Letícia Rittner

2.0k total citations
83 papers, 940 citations indexed

About

Letícia Rittner is a scholar working on Radiology, Nuclear Medicine and Imaging, Pediatrics, Perinatology and Child Health and Computer Vision and Pattern Recognition. According to data from OpenAlex, Letícia Rittner has authored 83 papers receiving a total of 940 indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Radiology, Nuclear Medicine and Imaging, 21 papers in Pediatrics, Perinatology and Child Health and 18 papers in Computer Vision and Pattern Recognition. Recurrent topics in Letícia Rittner's work include Advanced Neuroimaging Techniques and Applications (25 papers), Advanced MRI Techniques and Applications (19 papers) and Fetal and Pediatric Neurological Disorders (19 papers). Letícia Rittner is often cited by papers focused on Advanced Neuroimaging Techniques and Applications (25 papers), Advanced MRI Techniques and Applications (19 papers) and Fetal and Pediatric Neurological Disorders (19 papers). Letícia Rittner collaborates with scholars based in Brazil, Canada and United States. Letícia Rittner's co-authors include Roberto Lotufo, Roberto Souza, Simone Appenzeller, Pedram Ghamisi, Xiao Xiang Zhu, Jón Atli Benediktsson, Richard Frayne, Oeslle Lucena, Mariana Bento and David G. Gobbi and has published in prestigious journals such as Blood, PLoS ONE and NeuroImage.

In The Last Decade

Letícia Rittner

71 papers receiving 918 citations

Peers

Letícia Rittner

RNMI

CVPR

RHEUM

Steve De Backer Belgium

Clayton Chi‐Chang Chen Taiwan

Mehul Sampat United States

Qiang Zheng China

Ahmed Elazab China

Mariano Cabezas Spain

Emanuele Torti Italy

Rongjian Li China

Andrea U. J. Mewes United States

Vikram Chalana United States

Steve De Backer Belgium

Letícia Rittner

430 ×1.3

RNMI

398 ×1.9

CVPR

417 ×2.0

28 ×0.2

RHEUM

63 ×0.5

Citations per year, relative to Letícia Rittner Letícia Rittner (= 1×) peers Steve De Backer

Countries citing papers authored by Letícia Rittner

Since Specialization

Citations

This map shows the geographic impact of Letícia Rittner's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Letícia Rittner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Letícia Rittner more than expected).

Fields of papers citing papers by Letícia Rittner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Letícia Rittner. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Letícia Rittner. The network helps show where Letícia Rittner may publish in the future.

Co-authorship network of co-authors of Letícia Rittner

This figure shows the co-authorship network connecting the top 25 collaborators of Letícia Rittner. A scholar is included among the top collaborators of Letícia Rittner based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Letícia Rittner. Letícia Rittner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Bocchetta, Martina, Oula Puonti, Douglas N. Greve, et al.. (2025). H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI for hypothalamus subregion segmentation. Artificial Intelligence in Medicine. 170. 103271–103271.

Rittner, Letícia, et al.. (2025). Fiber Sensing in the 6G Era: Vision Transformers for ϕ-OTDR-Based Road-Traffic Monitoring. Applied Sciences. 15(6). 3170–3170. 1 indexed citations

Appenzeller, Simone, et al.. (2025). Brain volumetric reductions in patients with thrombotic thrombocytopenic purpura. PLoS ONE. 20(11). e0334903–e0334903.

Rittner, Letícia, et al.. (2024). FOD-Swin-Net: Angular Super Resolution of Fiber Orientation Distribution Using a Transformer-Based Deep Model. 1–5.

Rittner, Letícia, et al.. (2024). Vision Transformer MST++: Efficient Hyperspectral Skin Reconstruction. 51–52.

Rittner, Letícia, et al.. (2024). Assessing MedSAM's Generalization to White Matter Hyperintensities in Magnetic Resonance Imaging. 1–5.

Dertkigil, Sérgio San Juan, et al.. (2024). Spectro-ViT: A vision transformer model for GABA-edited MEGA-PRESS reconstruction using spectrograms. Magnetic Resonance Imaging. 113. 110219–110219. 1 indexed citations

Marini, Roberto, Fernando Cendes, Paula Teixeira Fernandes, et al.. (2023). Microstructural Changes in the Corpus Callosum in Systemic Lupus Erythematous. Cells. 12(3). 355–355. 4 indexed citations

Appenzeller, Simone, et al.. (2023). Volumetric corpus callosum segmentation integrated to inCCsight software for supporting DTI-based studies. 13. 106–106.

10.

Appenzeller, Simone, et al.. (2023). Thalamus Segmentation Using Deep Learning with Diffusion MRI Data: An Open Benchmark. Applied Sciences. 13(9). 5284–5284.

11.

Baumgärtner, Christian, et al.. (2023). Machine and Deep Learning Dominate Recent Innovations in Sensors, Signals and Imaging Informatics. Yearbook of Medical Informatics. 32(1). 282–285.

12.

Appenzeller, Simone, et al.. (2022). Neuropsychiatric manifestations in childhood-onset systemic lupus erythematosus. The Lancet Child & Adolescent Health. 6(8). 571–581. 17 indexed citations

13.

Rezende, Thiago Junqueira Ribeiro de, et al.. (2022). A benchmark for hypothalamus segmentation on T1-weighted MR images. NeuroImage. 264. 119741–119741. 9 indexed citations

14.

Bento, Mariana, et al.. (2022). Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets. Frontiers in Neuroinformatics. 15. 805669–805669. 29 indexed citations

15.

Cruciani, Federica, Mauro Zucchelli, F. Setti, et al.. (2021). Interpretable deep learning as a means for decrypting disease signature in multiple sclerosis. Journal of Neural Engineering. 18(4). 0460a6–0460a6. 13 indexed citations

16.

Peres, Fernando Augusto, Aline Tamires Lapa, Nailú Angélica Sinicato, et al.. (2018). Olfactory function in systemic lupus erythematosus and systemic sclerosis. A longitudinal study and review of the literature. Autoimmunity Reviews. 17(4). 405–412. 27 indexed citations

17.

Bento, Mariana, et al.. (2017). Computational methods for corpus callosum segmentation on MRI: A systematic literature review. Computer Methods and Programs in Biomedicine. 154. 25–35. 27 indexed citations

18.

Sinicato, Nailú Angélica, et al.. (2017). Prevalence and features of metabolic syndrome in childhood-onset systemic lupus erythematosus. Clinical Rheumatology. 36(7). 1527–1535. 9 indexed citations

19.

Ghamisi, Pedram, Roberto Souza, Jón Atli Benediktsson, et al.. (2016). Hyperspectral Data Classification Using Extended Extinction Profiles. IEEE Geoscience and Remote Sensing Letters. 13(11). 1641–1645. 68 indexed citations

20.

Ghamisi, Pedram, Roberto Souza, Letícia Rittner, et al.. (2016). Extinction profiles: A novel approach for the analysis of remote sensing data. elib (German Aerospace Center). 5122–5125. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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