Biomedical Imaging Technology

Ayush Dogra, PhD, is an Assistant Director at Chitkara University, Punjab, India. His research areas include image fusion, image enhancement, image registration, and image denoising. Shalli Rani, PhD, is a Professor and Director at Chitkara University, Punjab, India. She is a Senior Member of the IEEE and her research interests include Internet of Things, WSN, cloud computing, network security, and machine learning. Ankita Sharma, PhD, is an Assistant Professor at Chitkara University, Punjab, India. She has authored numerous national and international publications in peer-reviewed journals.

• List of Contributors xixAbout the Editors xxiiPreface xxvAcknowledgments xxvi1 Historical Evolution and Technological Advancements in Biomedical Imaging 1Shubham Gupta and Suhaib Ahmed1.1 Introduction 11.2 Early Milestones in Biomedical Imaging 51.2.1 Pre-Imaging Era: Anatomy and Physical Diagnosis 51.2.2 Discovery of X-Rays and Birth of Radiography 71.2.3 Development of Radioisotope Imaging (Nuclear Medicine) 71.3 Signal Processing Strategies in Biomedical Imaging 81.3.1 Data Acquisition and Preprocessing 81.3.2 Image Reconstruction Algorithms 91.3.3 Feature Extraction and Enhancement 101.3.4 Real-Time Processing Strategies 111.4 Innovations in Signal Processing for Biomedical Imaging 111.4.1 Machine Learning and AI-Driven Techniques 121.4.2 Quantum Signal Processing in Imaging 121.4.3 Multimodal Imaging and Data Fusion 131.4.4 Emerging Trends in Signal Processing Hardware 131.5 Case Studies 141.5.1 Innovations in Signal Processing for MRI 141.5.2 Deep Learning in Ultrasound Imaging 151.5.3 Hybrid Imaging Modalities 161.6 Challenges and Future Directions 171.6.1 Ethical and Regulatory Concerns 171.6.2 Scalability and Cost Effectiveness of Signal Processing Techniques 181.6.3 Future Trends in Biomedical Signal Processing 181.6.3.1 Image Systems at the Crossroads of Edge AI and IoT 181.6.3.2 Signal Processing for Personalized Imaging 191.7 Advancements in Signal Processing Techniques and Innovations 191.7.1 Future Perspectives on Biomedical Imaging 201.8 Conclusion 21References 222 Deep Learning Techniques for Biomedical Imaging 25Vandana and Chetna Sharma2.1 Introduction 252.2 Overview of DL Architecture in Biomedical Imaging 262.3 CNN Architecture 282.4 Basic Concepts in Biomedical Imaging 292.4.1 Data Representation in Imaging 292.4.2 Image Reconstruction with dl 292.4.2.1 Concept of Image Reconstruction 302.4.3 Image Segmentation 312.4.3.1 Traditional Image Segmentation Techniques 312.4.3.2 dl Image Segmentation Models 322.4.4 Image Registration 322.4.5 Diagnosis and Classification 332.4.5.1 Types of Image Classification 332.4.5.2 Working of Image Classification 342.4.6 Functional and Molecular Imaging 362.4.7 Explainability and Interpretability 372.4.7.1 Significance of Interpretability and Explainability 372.5 Future Study and Application of Image Processing in Biomedical 382.6 Conclusion 39References 393 Advanced Methods and Approaches in Image Reconstruction 45Navneet Kaur and Gurbinder Singh Brar3.1 Introduction 453.1.1 Fundamental Principles of Image Reconstruction 473.1.2 Forward and Inverse Problems in Image Reconstruction 473.1.2.1 Forward Problems 473.1.2.2 Inverse Problems 483.2 Classical Analytical Methods 493.2.1 Filtered Back Projection (FBP) 493.2.2 Fourier-Based Methods 503.2.3 Algebraic and Iterative Techniques 513.2.3.1 Algebraic Reconstruction Techniques (ARTs) 513.2.3.2 Simultaneous Algebraic Reconstruction Technique (SART) 513.3 Convergence and Computational Challenges 523.4 Signal Processing for Noise and Artifact Management 523.4.1 Sources of Noise and Artifacts 543.4.2 Sources of Noise 553.4.3 Sources of Artifacts 573.5 Denoising Techniques 593.5.1 Spatial Domain Filtering 593.5.2 Transform Domain Approaches 593.6 Artifact Correction Methods 603.6.1 Model-Based Correction Techniques 603.6.2 Deep Learning Approaches for Artifact Reduction 613.6.3 Advanced Signal Processing Strategies 613.7 Compressed Sensing in Imaging 623.7.1 Sparse Representation and Sampling 623.7.2 Applications in MRI and CT 623.7.3 Model-Based Reconstruction Techniques 633.7.4 Bayesian Inference Models 633.8 Statistical Methods for Noise Modeling 643.8.1 Machine Learning and Neural Networks 643.8.2 Supervised vs Unsupervised Approaches 643.8.3 Deep Learning for Artifact Removal and Reconstruction 643.8.4 Emerging Innovations in Image Reconstruction 653.9 Hybrid Computational Methods 653.9.1 Optimization-Based Deep Networks 663.9.2 Multimodal and Multiresolution Techniques 663.9.3 Super-Resolution Approaches for Enhanced Detail 673.10 Quantum Signal Processing 683.10.1 Quantum Imaging and Sensing 683.11 AI-Assisted Real-Time Reconstruction 693.12 Conclusion 70References 714 Integrative Approaches in Image Analysis and Signal Interpretation 75Tanishq Soni, Deepali Gupta, and Mudita Uppal4.1 Introduction 754.2 Related Work 784.3 Materials and Methodology 814.3.1 Description of Dataset 814.3.2 Proposed Methodology 824.3.2.1 Input Dataset and Pre-Processing 824.3.2.2 Designing of Deep Learning Models 844.4 Results and Discussion 884.4.1 Analysis Based on Confusion Matrix 884.4.2 Analysis Based on Accuracy 884.4.3 Analysis Based on Loss 884.5 Conclusion and Future Scope 93References 935 Multimodal Imaging: Combining Molecular and Optical Approaches 97Haewon Byeon, Azzah AlGhamdi, Ismail Keshta, Mukesh Soni, Mohammad Shabaz, and Mohammed Wasim Bhatt5.1 Introduction 975.2 Network Model 1005.2.1 Dataset Selection 1035.2.2 Image Patches for Classification and Regression Localization 1045.2.3 Candidate Block Screening Network 1065.2.4 Verification Module—Task-Guided Radial Basis Network 1075.2.5 Loss Function 1105.3 Evaluation and Results from Experiments 1115.3.1 Experimental Setting 1115.3.2 Performance Evaluation Metrics 1125.3.3 The Impact of Picture Block Size on the Efficiency of the Model 1125.3.4 The Impact of Deep Supervision and Attention Mechanism on Model Performance 1135.3.5 The Impact of the Number of Cluster Centers on Model Performance 1145.3.6 Experiments on ICPR 2014 Dataset 1145.3.7 Experiments on the AMIDA 2013 Dataset 1165.4 Conclusion 118References 1186 Advancements in Biomedical Imaging Using Fluorescence and Bioluminescence 123Ashish Kashyap, Manju Jakhar, Nidhi Rani, and Thakur Gurjeet Singh6.1 Introduction 1236.2 Advancements in Imaging Bioluminescence 1246.2.1 Advances in Bioluminescence Imaging 1246.2.2 Fluorescence Imaging Challenges 1246.2.3 Recent Innovations in Imaging Technologies 1246.3 Key Innovations in Bioluminescence Imaging (BLI) 1256.3.1 Recent Advances 1256.3.1.1 Luciferase-Loaded Nanoparticles 1256.3.1.2 Synthetic Bioluminescent Reactions 1256.3.1.3 Bioluminescent Reporters 1256.3.1.4 Bacterial Bioluminescence 1256.3.1.5 Applications and Future Directions 1266.4 Limitations of Bioluminescence Imaging (BLI) 1266.4.1 Depth Limitations 1266.4.2 Variation in Outputs 1266.4.3 Limitations to Quantitative Precision 1276.4.4 Other Major Limitations 1276.5 Evolution of BLI Technology 1276.5.1 Enhanced Luminescent Units 1286.5.2 Advanced Imaging Methods 1286.5.3 Improvements in Photon Detection 1296.5.3.1 High-Sensitivity Photon Detectors 1296.6 Applications of Bioluminescence Imaging 1296.6.1 Gene Expressions and Protein Localizations 1296.6.1.1 Multicolor Auto-Bioluminescence Systems 1296.6.2 Tumor Imaging 1306.6.2.1 Long-Term Imaging with Nanoparticles 1306.6.3 Optogenetic Biosensing 1306.6.3.1 Bioluminescence-Induced Optogenetic Biosensors 1306.6.4 Biomedical Research and Diagnostics 1316.6.4.1 Studies of Infectious Disease and Compounds for Treatment 1316.6.4.2 Challenges and Direction for the Future 1316.7 Innovations in Fluorescence Imaging 1316.7.1 Miniaturized Fluorescent Probes 1316.7.2 Computational Photography in Surgery 1326.7.3 Advanced Imaging Methods 1326.7.3.1 Challenges and Future Directions 1326.7.3.2 Fluorescence Imaging: Limitations 1336.8 Advances in Fluorescence Imaging Technology 1336.8.1 From Computational Photography to Fluorescence Imaging 1336.8.2 Near-Infrared Fluorescence Imaging in Cancer Diagnosis 1346.8.3 Advances in Fluorescence Molecular Tomography (FMT) 1346.8.4 Small-Molecule Probes in Bioimaging 1346.8.5 Light Sheet Fluorescence Microscopy (LSFM) 1346.9 Comparative Analysis of Bioluminescence and Fluorescence Imaging 1356.9.1 Sensitivity and the Strength of the Signal 1356.9.2 Application and Versatility 1356.9.3 Hybrid Methods 1356.10 Emerging Trends in Imaging Technological Development 1366.10.1 Challenges and Suggestions 1366.11 Conclusion 137References 1377 Innovative Diagnostic Imaging Techniques and Protocols 147Kamini Lamba, Shalli Rani, Ayush Dogra, and Ankita Sharma7.1 Introduction 1477.1.1 Evolution of Multi-Modal and Hybrid Imaging 1477.1.2 AI-Driven Image Analysis and Explainability in Medical Imaging 1487.1.3 Advancement in Molecular and Functional Imaging 1487.1.4 Radiomics and Predictive Analytics in Imaging 1497.1.5 Standardized Imaging Protocols and Future Trends 1497.2 Diagnosing Imaging Methods 1497.2.1 Conventional Diagnostic Imaging Techniques 1507.2.1.1 X-Ray Radiography and Its Limitations 1507.2.1.2 Pneumoencephalography (PEG): A Historical Perspective and Its Limitations 1507.2.1.3 Cerebral Angiography: Detecting Tumor-Related Vascular Abnormalities 1507.2.2 Advanced Imaging Modalities in Brain Tumor Detection 1517.2.2.1 Computed Tomography (CT) and Its Advancements 1517.2.2.2 Magnetic Resonance Imaging (MRI) and Functional Variants 1517.2.2.3 Positron Emission Tomography (PET) and Hybrid Imaging 1517.3 Comparison of Innovative Diagnostic Imaging Techniques and Protocols 1527.4 Challenges in Innovative Diagnostic Imaging 1537.4.1 Data Heterogeneity and Standardization Issues 1537.4.2 High Computational and Infrastructural Cost 1537.4.3 Lack of Explainability and Trust in Artificial-Intelligence Models 1567.4.4 Privacy and Ethical Concerns in Medical Data Sharing 1577.4.5 Clinical Validation and Regulatory Challenges 1577.4.6 Dataset Imbalance and Limited Availability of Rare Tumor Cases 1577.4.7 Ethical Biases and Fairness in AI Models 1587.4.8 Real-Time Processing and Latency Issues 1587.4.9 Vulnerability to Adversarial Attacks in Medical AI 1587.5 Future Directions in Innovative Diagnostic Imaging for Brain Tumor Detection 1587.5.1 Explainable AI (XAI) in Imaging 1597.5.2 Multimodal Imaging and Data Fusion 1597.5.3 Low-Cost and Portable Imaging Solutions 1607.5.4 AI and Quantum Computing in Medical Imaging 1607.5.5 Non-Invasive Tumor Monitoring and Early Detection 1607.6 Conclusion 160References 1618 Applications and Clinical Impacts of Biomedical Imaging 165Divya Gupta, Jaspreet Kaur, and Sheenam Middha8.1 Introduction 1658.2 Essential Techniques in Biomedical Imaging 1668.2.1 Computed Tomography (CT) 1668.2.2 Ultrasound Imaging 1688.2.3 Magnetic Resonance Imaging (MRI) 1688.2.4 Positron Emission Tomography (PET) 1698.3 Applications of Biomedical Imaging 1698.3.1 Diagnostic Imaging 1708.3.1.1 Detection of Disease 1708.3.1.2 Radiology and Pathology 1718.3.2 Treatment Planning 1718.3.2.1 Surgical Planning 1718.3.2.2 Radiation Therapy 1728.3.3 Monitoring and Evaluation 1728.3.3.1 Disease Monitoring 1728.3.3.2 Chronic Disease Management 1728.4 Clinical Impacts of Biomedical Imaging 1738.4.1 Early Diagnosis 1738.4.2 Improved Treatment Planning 1748.4.3 Monitoring and Assessment 1758.4.4 Minimizing Invasive Procedures 1758.4.5 Accelerating Research and Innovation 1768.4.6 Cost Efficiency 1768.5 Case Studies 1778.5.1 Advanced PET/CT Imaging for Tracking Cancer Metastases 1778.5.2 High-Resolution MRI for the Prompt Identification of Alzheimer’s Disease 1788.5.3 Breast Shape Analysis 1798.6 Conclusion 180References 1809 Emerging Technologies and Innovations in Medical Imaging 183Puneet Bawa and Manisha Rajput9.1 Introduction 1839.1.1 Background 1849.1.2 Contribution 1869.1.3 Organization 1869.2 Methodology 1879.3 Analysis 1889.3.1 Research Dynamics 1889.3.1.1 Publication Trends 1889.3.1.2 Publication Types 1899.3.1.3 Country Impact Analysis 1909.3.2 Key Contributors 1909.3.2.1 Most Cited Papers 1919.3.2.2 Author Impact Analysis 1919.3.2.3 Journal Impact Analysis 1929.3.2.4 Institutional Impact Analysis 1939.3.3 Research Focus and Emerging Topics 1949.3.3.1 Keyword Co-occurrence Analysis 1949.3.3.2 Hot Topics and Emerging Trends 1959.3.4 Collaboration Patterns 1979.3.4.1 Author Collaboration Network Analysis 1979.3.4.2 Regional Collaboration Network Analysis 1989.4 Discussions and Limitations 1999.5 Conclusion 200References 20010 Therapeutic Interventions Guided by Advanced Imaging Modalities 205Manisha Pathania and Chander Partap Singh10.1 Introduction 20510.1.1 The Role of AR in Medical Training and Imaging-Guided Therapeutic Interventions 20610.1.2 Educational Theories Underpinning AR Use 20610.1.3 Evidence of AR’s Impact on Learning Outcomes 20710.1.4 Evidence of AR’s Impact on Learning Outcomes 20710.1.5 Future Perspectives 20810.2 Background and Significance 20810.2.1 The Current Landscape of Medical Education 20810.2.2 Augmented Reality as a Solution 20910.2.3 Historical Development of AR in Medical Training 20910.2.4 Addressing the Gap Between Theory and Practice 20910.2.5 Accessibility and Scalability 21010.2.6 Enhancing Learner Engagement and Retention 21010.2.7 Challenges in AR Adoption 21010.3 Core Applications of AR in Medical Training 21110.3.1 Anatomy Education 21110.3.2 Surgical Training 21110.3.3 Emergency Medicine and Trauma Training 21210.3.4 Medical Imaging and Diagnostics 21210.3.5 Procedural Simulations and Skill Training 21310.3.6 Patient Communication and Empathy Training 21310.3.7 Remote and Collaborative Learning 21310.4 Bridging Theory and Practice 21410.4.1 Challenges in Traditional Medical Education 21410.4.2 AR as a Link Between Theory and Application 21510.4.3 Enhancing Skill Acquisition and Retention 21510.4.4 Bridging Cognitive and Procedural Learning 21610.4.5 Collaboration and Remote Learning 21610.4.6 Industry and Academic Partnerships 21610.4.7 Future Directions 21710.5 Challenges and Limitations 21710.5.1 Technological Constraints 21710.5.1.1 Hardware Limitations 21710.5.1.2 Software Challenges 21810.5.1.3 Latency and Real-Time Interactivity 21810.5.2 Pedagogical and Integration Challenges 21810.5.2.1 Lack of Faculty Training 21810.5.2.2 Curriculum Design and Overcrowding 21810.5.2.3 Learning Curve for Students 21910.5.3 Financial and Accessibility Barriers 21910.5.3.1 High Costs of Implementation 21910.5.3.2 Maintenance and Updates 21910.5.3.3 Disparities in Accessibility 21910.5.4 Ethical and Regulatory Concerns 21910.5.4.1 Data Privacy and Security 21910.5.4.2 Simulation Limitations 21910.5.4.3 Ethical Use of Patient Data 22010.5.5 Standardization and Accreditation Issues 22010.5.5.1 Lack of Standardized Guidelines 22010.5.5.2 Efficacy Validation 22010.5.6 Cultural and Psychological Barriers 22010.5.6.1 Resistance to Change 22010.5.6.2 Cognitive Overload 22010.6 Future Directions 22010.6.1 Industry and Academic Partnerships 22110.6.1.1 Improved Hardware Design 22110.6.1.2 AI-Integrated AR Platforms 22110.6.1.3 Interoperability Standards 22110.6.2 Personalized and Collaborative Learning 22110.6.2.1 Customized Training Modules 22110.6.2.2 Collaborative and Remote Learning 22110.6.2.3 Integration with Telemedicine 22210.6.3 Expanding Access and Equity 22210.6.3.1 Affordable AR Solutions 22210.6.3.2 Partnerships with NGOs and Governments 22210.6.4 Regulatory and Ethical Frameworks 22210.6.4.1 Establishing Robust Guidelines 22210.6.4.2 Ethical AI Integration 22210.6.5 Enhanced Simulation Capabilities 22310.6.5.1 Multimodal Simulations 22310.6.5.2 Advanced Scenario Modeling 22310.6.6 Long-Term Impact Studies 22310.6.6.1 Measuring Outcomes 22310.6.6.2 Working with Educational Researchers 22310.6.7 Vision for the Future 22310.7 Conclusion 22310.7.1 Long-Term Vision for Medical Training Using AR 22410.7.2 Hardware and Software 22410.7.3 Scalability and Accessibility 22510.7.4 Very Thorough Research and Validation 22510.7.5 Regulation and Ethical Frameworks 22510.7.6 The Broader Implications of AR in Healthcare 225References 22611 Addressing Technical and Clinical Challenges in Next-Generation Imaging 231Jaspreet Kaur, Divya Gupta, and Sheenam Middha11.1 Introduction 23111.2 Technical Innovations and Challenges 23311.2.1 Technology Innovations 23311.2.2 Challenges Associated with Technical Innovations 23411.3 Clinical Implications and Hurdles 23511.3.1 Clinical Hurdles 23611.4 Regulatory and Policy Challenges 23611.4.1 Data Privacy and Security Regulations 23611.4.2 AI Algorithm Certification and Approval 23711.4.3 Interoperability Standards 23711.4.4 Equity and Accessibility 23811.5 Emerging Trends and Solutions 23911.6 Real-World Applications 24011.7 Future Perspectives and Roadmap 24111.8 Conclusion 243References 243Index 245