{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":30,"n_cols":5,"rows_kept":30,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[11,2,11,3],[11,4,11,5],[12,2,12,3],[12,4,12,5],[13,2,13,3],[15,2,15,3],[16,2,16,3],[17,2,17,3],[18,2,18,3],[19,2,19,3],[22,2,22,3],[23,2,23,3],[26,2,26,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA990006198380403321"]
[null,null,"Autore",null,"Carlos, M."]
[null,null,"Titolo",null,"Reflexoes finais a' luz da filosofia universal : solucao dos problemas sociais / M. Carlos."]
[null,null,"Pubbl/distr/stampa",null,"Rio de Janeiro : A. Leite, 1945"]
[null,null,"Descrizione fisica",null,"85 p. ; 24 cm"]
[null,null,"Disciplina",null,"111.2"]
[null,null,"Locazione",null,"FGBC"]
[null,null,"Collocazione",null,"XI N 166"]
[null,null,"Lingua di pubblicazione",null,"Non definito"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNISA996500061103316"]
[null,null,"Titolo",null,"Computational diffusion MRI : 13th international workshop, CDMRI 2022, held in conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, proceedings / / editors : Suheyla Cetin-Karayumak [and six others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-21206-1"]
[null,null,"Descrizione fisica",null,"1 online resource (156 pages)"]
[null,null,"Collana",null,"Lecture notes in computer science ; ; Volume 13722"]
[null,null,"Disciplina",null,"616.07548"]
[null,null,"Soggetti",null,"Diffusion magnetic resonance imaging"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer vision"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Data Preprocessing -- Slice Estimation in Diffusion MRI of Neonatal and Fetal Brains in Image and Spherical Harmonics Domains Using Autoencoders -- 1 Introduction -- 2 Methodology -- 2.1 Materials -- 2.2 Model -- 3 Results -- 3.1 DWI Assessment -- 3.2 FA and MD in Newborns -- 3.3 Qualitative Results of FA and MD in Fetuses -- 4 Conclusion -- References -- Super-Resolution of Manifold-Valued Diffusion MRI Refined by Multi-modal Imaging -- 1 Introduction -- 2 Background and Methods -- 2.1 Background -- 2.2 Proposed Method -- 3 Experiments -- 3.1 Data -- 3.2 Implementation and Training Details -- 3.3 Models and Evaluation Metrics -- 4 Results -- 4.1 Proposed Model Performance -- 4.2 Model Ablation Results -- 5 Discussion -- References -- .28em plus .1em minus .1emLossy Compression of Multidimensional Medical Images Using Sinusoidal Activation Networks: An Evaluation Study -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Discussion and Conclusion -- References -- Correction of Susceptibility Distortion in EPI: A Semi-supervised Approach with Deep Learning -- 1 Introduction -- 2 Background -- 2.1 Distortion Model -- 2.2 Distortion Correction Using Image Registration -- 3 Method -- 3.1 Model Architecture -- 3.2 Models -- 4 Evaluations -- 4.1 Datasets -- 4.2 Models -- 4.3 Assessment Metrics -- 5 Results -- 6 Discussion -- "]
[{"$nulls":4},"7 Conclusion -- References -- The Impact of Susceptibility Distortion Correction Protocols on Adolescent Diffusion MRI Measures -- 1 Introduction -- 2 Methods -- 2.1 Study Participants and MRI Data -- 2.2 dMRI Preprocessing and Subsampling -- 2.3 dMRI Models and Regional Measures -- 2.4 Statistics -- 3 Results -- 3.1 DTI and NODDI Map Comparisons -- 3.2 DTI and NODDI Fit Evaluations -- 3.3 GAM Age Associations -- 4 Discussion -- References -- Signal Representations."]
[{"$nulls":4},"Diffusion MRI Fibre Orientation Distribution Inpainting -- 1 Introduction -- 2 Methods -- 2.1 The Human Connectome Project Dataset -- 2.2 Data Preprocessing -- 2.3 3D FOD Inpainting Framework -- 2.4 Feature Encoding Stage -- 2.5 Order-Wise Coefficient Decoders -- 2.6 Implementation Details -- 3 Experimental Results -- 3.1 Inpainting Quality Analysis -- 3.2 Connectome Matrix Analysis -- 4 Conclusion -- References -- Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-supervised Machine Learning -- 1 Introduction -- 2 Methods -- 2.1 Microstructure Model -- 2.2 Combined T1-Diffusion in Vivo Data -- 2.3 Simulated Data -- 2.4 Non-linear Least Squares Fitting -- 2.5 Self-supervised Model Fitting -- 3 Results -- 3.1 Simulated Data -- 3.2 Real Data -- 4 Discussion -- 5 Conclusion -- References -- Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting -- 1 Introduction -- 2 Methods -- 2.1 Biophysical Diffusion Model -- 2.2 Orientation Distribution Function Fingerprinting -- 2.3 Stepwise Stochastic Adaptation of a Dictionary -- 2.4 Data -- 2.5 Evaluation -- 3 Results -- 4 Discussion -- 5 Conclusions -- References -- How Can Spherical CNNs Benefit ML-Based Diffusion MRI Parameter Estimation? -- 1 Introduction -- 2 ML Solutions to the dMRI Parameter Estimation Problem and the Theoretical Benefits of S-CNNs -- 2.1 Fully-Connected Networks -- 2.2 Spherical CNNs -- 3 Experiments -- 3.1 Experiment 1 -- 3.2 Experiment 2 -- 4 Results and Discussion -- 4.1 For Experiment 1 -- 4.2 For Experiment 2 -- 5 Conclusion -- References -- Tractography and WM Pathways -- DC2U-Net: Tract Segmentation in Brain White Matter Using Dense Criss-Cross U-Net -- 1 Introduction -- 2 Methods -- 2.1 Dense Criss-Cross U-Net -- 2.2 Dense Criss-Cross Attention (DCCA) Block."]
[{"$nulls":4},"2.3 Deeply Supervised Loss Function -- 3 Experiments -- 3.1 Dataset and Implementation Details -- 3.2 Results -- 3.3 Ablation Analysis of DC2U-Net -- 4 Conclusion -- References -- Clustering in Tractography Using Autoencoders (CINTA) -- 1 Introduction -- 1.1 Related Work -- 2 Material and Methods -- 3 Experiments -- 4 Results -- 5 Discussion -- 6 Conclusion -- A Appendix -- A.1 Misclassified Streamlines -- A.2 Time Computational Requirements -- References -- Tractometric Coherence of Fiber Bundles in DTI -- 1 Introduction -- 2 Theory -- 3 Experiments -- 4 Discussion -- References -- Author Index."]