Magnetic Resonance Imaging Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Magnetic Resonance Imaging ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.

Magnetic Resonance Imaging Projects for Final Year Students

Latest Magnetic Resonance Imaging Projects

  1. An Overview of Data Integration in Neuroscience With Focus on Alzheimer’s Disease
    This project explores how to combine different types of medical and scientific data to better understand complex brain diseases. It explains the challenges of working with large and noisy datasets. The study gives guidance for beginners in data integration. It also shows how these methods can help in early detection of Alzheimer's Disease using machine learning.
  2. Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification
    This project focuses on detecting lung problems in children using recorded breathing sounds. It uses a computer program that learns patterns in these sounds to tell if a child has a respiratory disease. The system works remotely, so doctors can diagnose patients without face-to-face visits. It is more accurate than older methods and can help in situations like pandemics.
  3. Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review
    This project focuses on using deep learning to fix problems caused by movement during MRI scans. When a person moves, the MRI images can become blurry or distorted. The study reviews different deep learning methods that can correct these motion errors and improve image quality. It also discusses challenges, trends, and future research directions in this field.
  4. EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning
    This project focuses on using brain scans to detect autism. It studies how different regions of the brain interact in complex ways. The method finds important brain areas that help distinguish autistic patients from others. The approach is explainable, meaning it shows why it makes each diagnosis and works better than earlier methods.
  5. Fetal-BET: Brain Extraction Tool for Fetal MRI
    This project focuses on automatically identifying the fetal brain in MRI images. The researchers created a large dataset of fetal brain scans from different MRI types. They used deep learning to teach a computer to detect and separate the brain from other tissues. This method works accurately even when the scans are from different machines or show abnormal brains.
  6. Identifying Biases in a Multicenter MRI Database for Parkinson’s Disease Classification: Is the Disease Classifier a Secret Site Classifier
    This project studies how deep learning models for Parkinson's disease (PD) classification can unintentionally learn unrelated information from MRI scans. The researchers tested if the model could detect patient sex, scanner type, or hospital location even though it was only trained to detect PD. They found that the model could do this with high accuracy, showing it may rely on shortcuts instead of true disease patterns. This explains why such models sometimes fail on data from new hospitals.
  7. Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome
    This project uses a special type of artificial intelligence to study brain scans. It helps to identify patterns in the brain related to Alzheimer’s and Parkinson’s diseases. The method focuses on key brain connections while ignoring irrelevant data. This makes the model faster, more accurate, and able to find important brain regions linked to these diseases.
  8. Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines
    This project focuses on teaching a computer to convert MRI scans of spines into CT-like images. It also measures how confident the computer is about its predictions. By looking at uncertainties, the model can better separate bones from soft tissues. This helps doctors and researchers trust the results more when no expert labels are available.
  9. An Overview of Data Integration in Neuroscience With Focus on Alzheimers Disease
    This project looks at how to combine different types of medical and scientific data to study complex brain diseases. It explains common problems and suggests solutions for handling large and messy datasets. The work shows how computer tools like machine learning can help understand and detect Alzheimer's Disease earlier. It guides researchers on how to work across medicine and data science.
  10. Computer-Aided Intra-Operatory Positioning of an MRgHIFU Applicator Dedicated to Abdominal Thermal Therapy Using Particle Swarm Optimization
    This project is about improving a medical treatment that uses focused sound waves to heat and destroy liver tissue. The researchers made a computer program that finds the best position for the treatment device. It uses images from MRI scans and tests many positions to choose the safest and most effective one. The program works quickly and gives results very close to what doctors would choose manually.
  11. Deep Learning for Retrospective Motion Correction in MRI A Comprehensive Review
    This project looks at how movement affects MRI scans and makes the images unclear. It studies how deep learning can fix these motion problems at different stages. The work reviews many methods, comparing how they use data and are trained. It also suggests ways to improve motion correction in future MRI research.
  12. Identifying Biases in a Multicenter MRI Database for Parkinsons Disease Classification Is the Disease Classifier a Secret Site Classifier
    This project studied how a deep learning model for Parkinson's disease might rely on unintended shortcuts in brain MRI data. The researchers tested whether the model's internal features could reveal the patient’s sex, the scanner used, or the hospital the scan came from. They found that the model could predict these factors even though it was only trained to detect Parkinson's. The study shows that such shortcuts can affect the model’s reliability on new data from other centers.
  13. Motion-Compensated MR CINE Reconstruction With ReconstructionDriven Motion Estimation
    This project improves heart MRI imaging by creating clearer pictures from very fast scans. It combines motion tracking and image reconstruction into one step, which reduces errors and avoids blurry artifacts. The method works well even when the scan is highly accelerated. Tests show it produces more accurate and realistic heart motion images than current approaches.
  14. Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET
    This project develops a new method to improve heart PET scans. It uses a small external source together with the patient’s own signals to correct image errors. The approach does not rely on prior images or assumptions. Tests show it produces accurate images with low bias and less noise, making heart PET scans more reliable for patients.
  15. Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference
    This project focuses on creating a digital model of the heart to study heart attacks without surgery. It uses heart scans and ECG data to estimate tissue damage. A computer model learns to identify where and how much of the heart is affected. The approach could help doctors plan personalized treatments in the future.
  16. Unrolled Optimization via Physics-Assisted Convolutional Neural Network for MR-Based Electrical Properties Tomography A Numerical Investigation
    This project focuses on a new way to measure electrical properties of tissues using MRI without surgery. The method uses a series of neural networks that combine physics knowledge with data to improve the measurements. It was tested on brain models and produces accurate results much faster than traditional methods.
  17. Is Attention all You Need in Medical Image Analysis? A Review
    This project reviews the use of hybrid models that combine CNNs and Transformers for medical image analysis. CNNs capture local details in images, while Transformers capture global patterns. By combining both, these models can better understand complex medical images. The study analyzes existing designs, their strengths, and future opportunities for improving medical diagnosis and research.
  18. Quantification of Hypsarrhythmia in Infantile Spasmatic EEG: A Large Cohort Study
    This study focuses on helping doctors detect a brain disorder in infants called infantile spasms. The researchers analyzed brain wave recordings (EEG) from both affected and healthy babies. They found specific patterns in the signals that can reliably indicate the disorder. These patterns could be used to automatically diagnose infantile spasms, making the process faster and more accurate.
  19. Automated Segmentation of Brain Tumor MRI Images Using Deep Learning
    This project focuses on automatically identifying and separating brain tumors in MRI images. It uses advanced image analysis and deep learning to improve accuracy. The method combines two neural networks to give more complete and precise results. Overall, it achieves very high accuracy in detecting different parts of tumors.
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How We Help You with Magnetic Resonance Imaging Projects

At UniPhD, we provide complete guidance and support for Magnetic Resonance Imaging ieee projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.

We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.

Our experts guide students all over India, including in Mumbai, Delhi, Bangalore, Hyderabad, Ahmedabad, Chennai, Kolkata, Pune, Jaipur, and Surat. We also assist students in the USA, UK, Canada, Australia, Singapore, Malaysia, and Thailand. They have extensive experience in computer science, electronics, electrical and all engineering domains.

Magnetic Resonance Imaging Thesis and Dissertation Writing

UniPhD has a team of experienced academic writers who specialize in Magnetic Resonance Imaging research and thesis development. We offer fast-track dissertation writing services to help you complete your Magnetic Resonance Imaging thesis or dissertation smoothly and on time.

Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.

Magnetic Resonance Imaging Research Paper Publishing Support

UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.

Project Synopsis and Presentation Support

We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Magnetic Resonance Imaging final year project.

Magnetic Resonance Imaging Research Support for PhD Scholars

UniPhD offers advanced Magnetic Resonance Imaging research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.

Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.

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