Related Experiment Video
Updated: Jun 12, 2026
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Diffuse lung disease: CT of the chest with adaptive statistical iterative reconstruction technique
Priyanka Prakash1, Mannudeep K Kalra, Jeanne B Ackman
1Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 4th Floor, Boston, MA 02114, USA.
Purpose:
To compare visualization of subtle normal and abnormal findings at computed tomography (CT) of the chest for diffuse lung disease with images reconstructed with filtered back projection and adaptive statistical iterative reconstruction (ASIR) techniques.
Materials And Methods:
In this HIPAA-compliant, institutional review board-approved study, 24 patients underwent 64-section multi-detector row CT of the chest for evaluation of diffuse lung disease. Scanning parameters included a pitch of 0.984:1 and 120 kVp in thin-section mode, with 2496 views per rotation compared with 984 views acquired for normal mode. The 0.625-mm-thick images were reconstructed with filtered back projection, ASIR, and ASIR high-definition (ASIR-HD) kernels. Two thoracic radiologists independently assessed the filtered back projection, ASIR, and ASIR-HD images for small anatomic details (interlobular septa, centrilobular region, and small bronchi and bronchioles), abnormal findings (reticulation, tiny nodules, altered attenuation, bronchiectasis), image quality (graded by using a six-point scale, where 1 = excellent image quality, and 5 = interpretation impossible), image noise, and artifacts. Data were tabulated for statistical testing.
Results:
For visualization of normal and pathologic structures, CT image series reconstructed with ASIR-HD were rated substantially better than those reconstructed with filtered back projection and ASIR (P < .001). ASIR-HD images were superior to filtered back projection images in 15 of 24 (62%) patients for visualization of normal structures and in 24 of 24 (100%) patients for pathologic findings. ASIR-HD was superior to ASIR in three of 24 (12%) images for normal anatomic findings and in seven of 24 (29%) images for pathologic evaluation. None of the images in the three groups were rated as unacceptable for noise (P < .001).
Conclusion:
ASIR-HD reconstruction results in superior visualization of subtle and tiny anatomic structures and lesions in diffuse lung disease compared with ASIR and filtered back projection reconstructions.
