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Search · four archives
20 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…
Philippe Karan, Alexis Reymbaut, Guillaume Gilbert, Maxime Descoteaux
Diffusion tensor imaging (DTI) is widely used to extract valuable tissue measurements and white matter (WM) fiber orientations, even though its lack of specificity is now well-known, especially for WM fiber crossings. Models such as constrained spherical deconvolution (CSD) take advantage of high angular resolution…
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…
Maxime Yon, Omar Narvaez, Daniel Topgaard, Alejandra Sierra
Massively Multidimensional Diffusion MRI combines tensor-valued encoding, oscillating gradients, and diffusion-relaxation correlation to provide multicomponent sub-voxel parameters depicting the tissue microstructure. This method was successfully implemented ex vivo in micro-imaging systems and in clinical conditions…
Alexandru V. Avram, Kadharbatcha S. Saleem, Peter J. Basser
Diffusion MRI studies with resolutions of a few hundred micrometers have consistently shown that in the cortex water diffusion occurs preferentially along radial and tangential orientations with respect to the cortical surface, in agreement with histology. These dominant orientations do not change significantly even if…
Maryam Afzali, Santiago Aja-Fernández, Derek K Jones
It has been shown previously that for a very specific form of diffusion-encoding, i.e., the conventional Stejskal-Tanner pulsed gradient, or ‘linear tensor encoding’ (LTE), and in tissue in which diffusion exhibits a ‘stick-like’ geometry, the diffusion-weighted MRI signal at extremely high b-values follows a power…
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…
Maryam Afzali, Sam Coveney, Lars Mueller, Sarah Jones + 7 more
Diffusion tensor imaging (DTI) is commonly used in cardiac diffusion magnetic resonance imaging (dMRI). However, the tissue’s microstructure (cells, membranes, etc.) restricts the movement of the water molecules, making the spin displacements deviate from Gaussian behaviour. This effect may be observed with 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…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Angela F Harper, Simone S Köcher, Karsten Reuter, Christoph Scheurer
Machine learning (ML) surrogate modeling is a powerful approach to reduce the computational cost of first-principles calculations. While well established for the prediction of scalar observables like energetics or band gaps, performance metrics for the learning of tensor-based observables have not yet been formalized.…
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…
Maxwell Venetos, Mingjian Wen, Kristin Persson
The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Re- cently, machine learning has been applied to NMR in the prediction of isotropic chemi- cal shifts from a structure. Current machine learning models…
Jie Lin, Mingyuan Xu, Hongming Chen
Shape-based virtual screening is a widely utilized method in ligand-based de novo drug design, aiming to identify molecules in chemical libraries that share similar 3D shapes but simultaneously possess novel 2D chemical structures compared to the reference compound. As an emerging technology, generative model is an…