2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 12 , Issue 2 , PP: 187-197, 2024 | Cite this article as | XML | Html | PDF
Suresh Kumar Mandala1 1 * , Shahnaz K. V. 2 , Chopparapu Gowthami 3 , S. Shiek Aalam 4 , B. Laxmi Kantha 5 , K. Chandran 6
Doi: https://doi.org/10.54216/JISIoT.120214
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.
Dynamic Imaging , Image Quality , Individualized Healthcare , Iterative Algorithms , Medical Imaging , Motion Artifact Mitigation , Personalized Medicine , Temporal Compressed Sensing.
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