Results 101 to 110 of about 7,866 (212)

Circumstantial, but Compelling: Multimodal Phase 1 Data and the Seizure Onset Zone. [PDF]

Epilepsy Curr
Abel TJ.
europepmc   +1 more source

Collaborative Reconstruction of PROPELLER‐EPI Data Using POCSMUSE (CORPUSE) for High‐Fidelity Diffusion MRI

Magnetic Resonance in Medicine, Volume 96, Issue 4, Page 1929-1946, October 2026.
ABSTRACT Purpose To develop a reconstruction framework for DW‐PROPELLER‐EPI that improves image quality and SNR efficiency under per‐blade acceleration while minimizing EPI‐related artifacts, enabling high‐resolution diffusion‐tensor imaging (DTI) with fewer blades.
Hailin Xiong, Liyuan Liang, Shihui Chen, Yi Li, Chenglang Yuan, Tianbaige Liu, Xiaorui Xu, Hing‐Chiu Chang   +7 more
wiley   +1 more source

Cortical processing for the vestibular and visual input of egomotion in macaque monkeys: Separate networks with targeted convergence. [PDF]

Imaging Neurosci (Camb)
Marchand S, De Castro V, Excoffier E, Laurent MA, Rosito M, Vayssière N, Cottereau BR, Séverac Cauquil A, Durand JB.   +8 more
europepmc   +1 more source

Multiple‐TE Based Blood–Brain‐Barrier Water Exchange Time Measurement Using a TE‐Resolved 3D TSE Stack‐Of‐Spirals Readout

Magnetic Resonance in Medicine, Volume 96, Issue 4, Page 1629-1644, October 2026.
ABSTRACT Purpose To develop a slice‐wise blurring‐free and densely sampled TE‐resolved multiple‐TE (mTE) ASL sequence (TASL) for measuring blood–brain barrier (BBB) water exchange time. Methods A 3D TSE spiral‐readout pCASL sequence was modified to enable TE‐resolved acquisition.
Bo Li, Yiran Li, Xiao Liang, Amnah Mahroo, Manuel Taso, Marta Vidorreta Díaz de Cerio, Lucas Lemos Franco, Yulin Chang, María Asunción Fernández Seara, Matthias Günther, John A. Detre, Ze Wang   +11 more
wiley   +1 more source

Equivariant valuations on convex functions. [PDF]

Calc Var Partial Differ Equ
Hofstätter GC, Knoerr J.
europepmc   +1 more source

Simultaneous T2, T2*, and R2′ Mapping for Multiple Sclerosis Using Nonlinear Model‐Based Reconstruction of Undersampled Radial RARE‐EPI MRI

Magnetic Resonance in Medicine, Volume 96, Issue 4, Page 1872-1892, October 2026.
ABSTRACT Purpose To demonstrate the synergy of undersampled radial 2in1‐RARE‐EPI acquisition and nonlinear model‐based reconstruction for accelerated and simultaneous T2, T2*, and R2′ mapping in brains of patients with multiple sclerosis (MS). Methods 2in1‐RARE‐EPI combines a RARE module with an EPI module to capture T2 and T2* information.
Jose Raul Velasquez Vides, Carl J. J. Herrmann, Thomas Gladytz, Hoby P. Hetherington, Hendrik Mattern, Xiaoqing Wang, Jason M. Millward, Shahriar Shalikar, Igor Fabian Tellez Ceja, Beate Endemann, Sonia Waiczies, Joseph Kuchling, Friedemann Paul, Georg Rose, Min‐Chi Ku, Franz Schmitt, Thoralf Niendorf   +16 more
wiley   +1 more source

Wearable Data in Epilepsy Care. [PDF]

Epilepsy Curr
Aungaroon G.
europepmc   +1 more source

Laterally Oscillating Trajectory for Undersampling Slices: LOTUS

Magnetic Resonance in Medicine, Volume 96, Issue 4, Page 1682-1695, October 2026.
ABSTRACT Purpose While spiral sampling offers SNR advantages for diffusion MRI, its acceleration with simultaneous multislice remains relatively unexplored. This study introduces Laterally Oscillating Trajectory for Undersampling Slices (LOTUS), which is a 3D spiral‐like k‐space trajectory that aims to minimize g‐factor via controlled incoherent ...
Mayuri Sothynathan, Paul I. Dubovan, Corey A. Baron   +2 more
wiley   +1 more source

Quantitative Diffusion and T2 Mapping Using RF-Modulated Phase-Based Gradient Echo Imaging. [PDF]

Magn Reson Med
Tamada D, Pirasteh A, Jarrard DF, Hernando D, Reeder SB.   +4 more
europepmc   +1 more source

Denoising of ASL Data Using Deep Learning Priors Generated From Distribution Remapping

Magnetic Resonance in Medicine, Volume 96, Issue 4, Page 1972-1982, October 2026.
ABSTRACT Purpose To develop an effective deep learning (DL)–based method to denoise arterial spin labeling (ASL) data. Methods Conventional DL–based ASL denoising methods often suffer from overfitting and poor generalization when training data are limited.
Ziyang Xu, Rong Guo, Ziwen Ke, Yudu Li, Yibo Zhao, Wen Jin, Ruihao Liu, Ziyu Meng, Yao Li, Zhi‐Pei Liang   +9 more
wiley   +1 more source