4 citations · 6 across the 3 of their papers we have counts for
5 papers · 1 filter
Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients
Misha P. T. Kaandorp, Sebastiano Barbieri, Remy Klaassen +5
: Earlier work showed that IVIM-NET, an unsupervised physics-informed deep neural network, was more accurate than other state-of-the-art intravoxel-incohere…
Compressed Sensing MRI With Variable Density Averaging (CS-VDA) Outperforms Full Sampling At Low SNR
Jasper Schoormans, Gustav J. Strijkers, Anders C. Hansen +2
We investigated whether a combination of k-space undersampling and variable density averaging enhances image quality for low-SNR MRI acquisitions. We implemented 3D Cartesian k-spa…
A scale space based algorithm for automated segmentation of single shot tagged MRI of shearing deformation
André M. J. Sprengers, Matthan W. A. Caan, Kevin M. Moerman +3
Object This study proposes a scale space based algorithm for automated segmentation of single-shot tagged images of modest SNR. Furthermore the algorithm was designed for analysis…
Validation of SPAMM Tagged MRI Based Measurement of 3D Soft Tissue Deformation
Kevin M. Moerman, Andre M. J. Sprengers, Ciaran K. Simms +3
This study presents and validates a novel (non-ECG-triggered) MRI sequence based on SPAtial Modulation of the Magnetization (SPAMM) to non-invasively measure 3D (quasi-static) soft…
Validation of Continuously Tagged MRI for the Measurement of Dynamic 3D Skeletal Muscle Tissue Deformation
Kevin M. Moerman, Andre M. J. Sprengers, Ciaran K. Simms +3
A SPAMM tagged MRI methodology is presented allowing continuous (3.3-3.6 Hz) sampling of 3D dynamic soft tissue deformation using non-segmented 3D acquisitions. The 3D deformation…