2026-04-13T01:02:47Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/2232952025-12-04T19:25:28Zcom_2072_1057col_2072_478800col_2072_478858col_2072_478917

00925njm 22002777a 4500 dc Kalantari, Aref author Shahbazi, Mehrab author Schneider, Marc author Raikes, Adam C. author Frazão, Victor Vera author Bhattrai, Avnish author Carnevale, Lorenzo author Diao, Yujian author Franx, Bart A. A. author Gammaraccio, Francesco author Goncalves, Lisa Marie author Lee, Susan author Leeuwen, Esther M. van author Michalek, Annika author Mueller, Susanne author Rivera Olvera, Alejandro author Padro, Daniel author Kotb Selim, Mohamed author Toorn, Annette van der author Varriano, Federico author Vrooman, Roël author Wenk, Patricia author Albers, H. Elliott author Boehm Sturm, Philipp author Budinger, Eike author Canals, Santiago author Santis, Silvia de author Diaz Brinton, Roberta author Dijkhuizen, Rick M. author Eixarch Roca, Elisenda author Forloni, Gianluigi author Grandjean, Joanes author Hekmatyar, Khan author Jacobs, Russell E. author Jelescu, Ileana author Kurniawan, Nyoman D. author Lembo, Giuseppe author Longo, Dario Livio author Sta Maria, Naomi S. author Micotti, Edoardo author Muñoz Moreno, Emma author Ramos Cabrer, Pedro author Reichardt, Wilfried author Soria, Guadalupe author Ielacqua, Giovanna D. author Aswendt, Markus author 2025-09-19T12:56:36Z 2025-09-19T12:56:36Z 2024-09-27 2025-09-19T12:56:36Z Magnetic resonance imaging (MRI) is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large datasets for potential poor-quality outliers can be a challenge. We present AIDAqc, a machine-learning-assisted automated Python-based command-line tool for small animal MRI quality assessment. Quality control features include signal-to-noise ratio (SNR), temporal SNR, and motion. All features are automatically calculated and no regions of interest are needed. Automated outlier detection for a given dataset combines the interquartile range and the machine-learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. To evaluate the reliability of individual quality control metrics, a simulation of noise (Gaussian, salt and pepper, speckle) and motion was performed. In outlier detection, single scans with induced artifacts were successfully identified by AIDAqc. AIDAqc was challenged in a large heterogeneous dataset collected from 19 international laboratories, including data from mice, rats, rabbits, hamsters, and gerbils, obtained with different hardware and at different field strengths. The results show that the manual inter-rater agreement (mean Fleiss Kappa score 0.17) is low when identifying poor-quality data. A direct comparison of AIDAqc results, therefore, showed only low-to-moderate concordance. In a manual post hoc validation of AIDAqc output, precision was high (>70%). The outlier data can have a significant impact on further postprocessing, as shown in representative functional and structural connectivity analysis. In summary, this pipeline optimized for small animal MRI provides researchers with a valuable tool to efficiently and effectively assess the quality of their MRI data, which is essential for improved reliability and reproducibility. Imatges per ressonància magnètica Neuroanatomia Mapatge del cervell Magnetic resonance imaging Neuroanatomy Brain mapping Automated quality control of small animal MR neuroimaging data