Abstract #0466

Effect of iterative denoising in automated white matter hyperintensities segmentation using accelerated FLAIR sequences

Ricardo Alberto Corredor-Jerez^1,2,3, Mathilde Carrière⁴, Thierry Chaptal⁵, Bénédicte Maréchal^1,2,3, Tobias Kober^1,2,3, Xavier Ayrignac⁶, Nicolas Menjot de Champfleur^4,5, Thomas Troalen⁷, and Emmanuelle Le Bars^4,5

¹Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland, ²Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ³LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁴Department of Neuroradiology, Hospital and University of Montpellier, Montpellier, France, ⁵Institut d'Imagerie Fonctionnelle Humaine, I2FH, Hospital and University of Montpellier, Montpellier, France, ⁶Department of Neurology, Gui de Chauliac Montpellier University Hospital, Montpellier, Switzerland, ⁷Siemens Healthcare SAS, Saint-Denis, France

Synopsis

Keywords: Multiple Sclerosis, Segmentation, Fast MR ProtocolsAcquisition time in brain MR protocols can be reduced using acceleration techniques like CAIPIRINHA. The inherent increase of noise due to their undersampling schemes can be mitigated with additional processing methods. Iterative denoising has shown good performance filtering images in k-space while preserving image details. This work evaluates the impact of iterative denoising on automated segmentation of white matter hyperintensities using CAIPIRINHA 3D FLAIR and compressed sensing 3D MPRAGE. Reliable segmentations were generated across different denoising levels (45% to 85%); small structures presented lower detection rates with stronger denoising (≥75%). These findings are revelant for designing optimized brain imaging protocols.

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