Back

Status of Magnetic Resonance Imaging Systems and Quality Control Programs in Nigeria

Adewole, M.; Ige, T.; Irurhe, N.; Adewole, P.; Akpochafor, M.; Ibitoye, A.; Adeneye, S.

2023-06-27 radiology and imaging
10.1101/2023.06.20.23290883 medRxiv
Show abstract

Magnetic Resonance Imaging (MRI) employs the use of magnetic field and radio waves to produce images of the body. Quality Control (QC) is essential for ensuring optimal performance of MRI systems, as recommended by American College of Radiology (ACR), American Association of Physicists in Medicine (AAPM), and the International Society of Magnetic Resonance in Medicine (ISMRM). This survey examines the status of MRI systems and QC in Nigeria. Questionnaires were administered through google form to Radiologists, Radiographers, Medical Physicists, and biomedical engineers working in various MRI centers across the country, with a total of 44 responses received from 24 centers. The professional bodies of the professionals involved facilitated the questionnaire administration. The survey results indicate that 1.5T is the most common field strength of MRI systems in the country. 83% of the imaging centers rely solely on the service engineer to keep the MRI operational. Although 71% of the centers have Radiation Safety Advisors (RSA), their services do not include MRI. Moreover, 45% of the centers lack an understanding of the composition and importance of MRI QC. This is due to factors such as the absence of regulatory requirements, high patient workload, no trained personnel, and the unavailability of QC equipment. The findings of this survey highlight the need for improved QC programs in the country to improve image quality and longevity of MRI systems. It also underscores the need for the establishment of a regulatory framework and national policy to ensure the safe use of MRI in Nigeria.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.