{"doi":"10.1117/1.jmi.12.6.064003","title":"Cross-modality 3D MRI synthesis via cycle-guided denoising diffusion probability model","abstract":"PurposeWe propose a deep learning framework, the cycle-guided denoising diffusion probability model (CG-DDPM), for cross-modality magnetic resonance imaging (MRI) synthesis. The CG-DDPM aims to generate high-quality MRIs of a target modality from an existing modality, addressing the challenge of missing MRI sequences in clinical practice.ApproachThe CG-DDPM employs two interconnected conditional diffusion probabilistic models, with a cycle-guided reverse latent noise regularization to enhance synthesis consistency and anatomical fidelity. The framework was evaluated using the BraTS2020 dataset, which includes three-dimensional brain MRIs with T1-weighted, T2-weighted, and FLAIR modalities. The synthetic images were quantitatively assessed using metrics such as multi-scale structural similarity measure (MSSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE). The CG-DDPM was benchmarked against state-of-the-art methods, including IDDPM, IDDIM, and MRI-cGAN.ResultsThe CG-DDPM demonstrated superior performance across all cross-modality synthesis tasks (T1 → T2, T2 → T1, T1 → FLAIR, and FLAIR → T1). It consistently achieved the highest MSSIM values (ranging from 0.966 to 0.971), the lowest MAE (0.011 to 0.013), and competitive PSNR values (27.7 to 28.8 dB). Across all tasks, CG-DDPM outperformed IDDPM, IDDIM, and MRI-cGAN in most metrics and exhibited significantly lower uncertainty and inconsistency in MC-based sampling. Statistical analyses confirmed the robustness of CG-DDPM, with p-values<0.05 in key comparisons.ConclusionsThe proposed CG-DDPM provides a robust and efficient solution for cross-modality MRI synthesis, offering improved accuracy, stability, and clinical applicability compared with existing methods. This approach has the potential to streamline MRI-based workflows, enhance diagnostic imaging, and support precision treatment planning in medical physics and radiation oncology.","journal":"Journal of Medical Imaging","year":2025,"id":548860,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9576,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":872228,"name":"Shaoyan Pan","orcid":"0009-0007-1040-0189","position":1,"is_corresponding":false},{"id":849942,"name":"Chih‐Wei Chang","orcid":"0000-0002-3818-4381","position":2,"is_corresponding":false},{"id":1017022,"name":"Richard L. J. Qiu","orcid":"0000-0002-7877-1900","position":3,"is_corresponding":false},{"id":1018421,"name":"Junbo Peng","orcid":"0000-0002-5095-3016","position":4,"is_corresponding":false},{"id":236221,"name":"Tonghe Wang","orcid":"0000-0001-9021-1204","position":5,"is_corresponding":false},{"id":702655,"name":"Justin Roper","orcid":null,"position":6,"is_corresponding":false},{"id":289399,"name":"Hui Mao","orcid":"0000-0002-0147-6022","position":7,"is_corresponding":false},{"id":323946,"name":"David S. Yu","orcid":"0000-0002-2781-5379","position":8,"is_corresponding":false},{"id":233133,"name":"Xiaofeng Yang","orcid":"0000-0002-6854-6195","position":9,"is_corresponding":false},{"id":872494,"name":"Mingzhe Hu","orcid":"0000-0001-9808-4967","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:54:03.053965Z","pmid":"41292517","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":[]}