{"doi":"10.58530/2022/0049","title":"Deep-learning-based raw data generation for deep-learning-based image reconstruction","abstract":"<jats:p>This study demonstrates the potential of using deep learning for generating raw data to augment raw datasets of limited size for use in deep-learning-based MR image reconstruction. Using an adversarial autoencoder architecture, variability in 10 T1-weighted raw datasets from the fastMRI database was learned and recombined into new, random raw data. We trained a deep-learning-based Compressed Sensing reconstruction using both conventional approaches with real raw data and compared the results to training with generated raw data. The reconstruction from generated raw data showed improved reconstruction results and better generalization to scans with slightly different echo times.</jats:p>","journal":"ISMRM Annual Meeting","year":null,"id":631627,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1636973,"name":"Peter While","orcid":null,"position":1,"is_corresponding":false},{"id":1636972,"name":"Frank Zijlstra","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Deep-learning-based raw data generation for deep-learning-based image reconstruction","abstract":"This study demonstrates the potential of using deep learning for generating raw data to augment raw datasets of limited size for use in deep-learning-based MR image reconstruction. Using an adversarial autoencoder architecture, variability in 10 T1-weighted raw datasets from the fastMRI database was learned and recombined into new, random raw data. We trained a deep-learning-based Compressed Sensing reconstruction using both conventional approaches with real raw data and compared the results to training with generated raw data. The reconstruction from generated raw data showed improved reconstruction results and better generalization to scans with slightly different echo times.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W4385544042","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.15668106,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":null,"oa_locations":[{"url":"https://doi.org/10.58530/2022/0049","host_type":"journal"}],"fields_of_study":["Advanced MRI Techniques and Applications","Medical Imaging Techniques and Applications","Sparse and Compressive Sensing Techniques"],"mesh_terms":[],"keywords":["Autoencoder","Raw data","Deep learning","Artificial intelligence","Computer science","Generalization","Iterative reconstruction","Pattern recognition (psychology)","Computer vision","Mathematics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T00:15:01.053405Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}