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By leveraging an adapted version of Stein’s unbiased risk estimator (SURE) and exploiting a phase-corrected combination of repeated acquisitions, we outperform both state-of-the-art self-supervised denoising methods and conventional non-learning-based approaches. Additionally, we demonstrate the applicability of our proposed approach in accelerating DWI scans by acquiring fewer image repetitions. To evaluate denoising performance, we introduce a self-supervised methodology that relies on analyzing the characteristics of the residual signal removed by the denoising approaches.</jats:p>","journal":"Scientific Reports","year":2024,"id":643219,"datarank":0.41588830833596724,"base_score":2.772588722239781,"endowment":2.772588722239781,"self_citation_contribution":0.41588830833596724,"citation_network_contribution":0.0,"self_endowment_contribution":0.41588830833596724,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"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":689279,"name":"Omar Darwish","orcid":"0000-0003-4747-9007","position":1,"is_corresponding":false},{"id":708805,"name":"Fabian Wagner","orcid":"0000-0003-3429-2374","position":2,"is_corresponding":false},{"id":471993,"name":"Mareike Thies","orcid":"0000-0002-1364-4337","position":3,"is_corresponding":false},{"id":1673472,"name":"Nastassia Vysotskaya","orcid":null,"position":4,"is_corresponding":false},{"id":1673473,"name":"Julian Hossbach","orcid":null,"position":5,"is_corresponding":false},{"id":1673474,"name":"Elisabeth Weiland","orcid":null,"position":6,"is_corresponding":false},{"id":480638,"name":"Thomas Benkert","orcid":"0000-0002-3794-5580","position":7,"is_corresponding":false},{"id":862279,"name":"Cornelius Eichner","orcid":"0000-0001-5611-6748","position":8,"is_corresponding":false},{"id":480639,"name":"Dominik Nickel","orcid":"0000-0002-0360-7233","position":9,"is_corresponding":false},{"id":1673475,"name":"Tobias Wuerfl","orcid":null,"position":10,"is_corresponding":false},{"id":1302536,"name":"Andreas Maier","orcid":"0000-0003-1309-5435","position":11,"is_corresponding":false},{"id":1673471,"name":"Laura Pfaff","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation","abstract":"<jats:title>Abstract</jats:title><jats:p>Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information about the Brownian motion of water molecules within biological tissues. DWI plays a crucial role in stroke imaging and oncology, but its diagnostic value can be compromised by the inherently low signal-to-noise ratio (SNR). Conventional supervised deep learning-based denoising techniques encounter challenges in this domain as they necessitate noise-free target images for training. This work presents a novel approach for denoising and evaluating DWI scans in a self-supervised manner, eliminating the need for ground-truth data. By leveraging an adapted version of Stein’s unbiased risk estimator (SURE) and exploiting a phase-corrected combination of repeated acquisitions, we outperform both state-of-the-art self-supervised denoising methods and conventional non-learning-based approaches. Additionally, we demonstrate the applicability of our proposed approach in accelerating DWI scans by acquiring fewer image repetitions. 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