Deep learning-based Hounsfield unit value measurement method for bolus tracking images in cerebral computed tomography angiography

Background: Patient movement during bolus tracking (BT) impairs the accuracy of Hounsfield unit (HU) measurements. This study assesses the accuracy of measuring HU values in the internal carotid artery (ICA) using an original deep learning (DL)-based method as compared with using the conventional region of interest (ROI) setting method. Method: A total of 722 BT images of 127 patients who underwent cerebral computed tomography angiography were selected retrospectively and divided into groups for training data, validation data, and test data. To segment the ICA using our proposed method, DL was performed using a convolutional neural network. The HU values in the ICA were obtained using our DL-based method and the ROI setting method. The ROI setting was performed with and without correcting for patient body movement (corrected ROI and settled ROI). We compared the proposed DL-based method with settled ROI to evaluate HU value differences from the corrected ROI, based on whether or not patients experienced involuntary movement during BT image acquisition. Results: Differences in HU values from the corrected ROI in the settled ROI and the proposed method were 23.8 +/- 12.7 HU and 9.0 +/- 6.4 HU in patients with body movement and 1.1 +/- 1.6 HU and 3.9 +/- 4.7 HU in patients without body movement, respectively. There were significant differences in both comparisons (P < 0.01). Conclusion: DL-based method can improve the accuracy of HU value measurements for ICA in BT images with patient involuntary movement.

Automated summary

This study compared the accuracy of measuring HU values in the internal carotid artery using a DL-based method and the conventional ROI setting method. The results showed that the DL-based method can improve the accuracy of HU value measurements for ICA in BT images, especially in cases of patient involuntary movement.