Quantifiable Measures of Abdominal Wall Motion for Quality Assessment of Cine-MRI Slices in Detection of Abdominal Adhesions

Abstract

Abdominal adhesions present a diagnostic challenge, and classic imaging modalities can miss their presence. Cine-MRI, which records visceral sliding during patient-controlled breathing, has proven useful in detecting and mapping adhesions. However, patient movements can affect the accuracy of these images, despite there being no standardized algorithm for defining sufficiently high-quality images. This study aims to develop a biomarker for patient movements and determine which patient-related factors influence movement during cine-MRI. Included patients underwent cine-MRI to detect adhesions for chronic abdominal complaints, data were collected from electronic patient files and radiologic reports. Ninety slices of cine-MRI were assessed for quality, using a five-point scale to quantify amplitude, frequency, and slope, from which an image-processing algorithm was developed. The biomarkers closely correlated with qualitative assessments, with an amplitude of 6.5 mm used to distinguish between sufficient and insufficient-quality slices. In multivariable analysis, the amplitude of movement was influenced by age, sex, length, and the presence of a stoma. Unfortunately, no factor was changeable. Strategies for mitigating their impact may be challenging. This study highlights the utility of the developed biomarker in evaluating image quality and providing useful feedback for clinicians. Future studies could improve diagnostic quality by implementing automated quality criteria during cine-MRI.

Keywords: adhesions; algorithm; biomarker; cine-MRI; diagnostic quality.

Figure 1

Comparison of biomarkers to manual annotation. Comparison of the biomarkers as calculated by the algorithm (y-axis) to manual annotation (x-axis). (Left) The movement amplitude was measured in mm with a Pearson’s Correlation Coefficient (R) of 0.98, (Right) the movement frequency was measured in Hz with a Pearson’s Correlation Coefficient (R) of 0.93.

Figure 2

ROC curves on train set for classification of insufficient vs. sufficient slices. ROC curves on the training set for classification of insufficient (0, 1) vs. sufficient (2, 3, 4) slices, using only the Amplitude biomarker (blue) or only the Frequency biomarker (orange). The Amplitude biomarker has an AUC of 0.79, and the Frequency biomarker has an AUC of 0.51.

Figure 3

Quality criterion differentiates between sufficient and insufficient quality. (a) The Frequency and Amplitude biomarker for each case in the 30-slice test set, where the colors of the circles, red to green, correspond to the subjective quality score, Poor to Good. The vertical line visualizes the amplitude criterion. (b) The same plot as (a), but with the subjective quality score on the y-axis.