{"doi":"10.1016/j.mri.2025.110395","title":"Depth-dependent characterization of cartilage nanostructures using MRI signal decays","abstract":"The multi-exponential nature of echo decay in nuclear magnetic resonance exam of cartilage complicates the determination of relaxation times. In this study, a novel method has been developed and applied to analyze the cartilage nanostructure using multi-exponential signals. This approach eliminates the need for relaxation time determination, avoids sample rotation, and removes the requirement for multiple experiments. A key feature of this method is its ability to provide detailed insights into the nanostructures of the sample. Quantitative T 2 imaging method was used to examine the signal delays in mature and healthy canine articular cartilage, at a transverse resolution of 35.1 μm. A modeling method was used to analyze the multi-exponential echo decay for each resolved tissue depth along the full thickness of articular cartilage. The developed approach provides detailed information on the nanostructure in the tissue, which varies with cartilage depth. The information contains the volumes of the water-filled nanocavities created by the fibril structure and their orientation. This information reveals that the superficial and transitional anatomic zones of cartilage contain two distinct types of nanocavities, while the radial zone contains only one type. The proposed voxel-based method of echo decay analysis enables the estimation of nanocavities, their angular distribution, and spatial variations of the nanocavity characteristics throughout the sample. This newly developed approach demonstrated that detailed structural tissue information can be obtained as a depth function, representing a significant advancement in understanding cartilage nanostructures and holds potential for future medical applications. Fig. Schematic representation of articular cartilage as a set of fibrils and water-filled nanocavities (black curves and blue ellipses, respectively). ζ is the angle between the cavity axis Z C and the cartilage axis N . In biological tissues containing water-filled nanocavities, various relaxations observed in NMR and MRI experiments are multicomponent. We applied a method for analyzing multi-exponential magnetic resonance signals, that does not require the determination of relaxation times. In contrast to earlier investigations, this method eliminates the need for sample rotation and a series of experiments and is applied to study the depth-dependence of tissue nanostructure. Within this method, we model tissues as a network of water-filled nanocavities (Fig.), with the signal from each of them being exponential. Fitting a single signal decay from a voxel, we have estimated the polar angle, ζ, distributions of water-filled nanocavities and their averaged volume in the voxel. The obtained dependence of the angular distribution of nanocavities on the depth correlates well with the anatomic structure of cartilage and the results obtained by small-angle X-ray scattering and electron microscopy studies. • Multiexponential MRI signals from each 35.1 μm voxel for cartilage are analyzed. • Analysis is based on the representation of tissue as a set of water-filled nanocavities. • Cavity dimensions in each anatomic cartilage zone were estimated. • The degree of orientational ordering of nanocavities in each zone was obtained • The tissue nanostructure depth-dependence was determined • The obtained depth-dependences correlate well with the anatomical cartilage structure","journal":"Magnetic Resonance Imaging","year":2025,"id":550492,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9589,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"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":748227,"name":"G. B. Furman","orcid":"0000-0001-7303-9414","position":1,"is_corresponding":false},{"id":748230,"name":"Vladimir Sokolovsky","orcid":"0000-0003-4887-413X","position":2,"is_corresponding":false},{"id":531678,"name":"Farid Badar","orcid":"0000-0002-5545-2296","position":3,"is_corresponding":false},{"id":531679,"name":"Yang Xia","orcid":"0000-0003-4653-1862","position":4,"is_corresponding":false},{"id":1314244,"name":"Theodore Aptekarev","orcid":"0000-0003-0869-2331","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-19T02:54:16.596730Z","pmid":"40254173","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":[]}