Innovation in quality control of radiology equipment: signal-to-noise ratio (SNR) calibration curve approach as an indicator of strengthening the internal quality of MRI equipment

  • Olivia Ganna Program Studi Sarjana Terapan Teknologi Radiologi Pencitraan, Politeknik Sandi Karsa, Sulawesi Selatan, Indonesia
  • Akhirida Putri Program Studi Sarjana Terapan Teknologi Radiologi Pencitraan, Politeknik Sandi Karsa, Sulawesi Selatan, Indonesia
Keywords: built-in phantom MRI, signal-to-noise ratio, time repetition (TR), time echo (TE)

Abstract

Quality control (QC) of Magnetic Resonance Imaging (MRI) equipment is a very important thing that must be done to ensure the continuity and accuracy of diagnostic images. The lack of MRI QC equipment, according to the American College Of Radiology (ACR) standards, is an obstacle to the implementation of strengthening the internal quality of MRI equipment. Objective: to provide an alternative solution for enhancing the internal quality of MRI equipment by creating a Signal To Noise Ratio (SNR) calibration curve. The research method is experimental and has two stages. The first stage of image acquisition is determining the structure of phantom materials with values of T1 (longitudinal relaxation time), T2 (transverse relaxation time), and proton density. This value creates a calibration curve of Time Repetition (TR) and Time Echo (TE) variations on the MRI signal value. The second stage is to create an MRI image acquisition curve with variations in TR and TE values to MRI signal values. Through exponential regression analysis, Compare calibration curves, shape, and correlation. Results: The calibration curve of the variation of the TR value to the MRI signal value follows the exponential regression equation y = 32.283e0.0007x with a correlation of R² = 0.5278, and the variation of the TE value results in an exponential regression equation y = 63.455e-0.01x with a correlation value of R² = 0.76. The results of MRI images with the exact parameters of the resulting curve follow the exponential equation y = 170.74e0.0011x with a correlation of R² = 0.418. In contrast, the variation of the TE value results in an exponential regression equation y = 1652.1e-0.004x with a correlation of R² = 0.6756. The ratio of the correlation value of the curve of the MRI image with the calibration curve of TR value variation is 80.76% and TE variation 89.01%; the noise value of TR and TE variation produces an average value of 9.5 and 9.9. The T-test of 2 samples produced a value of P=0.13, meaning there was no difference in the noise value produced. Conclusion: Measurement of SNR value can be an alternative solution for strengthening the internal quality of MRI equipment even though the hospital does not own the ACR phantom

Downloads

Download data is not yet available.

References

A. England et al., “A comparison of perceived image quality between computer display monitors and augmented reality smart glasses,†Radiography, vol. 29, no. 3, pp. 641–646, 2023, doi: https://doi.org/10.1016/j.radi.2023.04.010.

N. Goyal et al., “The need for systematic quality controls in implementing N95 reprocessing and sterilization,†J. Hosp. Infect., vol. 133, pp. 38–45, 2023, doi: https://doi.org/10.1016/j.jhin.2022.11.023.

J. Zhang et al., “Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review,†Comput. Biol. Med., vol. 153, p. 106517, 2023, doi: https://doi.org/10.1016/j.compbiomed.2022.106517.

N. Kobyliak et al., “Accuracy of attenuation coefficient measurement (ACM) for real-time ultrasound hepatic steatometry: Comparison of simulator/phantom data with magnetic resonance imaging proton density fat fraction (MRI-PDFF),†Heliyon, vol. 9, no. 10, p. e20642, 2023, doi: https://doi.org/10.1016/j.heliyon.2023.e20642.

O. Sehrawat, P. A. Noseworthy, K. C. Siontis, T. C. Haddad, J. D. Halamka, and H. Liu, “Data-Driven and Technology-Enabled Trial Innovations Toward Decentralization of Clinical Trials: Opportunities and Considerations,†Mayo Clin. Proc., vol. 98, no. 9, pp. 1404–1421, 2023, doi: https://doi.org/10.1016/j.mayocp.2023.02.003.

T. A. Potretzke et al., “Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance–Derived Measurement of Total Kidney Volume,†Mayo Clin. Proc., vol. 98, no. 5, pp. 689–700, 2023, doi: https://doi.org/10.1016/j.mayocp.2022.12.019.

C. M. Mullins, R. Helton, T. Owens-Tyson, P. Hill-Collins, and S. N. Domby, “Facing Healthcare Access Challenges With Specialty Care Clinics in Central Appalachia,†J. Radiol. Nurs., vol. 42, no. 1, pp. 43–51, 2023, doi: https://doi.org/10.1016/j.jradnu.2022.09.006.

C. Sumner et al., “Approaches to Greening Radiology,†Acad. Radiol., vol. 30, no. 3, pp. 528–535, 2023, doi: https://doi.org/10.1016/j.acra.2022.08.013.

A. Roguin et al., “Update on Radiation Safety in the Cath Lab – Moving Toward a ‘Lead-Free’ Environment,†J. Soc. Cardiovasc. Angiogr. Interv., vol. 2, no. 4, p. 101040, 2023, doi: https://doi.org/10.1016/j.jscai.2023.101040.

N. L. Andersen et al., “Immersive Virtual Reality in Basic Point-of-Care Ultrasound Training: A Randomized Controlled Trial,†Ultrasound Med. Biol., vol. 49, no. 1, pp. 178–185, 2023, doi: https://doi.org/10.1016/j.ultrasmedbio.2022.08.012.

P. O. Ukoha et al., “Clinical indication diagnostic reference level (DRLCI) and post-optimization image quality for Adult Computed Tomography Examinations in Enugu, south eastern Nigeria,†Radiat. Phys. Chem., vol. 206, p. 110728, 2023, doi: https://doi.org/10.1016/j.radphyschem.2022.110728.

M.-L. Ho, C. W. Arnold, S. J. Decker, J. D. Hazle, E. A. Krupinski, and D. A. Mankoff, “Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic,†Acad. Radiol., vol. 30, no. 4, pp. 631–639, 2023, doi: https://doi.org/10.1016/j.acra.2022.12.045.

R. Matheoud et al., “EFOMP’s protocol quality controls in PET/CT and PET/MR,†Phys. Medica, vol. 105, p. 102506, 2023, doi: https://doi.org/10.1016/j.ejmp.2022.11.010.

S. A. Woolen et al., “Radiology Environmental Impact: What Is Known and How Can We Improve?,†Acad. Radiol., vol. 30, no. 4, pp. 625–630, 2023, doi: https://doi.org/10.1016/j.acra.2022.10.021.

Q. Hu, X. Shen, X. Qian, G. Huang, and M. Yuan, “The personal protective equipment (PPE) based on individual combat: A systematic review and trend analysis,†Def. Technol., vol. 28, pp. 195–221, 2023, doi: https://doi.org/10.1016/j.dt.2022.12.007.

S. Shinde, R. Mane, A. Vardikar, A. Dhumal, and A. Rajput, “4D printing: From emergence to innovation over 3D printing,†Eur. Polym. J., vol. 197, p. 112356, 2023, doi: https://doi.org/10.1016/j.eurpolymj.2023.112356.

C. Anderson, M. Algorri, and M. J. Abernathy, “Real-time algorithmic exchange and processing of pharmaceutical quality data and information,†Int. J. Pharm., vol. 645, p. 123342, 2023, doi: https://doi.org/10.1016/j.ijpharm.2023.123342.

S. Hegde et al., “A proactive learning approach toward building adaptive capacity during COVID-19: A radiology case study,†Appl. Ergon., vol. 110, p. 104009, 2023, doi: https://doi.org/10.1016/j.apergo.2023.104009.

J. Yu, J. Zhang, and S. Sengoku, “Innovation Process and Industrial System of US Food and Drug Administration–Approved Software as a Medical Device: Review and Content Analysis,†J. Med. Internet Res., vol. 25, 2023, doi: https://doi.org/10.2196/47505.

Z. Benmamoun, W. Fethallah, S. Bouazza, A. A. Abdo, D. Serrou, and H. Benchekroun, “A framework for sustainability evaluation and improvement of radiology service,†J. Clean. Prod., vol. 401, p. 136796, 2023, doi: https://doi.org/10.1016/j.jclepro.2023.136796.

M. N. K. Anudjo et al., “Considerations for environmental sustainability in clinical radiology and radiotherapy practice: A systematic literature review and recommendations for a greener practice,†Radiography, vol. 29, no. 6, pp. 1077–1092, 2023, doi: https://doi.org/10.1016/j.radi.2023.09.006.

A. H. Matsumoto and M. D. Dake, “Implications of IR Being a Primary Specialty on the Professional Organizational Relationship between Interventional and Diagnostic Radiology,†J. Vasc. Interv. Radiol., vol. 34, no. 12, pp. 2080–2084, 2023, doi: https://doi.org/10.1016/j.jvir.2023.08.011.

A. Elsakka, B. J. Park, B. Marinelli, N. C. Swinburne, and J. Schefflein, “Virtual and Augmented Reality in Interventional Radiology: Current Applications, Challenges, and Future Directions,†Tech. Vasc. Interv. Radiol., vol. 26, no. 3, p. 100919, 2023, doi: https://doi.org/10.1016/j.tvir.2023.100919.

N. J. Beauchamp et al., “Integrative Diagnostics: The Time Is Now—A Report From the International Society for Strategic Studies in Radiology,†J. Am. Coll. Radiol., vol. 20, no. 4, pp. 455–466, 2023, doi: https://doi.org/10.1016/j.jacr.2022.11.015.

O. Isaac and O. A. Awan, “Global Medical Education and Its Value to Radiology,†Acad. Radiol., vol. 30, no. 10, pp. 2222–2224, 2023, doi: https://doi.org/10.1016/j.acra.2023.06.014.

Published
2023-12-31
How to Cite
Ganna, O. and Putri, A. (2023) “Innovation in quality control of radiology equipment: signal-to-noise ratio (SNR) calibration curve approach as an indicator of strengthening the internal quality of MRI equipment”, Jurnal Ilmiah Kesehatan Sandi Husada, 12(2), pp. 518-527. doi: 10.35816/jiskh.v12i2.1238.

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.