Learning effects in visual grading assessment of new reconstruction algorithms in abdominal Computed Tomography

Abstract

Abstract ObjectivesImages reconstructed with higher strengths of iterative reconstruction algorithms impair radiologists’ subjective perception and diagnostic performance due to changes in the amplitude of different spatial frequencies of noise. The hypothesis was that there was a change in radiologists´ assessments towards a more positive attitude to the higher strengths of Advanced modeled iterative reconstruction algorithm (ADMIRE). Can radiologists learn to adapt to the unusual appearance of images produced by higher strengths of ADMIRE?MethodsThe present study is based on two ethical board, previously published, studies that evaluated the performance of ADMIRE in non-contrast and contrast-enhanced abdominal CT. Images from 25 (first material) and 50 (second material) clinical examinations, were reconstructed with ADMIRE strengths 3, 5 and filtered back projection (FBP). These images were assessed by local radiologists using image criteria obtained from the European guidelines for quality criteria in CT. To ascertain if there was a learning effect as the reviews progressed, results from these two studies were used in the new analyses of existing data by introducing a time variable in the mixed-effects ordinal logistic regression model.ResultsFor the highest strength (5) of the ADMIRE algorithm, the significant negative attitude for both liver parenchyma and overall image quality for diagnostic purposes, at the beginning of the reviews was strengthened during the progress of the reviews in both materials. For ADMIRE strength 3, an early positive attitude for the algorithm was perceived with no significant change over time for majority of the criteria except for one criterion, i.e., the overall image quality, where a significant negative trend over time was seen in the second material. ConclusionsAs the reviews in both materials progressed, an increasing dislike for ADMIRE 5 images was apparent for at least two image criteria. In the time perspective of weeks or months, no learning effect towards accepting the new algorithm could be demonstrated.