Results 131 to 140 of about 13,919 (263)

Free‐Breathing Fat Quantification Using a Phase Error‐Corrected Cartesian Acquisition With Spiral Profile Ordering

Magnetic Resonance in Medicine, Volume 96, Issue 4, Page 1709-1725, October 2026.
ABSTRACT Purpose To develop a phase‐corrected time‐interleaved multi‐echo gradient echo Cartesian acquisition with spiral profile ordering (TIMGRECASPR) for abdominal large‐FOV proton density fat fraction (PDFF) mapping at 3 T, demonstrating its sampling flexibility and inherent self‐gating capabilities at high isotropic resolutions.
Philipp Braun, Jonathan Stelter, Stefan Ruschke, Johannes M. Peeters, Holger Eggers, Kilian Weiss, Daniela Junker, Dimitrios C. Karampinos   +7 more
wiley   +1 more source

Deep Learning Reconstruction Enables Diagnostic-Quality 0.4T Knee and Spine MRI in One-Third of the Time. [PDF]

NMR Biomed
van den Berg DM, Varriale R, Ferrando F, Traverso P, Balbi L, Belgiorno G, Beekman K, Kox L, Strijkers GJ, Caan MWA.   +9 more
europepmc   +1 more source

Respiratory Motion Management in Abdominal MRI: Revisiting the Gap Between Technical Advances and Clinical Translation

Magnetic Resonance in Medicine, Volume 96, Issue 4, Page 1814-1833, October 2026.
ABSTRACT The inherently slow acquisition speed of MRI makes abdominal imaging highly sensitive to respiratory motion artifacts. Since the early days of MRI, the development of respiratory motion compensation techniques has been an active research topic, and this field has seen substantial progress.
Li Feng, Hersh Chandarana
wiley   +1 more source

Advancing Gastrointestinal Cancer Risk Prediction With Patient-Centered Machine Learning: Machine Learning Modeling Study. [PDF]

JMIR Med Inform
Baublyte D, Lee J, Gunathilake M, Kim J.
europepmc   +1 more source

Arc‐ZTE: Continuously‐Slewed Zero‐TE Imaging With Incoherent Temporal Sampling for Near‐Silent Dynamic MRI

Magnetic Resonance in Medicine, Volume 96, Issue 4, Page 1726-1740, October 2026.
ABSTRACT Purpose To enable flexible dynamic and DCE MRI with low acoustic noise and high spatiotemporal resolution using zero echo time (ZTE) imaging. Methods This work proposes Arc‐ZTE, a technique in which the readout gradients of ZTE are continuously‐slewed to form arcs in k‐space that are sequentially rotated to yield an incoherent trajectory ...
Shreya Ramachandran, Tobias C. Wood, Gavin Zhang, Ali B. Syed, Shreyas S. Vasanawala, Michael Lustig   +5 more
wiley   +1 more source

Comparison of Machine Learning Models and the FMF Competing-Risks Algorithm for First-Trimester Preeclampsia Screening in a Romanian Cohort. [PDF]

Diagnostics (Basel)
Cristofor AE, Carauleanu A, Vasilache IA, Condriuc I, Bica O, Nemescu D.   +5 more
europepmc   +1 more source

Improving Truck Driver Turnover Predictions With Survival Stacking and Reference‐Dependent Covariates

Journal of Business Logistics, Volume 47, Issue 4, October 2026.
ABSTRACT The U.S. motor carrier industry has suffered from high driver turnover rates for decades. The need is high for developing techniques that enable carriers to predict the turnover risk (quit probability) of each driver for each time period, as they can provide timely warnings to carriers regarding which drivers are about to quit, allowing ...
Yoshinori Suzuki
wiley   +1 more source

Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction. [PDF]

NMR Biomed
Zhang L, Yuwen Zhou I, Montesi SB, Feng L, Liu F.   +4 more
europepmc   +1 more source

Comparison of Retrospective Motion Compensation Techniques for Pulmonary Dynamic Ultrashort Time to Echo MRI in Suspected Idiopathic Pulmonary Fibrosis

Journal of Magnetic Resonance Imaging, Volume 64, Issue 3, Page 644-654, September 2026.
ABSTRACT Background Motion can degrade image quality during Ultrashort Time‐to‐Echo (UTE) pulmonary MRI and is particularly prevalent in patients with lung disease. Comprehensive assessment of the impact of motion compensation techniques on image quality and clinical interpretation is needed.
Abhilash S. Kizhakke Puliyakote, Luis A. Torres, Aiah AlAtoum, Natally AlArab, Kevin M. Johnson, Rodrigo M. Bello, Bryan O'Sullivan‐Murphy, Joseph G. Mammarappallil, Andrew D. Hahn, Sean B. Fain   +9 more
wiley   +1 more source

Unified comparison of machine learning paradigms for blood transfusion prediction in pediatric congenital heart surgery. [PDF]

iScience
Yin MW, Li J, Huang J, Zhu Z, Chen BH, Shi Z, Jiang Q, Chen XJ, Yu G.   +8 more
europepmc   +1 more source