Publications by authors named "Naofumi Yasuda"
Background: The utility of synthetic ECV, which does not require hematocrit values, has been reported; however, high-quality CT images are essential for accurate quantification. Second-generation Deep Learning Reconstruction (DLR) enables low-noise and high-resolution cardiac CT images. The aim of this study is to compare the differences among four reconstruction methods (hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and second-generation DLR) in the quantification of synthetic ECV.
View Article and Find Full Text PDFBackground: Monitoring the progression of idiopathic pulmonary fibrosis (IPF) using CT primarily focuses on assessing the extent of fibrotic lesions, without considering the distortion of lung architecture.
Objectives: To evaluate three-dimensional average displacement (3D-AD) quantification of lung structures using deformable registration of serial CT images as a parameter of local lung architectural distortion and predictor of IPF prognosis.
Materials And Methods: Patients with IPF evaluated between January 2016 and March 2017 who had undergone CT at least twice were retrospectively included ( = 114).
Article Synopsis
- The study aimed to compare the visibility of in-stent restenosis using conventional-resolution CT (CRCT) and ultra-high-resolution CT (U-HRCT) while examining different image reconstruction techniques.
- Using a 3.0-mm stent with non-calcified plaque, the researchers scanned samples with different angles and applied various reconstruction methods, assessing lumen size and image quality.
- Results showed that U-HRCT offered significantly better lumen visibility and overall image quality compared to CRCT, although the angle of the stent did have an impact on visualization.