contours regulated from high-level anatomical primitives, morphological knowledge and epipolar constraints is then applied to globally refine the 3-D anatomical landmarks on each vertebra level of the spine. This method was validated on twenty scoliotic patients by comparing results to a standard manual approach. The qualitative evaluation of the retro-projection of the vertebral contours confirms that the proposed method can achieve better consistency to the X-ray image's natural content. A comparison to synthetic models and real patient data also yields good accuracy on the localization of low-level primitives such as anatomical landmarks identified by an expert on each vertebra. The experiments reported in this paper demonstrate that the proposed method offers a better matching accuracy on a set of landmarks from biplanar views when compared to a manual technique for each evaluated cases, and its precision is comparable to 3-D models generated from magnetic resonance images, thus suitable for routine 3-D clinical assessment of spinal deformities.

Spinal ultrasound imaging is emerging as a low-cost, radiation-free alternative to conventional X-ray imaging for the clinical follow-up of patients with scoliosis. Currently, deformity measurement relies almost entirely on manual identification of key vertebral landmarks. However, the interpretation of vertebral ultrasound images is challenging, primarily because acoustic waves are entirely reflected by bone. To alleviate this problem, we propose an algorithm to segment these images into three regions: the spinous process, its acoustic shadow and other tissues. This method consists, first, in the extraction of several image features and the selection of the most relevant ones for the discrimination of the three regions. Then, using this set of features and linear discriminant analysis, each pixel of the image is classified as belonging to one of the three regions. Finally, the image is segmented by regularizing the pixel-wise classification results to account for some geometrical properties of vertebrae. The feature set was first validated by analyzing the classification results across a learning database. The database contained 107 vertebral ultrasound images acquired with convex and linear probes. Classification rates of 84%, 92% and 91% were achieved for the spinous process, the acoustic shadow and other tissues, respectively. Dice similarity coefficients of 0.72 and 0.88 were obtained respectively for the spinous process and acoustic shadow, confirming that the proposed method accurately segments the spinous process and its acoustic shadow in vertebral ultrasound images. Furthermore, the centroid of the automatically segmented spinous process was located at an average distance of 0.38 mm from that of the manually labeled spinous process, which is on the order of image resolution. This suggests that the proposed method is a promising tool for the measurement of the Spinous Process Angle and, more generally, for assisting ultrasound-based assessment of scoliosis progression.

Generating computational anatomical models of cerebrovascular networks is vital for improving clinical practice and understanding brain oxygen transport. This is achieved by extracting graph-based representations based on pre-mapping of vascular structures. Recent graphing methods can provide smooth vessels trajectories and well-connected vascular topology. However, they require water-tight surface meshes as inputs. Furthermore, adding vessels radii information on their graph compartments restricts their alignment along vascular centerlines. Here, we propose a novel graphing scheme that works with relaxed input requirements and intrinsically captures vessel radii information. The proposed approach is based on deforming geometric graphs constructed within vascular boundaries. Under a laplacian optimization framework, we assign affinity weights on the initial geometry that drives its iterative contraction toward vessels centerlines. We present a mechanism to decimate graph structure at each run and a convergence criterion to stop the process. A refinement technique is then introduced to obtain final vascular models. Our implementation is available on https://github.com/Damseh/VascularGraph. We benchmarked our results with that obtained using other efficient and state-of-the-art graphing schemes, validating on both synthetic and real angiograms acquired with different imaging modalities. The experiments indicate that the proposed scheme produces the lowest geometric and topological error rates on various angiograms. Furthermore, it surpasses other techniques in providing representative models that capture all anatomical aspects of vascular structures.

This paper presents a method that registers MRIs acquired in prone position, with surface topography (TP) and X-ray reconstructions acquired in standing position, in order to obtain a 3D representation of a human torso incorporating the external surface, bone structures, and soft tissues.