Evaluation of an adaptive filter for CT under low-CNR condition: comparison with linear filter

Issei Mori, Miho Uchida, Ami Sato, Shingo Sato, Hajime Tamura, Yoshihiro Takai, Tadashi Ishibashi, Haruo Saito, Yoshiyuki Hosokai, Takahide Ogura, Koichi Chida, Yoshio Machida

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The use of an adaptive filter for CT images is becoming a common procedure and is said to reduce image noise while preserving sharpness and helping to reduce the required X-ray dose. Although many reports support this view, the validity of such evaluations is arguable. When the linearity of a system is in question, physical performance indexes should be measured under conditions similar to those of clinical use. Evaluations of diagnosis using clinical images may be fallible because the non-filtered image used as the reference might not have been optimally reconstructed. We have chosen simple, but commonly used, adaptive filters for our evaluation. As a reference for comparing performance, we designed linear filters that best approximate the noise characteristics of the adaptive filters. MTF is measured through observation of the edge-spread function. Clinical abdominal images are used to compare the performance of adaptive filters and linear filters. We conclude that the performance of the type of adaptive filter we have chosen is virtually the same as that of the linear filter, as long as the image quality of soft tissues is our interest. Both the noise SD and MTF are virtually the same if the contrast of the object is not substantially higher than 150 HU. Images of soft tissues obtained with the use of adaptive filters are also virtually the same as those obtained by linear filters. The edge-preservation characteristic of this adaptive filter is not observable for soft tissues.

Original languageEnglish
Pages (from-to)15-24
Number of pages10
JournalNippon Hoshasen Gijutsu Gakkai zasshi
Issue number1
Publication statusPublished - 2009 Jan 20

ASJC Scopus subject areas

  • Medicine(all)


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