AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis

• URL: http://arxiv.org/abs/2604.17846v1
• Date: Mon, 20 Apr 2026 05:57:06 GMT
• Title: AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis
• Abstract summary: MRI is preferred over CT in paediatric imaging because it avoids ionising radiation.<n>Its use in spine deformity assessment is largely limited by the lack of automated, high-resolution 3D bony reconstruction.<n>This study introduces an AI framework that enables fully automated thoracolumbar spine (T1-L5) segmentation and 3D reconstruction from MRI alone.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: MRI is preferred over CT in paediatric imaging because it avoids ionising radiation, but its use in spine deformity assessment is largely limited by the lack of automated, high-resolution 3D bony reconstruction, which continues to rely on CT. MRI-based 3D reconstruction remains impractical due to manual workflows and the scarcity of labelled full-spine datasets. This study introduces an AI framework that enables fully automated thoracolumbar spine (T1-L5) segmentation and 3D reconstruction from MRI alone. Historical low-dose CT scans from adolescent idiopathic scoliosis (AIS) patients were converted into MRI-like images using a GAN and combined with existing labelled thoracic MRI data to train a U-Net-based model. The resulting algorithm accurately generated continuous thoracolumbar 3D reconstructions, improved segmentation accuracy (88% Dice score), and reduced processing time from approximately 1 hour to under one minute, while preserving AIS-specific deformity features. This approach enables radiation-free 3D deformity assessment from MRI, supporting clinical evaluation, surgical planning, and navigation in paediatric spine care.

Related papers

• Rendering Novel Views of MRI Using 3D Gaussian Splatting [54.6286495315908]
  We adapt 3D Gaussian Splatting to form a reconstruction starting from sparse anisotropic MRIs, and imaging planes aligned with the anatomy relevant for clinical evaluation are then sampled and rendered.<n>The novel view plane is optimal for diagnostic radiological grading of the target anatomy, whereas the original MRI is not.
  arXiv  Detail & Related papers  (2026-06-24T18:00:03Z)
• Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning [4.3531418255555545]
  Mamba is a state-space model for cross-modality translation.<n>We adapt both the U-Mamba and the SegMamba architecture, originally proposed for segmentation, to perform cross-modality image generation.<n>Our 3D Mamba architecture effectively captures complex features and long-range volumetric dependencies, thus allowing accurate CT synthesis.
  arXiv  Detail & Related papers  (2026-03-24T14:56:39Z)
• Feasibility of Augmented Reality-Guided Robotic Ultrasound with Cone-Beam CT Integration for Spine Procedures [35.940780679292025]
  We present an optical see-through augmented reality (OST-AR)-guided robotic system for spine procedures.<n>We integrate a cone-beam CT (CBCT)-derived 3D spine model which is co-registered with live ultrasound.<n>Results show that AR significantly reduces execution time and across-task placement error.
  arXiv  Detail & Related papers  (2026-03-23T16:35:17Z)
• ReBrain: Brain MRI Reconstruction from Sparse CT Slice via Retrieval-Augmented Diffusion [17.281214782353658]
  Low-dose protocols often result in highly sparse CT volumes with poor through-plane resolution.<n>We propose ReBrain, a retrieval-augmented diffusion framework for brain MRI reconstruction.<n>Experiments on SynthRAD2023 and BraTS demonstrate that ReBrain achieves state-of-the-art performance in cross-modal reconstruction under sparse conditions.
  arXiv  Detail & Related papers  (2025-11-21T09:18:35Z)
• US-X Complete: A Multi-Modal Approach to Anatomical 3D Shape Recovery [38.0728021642047]
  We present a novel multi-modal learning method for completing occluded anatomical structures in 3D ultrasound.<n>We generate paired training data consisting of: (1) 2D lateral vertebral views that simulate X-ray scans, and (2) 3D partial vertebrae representations.<n>Our method integrates morphological information both imaging modalities and demonstrates significant improvements in vertebral reconstruction.
  arXiv  Detail & Related papers  (2025-11-19T16:45:04Z)
• Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis [0.0]
  Flow Matching framework is used to generate synthetic CT from MRI or CBCT images.<n>We train models for MRI sCT and CBCT sCT across three anatomical regions.<n>The results indicate that the method accurately reconstructs global anatomical structures.<n>Future work will explore patch-based training and latent-space flow models to improve resolution and local structural fidelity.
  arXiv  Detail & Related papers  (2025-10-06T14:07:03Z)
• The Brain Resection Multimodal Image Registration (ReMIND2Reg) 2025 Challenge [42.51640997446028]
  The ReMIND2Reg 2025 Challenge provides the largest public benchmark for this task, built upon the ReMIND dataset.<n>It offers 99 training cases, 5 validation cases, and 10 private test cases comprising paired 3D ceT1 MRI, T2 MRI, and post-resection 3D iUS volumes.<n>Data are provided without annotations for training, while validation and test performance are evaluated on manually annotated anatomical landmarks.
  arXiv  Detail & Related papers  (2025-08-13T09:31:06Z)
• X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models [9.97747208739885]
  X-Diffusion is a novel cross-sectional diffusion model that reconstructs detailed 3D MRI volumes from extremely sparse spatial-domain inputs.<n>A key aspect of X-Diffusion is that it models MRI data as holistic 3D volumes during the cross-sectional training and inference.<n>Our results demonstrate that X-Diffusion not only surpasses state-of-the-art methods in quantitative accuracy (PSNR) on unseen data but also preserves critical anatomical features.
  arXiv  Detail & Related papers  (2024-04-30T14:53:07Z)
• Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumors [39.58317527488534]
  We developed a deep learning-based method for MRI reconstruction from undersampled data acquired with RT-specific receiver coil arrangements. DeepMRIRec reduced scanning time by a factor of four producing a structural similarity score surpassing the evaluated state-of-the-art method.
  arXiv  Detail & Related papers  (2023-11-22T16:01:44Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine [72.85011943179894]
  We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
  arXiv  Detail & Related papers  (2022-05-16T10:59:55Z)
• A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction [50.1787181309337]
  We propose a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention.
  arXiv  Detail & Related papers  (2022-03-28T14:03:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.