Search · four archives
Search · four archives
14 papers · ranked by Valyu relevance
Filip Szczepankiewicz, Malwina Molendowska, Samo Lasič, Marcella E Safi + 7 more
Tensor-valued diffusion encoding employs gradient waveforms that enable unique sensitivity to microstructural features of tissue, but the interpretation of signal and parameters may be confounded by diffusion-time dependence. We introduce a framework for restriction-weighted q-space trajectory imaging (ResQ) that…
Filip Szczepankiewicz, Malwina Molendowska, Samo Lasič, Marcella E. Safi + 7 more
Tensor-valued diffusion encoding employs gradient waveforms that enable unique sensitivity to microstructural features of tissue, but the interpretation of signal and parameters may be confounded by diffusion-time dependence. We introduce a framework for restriction-weighted q-space trajectory imaging (ResQ) that…
Yiang Pan, Yuanjing Feng, Jianzhong He, William Consagra + 3 more
Diffusion MRI (dMRI) enables noninvasive characterization of white-matter fiber orientations and tissue microstructure, but widely used approaches, such as constrained spherical deconvolution (CSD) and parametric multicompartment models, typically address these features separately. The diffusion tensor distribution…
Joshua Mawuli Ametepe, James Gholam, Leandro Beltrachini, Mara Cercignani + 1 more
1## Introduction Diffusion-weighted imaging (DWI) is sensitive to the microscopic motion of water molecules within tissue, thus providing a unique contrast which complements T1 and T2-weighted MRI . As diffusion is anisotropic in brain white matter , typically three images with orthogonal diffusion directions are…
Junhyeok Lee, Kyu Sung Choi
Reconstructing diffusion tensors from sparse DWIs is critical for accelerating Diffusion Tensor Imaging (DTI) in clinical settings, yet current deep learning approaches frequently yield anatomically inconsistent or physically implausible tensors. We introduce TensorLDM, a component-wise latent diffusion model that…
Santiago Coelho, Gregory Lemberskiy, Ante Zhu, Hong‐Hsi Lee + 4 more
is the invariant of tensor , compare Reference . Figure [mrm70144-fig-0001] shows the dimensionless ratio for a generic , as well as the vector field distorted relative to , for the system used in this study. Figure [mrm70144-fig-0001] shows maps of mean and standard deviation of , Equations ((9-3)$(9)$, (9-4)$(10)$…
Xudong Wang, Saketh Rambhatla, Rohit Girdhar
Latent diffusion has become the default paradigm for visual generation, yet we observe a persistent reconstruction–generation trade-off as latent dimensionality increases: higher-capacity autoencoders improve reconstruction fidelity but generation quality eventually declines. We trace this gap to the different…
Jing Gu, Morteza Mardani, Wonjun Lee, Dongmian Zou + 1 more
Diffusion models often degrade when trained in latent spaces (e.g., VAEs), yet the formal causes remain poorly understood. We quantify latent-space diffusability through the rate of change of the Minimum Mean Squared Error (MMSE) along the diffusion trajectory. Our framework decomposes this MMSE rate into contributions…
Fuma Kimishima, Jinjia Zhou
DiffC provides a principled way to reuse pre-trained diffusion models for lossy compression, but its encoding and decoding procedures remain slow because they require many discretized forward and reverse steps. We study whether few-step generative models -- Rectified Flow, Consistency Trajectory Models (CTM), and…
Yaobin Ma, Hossein Aghababaei, Ling Chang, Jingbo Wei + 1 more
Translating Synthetic aperture radar (SAR) images into optical images is intrinsically ill-posed because microwave backscatter and optical reflectance describe different physical properties of the observed scene. Although frequency-domain modeling has been introduced into diffusion-based translation, existing methods…
Wang, Xiyuan, Zhang, Muhan
Standard Latent Diffusion Models rely on a complex, threepart architecture consisting of a separate encoder, decoder, and diffusion network, which are trained in multiple stages. This modular design is computationally inefficient, leads to suboptimal performance, and prevents the unification of diffusion with the…
Yajushi Khurana, Keisuke Ishihara
Three-dimensional biological morphologies encode functional and physiological state, yet the directional, orientational, and topological properties of these shapes are rarely captured by morphometric tools available for bioimage analysis. Minkowski tensors are mathematically rigorous tensor-valued measures that encode…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Laura Sainz Villalba, P. Michael Furlong, Madeleine Bartlett, Nicole Sandra-Yaffa Dumont
The brain faces the feature binding problem: how are multiple stimulus features and variables combined into coherent representations that support flexible behavior? A key finding from neuroscience is that some brain regions employ factorized representations, where distinct features are encoded in neural state space in…