3-Dimensional adaptive raw-data filter: Evaluation in low dose chest multidetector-row computed tomography

Takeshi Kubo, Mizuki Nishino, Aya Kino, Norihiko Yoshimura, Pei Jan Paul Lin, Masaya Takahashi, Vassilios Raptopoulos, Hiroto Hatabu

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


OBJECTIVES: To evaluate a 3-dimensional adaptive raw-data filter in reducing streak artifacts in low dose chest computed tomographic (CT) images. METHODS: Fourteen adult patients who underwent low dose chest CT examination (parameters: 25 or 50 mAs, 120 kV) on 64-detector CTscanner were included in this study. We prepared 2 sets of contiguous 5-mm thick images by reconstruction with and without 3-dimensional adaptive raw-data filter (filter-processed and unprocessed images). Streak artifacts and visualization of peripheral vessels in both filter-processed and unprocessed images were evaluated using a 5-point scale. Upper, middle, and lower thorax were evaluated separately. RESULTS: The difference in artifact severity was statistically significant in upper and lower thorax (P = 0.002 and 0.03, respectively), whereas it was not significant in middle thorax (P = 0.13). The difference in the visibility of peripheral pulmonary vessels was not statistically significant in all anatomical regions. CONCLUSIONS: The 3-dimensional adaptive raw-data filter reduced streak artifacts in low dose chest CT in upper and lower thorax.

Original languageEnglish (US)
Pages (from-to)933-938
Number of pages6
JournalJournal of computer assisted tomography
Issue number6
StatePublished - Nov 1 2006


  • Chest CT
  • Filter
  • Low dose CT
  • Streak artifacts

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


Dive into the research topics of '3-Dimensional adaptive raw-data filter: Evaluation in low dose chest multidetector-row computed tomography'. Together they form a unique fingerprint.

Cite this