JP6100772B2 - 画像処理方法及びコンピューティング装置 - Google Patents
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Description
〔外1〕
は、容積VEeにおけるボクセルを表し、ここで、その容積はエネルギーEeによって得られる。式1が、局所的な構造モデルを当てはめるために使用可能な、最小二乗手法を含む。
〔外2〕
は、その容積内の(i+i',j+j',k+k')のボクセルについてのモデル値であり、wi',j',k'は重み付け要素である。重み付け要素を、位置特定の核として考えてよく、それは、式2に示すような2つの重み付け関数の積である。
〔外3〕
は、ボクセルvi,j,kの局所的なノイズレベル推定であって、上述したようなノイズ推定部202によって推定される。
〔外4〕
は、第1のモデルについてのノイズなしと推定されたボクセルであり、閾値(threshold)は、基準メモリ306に保存されている基準に対応する。この例において、不等式1が満たされる場合、第2のモデルパラメータを用いてノイズ除去を行う。
〔外5〕
は、物質mに関する物質ベクトルであり、
〔外6〕
は、デノイザ118を用いてデノイズした後に取得されたエネルギーeに関する量であり、nは、エネルギービン(energy bin)の数であり、αmは、物質mに関して推定された物質分布マップである。別の適切な分解アルゴリズムが、確率論的分解手法に基づく。例えば、物質分析部402は、式6に基づいて物質分布マップを推定してよい。
〔外7〕
と、ヨード及び脂肪に基づいた第2のヨード分布マップである
〔外8〕
とを受信する。3つ以上の異なるエネルギー範囲を有する例において、ヨード、軟組織、及び脂肪についての単一のヨードマップを生成してよく、かつ/あるいは、より多くのヨードマップを生成してよい。
Claims (10)
- 種々のエネルギー範囲に対応する一式のスペクトル画像群のうちのスペクトル画像の1つ又は複数のボクセルについて、局所的なノイズ値を推定するステップであって、前記スペクトル画像についてのノイズモデルを生成する、推定するステップと、
前記スペクトル画像のボクセルについての局所的な構造モデル群を、対応するノイズモデルに基づいて推定するステップと、
前記ボクセルについての前記局所的な構造モデル群のうち1つを、所定のモデル選択基準に基づいて選択するステップと、
前記ボクセルの値を、前記の選択された局所的な構造モデルに基づいて推定される値に置換することによって、前記選択された局所的な構造モデルに基づいて、前記ボクセルをデノイズするステップと、
を含み、
前記一式のスペクトル画像群の中の複数スペクトル画像の複数ボクセルがデノイズされ、一式のデノイズされたスペクトル画像を生成する、
方法。 - 前記局所的な構造モデル群の一式を、前記スペクトル画像内のボクセルに関する前記スペクトル画像内の3次元の近傍ボクセルに、当てはめるステップ、
をさらに含む、請求項1に記載の方法。 - 前記スペクトル画像群は、第1の線量スキャンの間に取得されたデータを用いて生成され、前記スペクトル画像群についての前記デノイズするステップは、第2の線量スキャンの間に取得されたデータを用いて生成されるスペクトル画像に関する画像ノイズと同じレベルの量の画像ノイズを有する画像一式を生成し、前記第2の線量スキャンの線量は、前記第1の線量スキャンの線量よりも高い、請求項1乃至2のいずれか1項に記載の方法。
- 前記局所的な構造モデル群を当てはめるために、最小二乗最小化を用いるステップと、
前記最小二乗最小化を重み付け要素を用いて重み付けするステップであって、前記重み付け要素は、前記ボクセルに関する3次元の近傍ボクセルを前記ボクセルとその近隣ボクセルとの間のボクセル強度距離に基づいて重み付けする、第1の重み付け成分を含む、重み付けするステップと、
をさらに含む、請求項1乃至3のいずれか1項に記載の方法。 - 前記第1の重み付け成分は、前記ボクセルの前記局所的なノイズ値についての関数である、請求項4に記載の方法。
- 前記重み付け要素は、前記ボクセルに関する前記3次元の近傍を前記ボクセルとその近隣ボクセルとの間の空間的距離に基づいて重み付けする、第2の重み付け成分を含む、請求項4乃至5のいずれか1項に記載の方法。
- 前記局所的な構造モデル群は、少なくとも2つのノイズモデルを含み、前記少なくとも2つのノイズモデルは、同次の領域をモデル化する一定モデルと非同次の領域をモデル化する二次多項式とを少なくとも含む、請求項1乃至6のいずれか1項に記載の方法。
- 前記ボクセルをデノイズすることについて当てはめられたノイズモデルを含む局所的な構造モデルを、前記少なくとも2つのノイズモデルの局所的な標準偏差の比率と所定の閾値との間の所定の関係に基づいて選択するステップ、
をさらに含む、請求項6乃至7のいずれか1項に記載の方法。 - 種々のエネルギー範囲に対応する一式のスペクトル画像群のうちのスペクトル画像のノイズパターンを推定する、ノイズ推定部であって、前記ノイズパターンは前記スペクトル画像のボクセルについての局所的な構造モデル群を推定するために使用される、ノイズ推定部と、
前記ボクセルについての前記局所的な構造モデル群のうち1つを、所定のモデル選択基準に基づいて選択する、モデル選択部と、
前記ボクセルの値を、前記の選択された局所的な構造モデルに基づいて推定される値に置換することによって、前記選択された局所的な構造モデルに基づいて、前記ボクセルをデノイズするスペクトルノイズ除去部であって、前記一式のスペクトル画像群の中の複数スペクトル画像の複数ボクセルがデノイズされ、一式のデノイズされたスペクトル画像を生成する、スペクトルノイズ除去部と、
を含むコンピューティング装置。 - 前記局所的な構造モデル群の一式を、前記スペクトル画像内のボクセルに関する前記スペクトル画像内の3次元の近傍ボクセルに当てはめる、モデル当てはめ部であって、前記モデル選択部は、前記ボクセルについての前記局所的な構造モデル群のうち1つを、前記の当てはめと前記所定のモデル選択基準とに基づいて選択する、モデル当てはめ部、
をさらに含む、請求項9に記載のコンピューティング装置。
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US201161508178P | 2011-07-15 | 2011-07-15 | |
US61/508,178 | 2011-07-15 | ||
PCT/IB2012/053520 WO2013011418A2 (en) | 2011-07-15 | 2012-07-10 | Spectral ct |
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EP (1) | EP2732431B1 (ja) |
JP (1) | JP6100772B2 (ja) |
CN (1) | CN103649990B (ja) |
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