2026-04-27T12:55:22Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/783352025-12-15T20:03:30Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

París I Brandrés, Guillem Lluis Pieciak, Tomász Jones K., Derek Aja Fernández, Santiago Tristán Vega, Antonio Veraart, Jelle 2025-10-06T11:07:08Z 2025-10-06T11:07:08Z 2025 Magnetic Resonance in Medicine, 2025, p. 1-16 0740-3194 https://uvadoc.uva.es/handle/10324/78335 10.1002/mrm.70035 1 16 Magnetic Resonance in Medicine 1522-2594 Producción Científica Purpose: Rotational invariants (RIs) are at the root of many dMRI applications.Among others, they are presented as a sensible way of reducing the dimension-ality of biophysical models. While thermal noise impact on diffusion metrics hasbeen well studied, little is known on its effect on spherical harmonics-based RI(RISH) features and derived markers. In this work, we evaluate the effect of noiseon RISH features and downstream Standard Model Imaging (SMI) estimates. Theory and Methods: Using simulated and test/retest multishell MRI data,we assess the accuracy and precision of RISH features and SMI parameters inthe presence of thermal noise, as well as its robustness to variations in protocoldesign. We further propose and evaluate correction strategies that bypass theneed of rotational invariant features as an intermediate step. Results: Both RISH features and SMI estimates are impacted by SNR-dependentRician biases. However, higher-order RISH features are susceptible to a sec-ondary noise-related source of bias, which not only depends on SNR, but alsoprotocol and underlying microstructure. Rician bias-correcting techniques areinsufficient to maximize the accuracy of RISH and SMI features, or to ensureconsistency across protocols. SMI estimators that avoid RISH features by fit-ting the model to the directional diffusion MRI data outperform RISH-basedapproaches in accuracy, repeatability, and reproducibility across acquisitionprotocols. Conclusions: RISH features are increasingly used in dMRI analysis, yet theyare prone to various sources of noise that lower their accuracy and reproducibil-ity. Understanding the impact of noise and mitigating such biases is critical tomaximize the validity, repeatability, and reproducibility of dMRI studies. National Institute of Neurological Disorders and Stroke: R01 NS088040 Narodowa Agencja Wymiany Akademickiej: PPN/BEK/2019/1/00421 Consejería de Educación de la Junta de Castilla y León: contrato predoctoral de Guillem Lluis París Blandes: EDU/1100/2017 12/12 EPSRC Centre for Doctoral Training in Medical Imaging: EP/M029778/1 Ministerio de Ciencia e Innovación (MCIN) / Agencia Estatal de Investigación (AEI): PID2021-124407NB-I00 y TED2021-130758B-I00 Junta de Castilla y León: VA156P24 European Social Fund Plus Krajowy Naukowy Osrodek Wiodacy: 692/STYP/13/2018 National Institute of Biomedical Imaging and Bioengineering: NIH P41 EB017183 Open access funding provided by FEDER European Funds and the Junta De Castilla y León under the Research and Innovation Strategy for Smart Specialization (RIS3) of Castilla y León 2021-2027. application/pdf eng Wiley info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc/4.0/ © 2025 The Author(s) Attribution-NonCommercial 4.0 Internacional Modelos biofísicos Imágenes ponderadas por difusión Sesgo de Rician Invariantes rotacionales Imágenes del modelo estándar 2207.16 Resonancia Magnética Nuclear Thermal noise lowers the accuracy of rotationally invariant harmonics of diffusion MRI data and their robustness to experimental variations info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.70035 SI