CZT-based photon-counting-detector CT with deep-learning reconstruction: image quality and diagnostic confidence for lung tumor assessment.
Authors
Affiliations (7)
Affiliations (7)
- Department of Diagnostic Radiology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan. [email protected].
- Department of Diagnostic Radiology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
- Department of Medical Information, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
- Department of Thoracic Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
- Clinical Research Support Office, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
- Canon Medical Systems Corporation, 1385 Shimoishigami, Otawara, Tochigi, 324-8550, Japan.
Abstract
This is a preliminary analysis of one of the secondary endpoints in the prospective study cohort. The aim of this study is to assess the image quality and diagnostic confidence for lung cancer of CT images generated by using cadmium-zinc-telluride (CZT)-based photon-counting-detector-CT (PCD-CT) and comparing these super-high-resolution (SHR) images with conventional normal-resolution (NR) CT images. Twenty-five patients (median age 75 years, interquartile range 66-78 years, 18 men and 7 women) with 29 lung nodules overall (including two patients with 4 and 2 nodules, respectively) were enrolled to undergo PCD-CT. Three types of images were reconstructed: a 512 × 512 matrix with adaptive iterative dose reduction 3D (AIDR 3D) as the NR<sub>AIDR3D</sub> image, a 1024 × 1024 matrix with AIDR 3D as the SHR<sub>AIDR3D</sub> image, and a 1024 × 1024 matrix with deep-learning reconstruction (DLR) as the SHR<sub>DLR</sub> image. For qualitative analysis, two radiologists evaluated the matched reconstructed series twice (NR<sub>AIDR3D</sub> vs. SHR<sub>AIDR3D</sub> and SHR<sub>AIDR3D</sub> vs. SHR<sub>DLR</sub>) and scored the presence of imaging findings, such as spiculation, lobulation, appearance of ground-glass opacity or air bronchiologram, image quality, and diagnostic confidence, using a 5-point Likert scale. For quantitative analysis, contrast-to-noise ratios (CNRs) of the three images were compared. In the qualitative analysis, compared to NR<sub>AIDR3D</sub>, SHR<sub>AIDR3D</sub> yielded higher image quality and diagnostic confidence, except for image noise (all P < 0.01). In comparison with SHR<sub>AIDR3D</sub>, SHR<sub>DLR</sub> yielded higher image quality and diagnostic confidence (all P < 0.01). In the quantitative analysis, CNRs in the modified NR<sub>AIDR3D</sub> and SHR<sub>DLR</sub> groups were higher than those in the SHR<sub>AIDR3D</sub> group (P = 0.003, <0.001, respectively). In PCD-CT, SHR<sub>DLR</sub> images provided the highest image quality and diagnostic confidence for lung tumor evaluation, followed by SHR<sub>AIDR3D</sub> and NR<sub>AIDR3D</sub> images. DLR demonstrated superior noise reduction compared to other reconstruction methods.