Search · four archives
Search · four archives
9 papers · ranked by Valyu relevance
Filip Szczepankiewicz, Jens Sjölund, Freddy Ståhlberg, Jimmy Lätt + 2 more
'Markus Nilsson' 'Xi Chen'] Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encoding-applied along a single direction in each shot-tensor-valued encoding employs diffusion encoding along multiple…
Filip Szczepankiewicz, Jens Sjölund, Erica Dall’Armellina, Sven Plein + 3 more
1## INTRODUCTION Tissue movement during diffusion encoding can lead to phase dispersion that is erroneously attributed to diffusion or cause gross signal dropout. For example, the relatively slow and incoherent movement of blood in capillaries has a measurable impact on the diffusion-weighted signal at low b-values and…
Alexis Reymbaut, Alex Valcourt Caron, Guillaume Gilbert, Filip Szczepankiewicz + 4 more
'Filip Szczepankiewicz' 'Markus Nilsson' 'Simon K. Warfield' 'Maxime Descoteaux' 'Benoît Scherrer'] Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model…
Filip Szczepankiewicz, Carl‐Fredrik Westin, Markus Nilsson
- Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the 'shape of the b-tensor' as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to…
Samo Lasič, Nathalie Just, Markus Nilsson, Filip Szczepankiewicz + 2 more
'Matthew Budde' 'Henrik Lundell'] Title: Abstract Tensor-valued encoding in diffusion MRI allows probing of microscopic anisotropy in tissue, however, time-dependent diffusion (TDD) can bias results unless b-tensors are carefully tuned to account for TDD. We propose two novel strategies for tuning b-tensors to enable…
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…
Oddvar Christiansen, Tin-Man Lee, Johan Lie, Usha Sinha + 1 more
'Tony F. Chan'] We generalize the total variation restoration model, introduced by Rudin, Osher, and Fatemi in 1992, to matrix-valued data, in particular, to diffusion tensor images (DTIs). Our model is a natural extension of the color total variation model proposed by Blomgren and Chan in 1998. We treat the diffusion…
Tobias Alt, Karl Schrader, Joachim Weickert, Pascal Peter + 1 more
'Matthias Augustin'] Partial differential equation models and their associated variational energy formulations are often rotationally invariant by design. This ensures that a rotation of the input results in a corresponding rotation of the output, which is desirable in applications such as image analysis. Convolutional…
Tae-Hyung Kwon, Jihoon Ko, Jinhong Jung, Kijung Shin
In this work, we propose TENSORCODEC, a lossy compression algorithm for general tensors that do not necessarily adhere to strong input data assumptions. TENSORCODEC incorporates three key ideas. The first idea is Neural Tensor-Train Decomposition (NTTD) where we integrate a recurrent neural network into Tensor-Train…