Enhancing MSCT Image Quality and Equipment Maintenance: Daily Training Essentials for Radiographers at Mzuzu Central Hospital

Hui Zhang(Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University)
Yonghao Du(Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University)
Wenli Huo(Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University)
Jin Shang(Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University)

Abstract

Objective: In the Radiology Department of Mzuzu Central Hospital (MCH), daily training for radiographers now includes content on Computed Tomography (CT) image quality control and equipment maintenance to ensure the normal, continuous, and stable operation of the 16-slice spiral CT scanner.

Methods: Through comprehensive analysis of relevant equipment, we have identified key parameters that significantly impact CT image quality. Innovative optimization strategies and solutions targeting these parameters have been developed and integrated into daily training programs. Furthermore, starting from an examination of prevalent failure modes observed in CT equipment, we delve into essential maintenance and preservation techniques that CT technologists must master to ensure optimal system performance.

Results: (1) Crucial factors affecting CT image quality include artifacts, noise, partial volume effects, and surrounding gap phenomena, alongside spatial and density resolutions, CT dose, reconstruction algorithms, and human factors during the scanning process. In the daily training for radiographers, emphasis is placed on strictly implementing image quality control measures at every stage of the CT scanning process and skillfully applying advanced scanning and image processing techniques. By doing so, we can provide clinicians with accurate and reliable imaging references for diagnosis and treatment. (2) Strategies for CT equipment maintenance: ① Environmental inspection of the CT room to ensure cleanliness and hygiene. ② Rational and accurate operation, including calibration software proficiency. ③ Regular maintenance and servicing for minimizing machine downtime. ④ Maintenance of the CT X-ray tube. CT technicians can become proficient in equipment maintenance and upkeep techniques through training, which can significantly extend the service life of CT systems and reduce the occurrence of malfunctions.

Conclusion: Through the regular implementation of rigorous CT image quality control training for radiology technicians, coupled with diligent and proactive CT equipment maintenance, we have observed profound and beneficial impacts on improving image quality. The accuracy and fidelity of radiological data ultimately leads to more accurate diagnoses and effective treatments.

Keywords

Training; Multi-slice spiral CT; Image quality control; Equipment maintenance

References

Amin A, Lane JS. The future of anesthesia for interventional radiology. Curr Opin Anaesthesiol. 2018 Aug;31(4):469-472. doi: 10.1097/ACO.0000000000000609.

Manyak MJ. The emergence of imaging technology in advanced prostate cancer. Can J Urol. 2007 Dec;14 Suppl 1:32-8. PMID: 18163943.

Wagner JB. Artificial Intelligence in Medical Imaging. Radiol Technol. 2019 May;90(5):489-501. PMID: 31088949.

Greffier J, Villani N, Defez D, Dabli D, Si-Mohamed S. Spectral CT imaging: Technical principles of dual-energy CT and multi-energy photon-counting CT. Diagn Interv Imaging. 2023 Apr;104(4):167-177. doi: 10.1016/j.diii.2022.11.003.

Seeram E. Computed Tomography: A Technical Review. Radiol Technol. 2018 Jan;89(3):279CT-302CT. PMID: 29298954.

Vaishnav JY, Jung WC, Popescu LM, Zeng R, Myers KJ. Objective assessment of image quality and dose reduction in CT iterative reconstruction. Med Phys. 2014 Jul;41(7):071904. doi: 10.1118/1.4881148.

Li D, Ma L, Li J, Qi S, Yao Y, Teng Y. A comprehensive survey on deep learning techniques in CT image quality improvement. Med Biol Eng Comput. 2022 Oct;60(10):2757-2770. doi: 10.1007/s11517-022-02631-y.

Wood K. How is the reporting radiographer role portrayed in published studies? A scoping review. Radiography (Lond). 2022 Feb;28(1):215-221. doi: 10.1016/j.radi.2021.07.027.

Muharemovic O, Troelsen A, Thomsen MG, Kallemose T, Gosvig KK. A pilot study to determine the effect of radiographer training on radiostereometric analysis imaging technique. Radiography (Lond). 2018 May;24(2):e37-e43. doi: 10.1016/j.radi.2017.12.003.

Murphy L, Nightingale J, Calder P. Difficulties associated with Reporting Radiographer working practices - A narrative evidence synthesis. Radiography (Lond). 2022 Nov;28(4):1101-1109. doi: 10.1016/j.radi.2022.08.007.

Endo M. History of medical physics. Radiol Phys Technol. 2021 Dec;14(4):345-357. doi: 10.1007/s12194-021-00642-2.

Zhou H, Liu Q, Liu H, Chen Z, Li Z, Zhuo Y, Li K, Wang C, Huang J. Healthcare facilities management: A novel data-driven model for predictive maintenance of computed tomography equipment. Artif Intell Med. 2024 Mar;149:102807. doi: 10.1016/j.artmed.2024.102807.

Abd Rahman NH, Mohamad Zaki MH, Hasikin K, Abd Razak NA, Ibrahim AK, Lai KW. Predicting medical device failure: a promise to reduce healthcare facilities cost through smart healthcare management. PeerJ Comput Sci. 2023 Apr 3;9:e1279. doi: 10.7717/peerj-cs.1279.

Zamzam AH, Al-Ani AKI, Wahab AKA, Lai KW, Satapathy SC, Khalil A, Azizan MM, Hasikin K. Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management. Front Public Health. 2021 Nov 17;9:782203. doi: 10.3389/fpubh.2021.782203.

Arab-Zozani M, Imani A, Doshmangir L, Dalal K, Bahreini R. Assessment of medical equipment maintenance management: proposed checklist using Iranian experience. Biomed Eng Online. 2021 May 21;20(1):49. doi: 10.1186/s12938-021-00885-5.

DOI: http://dx.doi.org/10.26549/jams.v8i1.24858