[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"diffusion-magnetic-resonance-imaging\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:diffusion-magnetic-resonance-imaging":28},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,3,0,[8,47,72],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":30,"overallStatus":35,"whyStopped":4,"lastUpdateSubmitDate":36,"lastUpdatePostDateStruct":37,"startDateStruct":40,"completionDateStruct":42,"leadSponsor":44,"locationsCount":4},"100643796","non-contrast-breast-mri-diagnosis-and-risk-stratification-using-dwi-generated-synthetic-contrast-enhancement-100643796",false,"NCT07598084","Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement","Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI: Development and Clinical Validation of a Diffusion-Weighted Imaging-Based Synthetic Contrast-Enhanced MRI System for Non-Contrast Breast Cancer Diagnosis and Risk Stratification","Inclusion Criteria:\n\n1. Complete breast MRI data;\n2. Negative pathology biopsy results or negative follow-up examinations for at least 12 months for non-cancer cases;\n3. Positive biopsy results that meet the requirements for the pathological subtype of cancer for cancer cases;\n4. Original data that can be used to verify clinical status, including radiological and pathological reports;\n\nExclusion Criteria:\n\n1. Partial mastectomy or puncture biopsy on the diseased side of the breast prior to breast MRI examination;\n2. Poor image quality;\n3. Implants in the affected breast;",true,"ALL","18 Years",{"count":20,"type":21},12000,"ESTIMATED","OBSERVATIONAL","This study is conducted under the ethics-approved project titled \"Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI''.The goal of this observational study is to develop an integrated breast MRI system that uses diffusion-weighted imaging (DWI) to create synthetic contrast-enhanced images. This system aims to diagnose and screen for breast cancer without the need for contrast agents, while using a generated risk score to perform imaging-based triage and risk stratification.\n\nParticipants will include people aged 18 and older who require a breast MRI either for evaluation of a suspicious finding or for high-risk screening.\n\nThis study seeks to answer two main questions:\n\n* Can synthetic contrast-enhanced images generated from DWI match real contrast-enhanced images in their ability to distinguish benign from malignant breast lesions?\n* Can the risk score derived from DWI-based synthetic images enable imaging-level risk stratification, allowing people at lower risk to avoid contrast agent injection? Researchers will compare the quality of synthetic images against real contrast-enhanced images and will recruit radiologists to assess how well these images perform for diagnostic and screening tasks. MRI data from participants undergoing breast MRI will be used to train, validate, and test this integrated system.",[25,26,27,28,29],"Breast Neoplasms","Artificial Intelligence (AI)","Magnetic Resonance Imaging (MRI)","Diffusion Magnetic Resonance Imaging","Deep Learning",[31,32,33,34],"Breast","Magnetic Resonance Imaging","Artificial Intelligence","Deep learning","NOT_YET_RECRUITING","2026-06-05",{"date":38,"type":39},"2026-06-09","ACTUAL",{"date":41,"type":21},"2026-06",{"date":43,"type":21},"2027-05",{"name":45,"class":46},"Peking University People's Hospital","OTHER",{"id":48,"slug":49,"hasResults":11,"nctId":50,"briefTitle":51,"officialTitle":52,"acronym":4,"eligibilityCriteria":53,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":54,"enrollmentInfo":55,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":57,"conditions":58,"keywords":4,"overallStatus":60,"whyStopped":4,"lastUpdateSubmitDate":61,"lastUpdatePostDateStruct":62,"startDateStruct":64,"completionDateStruct":66,"leadSponsor":68,"locationsCount":71},"100601301","diffusion-magnetic-resonance-imaging-dmri-in-the-early-evaluation-of-brain-white-matter-diseases-100601301","NCT07108712","Diffusion Magnetic Resonance Imaging (dMRI) in the Early Evaluation of Brain White Matter Diseases","The Role of Diffusion Magnetic Resonance Imaging (dMRI) in the Early Evaluation of Brain White Matter Diseases","Inclusion Criteria:\n\n* Adults aged 18-80 years.\n* Clinical suspicion of early-stage white matter disease.\n* No prior diagnosis of significant neurological disorders (e.g., stroke, brain tumors).\n* Ability to provide informed consent.\n\nExclusion Criteria:\n\n* Pregnancy or breastfeeding.\n* Contraindications for magnetic resonance imaging (MRI) (e.g., metal implants, pacemakers).\n* Severe psychiatric or cognitive impairment that prevents participation.\n* Control Group: Healthy individuals without any clinical history of neurological diseases, matched by age and sex to the patient cohort.","80 Years",{"count":56,"type":21},150,"To assess the sensitivity and accuracy of diffusion magnetic resonance imaging (MRI), including diffusion-weighted imaging (DWI) metrics, in identifying early-stage changes in brain white matter related to various white matter diseases.",[28,59],"Brain White Matter Diseases","RECRUITING","2025-07-31",{"date":63,"type":39},"2025-08-07",{"date":65,"type":39},"2025-05-01",{"date":67,"type":21},"2025-12-01",{"name":69,"class":70},"The General Authority for Teaching Hospitals and Institutes","NETWORK",1,{"id":73,"slug":74,"hasResults":11,"nctId":75,"briefTitle":76,"officialTitle":76,"acronym":77,"eligibilityCriteria":78,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":79,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":81,"conditions":82,"keywords":4,"overallStatus":60,"whyStopped":4,"lastUpdateSubmitDate":89,"lastUpdatePostDateStruct":90,"startDateStruct":92,"completionDateStruct":94,"leadSponsor":96,"locationsCount":71},"100504038","the-stereo-dbs-study-7-tesla-mri-brain-network-analysis-for-deep-brain-stimulation-100504038","NCT05843084","The STEREO-DBS Study: 7-Tesla MRI Brain Network Analysis for Deep Brain Stimulation","STEREO-DBS","1.2 Inclusion criteria\n\nIn order to be eligible to participate in this study, a subject must meet all of the following criteria:\n\n* Age \\> 18 years;\n* Idiopathic PD who underwent STN DBS\n\n1.3 Exclusion criteria\n\nA potential subject who meets any of the following criteria will be excluded from participation in this study:\n\n* Legally incompetent adults;\n* No written informed consent.",{"count":80,"type":21},500,"Rationale: Deep brain stimulation (DBS) of the nucleus subthalamicus (STN) is an effective surgical treatment for the patients with advanced Parkinson's disease, despite optimal pharmacological treatment. However, individual improvement after DBS remains variable and 50% of patients show insufficient benefit. To date, DBS-electrode placement and settings in the highly connected STN are based on 1,5-Tesla or 3-Tesla MR-images. These low resolution and solely structural modalities are unable to visualize the multiple brain networks to this small nucleus and prevent electrode activation directed at its cortical projections. By using structural 7-Tesla MRI (7T MRI) connectivity to visualize (malfunctioning) brain networks, DBS-electrode placement and activation can be individualized.\n\nObjective: Primary objective of the study is to determine whether visualisation of cortical projections originating in the STN and the position of the DBS electrode relative to these projections using 7T MRI improves motor symptoms as measured by the disease-specific Unified Parkinson's Disease Rating Scale (UPDRS-III).\n\nSecondary outcomes are: disease related daily functioning, adverse effects, operation time, quality of life, patient satisfaction with treatment outcome and patient evaluation of treatment burden.\n\nStudy design: The study will be a single center prospective observational study.\n\nStudy population: Enrollment will be ongoing from April 2022. Intervention (if applicable): No intervention will be applied. Application of 7T MRI for DBS is standard care and outcome scores used will be readily accessible from the already existing advanced electronic DBS database.\n\nMain study parameters\u002Fendpoints: The primary outcome measure is the change in motor symptoms as measured by the disease-specific Unified Parkinson's Disease Rating Scale (UPDRS-III). This is measured after 6 months of DBS as part of standard care. The secondary outcome measures are the Amsterdam Linear Disability Score for functional health status, Parkinson's Disease Questionnaire 39, Starkstein apathy scale, patient satisfaction with the treatment, patient evaluation of treatment burden, operating time, hospitalization time, change of tremor medication, side effects and complications.\n\nNature and extent of the burden and risks associated with participation, benefit and group relatedness: The proposed observational research project involves treatment options that are standard care in daily practice. The therapies will not be combined with other research products. Participation in this study constitutes negligible risk according to NFU criteria for human research.",[83,28,84,85,86,87,88],"Parkinson Disease","Brain","Subthalamic Nucleus","Deep Brain Stimulation","Human","Treatment Outcome","2025-07-22",{"date":91,"type":39},"2025-07-25",{"date":93,"type":39},"2022-04-11",{"date":95,"type":21},"2033-04-11",{"name":97,"class":46},"Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)"]