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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]
| 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] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (156 pages) |
| Disciplina | 616.07548 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Diffusion magnetic resonance imaging
Artificial intelligence Computer vision |
| ISBN | 3-031-21206-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 -- 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.
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. 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. |
| Record Nr. | UNINA-9910633928503321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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]
| 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] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (156 pages) |
| Disciplina | 616.07548 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Diffusion magnetic resonance imaging
Artificial intelligence Computer vision |
| ISBN | 3-031-21206-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 -- 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.
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. 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. |
| Record Nr. | UNISA-996500061103316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Computational diffusion MRI : 12th international workshop, CDMRI 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, proceedings / / Suheyla Cetin-Karayumak [and five others], editors
| Computational diffusion MRI : 12th international workshop, CDMRI 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, proceedings / / Suheyla Cetin-Karayumak [and five others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (174 pages) |
| Disciplina | 621.367 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Optical data processing |
| ISBN | 3-030-87615-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910503010403321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational diffusion MRI : 12th international workshop, CDMRI 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, proceedings / / Suheyla Cetin-Karayumak [and five others], editors
| Computational diffusion MRI : 12th international workshop, CDMRI 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, proceedings / / Suheyla Cetin-Karayumak [and five others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (174 pages) |
| Disciplina | 621.367 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Optical data processing |
| ISBN | 3-030-87615-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464405503316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||