Publications of J Steiglechner

Birk, F.; Mahler, L.; Steiglechner, J.; Wang, Q.; Scheffler, K.; Heule, R.: Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP. Scientific Reports 15 (1), 4825 (2025)

Wang, Q.; Mahler, L.; Steiglechner, J.; Birk, F.; Scheffler, K.; Lohmann, G.: DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise Cleaning. In: IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 2452 - 2461. 2nd Workshop on Computer Vision for Automated Medical Diagnosis: International Conference on Computer Vision (ICCV/CVF 2023) , Paris, France, October 02, 2023 - October 06, 2023. (2023)

Mahler, L.; Wang, Q.; Steiglechner, J.; Birk, F.; Heczko, S.; Scheffler, K.; Lohmann, G.: Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification. In: Machine Learning in Clinical Neuroimaging: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, pp. 123 - 132 (Eds. Abdulkadir, A.; Bathula, D.; Dvornek, N.; Govindarajan, S.; Habes, M. et al.). 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023), Vancouver, BC, Canada, October 08, 2023. Springer, Cham, Switzerland (2023)

Wang, Q.; Mahler, L.; Steiglechner, J.; Birk, F.; Scheffler, K.; Lohmann, G.: A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging. In: Machine Learning in Clinical Neuroimaging: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, pp. 23 - 33 (Eds. Abdulkadir, A.; Bathula, D.; Dvornek, N.; Govindarajan, S.; Habes, M. et al.). 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023), Vancouver, BC, Canada, October 08, 2023. Springer, Cham, Switzerland (2023)

Steiglechner, J.; Wang, Q.; Ramadan, D.; Mahler, L.; Scheffler, K.; Bender, B.; Lindig, T.; Lohmann, G.: FLEXseg: Next Generation Brain MRI Segmentation at 9.4 T. In: Medical Imaging with Deep Learning (MIDL 2022). Medical Imaging with Deep Learning (MIDL 2022), Zürich, Swtzerland, July 06, 2022 - July 08, 2022. (2022)

Wang, Q.; Steiglechner, J.; Lindig, T.; Bender, B.; Scheffler, K.; Lohmann, G.: Super-Resolution for Ultra High-Field MR Images. In: Medical Imaging with Deep Learning (MIDL 2022). Medical Imaging with Deep Learning (MIDL 2022) , Zürich, Swtzerland , July 06, 2022 - July 08, 2022. (2022)

Birk, F.; Mahler, L.; Steiglechner, J.; Wang, Q.; Scheffler, K.; Heule, R.: Distribution-free uncertainty estimation in multi-parametric quantitative MRI through conformalized quantile regression. In ISMRM & ISMRT Annual Meeting & Exhibition 2024, 2228. ISMRM & ISMRT Annual Meeting & Exhibition 2024, Singapore, May 04, 2024 - May 09, 2024. (2024)

Steiglechner, J.; Bender, B.; Lohmann, G.; Scheffler, K.; Ernemann, U.; Lindig, T.: Integration of Automated Brain MRI Segmentation and Longitudinal Atrophy Analysis Generalizes In Forecasting Progression from Mild Cognitive Impairment to Alzheimer’s Disease. In Clinical Neuroradiology, 33 (Supplement 1), 321, pp. S78 - S79. 58. Jahrestagung der Deutschen Gesellschaft für Neuroradiologie e.V. und 30. Jahrestagung der Österreichischen Gesellschaft für Neuroradiologie e.V. (NEURORAD 2023), Kassel, Germany, October 04, 2023 - October 06, 2023. Springer (2023)

Steiglechner, J.; Bender, B.; Ernemann, U.; Scheffler, K.; Lindig, T.: Automated detection of MCI to Alzheimer's disease conversion before clinical onset by evaluation of atrophy rates. In European Congress of Radiology (ECR 2022): Building Bridges, RPS 1505-9, pp. 629 - 630. European Congress of Radiology (ECR 2022): Building Bridges, Wien, Austria, July 13, 2022 - July 17, 2022. (2022)

Wang, Q.; Steiglechner, J.; Lohmann, G.: Sythetic 9T-like structural MRI using Generative Neural Network. In NeNa Conference 2021: Neurowissenschaftliche Nachwuchskonferenz (Conference of Junior Neuroscientists), T13, p. 14. 22nd Conference of Junior Neuroscientists (NeNa 2021), Tübingen, Germany, October 07, 2021. (2021)

Mahler, L.; Steiglechner, J.; Wang, Q.; Scheffler, K.; Lohmann, G.: JudgeMI: Towards Accurate Metrics for Assessing Deep Learning Based Structural MRI Motion Correction. 29th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2023), Montreal, Canada (2023)

Wang, Q.; Steiglechner, J.; Scheffler, K.; Lohmann, G.: Super Resolution Improves Cortical Segmentation Accuracy in Ultra-high Resolution MRI. 28th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2022), Glasgow, UK (2022)

Birk, F.; Steiglechner, J.; Scheffler, K.; Heule, R.: Investigating complex-valued neural networks applied to phase-cycled bSSFP for multi-parametric quantitative tissue characterization. Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022), London, UK (2022)

Mahler, L.; Steiglechner, J.; Bender, B.; Lindig, T.; Ramadan, D.; Bause, J.; Birk, F.; Heule, R.; Charyasz, E.; Erb, M. et al.; Kumar, V.; Hagberg, G.; Martin, P.; Lohmann, G.; Scheffler, K.: UltraCortex: Submillimeter Ultra-High Field 9.4 T Brain MR Image Collection and Manual Cortical Segmentations. (submitted)

Mahler, L.; Steiglechner, J.; Wang, Q.; Scheffler, K.; Heule, R.: Flexible and Cost-Effective Deep Learning for Fast Multi-Parametric Relaxometry using Phase-Cycled bSSFP. (submitted)

Lohmann, G.; Heczko, S.; Mahler, L.; Wang, Q.; Steiglechner, J.; Kumar, V.; Roost, M.; Jost, J.; Scheffler, K.: Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning. (submitted)