Investigating the Impact of Compressed Sensing Techniques and IoT in Medical Imaging




Suresh Kumar Mandala1*, Shahnaz K. V.2, Chopparapu Gowthami3, S. Shiek Aalam4, B. Laxmi Kantha5, K. Chandran6
1Department of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India.

2 Department of ECE, Veltech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, TN, India.

3Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.

4 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, TN, India.

5 Department of IT, St. Martin's Engineering College, Secunderabad, Telangana, India.

6 Department of Humanities, Madanapalle Institute of Technology and Science, Madanapalle-517325, Andhra Pradesh, India
Emails: mandala.suresh83@gmail.com; shahnaznitc@gmail.com; gouthami526@gmail.com; sheikaalam@gmail.com; drblaxmiit@smec.ac.in;  chanrohit@gmail.com

Text Box: Abstract This research paper examines compressed sensing's impact on medical imaging. Math and signal processing inspired compressed sensing. Future picture-capturing will be radically different. The paper focuses on adaptive random sampling (ARS), iterative shrinkage-thresholding algorithms (ISTA), and temporal compressed sensing (TCS). These approaches were rigorously tested using MRIs, X-rays, and dynamic imaging patterns. Low scan times, picture quality, and dynamic imaging were the main test criteria. The technologies considerably reduced scan time, demonstrating their potential to speed up imaging procedures. The reconstructed photos had higher SNRs and SSIs than those obtained using normal techniques, indicating greater accuracy. The TCS algorithm's dynamic imaging skills, especially evident in heart and musculoskeletal imaging, eliminated motion defects while exhibiting real-time physiological changes. The study was expanded to incorporate customized treatment, and the recommended procedures have proven amazing adaptability to each patient's demands. This adaptability fits current medical treatments, making unique imaging technologies viable.

Received: August 21, 2023 Revised: November 25, 2023 Accepted: April: 27, 2024

 

Keywords: Dynamic Imaging; Image Quality; Individualized Healthcare; Iterative Algorithms; Medical Imaging; Motion Artifact Mitigation; Personalized Medicine; Temporal Compressed Sensing.