Artificial intelligence iterative reconstruction in lower extremity computed tomography angiography (CTA) of diabetic patients: Improved visualization of distal and collateral arteries

Abstract

Background

The impact of deep learning (DL)-based computed tomography (CT) reconstruction on the visualization of distal and collateral arteries in diabetic lower extremity CT angiography (CTA) remains unclear.

Purpose

To investigate the performance of a novel DL-based CT reconstruction algorithm, artificial intelligence iterative reconstruction (AIIR), in visualizing distal and collateral arteries on lower extremity CTA of diabetic patients, compared to the routine hybrid iterative reconstruction (HIR).

Material and Methods

This retrospective study included 59 diabetic patients who underwent clinically indicated lower extremity CTA. The images were reconstructed with both AIIR and HIR. Distal arterial visualization, collateral circulation depiction, and overall image quality were assessed and compared between two reconstruction methods.

Results

Compared with HIR, AIIR significantly improved the vessel visualization scores in the posterior tibial, dorsalis pedis, medial plantar, dorsal metatarsal, and dorsal digital arteries (all P <0.05). The scores for collateral circulation depiction were also higher with AIIR than those with HIR (all P <0.001). AIIR yielded significantly lower noise as well as higher signal-to-noise ratio and contrast-to-noise ratio compared with HIR (all P <0.001). The subjective score on overall image quality was significantly higher with AIIR than that with HIR (P <0.001).

Conclusion

Compared with HIR, AIIR provides improved visualization of distal and collateral arteries, as well as better overall image quality, in lower extremity CTA of diabetic patients.