REVIEW ARTICLE
ARAGÃO, José Aderval [1], SANT’ANNA ARAGÃO, Felipe Matheus [2], SANT’ANNA ARAGÃO, Iapunira Catarina [3], CARVALHO, Adler Oliveira Silva Jacó [4], CUNHA, Henrique Montalvão Routman da [5], COSTA, Giovanna de Oliveira Sá [6], RAMOS, Tiago Mateus da Silva [7], MENDONÇA, Deise Maria Furtado de [8], REIS, Francisco Prado [9]
ARAGÃO, José Aderval et al. Living anatomy: The transformative impact of magnetic resonance imaging on the study of the human body. Revista Científica Multidisciplinar Núcleo do Conhecimento. Year 10, Ed. 11, Vol. 01, pp. 84-98. November 2025. ISSN: 2448-0959. Access link: https://www.nucleodoconhecimento.com.br/health/living-anatomy, DOI: 10.32749/nucleodoconhecimento.com.br/health/living-anatomy
Magnetic Resonance Imaging (MRI) has revolutionized the study of anatomy, transitioning it from a post-mortem morphological discipline to a dynamic and detailed in vivo exploration of human structure and function. MRI, by virtue of its ability to generate high-resolution images with superior tissue contrast (especially for soft tissues) and without the use of ionizing radiation, enhances diagnostic precision, allowing for early detection of pathologies and differentiation between subtle variations and anomalies. Methodologically, modern MRI enables dynamic understanding of physiological processes through functional sequences (fMRI and cine-MRI), in addition to facilitating optimized therapeutic planning with high-fidelity 3D reconstructions for surgical navigation. Innovations include Diffusion Tensor Imaging (DTI), which maps the microstructure of nerve fibers, and ultra-high-field MRI (7 Tesla), which reveals unprecedented microscopic anatomical details. The integration of Artificial Intelligence (AI) and Machine Learning (ML) represents a significant advance in image analysis, with algorithms capable of automatically segmenting organs and pathologies, detecting subtle lesions, and quantifying volumes, thereby standardizing measurements and optimizing the radiological workflow. Technical challenges, such as motion artifacts, are continuously mitigated by protocol optimizations, founded on iterative analysis and clinical feedback. The future of MRI in anatomy points towards predictive AI, multimodal data fusion, and the development of quantitative imaging biomarkers, solidifying its role as a pillar of diagnostic and interventional medicine. Despite limitations such as motion and susceptibility artifacts, continuous optimization of protocols and iterative clinical feedback are improving accuracy and reliability. Future directions include predictive AI models, multimodal image fusion, and the development of quantitative imaging biomarkers. MRI thus stands as a cornerstone of diagnostic and interventional medicine, bridging classical anatomy with emerging digital frontiers.
Keywords: Magnetic Resonance Imaging, Anatomy, Artificial Intelligence, Imaging Biomarkers, Surgical Planning.
For centuries, the study of human anatomy was almost exclusively done on the dissection table. The body, in its physical and palpable form, served as the primary textbook, and the understanding of its internal structures relied on the acuity of the human eye and the dexterity of the anatomist’s hands (Gray, 1918; Netter, 2018). However, this approach, while fundamental and unparalleled in certain aspects, had inherent limitations: the static nature of cadaveric material, the impossibility of observing physiological processes in real-time, and the inherent invasiveness of the method.
The advent of medical imaging technologies marked a silent yet profound revolution in how we understand and interact with anatomy. From Wilhelm Röntgen’s first X-rays, which provided the initial non-invasive “window” into the skeleton (Röntgen, 1896), to the complex Magnetic Resonance Imaging (MRI) scans that today reveal the secrets of soft tissues with remarkable clarity, developed by pioneers such as Paul Lauterbur and Peter Mansfield (Lauterbur, 1973; Mansfield & Maudsley, 1977), medical imaging has redefined the field of anatomy. It transcended post-mortem observation, enabling the in vivo study of human structure and function, opening a new chapter in diagnostic and therapeutic medicine. MRI, in particular, emerged as a paradigm-shifting tool, offering a detailed and multifaceted view of the body, driving diagnostic precision to previously unimaginable levels.
MRI’s ability to generate high-resolution images without the use of ionizing radiation and with superior tissue contrast, especially for soft tissue structures, has positioned it as a unique modality.
Enhanced Diagnostic Precision: MRI allows for the identification of subtle anatomical variations, congenital anomalies, and pathologies in their early stages, often before clinical symptoms become overtly manifest. Its ability to differentiate, for example, inflammatory edema from a malignant tumor, or a ligamentous injury from a bone bruise, is crucial for accurate diagnosis and early intervention (Westbrook & Talbot, 2018).
Dynamic Understanding: Unlike the static view provided by traditional methods, modern MRI, especially with the advent of functional sequences (fMRI, cine-MRI), enables the observation of physiological processes (Ogawa et al., 1990). It is possible to monitor blood flow in large vessels, ventricular contraction of the heart, or the activation of specific brain areas in response to stimuli (Wu et al., 2021), offering an anatomical understanding that extends beyond form to encompass function.
Optimized Therapeutic Planning: MRI provides detailed images in multiple planes (axial, coronal, sagittal) and allows for three-dimensional (3D) reconstructions. This capability to visualize anatomy in its spatial complexity is invaluable for surgical, radiotherapeutic, and even rehabilitation planning. Surgeons can virtually “navigate” through the area of interest, identifying vital structures to be preserved and precisely delineating the path for intervention, thereby minimizing risks and optimizing outcomes (Moore, Dalley, & Agur, 2018).
A Clinical Dialogue: The Case of the Supraspinatus Tendon
To illustrate the synergy between anatomical knowledge and imaging technology, let’s consider a common yet challenging clinical case: shoulder pain.
Imagine a 55-year-old patient complaining of chronic and intense pain in her right shoulder, particularly when performing arm elevation and abduction movements. She reports difficulty combing her hair and getting dressed. The initial clinical evaluation revealed limited range of motion and weakness during elevation tests. The suspicion falls on a rotator cuff injury, a group of four tendons surrounding the humeral head, with the supraspinatus tendon being the most frequently affected (Rockall et al., 2025; Parkar & Adriaensen, 2024).
– The Clinical Approach: Clinical assessment is crucial. Specific range of motion and strength tests for the supraspinatus (such as the “empty can test”) were performed. However, pain and inflammation can mask the true extent of the injury. Is it a tendinopathy? A partial tear? A complete tear? Differentiation is fundamental for the treatment plan.
– The Choice of Tool: To deepen the investigation and visualize soft tissues with the necessary clarity, MRI is the imaging modality of choice. Radiographs would be useful for assessing bone structures, but not tendons; Computed Tomography (CT) has limited contrast for soft tissues; and ultrasound, while valuable and dynamic, is operator-dependent and may not assess the full extent of complex lesions or tendon retraction.
– The Dialogue of Imaging with Anatomy: In the MRI scanner, specific protocols are applied. Sequences like PD-fat sat (Proton Density with Fat Saturation) are chosen for their ability to highlight fluid and edema, which are signs of injury. My anatomical knowledge guides me: I look for the supraspinatus muscle tendon, which originates in the supraspinous fossa of the scapula and inserts into the greater tubercle of the humerus (Drake, Vogl, & Mitchell, 2014). My attention turns to its continuity, morphology, and signal intensity.
– Revealing the Injury: The MRI images reveal the truth: there is a complete discontinuity of the supraspinatus tendon fibers. Not only that, but there is significant retraction of the proximal portion of the tendon, approximately 2 cm, and an accumulation of fluid in the subacromial-subdeltoid bursa, indicating inflammation. The integration of where the tendon should be and what I am seeing in the image allows for the precise diagnosis of a complete rupture with tendon retraction.
– Impact on Treatment: This detailed information, which no clinical assessment alone could provide, is vital. A complete tendon rupture with retraction, as in this case, generally requires surgical intervention for repair, unlike a tendinopathy or partial rupture that might be managed conservatively. By precisely detailing the extent of the injury, MRI provided the surgeon with the necessary foundation for planning an optimized surgical intervention, aiming to restore the patient’s shoulder function.
This case is not merely a technical example; it is a demonstration of the intimate dialogue between fundamental anatomy and the revealing power of imaging. It is proof that technology does not replace anatomical knowledge but rather amplifies it, making it visible and dynamic.
The evolution of MRI is continuous, with innovations that have expanded our ability to unravel the tissues and organs and their pathology (Hashemi, Bradley, & Lisanti, 2010; Yousaf, Dervenoulas, & Politis, 2018):
– Advanced Magnetic Resonance Sequences: Diffusion Tensor Imaging (DTI): DTI goes beyond conventional structural imaging. It maps the movement of water molecules in tissues, providing information about the microstructure and direction of nerve fibers in the brain (Mori, 2007; Wu et al., 2021). For the anatomist, this means in vivo visualization of white matter tracts, revealing intricate brain connections. In neurosurgery, it allows for the identification of critical pathways (such as the corticospinal tract) adjacent to tumors, enabling safer and more precise resection (Mori et al., 2013).
– Ultra-High-Field MRI (7 Tesla and Above): While most clinical examinations are performed on 1.5T or 3T systems, 7T scanners possess a significantly stronger magnetic field. This translates into unprecedented spatial resolution and signal contrast. Microscopic anatomical details, such as the cortical layers of the brain, small blood vessels, or complex brainstem structures, become visible, opening new avenues for the study of neurodegenerative diseases in their early stages (Inglese et al., 2018).
– 3D Visualization and Augmented/Virtual Reality (AR/VR): 3D Reconstructions from 2D Data: Post-processing software transforms the multiple two-dimensional slices acquired by MRI into interactive three-dimensional models. This allows surgeons and anatomy students to virtually “navigate” through patient structures, rotate, section, and isolate organs and vessels. This virtual pre-operative rehearsal is fundamental for planning complex procedures, improving spatial understanding, and reducing surgical time (Peters, 2016; Cleary & Peters, 2010).
– Integration of AR in Surgery: Augmented Reality projects virtual information (such as MRI images) onto the surgeon’s real field of view. During spinal surgery, for instance, the patient’s spine image obtained via MRI can be directly superimposed onto the patient, guiding the surgeon in the precise insertion of screws without the need to avert their gaze to external monitors (Navab et al., 2015).
– Artificial Intelligence (AI) and Machine Learning (ML): Automatic Segmentation of Organs and Pathologies: AI algorithms trained on vast datasets of images are capable of automatically identifying and contouring anatomical structures (heart, kidneys, brain) or lesions (tumors, multiple sclerosis). This not only accelerates the analysis process but also standardizes measurements and can reduce inter-observer variability, freeing the radiologist to focus on clinical interpretation (Topol, 2019; Seyithanoglu et al., 2024).
– Detection of Subtle Lesions and Quantification: ML can identify patterns in images that are extremely difficult, or even impossible, for the human eye to perceive. This is particularly relevant for the early detection of micro-lesions or for the precise quantification of lesion volumes over time, aiding in monitoring disease progression and treatment response (LeCun, Bengio, & Hinton, 2015).
– The Continuous Refinement of Vision: Iterative Analysis in Imaging The acquisition of a high-quality image is not an isolated act but rather the result of a continuous refinement process. Iterative analysis is a fundamental feedback loop for optimizing MRI protocols and ensuring maximum diagnostic accuracy. Protocol Definition and Acquisition: Based on the clinical question, specific MRI acquisition parameters are selected.
Post-Acquisition Evaluation: After acquisition, images are technically reviewed. We check the signal-to-noise ratio, the presence of artifacts (distortions that can compromise the image), anatomical coverage, and contrast. Optimizing these parameters is crucial for image quality (Mikkelsen, Thygesen, & Fledelius, 2025; Greffier et al., 2015).
– Diagnostic Interpretation and Clinical Feedback: The radiologist interprets the images, and the report is sent to the referring physician. It is crucial to obtain feedback from the clinician regarding the usefulness and clarity of the images (Rockall et al., 2025; European Society of Radiology (ESR), 2022). Questions like “Did the images provide all the necessary information for surgical planning?” or “Was there any area that needed more detail?” are vital.
– Multidisciplinary Discussion: In complex cases, meetings between radiologists, surgeons, neurologists, oncologists, etc., become a forum to identify gaps in imaging protocols or areas where clarity could be improved.
– Protocol Adjustment and Optimization: Based on feedback, acquisition parameters are adjusted. This may include modifying pulse sequences, Echo Times (TE) and Repetition Times (TR), the Field Of View (FOV), slice thickness, or adding functional sequences.
– Re-evaluation and Iteration: The modified protocol is then applied to future patients, and the feedback cycle repeats. This approach ensures that imaging protocols are dynamic, continuously adapting to clinical needs and technological advancements, resulting in a constant improvement in image quality and diagnostic accuracy (Ma et al., 2024).
Despite its remarkable capabilities, MRI is not without its challenges. Mastery of interpretation requires not only anatomical and radiological knowledge but also the ability to overcome technical obstacles and to discern between normal and pathological in atypical situations.
Motion Artifacts: Patient movements (breathing, heartbeat, swallowing, agitation) degrade image quality. Solutions include using fast acquisition sequences (e.g., single-shot), synchronizing with physiological movement (gating), or, in extreme cases, patient sedation.
Magnetic Susceptibility Artifacts: Metals (orthopedic implants, surgical clips, metallic fragments) distort the local magnetic field, creating “low signal” (black hole) or “high signal” (blooming) artifacts. Strategies include using optimized sequences for metal suppression (such as SEMAC or MAVRIC) or careful patient positioning.
Low Signal-to-Noise Ratio (SNR): Images with low SNR appear grainy and hinder the visualization of fine details. To mitigate this, one can increase the number of acquisitions (NSA/NEX), use surface coils closer to the area of interest, or optimize sequence parameters to maximize signal (American Society of Neuroradiology, 2007; Ma et al., 2024).
Anatomical Variations: The human body presents a wide range of anatomical variations that do not show signs of pathology (e.g., accessory ossicles, anomalous muscles, unusual vascular bifurcations). A deep knowledge of these variations is crucial to avoid false positive or incorrect diagnoses, or the recommendation of unnecessary procedures (Gray, 1918).
Incidental Findings: During an examination focused on one area, other unrelated findings may be discovered (e.g., a small renal cyst on a spine MRI, or a thyroid nodule on a neck examination). It is vital to have a systematic process to identify, characterize, and, if necessary, recommend follow-up for these findings, always weighing their clinical significance.
Clinical-Radiological Discrepancy: When imaging findings do not align with the patient’s clinical picture, it is a red flag. In such cases, a holistic approach is essential: discussing the case with the referring physician, reviewing the patient’s complete history, considering additional tests or other imaging modalities. Imaging is one piece of the diagnostic puzzle, not the entirety (Rajkomar & Dhaliwal, 2011).
The journey of anatomy, from the dissection table to the digital interface, has been remarkable. MRI, as one of the most powerful tools in this evolution, has transformed anatomical study from a predominantly post-mortem morphological discipline to a dynamic and detailed in vivo exploration. The ability to visualize structures with high resolution, in multiple planes, and with exceptional tissue contrast has been the basis for more accurate diagnoses, optimized therapeutic planning, and a deeper understanding of human pathologies. The synergy between profound anatomical knowledge and the mastery of cutting-edge imaging tools will continue to be the cornerstone of diagnostic and interventional medicine, driving the precision and effectiveness of patient care.
AI and Predictive Imaging: The development of AI algorithms that not only identify pathologies but also predict disease progression, treatment response, or the risk of complications based on anatomical and functional imaging data. This would transform radiology from a diagnostic discipline into a prognostic one.
Integrated Multimodality Imaging: The advancement in fusing data from different imaging modalities (MRI, PET, CT, Ultrasound) to create even more comprehensive and personalized anatomical and functional models for each patient, optimizing diagnosis and therapeutic decision-making.
Mixed Reality and Haptics in Surgery: The enhancement of augmented and virtual reality technologies with haptic (tactile) feedback, allowing surgeons not only to see but also to “feel” virtual anatomy, improving training and the execution of complex procedures.
Quantitative MRI and Imaging Biomarkers: The development and standardization of MRI techniques that not only show structure but also quantify tissue properties (e.g., degree of fibrosis, inflammation, fat or iron content) at a molecular level, transforming imaging into a non-invasive biomarker of tissue health and disease.
Accessibility of Ultra-High Field MRI: Making ultra-high field MRI technology more practical, economically viable, and widely available for routine clinical applications, overcoming current technical and cost challenges to democratize access to this unprecedented resolution.
The “invisible gaze” of MRI will continue to illuminate the secrets of the human body, shaping the future of medicine and deepening our admiration for the complexity and beauty of anatomy.
MRI has radically transformed the study of anatomy, elevating it from a predominantly post-mortem morphological discipline to a dynamic and detailed in vivo exploration. Its ability to visualize structures with high resolution, in multiple planes, and with exceptional tissue contrast has been fundamental in enhancing diagnosis, optimizing therapeutic planning, and deepening the understanding of human pathologies. As exemplified by the case of supraspinatus muscle tendon rupture, the integration of profound anatomical knowledge with MRI capabilities is crucial for precise and individualized clinical decisions.
Continuous innovations in imaging sequences, 3D/AR/VR visualization, and, increasingly, the application of AI, promise to further refine this capability, overcoming technical challenges and improving interpretation. The constant optimization of imaging protocols, driven by iterative analysis and clinical feedback, ensures that MRI remains at the forefront of diagnostic medicine. The synergy between anatomical knowledge and cutting-edge imaging tools is, and will continue to be, the cornerstone of medicine, driving the precision and effectiveness of patient care and increasingly elucidating the complex beauty of the human body.
MRI has revolutionized the study of anatomy by providing detailed and functional images of the body in real-time. Beyond diagnosis, it supports optimized therapeutic planning, enhances surgical navigation, and expands educational applications. Advances in high-field systems, functional imaging, and AI are making MRI more predictive, quantitative, and personalized. However, interpreting these advanced images still requires anatomical expertise. The convergence of classical anatomical knowledge with advanced imaging ensures that MRI will continue to play a crucial role in clinical decision-making and patient care.
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[1] Titular Professor of Clinical Anatomy. ORCID: https://orcid.org/0000-0002-2300-3330. Currículo Lattes: http://lattes.cnpq.br/6911783083973582.
[2] Cardiology Resident. ORCID: https://orcid.org/0000-0001-9211-7000. Currículo Lattes: http://lattes.cnpq.br/4619345212343744.
[3] Medical Clinic. ORCID: https://orcid.org/0000-0002-5298-537X. Currículo Lattes: http://lattes.cnpq.br/6291628187714859.
[4] Medical Student. ORCID: https://orcid.org/0009-0001-8725-2811. Currículo Lattes: http://lattes.cnpq.br/0276122702312432.
[5] Medical Student. ORCID: https://orcid.org/0009-0008-1153-0601. Currículo Lattes: http://lattes.cnpq.br/3376903340637107.
[6] Medical Student. ORCID: https://orcid.org/0009-0006-8995-4303. Currículo Lattes: https://lattes.cnpq.br/9832516660992325.
[7] Medical Student. ORCID: https://orcid.org/0009-0008-4461-1256. Currículo Lattes: http://lattes.cnpq.br/2955797545615787.
[8] Associate Professor of Anatomy. ORCID: https://orcid.org/0009-0006-6772-0266. Currículo Lattes: http://lattes.cnpq.br/7452033697540038.
[9] Titular Professor of the Medical School. ORCID: https://orcid.org/0000-0002-7776-1831. Currículo Lattes: http://lattes.cnpq.br/6858508576490184.
Authors’ contributions:
José Aderval Aragão: JAA – Conception, design and Overall responsibility.
Felipe Matheus Sant’Anna Aragão: FMSA: Methodology and writing the article.
Iapunira Catarina Sant’Anna Aragão: ICSA: Methodology and writing the article.
Adler Oliveira Silva Jacó Carvalho: AOSJC: Methodology and writing the article.
Henrique Montalvão Routman da Cunha: HMRC: Methodology and writing the article.
Giovanna de Oliveira Sá Costa: GOSC: Methodology and writing the article.
Tiago Mateus da Silva Ramos: TMSR: Methodology and writing the article.
Deise Maria Furtado de Mendonça: DMFM: Critical revision of the article.
Francisco Prado Reis: FPR: Critical revision of the article.
Conflict of interest:
There is no conflict of interest.
Acknowledgments and Funding:
There is no funding.
Note:
The authors used the AI Artificial Intelligence CHATGPT GPT-5 version for spelling and text review. However, all content searches and article quality ratings were performed by the authors themselves.
Copyright and License Information:
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The names and addresses provided in this journal will be used exclusively for the services provided by this publication and will not be made available for other purposes or to third parties.
Publication History:
Material received: October 13, 2025.
Material approved by peers: October 21, 2025.
Edited material approved by authors: November 3, 2025.
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