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Jannis Müller

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YNICL Journal 2025 Journal Article

Fluid and White Matter Suppression contrasts MRI improves Deep Learning detection of Multiple Sclerosis Cortical Lesions

  • Pedro M. Gordaliza
  • Jannis Müller
  • Alessandro Cagol
  • Nataliia Molchanova
  • Francesco La Rosa
  • Charidimos Tsagkas
  • Cristina Granziera
  • Meritxell Bach Cuadra

PURPOSE: To investigate the efficacy of Fluid and White Matter Suppression (FLAWS) MRI sequence in improving Deep Learning (DL)-based detection and segmentation of cortical lesions in Multiple Sclerosis (MS) patients even, and to develop models that can generalize to clinical settings where only standard T1-weighted images (MPRAGE) are available. MATERIALS AND METHODS: -score for detection and DSC for segmentation accuracy. RESULTS: -score: 0.55[0.211-0.998]), demonstrating successful knowledge transfer from advanced research sequences to routine clinical sequences. CONCLUSION: Integration of FLAWS-derived contrasts and annotations significantly improves DL-based CL detection and segmentation. The models demonstrate capability in identifying lesions missed by individual raters and maintain robust performance when applied to standard clinical sequences at external sites. This cross-sequence generalization facilitates immediate clinical translation, supported by publicly available inference models on DockerHub.

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YNICL Journal 2022 Journal Article

Brain atrophy measurement over a MRI scanner change in multiple sclerosis

  • Tim Sinnecker
  • Sabine Schädelin
  • Pascal Benkert
  • Esther Ruberte
  • Michael Amann
  • Johanna M. Lieb
  • Yvonne Naegelin
  • Jannis Müller

BACKGROUND: A change in MRI hardware impacts brain volume measurements. The aim of this study was to use MRI data from multiple sclerosis (MS) patients and healthy control subjects (HCs) to statistically model how to adjust brain atrophy measures in MS patients after a major scanner upgrade. METHODS: We scanned 20 MS patients and 26 HCs before and three months after a major scanner upgrade (1.5 T Siemens Healthineers Magnetom Avanto to 3 T Siemens Healthineers Skyra Fit). The patient group also underwent standardized serial MRIs before and after the scanner change. Percentage whole brain volume changes (PBVC) measured by Structural Image Evaluation using Normalization of Atrophy (SIENA) in the HCs was used to estimate a corrective term based on a linear model. The factor was internally validated in HCs, and then applied to the MS group. RESULTS: Mean PBVC during the scanner change was higher in MS than HCs (-4.1 ± 0.8 % versus -3.4 ± 0.6 %). A fixed corrective term of 3.4 (95% confidence interval: 3.13-3.67)% was estimated based on the observed average changes in HCs. Age and gender did not have a significant influence on this corrective term. After adjustment, a linear mixed effects model showed that the brain atrophy measures in MS during the scanner upgrade were not anymore associated with the scanner type (old vs new scanner; p = 0.29). CONCLUSION: A scanner change affects brain atrophy measures in longitudinal cohorts. The inclusion of a corrective term based on changes observed in HCs helps to adjust for the known and unknown factors associated with a scanner upgrade on a group level.

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