Publications by authors named "Mao Konishiike"
Article Synopsis
- The study aimed to compare two CT reconstruction methods: deep learning reconstruction (DLR) and hybrid-iterative reconstruction (hybrid-IR), focusing on how they affect the depiction and detection of vertebral masses and spinal cord compression.
- Involving 49 patients, the research assessed the performance of three readers with varying experience in detecting vertebral masses and evaluating spinal cord compression through CT images reconstructed with both methods.
- Results indicated that DLR provided better detection rates and image quality, particularly benefiting less experienced readers, leading to improved diagnostic capabilities compared to hybrid-IR.
To evaluate the effects of deep learning reconstruction (DLR) on image quality of abdominal computed tomography (CT) in patients without arm elevation compared with hybrid-iterative reconstruction (Hybrid-IR) and filtered back projection (FBP). In this retrospective study, axial images of 26 patients who underwent CT without arm elevation were reconstructed using DLR, Hybrid-IR, and FBP. Streak artifact index (SAI) was calculated by dividing the standard deviation of CT attenuation in the liver or spleen by that in fat.
View Article and Find Full Text PDF