{"doi":"10.1109/tmi.2020.3002708","title":"Hierarchical Nonlocal Residual Networks for Image Quality Assessment of Pediatric Diffusion MRI With Limited and Noisy Annotations","abstract":"Fast and automated image quality assessment (IQA) of diffusion MR images is crucial for making timely decisions for rescans. However, learning a model for this task is challenging as the number of annotated data is limited and the annotation labels might not always be correct. As a remedy, we will introduce in this paper an automatic image quality assessment (IQA) method based on hierarchical non-local residual networks for pediatric diffusion MR images. Our IQA is performed in three sequential stages, i.e., 1) slice-wise IQA, where a nonlocal residual network is first pre-trained to annotate each slice with an initial quality rating (i.e., pass/questionable/fail), which is subsequently refined via iterative semi-supervised learning and slice self-training; 2) volume-wise IQA, which agglomerates the features extracted from the slices of a volume, and uses a nonlocal network to annotate the quality rating for each volume via iterative volume self-training; and 3) subject-wise IQA, which ensembles the volumetric IQA results to determine the overall image quality pertaining to a subject. Experimental results demonstrate that our method, trained using only samples of modest size, exhibits great generalizability, and is capable of conducting rapid hierarchical IQA with near-perfect accuracy.","journal":"IEEE Transactions on Medical Imaging","year":2020,"id":78024,"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":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9484,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":368634,"name":"Kim-Han Thung","orcid":"0000-0003-1379-2185","position":1,"is_corresponding":false},{"id":314308,"name":"Weili Lin","orcid":"0000-0002-2900-8204","position":2,"is_corresponding":false},{"id":270449,"name":"Dinggang Shen","orcid":"0000-0002-7934-5698","position":3,"is_corresponding":false},{"id":289667,"name":"Pew‐Thian Yap","orcid":"0000-0003-1489-2102","position":4,"is_corresponding":false},{"id":368633,"name":"Siyuan Liu","orcid":"0000-0002-0946-4683","position":0,"is_corresponding":true}],"reference_count":93,"raw_metadata":null,"created_at":"2026-07-18T21:49:40.769098Z","pmid":"32746115","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":[]}