Conventional χ-separation compared to self-calibrated method (BIOPHYSICSS-DL) with histological validation

Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F

Proc Intl Soc Mag Reson Med, Toronto, ON, Canada, 2023

Myelin, Iron, and Free Water Content Quantification Through BIOPHYSICSS Deep Learning.

Benslimane I., Grabner G., Hametner S., Jochmann T., Zivadinov R., Schweser F.

Proc Intl Soc Mag Reson Med, Toronto, ON, Canada, 2023

Nonsusceptibility frequency shift reveals subtypes of multiple sclerosis lesions and predicts 5-year disappearance of paramagnetic rim.

Jochmann T., Reeves J.A., Salman F., Jakimovski D., Hametner S., Mohebbi M., Bergsland N., Weinstock-Gutman B., Dwyer M.G., Zivadinov R., Haueisen J., Schweser F.

Proc Intl Soc Mag Reson Med, Toronto, ON, Canada, 2023

Nonsusceptibility frequency contributions and brain size affect quantitative susceptibility mapping in a region-dependent manner.

Jochmann T., Salman F., Adegbemigun A., Haueisen J., Schweser F.

Proc Intl Soc Mag Reson Med, Toronto, ON, Canada, 2023

Novel MRI Technique Reveals Subtypes of Paramagnetic Rim Lesions and Predicts 5-year Rim Disappearance

J.A. Reeves*, T. Jochmann*, M. Mohebbi, D. Jakimovski, S. Hametner, F. Salman, N. Bergsland, B. Weinstock-Guttman, M.G. Dwyer, R. Zivadinov, F Schweser

Americas Committee for Treatment and Research in Multiple Sclerosis Forum , San Diego, CA, 2023

Systematic assessment of published QSM inversion algorithms for detecting longitudinal changes in brain susceptibility

F. Salman, A. Ramesh, M. Prayer, T. Jochmann, N. Bergsland, G. M. Dwyer, R. Zivadinov, F Schweser

Proc Intl Soc Mag Reson Med, Toronto, ON, Canada, 2023

Improved self-calibrated signal model for χ-separation using single-subject (N=1) physics-constrained deep learning with histological validation.

Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F

Artificial Intelligence in Clinical Imaging (AICI) Forum, Graz, Austria, 2022

Self-calibrating χ-separation using single-subject (N=1) physics-constrained deep learning.

Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F

15th Annual Buffalo Neuroscience Research Day, Buffalo, NY, 2022

Beyond qMRI: Biological tissue properties forom single-subject unsupervised deep learning with theoretical signal constraints

Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F

Proc Intl Soc Mag Reson Med, 2022, p. 370

Self-calibration, histological validation, and an improved signal model for χ-separation using single-subject (N=1) physics-constrained deep learning.

Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F

Joint Workshop on MR phase, magnetic susceptibility and electrical properties mapping, Lucca, Italy, 2022

Nonsusceptibility frequency shifts in the human brain and their impact on quantitative suscep-tibility mapping

Jochmann T, Jakimovski D, Hametner S, Zivadinov R, Haueisen J, Schweser F

Workshop on MR phase, magnetic susceptibility and electrical properties mapping, Lucca, Italy, 2022

Embedding medium alters local phase contrast in postmortem MRI of the human brain

Schweser F, Hametner S, Jochmann T, Riedl C, Bachrata B, Laux M, Grabner G

Joint Workshop on MR phase, magnetic susceptibility and electrical properties mapping, Lucca, Italy, 2022

Unsupervised physics-informed deep learning (N=1) for solving inverse qMRI problems–Relaxometry and field mapping from multi-echo data.

Benslimane I, Jochmann T, Zivadinov R, Schweser F

Proc Intl Soc Mag Reson Med, 2021, p. 330

Quantitative mapping of susceptibility and non-susceptibility frequency from gradient-echo phase images

Jochmann T, Jakimovski D, Küchler N, Zivadinov R, Haueisen J, Schweser F

12th Ultrahigh Field Magnetic Resonance Symposium , 2021, p. 13

Quantitative mapping of susceptibility and non-susceptibility frequency with DEEPOLE QUASAR

Jochmann T, Jakimovski D, Küchler N, Zivadinov R, Haueisen J, Schweser F

Proc Intl Soc Mag Reson Med, 2021, p. 789

Mapping Magnetic Susceptibility and Non-Susceptibility Sources from MRI Frequency Shift with Physics-informed Deep Learning.

Jochmann T, Schweser F, Küchler N, Jakimovski D, Zivadinov R, Haueisen J

16th International Workshop on Optimization and Inverse Problems in Electromagnetism, 2021

U3-Net for Deep Vector QSM – Solving the Susceptibility Tensor Phase Model in Single-Orientation MRI

Baader EF, Jochmann T, Haueisen J, Zivadinov R, Schweser F

Proc Intl Soc Mag Reson Med, 2020, p. 3202

How to train a Deep Convolutional Neural Network for Quantitative Susceptibility Mapping (QSM)

Jochmann T, Haueisen J, Schweser F

Proc Intl Soc Mag Reson Med, 2020, p. 3195

SOJU-Net—Denoising MR phase images with physics-informed deep learning using artificial Rician noise augmentation

Jochmann T, Kuechler N, Haueisen J, Schweser F

Proc Intl Soc Mag Reson Med, 2020, p. 3196

A Fourier transformation based convolutional neural network layer for physics-informed deep learning of magnetic dipole inversion

Küchler N, Haueisen J, Schweser F, Jochmann T

German Society for Biomedical Engineering , 2020, p1570638759

Physics-aware augmentation, artificial noise, and synthetic samples to train a convolutional neural network for QSM

Jochmann T, Haueisen J, Schweser F

5th International Workshop on MRI Phase Contrast and QSM, Seoul, Korea, 2019

U2-Net for DEEPOLE QUASAR–A Physics-Informed Deep Convolutional Neural Network that Disentangles MRI Phase Contrast Mechanisms.

Jochmann T, Haueisen J, Zivadinov R, Schweser F

Proc Intl Soc Mag Reson Med, Montreal, QC, Canada, 2019, p. 320

DEEPOLE QUASAR–A deep learning-based approach for quantitative susceptibility and non-susceptibility phase mapping.

Jochmann T, Haueisen J, Zivadinov R, Schweser F

5th International Workshop on MRI Phase Contrast and QSM, Seoul, Korea, 2019

Overcoming the Rician Noise Bias of T2* Relaxometry with an Artificial Neural Network (ANN).

Schweser F, Jochmann T, Zivadinov R

Proc Intl Soc Mag Reson Med, Proc Intl Soc Mag Reson Med, Montreal, QC, Canada, 2019, p. 4854