De-Identification of Magnetic Resonance Imaging to Protect Patient Privacy in Research Use: A Comprehensive Review

Abstract

Objectives

Brain magnetic resonance imaging (MRI) contains identifiable facial and cranial features, creating privacy risks that can limit secondary research use. This review examines current MRI de-identification technologies, quantitative validation methods, and governance frameworks to identify practical strategies for preserving data utility while protecting patient privacy.

Methods

A descriptive narrative review was conducted across technical and policy domains. Studies of facial de-identification were analyzed according to the tools used, validation procedures, and downstream analytic performance. The reviewed approaches included traditional defacing, refacing, and deep-learning-based anonymization. Evaluation frameworks used the structural similarity index measure (SSIM), Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), and the paired t-test to quantify both privacy preservation and analytic fidelity. A parallel policy analysis compared the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), Japan’s Act on Anonymized Medical Information, Taiwan’s Personal Data Protection Act, and South Korea’s Personal Information Protection Act and 2024 Health Data Use Guidelines to assess policy convergence and institutional consistency.

Results

Visual inspection studies reported that FreeSurfer preserved cortical anatomy but incompletely removed facial features, whereas FSL_deface overmasked some nonfacial regions. Artificial intelligence (AI)-based recognition tests achieved 28%–38% accuracy on defaced data, confirming measurable residual re-identification risk. Quantitative assessments identified segmentation degradation, including a DSC decrease from 0.970 to 0.918, and regional volumetric variability, including a hippocampal ICC of 0.742, with p < 0.05. Generative adversarial network-based refacing improved perceptual similarity, with SSIM values >0.7, but retained subtle facial geometry. The governance analysis indicated that HIPAA and GDPR provide established standards, whereas South Korea’s Data Review Board oversight remains discretionary and nonuniform, limiting reproducibility across institutions.

Conclusions

MRI de-identification requires integrated pipelines that combine AI-based facial masking and metadata cleansing with standardized evaluation metrics and enforceable review protocols.

I. Introduction

Brain magnetic resonance imaging (MRI) provides high-resolution, three-dimensional visualization of soft tissues and is essential for clinical diagnosis and neuroscience research [1]. However, these scans also capture facial and cranial structures that can be reconstructed into recognizable faces using open-source software, creating substantial re-identification and privacy risks [2].

Accordingly, major data-protection frameworks require de-identification. The U.S. Health Insurance Portability and Accountability Act (HIPAA) specifies “Safe Harbor and Expert Determination” methods [3,4], whereas the European Union’s General Data Protection Regulation (GDPR) establishes explicit standards for anonymization and pseudonymization [4–6]. In South Korea, the Personal Information Protection Act and the 2024 Health Data Use Guidelines require identifiable information to be removed before data sharing. However, institutional Data Review Board (DRB) oversight remains heterogeneous and lacks imaging-specific standards. Although de-identification protects privacy, it may also compromise analytic validity: excessive removal of facial or adjacent cranial image regions can distort brain volumes, degrade segmentation, and bias morphometric analyses. Recent studies have shown that defacing can alter cortical volumes by more than 3% and affect quality-control metrics [7].

Conventional defacing and Digital Imaging and Communications in Medicine (DICOM) metadata anonymization remain common but may result in incomplete facial removal (Type I failure) or overmasking of nonfacial structures (Type II failure). Emerging methods, including refacing with population templates [8] and deep learning (DL)-based quality-control pipelines [9], aim to reduce privacy risk while preserving analytic integrity. This review integrates three domains: (1) technical strategies, including defacing, refacing, DL, and metadata cleansing; (2) evaluation frameworks that link privacy protection with data utility; and (3) governance issues, with emphasis on Korean DRB standardization. Together, these perspectives outline a roadmap for standardized, artificial intelligence (AI)-integrated, and ethically grounded MRI data-sharing practices.

II. Technical Aspects of Brain MRI De-Identification

1. Characteristics of Brain MRI and Privacy Risks

1) Facial reconstruction

Brain MRI provides exceptional soft-tissue contrast and high spatial resolution, capturing detailed three-dimensional (3D) anatomical structures of the entire head. These structural scans, particularly 3D T1-weighted sequences, are essential for clinical diagnosis and neuroscience research but also include facial and cranial morphology outside the intracranial volume [1]. Facial features derived from T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and fluid-attenuated inversion recovery (FLAIR) sequences may support re-identification. Advances in imaging resolution, surface-extraction algorithms, and widely available visualization tools allow high-fidelity reconstructions of the head surface to be generated [10]. As computational methods improve, the ability to convert MRI data into realistic facial renderings continues to increase. High-fidelity reconstructions from multiple sequences, including T1-weighted, T2-weighted, FLAIR, gradient-echo, and DWI sequences, may therefore support facial reconstruction [1,9–13].

2) Re-identification risks

When facial landmarks and unique biometric structures, such as the eyes, nose, mouth, and jaw, are preserved in MRI data, generated 3D reconstructions may closely resemble photographic facial features. These renderings can allow adversaries to use publicly available facial recognition software (FRS) to match reconstructed faces against personal photographs or facial databases. This capability shifts the risk from a theoretical concern to a tangible and quantifiable threat, allowing metrics such as cosine similarity between facial embeddings to distinguish intra-patient from inter-patient images. The demonstrated success of automated matching against facial databases challenges anonymity in neuroimaging research and underscores the need for rigorous de-identification protocols [14].

3) Verification of de-identification performance

Practical evaluation of de-identification performance must address the privacy–utility trade-off rigorously (Figure 1). This evaluation requires a dual assessment: confirming the complete removal of personally identifiable information and preserving the analytic validity of nonidentifiable data [8]. Along the privacy axis, performance is validated by demonstrating successful removal of facial features and the inability of FRS to re-identify the subject. Along the utility axis, de-identification should minimize data alteration because defacing algorithms can affect downstream DL models, including models used for brain-age prediction. This issue is important because facial features have been found to contribute to predictive performance in tasks such as age estimation, suggesting that inadvertent removal of adjacent informative brain tissue, or removal of correlated facial data, may reduce model accuracy. Therefore, verification should confirm adequate privacy protection without sacrificing the information needed for specialized quantitative analyses [7,15].

Figure 1.

Trade-off between personal privacy protection and data utility in research.

2. Methods for Facial De-Identification in MRI

1) Metadata-based anonymization

Metadata anonymization is a prerequisite for data sharing and focuses on removing or modifying personally identifiable information in the DICOM header. However, this method is inherently limited because it cannot mitigate re-identification risk arising from image pixel data, such as unique facial features or embedded text [1,16]. Practical implementation involves complexities beyond applying the DICOM standard’s Basic Application Level Confidentiality Profile. Researchers must address vendor-specific private tags, which often contain undocumented identifiers, as well as the unresolved question of how unique identifiers should be modified to maintain data traceability [1]. To support exhaustive cleansing, robust pipelines often use a two-stage de-identification process and configurable rules for handling different sensitive attributes (Table 1). Specialized mechanisms are also needed for burned-in annotations, or text labels embedded directly within image pixels; these annotations are often missed by simple header stripping and require supplementary cleanup methods, such as blacking out or optical character recognition (OCR)-based detection. Metadata security should therefore be integrated with image-based de-identification to provide comprehensive privacy protection [16].

Table 1.

Metadata anonymization in MRI

Study	Methods	Approach	Key points
Shahid et al. [1]	Custom DICOM Parser	Two-stage metadata anonymization	Classified DICOM attributes to remove identifiers selectively Preserved essential clinical fields Maintained DICOM standard compliance
Gonzalez et al. [16]	Open-source DICOM Toolkit	Metadata cleansing and burned-in text removal	Vendor-independent architecture Removed pixel-level annotations Supported cross-vendor MRI data formats

MRI: magnetic resonance imaging, DICOM: Digital Imaging and Communications in Medicine.

2) Tool-based methods

Conventional facial de-identification relies on rigid mask-based defacing or skull stripping, which offer high computational efficiency and broad availability (Table 2). Skull stripping isolates brain tissue, whereas defacing specifically targets facial features for privacy protection. Studies have reported variable efficacy and analytic impact. Although methods such as FreeSurfer and FSL defacing generally preserve structural brain measures, they can introduce small systematic biases [17]. Highly detailed structural sequences, including 3D T1-weighted, T2-weighted, and T2-FLAIR sequences, remain highly re-identifiable before processing, with reported rates of 96%–98% [10], underscoring the severity of the privacy risk. Other tools, however, have been robust for anonymization without substantially affecting global brain volumetry accuracy [13]. Despite their simplicity, these techniques are prone to registration failures, which can lead to Type I failures, defined as residual identifiable features, or Type II failures, defined as inadvertent loss of brain tissue that compromises data integrity. Defacing remains a minimum standard because of its accessibility, but careful tool selection and parameter optimization are essential for preserving data utility.

Table 2.

Tool-based approaches for facial de-identification in MRI

Study	Methods	Approach	Key points
Schwarz et al. [10]	MRI sequence classifier	Re-identification risk analysis	Recognition success rate 28%–38% after defacing Highlighted residual privacy vulnerability
Ryu et al. [13]	mri_deface BioImage Suite Web, Defacer	Volumetric defacing comparison	Quantified volumetric impact across methods Hippocampal ICC drop to 0.742 Demonstrated method sensitivity
Buimer et al. [17]	FreeSurfer, FSL	Conventional defacing algorithms	Compared multiple tools across age groups Reported minor cortical/subcortical volume bias Maintained structural integrity

MRI: magnetic resonance imaging, ICC: intraclass correlation coefficient.

3) Deep learning-based methods

DL methods offer more precise and robust MRI de-identification by treating facial-feature removal as an image-segmentation task. Convolutional neural networks (CNNs), particularly U-Net architectures, are trained to detect facial features automatically and generate precise masks, thereby removing identifiable information while preserving as much brain anatomy as possible (Table 3).

Table 3.

Deep learning approaches for facial de-identification in MRI

Study	Methods	Approach	Key points
Delbarre et al. [9]	CNN QC Classifier	Automated defacing quality assessment	235,000 MRI dataset Three QC categories 92% accuracy, 40%–50% reduced manual effort
Jeong et al. [11]	Deface (2D U-Net)	CNN-based facial segmentation and masking	Detected and replaced eyes, nose, ears voxels Complete feature removal verified via Azure Face API Preserved brain structures
Khazane et al. [18]	DeepDefacer (3D U-Net)	Volumetric CNN face masking	Multi-site MRI training Maintained segmentation consistency

MRI: magnetic resonance imaging, CNN: convolutional neural network, QC: quality control.

Several DL approaches illustrate this strategy. Jeong et al. [11] used a U-Net architecture to identify and de-identify the eyes, nose, and ears slice by slice, providing a DL-based software solution. Khazane et al. [18] extended this concept volumetrically by developing DeepDefacer, a streamlined 3D U-Net trained on multisite MRI data. DeepDefacer achieved high face-masking accuracy, with a Dice coefficient of 0.85 compared with 0.88 for a larger baseline model, while using fewer parameters, highlighting the balance between performance and computational efficiency. These methods are considered valid when post-defacing automated brain measurements, including volumes and segmentations, remain consistent, confirming that the methods selectively alter only nonbrain voxels. DL has also been integrated into quality-control (QC) processes. Delbarre et al. [9] applied a CNN to classify FSL-defaced results. Trained on a large dataset of 235,000 scans, their model achieved 92% accuracy in identifying defaced images and classifying three types of masking failures, allowing nearly half of scans to bypass manual checks. This automated approach reduced manual QC time by 40%–50% in the processing pipeline. The robustness of these DL-based methods, as confirmed by validation against automated face-recognition systems, lies in their ability to selectively modify nonbrain voxels while preserving the consistency of downstream volumetric and segmentation measures.

3. Implementation and QC

1) Overview of practical implementation needs

Effective de-identification requires robust implementation pipelines and stringent QC [19]. Inadequate protocols pose three primary threats: residual facial features that increase privacy risk, inadvertent removal of brain tissue that threatens data validity, and incomplete metadata anonymization [20,21]. This risk is compounded by evidence that a high structural similarity index measure (SSIM) can persist after de-identification, suggesting the possibility of residual facial geometry. Comprehensive de-identification therefore requires an integrated approach that addresses both image-based methods for voxel-level facial features and metadata-based methods for DICOM headers and burned-in annotations.

2) Image-based deep learning for face removal and QC

DL automates and improves facial de-identification in MRI by using CNNs for precise image segmentation [11]. Attention-gated 2D U-Nets, as implemented in the open-source Deface tool, replace facial features, including the eyes, nose, and ears, slice by slice; this approach has achieved complete feature removal while preserving brain anatomy, as validated against automated recognition systems such as the Azure Face API. Advanced 3D models, such as DeepDefacer, a compact 3D U-Net, have achieved high mask accuracy, with a Dice coefficient of 0.85 compared with 0.88 for the baseline model, while using fewer parameters and improving efficiency relative to traditional tools [7]. The main strength of DL-based models is their selective targeting of nonbrain voxels, which maintains volumetric and segmentation consistency while minimizing Type II failures, or brain-tissue loss [18]. DL has also been integrated into QC pipelines to classify masking failures automatically, reducing reliance on labor-intensive visual inspection.

4. Evaluation of Brain MRI De-Identification

1) Expert visual review

Although expert visual review remains a necessary component of final validation, it has limited utility as the primary mechanism for validating de-identified data. Trained raters must inspect two-dimensional axial, sagittal, and coronal slices, often supported by 3D surface renderings, to detect two main error types: Type I failure, defined as incomplete removal of facial features that may allow re-identification, and Type II failure, defined as inadvertent removal of brain tissue that compromises data integrity [7,11,12,17]. Tool-specific deficiencies illustrate this challenge. Buimer et al. [17] reported that FreeSurfer-based defacing generally preserved brain anatomy but lacked needed ear-removal options, whereas FSL_deface frequently extended into nonfacial regions. Despite its role in final QC, visual review is resource-intensive, subjective, and difficult to scale for the large population-level datasets used in modern neuroimaging research.

2) AI-based evaluation

To address the subjectivity and scalability limitations of manual review, AI-based FRS combined with embedding-similarity metrics can provide scalable and objective methods for quantifying residual privacy risk [7]. These approaches simulate a realistic privacy threat in which an adversary attempts to match anonymized MRI-derived face renderings to real-world photographs using automated algorithms [10]. Schwarz et al. [10] used this method and demonstrated persistent limitations of conventional defacing: recognition success rates ranged from 28% for FSL_deface to 38% for Py-Deface, quantifying residual risk. These verification systems rely on embedding vectors, or numerical representations of faces in latent feature space, and cosine similarity between embeddings is widely used to quantify identity retention. Although recent studies have incorporated FRS outcomes into comprehensive validation frameworks to support reproducible risk assessment [2,22], these pipelines may overestimate risk because FRS models are typically optimized for photographic rather than medical imaging data. This limitation underscores the need for domain-specific benchmarks (Table 4).

Table 4.

Representative studies evaluating the effectiveness of MRI de-identification

Study	Tool	Method/Metirc	Key point
Buimer et al. [17]	FreeSurfer, FSL	Expert visual inspection	Manual inspection across age groups FreeSurfer preserved brain anatomy FSL_deface over-removed non-facial regions
Schwarz et al. [10]	Azure Face API	AI-based face recognition risk testing	3D surface-based identity classifier Recognition rate 28%–38% Residual re-identification risk confirmed
Gao et al. [7]	FreeSurfer	DSC for segmentation reproducibility	Higher segmentation failure rate in defaced data Reduced pipeline robustness in FreeSurfer analysis
Ryu et al. [13]	mri_deface, FreeSurfer	ICC and paired t-test comparison	Global ICC > 0.995; hippocampal ICC 0.742 Significant volumetric differences (p < 0.05)
Bhalerao et al. [19]	PyDeface, FSL	Image quality metrics (SNR, CNR, entropy)	Signal and contrast degradation after defacing Verified signal loss via multi-metric analysis
Abramian & Eklund [23]	GAN model	SSIM for refaced images	SSIM > 0.7 between original and refaced MRIs Residual facial structures remained

MRI: magnetic resonance imaging, DSC: Dice similarity coefficient, ICC: intraclass correlation coefficient, SNR: signal-to-noise ratio, CNR: contrast-to-noise ratio, SSIM: structural similarity index measure.

3) Quantitative metrics for evaluating defacing effectiveness

Robust evaluation of de-identification requires quantitative metrics that assess both anonymization efficacy and preservation of anatomical integrity. These measures help characterize the privacy–utility trade-off in neuroimaging data [9]. The SSIM compares luminance, contrast, and structure between original and defaced images [7]; values >0.70 in generative adversarial network-refaced scans suggest residual facial geometry and potential re-identification risk [2,22,23]. The intraclass correlation coefficient (ICC) evaluates the consistency of quantitative brain measures. Global volumes often show high ICC values >0.995, whereas some regions appear more vulnerable; for example, hippocampal ICC decreased to 0.742 with certain tools [13].

The paired t-test detects systematic differences in volumetric or quality metrics, whereas the Dice similarity coefficient (DSC) quantifies voxel-level overlap. Gao et al. [7] reported DSC declines from 0.970 to 0.918 in FreeSurfer, reflecting segmentation loss [19]. Together, SSIM and DSC capture spatial similarity, whereas ICC and the paired t-test assess numerical consistency. However, these metrics may not fully represent downstream analytic validity: defaced MRIs can reduce segmentation reproducibility by 3%–5% and increase FreeSurfer failure rates. Subtle facial remnants may still influence morphometry, underscoring the need for task-specific evaluation frameworks that link identity risk with analytic reliability.

5. Legal and Policy Frameworks

1) International standards

Medical imaging data are among the most sensitive categories of health information because they often contain both clinical records and identifiable facial or biometric features (Table 5). The use of such data in research and clinical practice therefore requires strict legal and regulatory oversight to protect patient privacy while enabling appropriate secondary use. Standards for medical-image de-identification have been shaped mainly by the United States’ HIPAA and the European Union’s GDPR [24,25].

Table 5.

Regulatory comparison of de-identification standards in major jurisdictions, with emphasis on biometric data and research use

Country	Regulation	Facial MRI classification	Legal basis for use without consent	Key features
United States	HIPAA	PHI	Allowed if de-identified through Safe Harbor or Expert Determination	Safe Harbor: removal of 18 direct identifiers Expert Determination: Statistical assessment of re-identification risk De-identified data are exempt from HIPAA restrictions
EU	GDPR	Biometric data (special category)	Permitted only with explicit safeguards (e.g., informed consent, substantial public interest)	Outcome-based anonymization required Pseudonymized data remain regulated Accountability lies with data controllers
Japan	Act on Anonymized Medical Information	High-risk biometric identifier	Use permitted only if anonymized irreversibly	Mandatory irreversible anonymization Third-party review required Pseudonymized data continue to be regulated
Taiwan	Personal Data Protection Act	Personal medical and biometric data	Research use allowed after appropriate de-identification and ethics approval	Requires prior de-identification and data-use justification Emphasizes proportionality and purpose limitation
South Korea	PIPA 2020 Data 3 Act 2024 Health Data Guidelines	Personal information Biometric data	Pseudonymized data allowed with internal DRB review	Guidelines are non-binding Standards vary across institutions DRB discretion introduces inconsistency and limits reproducibility

MRI: magnetic resonance imaging, HIPAA: Health Insurance Portability and Accountability Act, PHI: Protected Health Information, GDPR: General Data Protection Regulation, PIPA: Personal Information Protection Act, DRB: Data Review Board.

Under HIPAA, de-identification requires a qualified expert to evaluate the probability of re-identification according to statistical principles. In contrast, the GDPR requires lawful processing of personal data, specifies limited exemptions for health-data use, and establishes the right to data portability. These international models have guided subsequent regulations in the Asia-Pacific region [26]. For example, Japan’s Act on Anonymized Medical Information requires irreversible anonymization and third-party oversight [27], whereas Taiwan’s Personal Data Protection Act requires appropriate de-identification before medical and clinical records are used [28].

2) South Korean governance challenges

In South Korea, the Personal Information Protection Act, the 2020 Data 3 Act, and the 2024 Health Data Use Guidelines collectively provide the legal foundation for medical-data governance. These frameworks permit pseudonymized data to be used for scientific research without individual consent, contingent on review and approval by an institutional DRB [29]. However, the current DRB system creates substantial heterogeneity in enforcement. Each DRB operates independently and retains broad discretion in assessing the adequacy of pseudonymization, the presence of residual identifiers, and the robustness of data-security measures. This issue is compounded because the 2024 Guidelines, although they provide recommended criteria, are advisory rather than mandatory. As a result, institutional requirements vary substantially: some DRBs require complete facial defacing, whereas others permit partial visibility, and requirements for external-expert involvement also differ.

Such inconsistencies create direct barriers to interoperability and reproducibility in multicenter research. The Organisation for Economic Co-operation and Development has also identified related domestic challenges in Korea, including a lack of social consensus on data sharing, the absence of a clear legal framework for research-data access, and inconsistent interpretation of regulations [30]. This fragmentation underscores the need for national standardization, mandatory protocols, and strengthened oversight to support privacy protection and reliable data sharing in AI-driven neuroimaging research.

III. Discussion

1. Challenges in Practical Implementation

The reviewed findings show both advances and remaining limitations in MRI de-identification. Quantitative metrics such as SSIM, ICC, and DSC provide structured methods for assessing privacy protection and data fidelity. However, inconsistent DRB practices in South Korea indicate a need for standardized and enforceable policies that balance regulatory compliance with reproducible research. Refacing and synthetic masking are promising approaches, whereas metadata anonymization remains an underaddressed source of risk. No single method currently ensures both adequate privacy protection and data utility preservation. Future priorities should include the following:

• Establish standardized benchmarks that link privacy risk with analytic validity.

• Harmonize governance frameworks, including DRB accreditation.

• Integrate advanced methods, including DL-based refacing, masking, and metadata cleansing, into scalable multicenter workflows.

Beyond these methods, federated learning offers an alternative approach by enabling model training without direct data transfer. Effective MRI de-identification ultimately requires coordination among technical innovation, regulatory rigor, and ethical oversight to support secure and reproducible data sharing in AI-enabled research.

2. Refacing and Synthetic Masking

Refacing offers a practical compromise in the privacy–utility trade-off by replacing facial structures with synthetic or template-derived faces rather than removing them entirely [8,31]. This approach preserves skull contours and nonbrain anatomy, thereby reducing the domain shift caused by blunt defacing. For example, mri_reface substitutes subject voxels with a population-averaged head, producing realistic renderings with minimal morphometric distortion. Generative adversarial network-based refacing further generates plausible facial features while maintaining near-identical volumetric measures and markedly lowering re-identification rates [23]. Although full defacing maximizes privacy protection, it can distort morphometry or impair segmentation. Refacing and synthetic masking tools, such as AnonyMI, may better balance fidelity and privacy by substantially reducing automated face-matching accuracy [32]. However, residual risk remains if synthetic faces lack randomness or metadata persist. Because full-face images can constitute protected health information, future work should refine synthetic methods and standardize validation procedures to quantify and minimize residual risk in multicenter neuroimaging studies.

3. Metadata De-Identification and Burned-in Annotation Removal

Extending the earlier discussion of metadata anonymization, subsequent analyses confirm that metadata-level risks persist, particularly because burned-in identifiers and residual header traces may remain after standard DICOM cleaning procedures. Identifiable information in MRI data resides not only in pixel data but also in DICOM headers. HIPAA and GDPR recognize standard attributes, such as patient names and unique identifiers, as protected health information and require their removal. This process is complicated by vendor-specific private tags, which may contain undocumented identifiers and therefore require either removal of all private tags or selective sanitization based on vendor-specific knowledge. Open-source tools such as DICOM Cleaner and converters such as dcm2niix offer configurable pipelines for header anonymization [33]. Burned-in annotations, or text labels embedded directly into image pixels, pose an additional pixel-level challenge that simple header stripping often misses and that may require supplementary steps such as OCR detection. Despite advances in integrating OCR with header cleansing, metadata de-identification remains inconsistently implemented. This heterogeneity creates residual re-identification risk even when facial de-identification is successful. Standardized tools that combine header cleansing, private-tag handling, and automated burned-in annotation detection are therefore needed to ensure that MRI de-identification comprehensively addresses both pixel-level and metadata-level risks.

4. Synthesis and Future Directions

The reviewed findings show both progress and limitations in current MRI de-identification strategies. As discussed above, quantitative metrics provide an important foundation but should be complemented by downstream task validation to ensure analytic reliability in applied research settings. The implementation challenges, particularly variability in South Korean DRB practices, underscore the need for standardized and enforceable policies that balance regulatory compliance with scientific reproducibility. Refacing and synthetic masking are promising alternatives for preserving morphometric validity, whereas metadata anonymization remains an underrecognized vulnerability requiring automated solutions. These observations indicate that no single approach currently provides both comprehensive privacy protection and preservation of data utility. Therefore, future work should prioritize three areas:

• Develop standardized benchmarks that link privacy risk directly with analytic validity.

• Harmonize governance frameworks, including national DRB accreditation systems and international alignment of technical standards.

• Integrate advanced technologies, such as DL-based face removal, refacing, and automated metadata cleansing, into scalable workflows for large multicenter datasets.

Beyond these traditional methods, privacy-preserving approaches such as federated learning offer an alternative to the privacy–utility trade-off by enabling DL models to be trained on distributed MRI data across multiple institutions without transferring sensitive data to a central server. This approach can reduce de-identification risks while enabling large-scale multicenter research. Effective MRI de-identification requires coordination among technical innovation, robust regulatory oversight, and ethical stewardship. Coordinated global and national efforts are needed to establish secure, interoperable, and scientifically valid practices for responsible data sharing in AI-enabled research.