Neural Spherical Harmonics for structurally coherent continuous representation of diffusion MRI signal -- A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-weighted MRI -- Diffusionphantomstudyof fiber crossings at varied angles reconstructed with ODF-Fingerprinting -- Improving Multi-Tensor Fitting with Global Information from Track Orientation Density Imaging -- BundleSeg: A versatile, reliable and reproducible approach to white matter bundle segmentation -- Automated Mapping of Residual Distortion Severity in Diffusion MRI -- Automatic fast and reliable recognition of a small brain white matter bundle -- Self Supervised Denoising Diffusion Probabilistic Models for Abdominal DW-MRI -- Voxlines: Streamline Transparency through Voxelization and View-Dependent Line Orders -- Subnet Communicability: Diffusive Communication Across the Brain Through a Backbone Subnetwork -- Fast Acquisition for Diffusion Tensor Tractography -- FASSt : Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography -- Anisotropic Fanning Aware Low-Rank Tensor Approximation Based Tractography -- BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data -- A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data -- ReTrace: Topological evaluation of white matter tractography algorithms using Reeb graphs -- Advanced diffusion MRI modeling sheds light on FLAIR white matter hyperintensities in an aging cohort.

This book constitutes the proceedings of the 14th International Workshop, CDMRI 2023, held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Vancouver, BC, Canada, on October 8, 2023. The 17regular papers presented in this book were carefully reviewed and selected from 19 submissions. These contributions cover various aspects, including preprocessing, signal modeling, tractography, bundle segmentation, and clinical applications. Many of these studies employ novel machine learning implementations, highlighting the evolving landscape of techniques beyond the more traditional physics-based algorithms.