Automatic bolus tracking in abdominal CT scans with convolutional neural networks

Background: Bolus tracking can optimize the time delay between contrast injection and diagnostic scan initiation in contrast-enhanced computed tomography (CT), yet the procedure is time-consuming and subject to inter-and intra-operator variances which affect the enhancement levels in diagnostic scans. The objective of the current study is to use artificial intelligence algorithms to fully automate the bolus tracking procedure in contrast-enhanced abdominal CT exams for improved standardization and diagnostic accuracy while providing a simplified imaging workflow. Methods: This retrospective study used abdominal CT exams collected under a dedicated Institutional Review Board (IRB). Input data consisted of CT topograms and images with high heterogeneity in terms of anatomy, sex, cancer pathologies, and imaging artifacts acquired with four different CT scanner models. Our method consisted of two sequential steps: (I) automatic locator scan positioning on topograms, and (II) automatic region-of-interest (ROI) positioning within the aorta on locator scans. The task of locator scan positioning is formulated as a regression problem, where the limited amount of annotated data is circumvented using transfer learning. The task of ROI positioning is formulated as a segmentation problem. Results: Our locator scan positioning network offered improved positional consistency compared to a high degree of variance in manual slice positionings, verifying inter-operator variance as a significant source of error. When trained using expert-user ground-truth labels, the locator scan positioning network achieved a sub-centimeter error (9.76 +/- 6.78 mm) on a test dataset. The ROI segmentation network achieved a submillimeter absolute error (0.99 +/- 0.66 mm) on a test dataset. Conclusions: Locator scan positioning networks offer improved positional consistency compared to manual slice positionings and verified inter-operator variance as an important source of error. By significantly reducing operator-related decisions, this method opens opportunities to standardize and simplify the workflow of bolus tracking procedures for contrast-enhanced CT.

Automated summary

The study aims to fully automate the bolus tracking procedure in contrast-enhanced abdominal CT exams using artificial intelligence algorithms to improve standardization and diagnostic accuracy. The method consists of two steps: automatic locator scan positioning on topograms and automatic region-of-interest (ROI) positioning within the aorta on locator scans. The results show that the locator scan positioning network offers improved positional consistency and reduces error compared to manual slice positionings, while the ROI segmentation network achieves high accuracy in positioning with minimal error.