Deep learning-based CT image reconstruction: Initial evaluation targeting hypovascular hepatic metastases

Abstract

Purpose: To evaluate the effect of a deep learning-based reconstruction (DLR) method on the conspicuity of hypovascular hepatic metastases on abdominal CT images. Materials and Methods: This retrospective study with institutional review board approval included 58 patients with hypovascular hepatic metastases. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and the contrast-to-noise ratio (CNR). CNR was calculated as region of interest ([ROI]L − ROIT)/N, here ROIL is the mean liver parenchyma attenuation, ROIT, the mean tumor attenuation, and N, the noise. Two other radiologists graded the conspicuity of the liver lesion on a five-point scale where 1 is unidentifiable and 5 is detected without diagnostic compromise. Only the smallest liver lesion in each patient, classified as smaller or larger than 10 mm, was evaluated. The difference between hybrid iterative reconstruction (IR) and DLR images was determined by using a two-sided Wilcoxon signed-rank test. Results: The image noise was significantly lower, and the CNR was significantly higher on DLR images than hybrid IR images (median image noise: 19.2 vs 12.8 HU, P, 001; median CNR: Tumors, 10 mm: 1.9 vs 2.5; tumors. 10 mm: 1.7 vs 2.2, both P, 001). The scores for liver lesions were significantly higher for DLR images than hybrid IR images (P, 01 for both in tumors smaller or larger than 10 mm). Conclusion: DLR improved the quality of abdominal CT images for the evaluation of hypovascular hepatic metastases.