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 - ROI_T)/N, where ROI_L is the mean liver parenchyma attenuation, ROI_T, 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

SUBMITTER: Nakamura Y 

PROVIDER: S-EPMC8017421 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature