A SURVEY ON RADIOGRAPHERS TO ASSESS THE REASON OF REJECTING X RAY IMAGES

Swati Bansal, Santosh Kumar, M. Naga Teja, Mehvish Maqbool Malik, Vani Pushpa M, Nandini Vyas, Dharavath Naveen, Piyush Kant*

Authors

Swati Bansal, Santosh Kumar, M. Naga Teja, Mehvish Maqbool Malik, Vani Pushpa M, Nandini Vyas, Dharavath Naveen, Piyush Kant*

Keywords:

Image Rejection, Image Quality, Rejection Criteria, Quality Assurance.

Abstract

Background: Reject analysis is a critical aspect of quality assurance in medical imaging departments, serving to identify the causes of image rejection and enhance radiographer training, departmental workflow, and radiation safety by reducing patient exposure.

Aim: The primary objective of this study was to evaluate the reasons for X-ray image rejection among radiographers in the Delhi-NCR region of India.

Methods: A cross-sectional study was conducted involving 288 participants, including radiographers . Data were collected using a structured, self-administered questionnaire consisting of 34 questions, distributed via Google Forms. The questionnaire covered demographic information and reasons for image rejection.

Results: A significant proportion of respondents (82.3%) reported challenges in effectively communicating reasons for image rejection to other team members. Although 76.7% indicated the existence of a standardized process for reporting rejected images, the findings suggest room for system-wide improvement. Pediatric imaging was identified by 49.7% of participants as the most challenging area for exposure factor adjustments. Enhanced quality control measures were recommended by 37.5% of respondents to minimize rejections, while 38.9% highlighted the need for better collaboration between radiographers and other departments. Additionally, the study revealed a gap in the general understanding and assessment skills among radiology students.

Conclusion: This study provides critical insights into the prevalent causes of X-ray image rejection and underscores the need for targeted interventions in training, workflow communication, and interdepartmental coordination. Addressing these issues can lead to improved diagnostic image quality and optimized clinical outcomes.