Opfer, RolandRolandOpferKrüger, JuliaJuliaKrügerBuddenkotte, ThomasThomasBuddenkotteSpies, LotharLotharSpiesBehrendt, FinnFinnBehrendtSchippling, SvenSvenSchipplingBuchert, RalphRalphBuchert2024-10-152024-10-152024-06-16International Journal of Computer Assisted Radiology and Surgery 19 (9): 1763–1771 (2024)https://hdl.handle.net/11420/49403Purpose: MRI-derived brain volume loss (BVL) is widely used as neurodegeneration marker. SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose “BrainLossNet”, a convolutional neural network (CNN)-based method for BVL-estimation. Methods: BrainLossNet uses CNN-based non-linear registration of baseline(BL)/follow-up(FU) 3D-T1w-MRI pairs. BVL is computed by non-linear registration of brain parenchyma masks segmented in the BL/FU scans. The BVL estimate is corrected for image distortions using the apparent volume change of the total intracranial volume. BrainLossNet was trained on 1525 BL/FU pairs from 83 scanners. Agreement between BrainLossNet and SIENA was assessed in 225 BL/FU pairs from 94 MS patients acquired with a single scanner and 268 BL/FU pairs from 52 scanners acquired for various indications. Robustness to short-term variability of 3D-T1w-MRI was compared in 354 BL/FU pairs from a single healthy men acquired in the same session without repositioning with 116 scanners (Frequently-Traveling-Human-Phantom dataset, FTHP). Results: Processing time of BrainLossNet was 2–3 min. The median [interquartile range] of the SIENA-BrainLossNet BVL difference was 0.10% [− 0.18%, 0.35%] in the MS dataset, 0.08% [− 0.14%, 0.28%] in the various indications dataset. The distribution of apparent BVL in the FTHP dataset was narrower with BrainLossNet (p = 0.036; 95th percentile: 0.20% vs 0.32%). Conclusion: BrainLossNet on average provides the same BVL estimates as SIENA, but it is significantly more robust, probably due to its built-in distortion correction. Processing time of 2–3 min makes BrainLossNet suitable for clinical routine. This can pave the way for widespread clinical use of BVL estimation from intra-scanner BL/FU pairs.en1861-64292024917631771Springerhttps://creativecommons.org/licenses/by/4.0/Brain volume lossConvolutional neural networkMagnetic resonance imagingMultiple sclerosisTechnology::610: Medicine, HealthComputer Science, Information and General Works::004: Computer SciencesJournal Article10.15480/882.1338610.1007/s11548-024-03201-310.15480/882.13386Journal Article