Low dose threshold for measuring cardiac functional metrics using four-dimensional CT with deep learning

Four-dimensional CT is increasingly used for functional cardiac imaging, including prognosis for conditions such as heart failure and post myocardial infarction. However, radiation dose from an acquisition spanning the full cardiac cycle remains a concern. This work investigates the possibility of dose reduction in 4DCT using deep learning (DL)-based segmentation techniques as an objective observer.

A 3D residual U-Net was developed for segmentation of left ventricle (LV) myocardium and blood pool. Two networks were trained: Standard DL (trained with only standard-dose [SD] data) and Noise-Robust DL (additionally trained with low-dose data). The primary goal of the proposed DL methods is to serve as an unbiased and consistent observer for functional analysis performance. Functional cardiac metrics including ejection fraction (EF), global longitudinal strain (GLS), circumferential strain (CS), and wall thickness (WT), were measured for an external test set of 250 Cardiac CT volumes reconstructed at five different dose levels.

Functional metrics obtained from DL segmentations of standard dose images matched well with those from expert manual analysis. Utilizing Standard-DL, absolute difference between DL-derived metrics obtained with standard dose data and 100 mA (corresponding to ∼76 ± 13% dose reduction) data was less than 0.8 ± 1.0% for EF, GLS, and CS, and 5.6 ± 6.7% for Average WT. Performance variation of Noise-Robust DL remained acceptable at even 50 mA.

We demonstrate that on average radiation dose can be reduced by a factor of 5 while introducing minimal changes to global functional metrics (especially EF, GLS, and CS). The robustness to reduced image quality can be further boosted by using emulated low-dose data in the DL training set.