Masahito Uesugi

Realization of reliable cerebral-blood-flow maps from low-dose CT perfusion images by statistical noise reduction using nonlinear diffusion filtering.

X-ray computed tomographic perfusion (CTP) imaging, a rapid method for measuring cerebral blood flow (CBF), is an effective modality for assessment of the severity and extent of brain tissue ischemia. Low-dose scanning has been required for CTP imaging for reducing the radiation exposure to patients, because the same plane is scanned repeatedly. Low-dose CTP imaging, however, results in substantial statistical noise in the images, which may negatively impact the accuracy of CBF values. Because CBF values are calculated from the set of CTP images, it is important to reduce the statistical noise in raw CTP images to make the values reliable. Noise reduction must be performed without blurring of vessel structures, because such blurring will overestimate CBF values. For this purpose, two-dimensional nonlinear diffusion filtering (NLDF) was introduced. It was applied to CTP images of a CTP phantom for evaluating the accuracy of CBF values in low-dose CTP and to clinical low-dose CTP images for determining its effectiveness in actual CTP examinations. NLDF successfully reduced the statistical noise in the CTP images while preserving the sharp edges. This feature generated CBF values close to the reference value, producing reliable CBF maps from low-dose CT perfusion images. The CBF maps obtained with NLDF were comparable to or better than those obtained by other, commercial CTP software programs. The use of NLDF was thus effective for manipulation of low-dose CT perfusion images.

Image Smoothing Based on Fuzzy Reasoning. I. Theory.

画像平滑化において相反する要素である統計的雑音除去とエッジ保存の双方を同時に満足させるため, ファジィ推論を平滑化に利用した。画素のエッジ情報から, その最適な平滑化の方法を言語的規則を用いて推論し, その画素の平滑値を決定した。この平滑化の方法は, 言語的規則を用いる点で, 数理解析的規則を使用する従来の平滑化の方法と根本的に異なる。