Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements

ConclusionAutomatic detection of the contrast phase in multicenter CT data was achieved with high accuracy, minimizing the contrast-induced error in BMD measurements.Key Points• A 2D DenseNet with anatomy-guided slice selection achieved an F1 score of 0.98 and an accuracy of 98.3% in the test set. In a public dataset, an F1 score of 0.93 and an accuracy of 94.2% were obtained.• Automated adjustment for contrast injection improved the accuracy of lumbar bone mineral density measurements (RMSE 18.7 mg/ml vs. 9.45 mg/ml respectively, in the portal-venous phase).• An artificial neural network can reliably reveal the presence and phase of iodinated contrast agent in multidetector CT scans (https://github.com/ferchonavarro/anatomy_guided_contrast_c). This allows minimizing the contrast-induced error in opportunistic bone mineral density measurements.

Source: European Radiology - February 11, 2022 Category: Radiology Source Type: research