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Scoliosis is currently assessed solely on 2D lateral deviations, but recent studies have also revealed the importance of other imaging planes in understanding the deformation of the spine. Consequently, exctracting the spinal geometry in 3D would help quantify these spinal deformations and aid diagnosis. In this study, we propose an automated general framework to estimate the 3D spine shape from 2D DXA scans. We achieve this by explicitly predicting the sagittal view of the spine from the DXA scan. Using these two orthogonal projections of the spine (coronal in DXA, and sagittal from the prediction), we are able to describe the 3D shape of the spine. The prediction is learnt from over 30k paired images of DXA and MRI scans. We assess the performance of the method on a held out test set, and achieve high accuracy. For more information, visit our website : Project Link

We present X-Diffusion, a cross-sectional diffusion model tailored for Magnetic Resonance Imaging (MRI) data. X- Diffusion is capable of generating the entire MRI volume from just a single MRI slice or optionally from few multiple slices, setting new benchmarks in the precision of synthesized MRIs from extremely sparse observations. The uniqueness lies in the novel view-conditional training and inference of X-Diffusion on MRI volumes, allowing for generalized MRIlearning. The generated MRIs retain essential features of the original MRI, including tumour profiles, spine curvature, brain volume, and beyond. For more information, visit our website : Project Link

Scoliosis is typically measured in 2D in the coronal plane, although it is a three-dimensional (3D) condition. Our objective in this work is to analyse the 3D geometry of the spine and its relationship to the vertebral canal. To this end, we make three contributions: first, we extract the 3D space curve of the spine automatically from low-resolution whole-body Dixon MRIs and obtain coronal, sagittal and axial projections for various degrees of scoliosis; second, we also extract the vertebral canal as a 3D curve from the MRIs, and examine the relationship between the two 3D curves; and third, we measure the angle of rotation of the spine and examine the correlation between this 3D measurement and the 2D curvature of the coronal projection. For this study, we use 48,384 MRIs from the UK Biobank.

We propose improvements to an automated method for scoliosis measurement. Our main novelty is the use of a spline to better model the curve of the spine, and we employ pseudo- labelling to re-train the segmentation step to mitigate the domain gap when adapting to a new dataset. We obtain promising results with a good fit of our smoothed curve to approximate the spinal midpoints in severe scoliosis cases, and obtain good agreement against human ground-truth. This work is relevant for improving the severity grading of scoliosis and potentially aiding in the treatment management of scoliosis.

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