JP6603656B2 - 脳画像パイプライン及び脳画像領域の位置及び形状予測のための方法並びにシステム - Google Patents
脳画像パイプライン及び脳画像領域の位置及び形状予測のための方法並びにシステム Download PDFInfo
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Description
電子デバイスが、データセット及び患者のデータの両方に7テスラ、3テスラ、又は1.5テスラ磁石のうちの少なくとも1つを含む、請求項1に記載の方法。
「電子デバイス」は、パーソナルコンピュータ、ラップトップ、タブレット、スマートフォン、及び本明細書に記載される用途をサポート可能な任意の他の電子デバイスを含むものとして定義される。
1.5T-MRIが、以下の標準臨床プロトコルを用いて取得された。まず、i)画像パラメータとして、FOV=192×256×176mm3、分解能0.98×0.98×1mm3、TR=1650ms、TE=3.02ms、公称フリップ角15°、帯域幅179Hz/ピクセルを用い、シーメンス(Siemens)社のT1W-AX-T1-MPR臨床プロトコルを使用してT1W画像を取得した。また、ii)画像パラメータとして、FOV:172×230×192mm3、分解能0.72×0.72×2.0mm3、TR/TEは2500/249msec、フリップ角120度、帯域幅539Hz/ピクセル、及び2平均を用いて、市販のシーメンス(Siemens)社のT2W-AX-T2-3D-SPC(スピンエコー)臨床プロトコルを使用してT2W画像を取得した。
事前処理。大脳基底核及び視床領域内の皮質下構造−SN、赤核(RN)、淡蒼球内節(GPi)、及び視床(Tha)は、STNへの隣接性及び分割するための可視性を考慮して、7T-MRIでのSTNの潜在的な予測子として選ばれる。1.5T(又は臨床)MRIデータでは、唯一検出可能なRN構造が、STNの予測子として使用される(その他の構造は、典型的な臨床撮像で識別することは難しいことがあり、識別可能な場合には使用することができる)。
統計学的形状モデルは、トレーニング形状セットから解剖学的構造の形状のばらつきについての事前情報を利用できるようにする。一般的な統計学的形状モデルは点分布モデル(PDM:point distributioon model)に基づき、PDMは、表面に沿って分布する1組のランドマーク点(すなわち、メッシュ内の頂点)によって形状を表し、形状のばらつきをモデリングする。特に、トレーニング形状のランドマーク点の中の対応性が、各形状のばらつきを捕捉し、形状予測の回帰モデルを構築するために必要とされる。最小記述長(MDL:minimum description length)ベースの手法が、有効な形状対応性方法として認識されている。トレーニング形状セットにわたるそのようなランドマークを対応して得るために、MDLベースの方法を採用し、この方法はまず、トレーニング形状への球形メッシュパラメータ化の等角写像によってランドマークを生成し、次に、勾配降下アルゴリズムを適用することによって対応性を最適化する。5つの7T-MRトレーニングセットにわたるSN、STN、RN、GPi、及びTha(これらの重要な構造を本発明の例として使用することを想起し、本技法は他の領域にも同様に適用される)の3D形状及び表面に沿って分布したランドマーク点は対応して、図10に示されている。
本開示の方法は、STN(又は予測する形状/領域)の形状パラメータ及び姿勢と、トレーニングセットでの予測子の形状パラメータ及び姿勢との依存性を見つける回帰問題に更に対処する。形状予測は劣決定(すなわち、低サンプルサイズ及び高次元問題)であるため、バイアス線形回帰が好ましいが、他の予測ツールも同様に使用可能である。
t=Xw,f=Yg、但し、wTw=1,tTt=1…(17)
によって得られ、X及びYの列の線形結合が最大共分散を有することはtTfが最大であることを意味する。
形状及び姿勢を予測する枠組みが、開示される方法について導出されている。予測方法によって使用される実際の形状について以下に更に開示する。
本開示で紹介される特定の実施形態の幾つかの価値を強調するために、これより、例示の形態で、上で説明され例示した実装形態を使用して得た実験結果の幾つかを説明する。
本開示の方法のSTNの予測が、7T-MRからの各構造−SN、RN、GPi、又はTha−を使用して実行され、そのような予測子の予測性能を評価した。各データセットでの構造のトレーニングセットは、1個抜き(leave-one-out)法を使用して構築される。例えば、データセット1のトレーニングセットは、データセット1からの構造を除いたデータセット2、3、4、及び5からの各構造のランドマーク点ベクトルからなる。ここで5つのセットが例示のために使用されるが、本開示の構成要素は任意の数のトレーニングデータセットに適用される。
高磁場(7T)MR撮像は、優れたコントラスト及びより高い分解能に起因して、STNの視覚化に成功し、隣接構造SNを分離した。しかし、7T-MRデータは、標準の臨床用途に常に利用可能であるとは限らない。したがって、従来の臨床1.5T(又は3T)データでのSTN予測は、DBS標的及び手術後プログラミングについて極めて重要な情報を提供することができる。4つの(説明のための例として)1.5T-MRデータセットでのSTNの予測は、臨床1.5T-MRIで可視のRN構造のみを使用して実行した。この比較のために、対応する(同じ被験者の)7T-MRデータセットでのRNを使用して得られた予測結果と、1.5T及び7T-MRテストセットの両方に位置合わせされたトレーニングデータセットにわたるSTNの形状の平均とを提示する。
Claims (14)
- コンピュータの作動方法であって、
予測子領域および関心領域を有する患者の脳画像と当該脳画像の特性を示す患者情報とを受信すること、ここで、前記患者情報は、前記脳画像に関連付けられた性別、年齢、病歴、脳サイズ、脳寸法、および撮像モダリティの1つまたは複数を含み、
前記患者の脳画像とは異なる複数の脳画像と前記患者の脳画像とは異なる当該複数の脳画像の各々の特性を示すデータベース画像情報とを有するとともに、複数の異なるタイプの磁気共鳴画像を含むデータベースにアクセスすること、ここで、前記データベース画像情報は、前記脳画像に関連付けられた性別、年齢、病歴、脳サイズ、脳寸法、および撮像モダリティの1つまたは複数を含み、
前記データベースから前記患者の脳画像の前記患者情報に一致するデータベース画像情報を有する複数の脳画像の患者固有のトレーニングセットを取得することであって、前記トレーニングセットの各脳画像は、予測子領域と、前記予測子領域とは解剖学的に異なる関連する関心領域とを有し、前記トレーニングセットの各脳画像の前記予測子領域は、前記患者の脳画像の前記予測子領域に対応しており、前記トレーニングセットの各脳画像の前記関心領域は、前記患者の脳画像の前記関心領域に対応しており、前記複数の脳画像の患者固有のトレーニングセットを取得すること、
前記複数の脳画像の患者固有のトレーニングセットを処理して、前記複数の脳画像の前記トレーニングセット内の前記予測子領域の形状、位置、及び向きに対する前記複数の脳画像の前記トレーニングセット内の前記関心領域の形状、位置、及び向きの関係に基づいて、予測形状と予測位置及び向きとを有する予測された関心領域を表す予測子情報を抽出すること、
前記予測子情報を用いて前記患者の脳画像を処理して、前記患者の脳画像の前記予測子領域に対して前記患者の脳画像内に前記予測形状と前記予測位置及び向きとを有する前記予測された関心領域を組み込むことにより、前記予測された関心領域を有する患者固有アトラスを生成することを備える方法。 - 前記データベースにアクセスすることは、メタデータが付された脳画像を含む脳画像データベースをアクセスすることを含み、前記メタデータは、前記脳画像に関連付けられた性別、年齢、病歴、脳サイズ、脳寸法、および撮像モダリティの1つまたは複数を含み、
前記複数の脳画像の患者固有のトレーニングセットを取得することは、前記脳画像データベース内の前記脳画像に関連付けられた前記メタデータに基づいて脳画像を選択することを含む、請求項1に記載の方法。 - 前記複数の脳画像の患者固有のトレーニングセットを取得することは、脳深部刺激電極配置の画像を選択することを含み、前記予測された関心領域は視床下核であり、前記予測子領域が赤核である、請求項1に記載の方法。
- 前記複数の脳画像の前記トレーニングセットを処理する工程の前に、前記複数の脳画像の前記トレーニング内の前記予測子領域と前記関心領域とを座標系に位置合わせすること、
前記患者の脳画像の前記予測子領域を前記座標系に位置合わせすること、
をさらに含み、
前記患者の脳画像の前記トレーニングセットを処理することは、前記組み込まれた予測された関心領域を前記座標系に位置合わせすることを含む、請求項1に記載の方法。 - 前記予測子領域は、視床下核、尾状核、被殻、淡蒼球内節、黒質、赤核、及び視床のうちの1つ又は複数である、請求項1に記載の方法。
- 脳画像および前記患者の脳画像の前記トレーニングセットにおける前記予測子領域、脳画像の前記トレーニングセットにおける前記関心領域、及び前記予測された関心領域を前記座標系に位置合わせすることは、線形レジストレーション及び非剛体レジストレーションの一方又は両方を含む、請求項5に記載の方法。
- 前記患者の脳画像を受信することは、第1の撮像モダリティ脳画像を受信することを含み、
前記複数の脳画像の患者固有のトレーニングセットを取得することは、前記第1の撮像モダリティ脳画像とは異なる第2の撮像モダリティ脳画像を選択することを含む、請求項1に記載の方法。 - 前記複数の脳画像の患者固有のトレーニングセットを取得することは、約7テスラ以上の磁場強度で撮影された磁気共鳴画像を選択することを含む、請求項1に記載の方法。
- 前記患者の脳画像を受信することは、約3テスラ以下の磁場強度で撮影された磁気共鳴画像を受信することを含む、請求項8に記載の方法。
- 前記複数の脳画像の患者固有のトレーニングセットを取得することは、1つ又は複数のコンピュータ断層画像を選択することを更に含む、請求項9に記載の方法。
- 前記複数の脳画像の患者固有のトレーニングセットを取得することは、脳深部刺激電極配置の画像を取得することを含む、請求項9に記載の方法。
- ネットワークを介して前記患者固有のアトラスと前記患者の脳画像とのうちの一方又は両方を送信することを更に備える請求項1に記載の方法。
- 前記患者固有のアトラスの前記予測された関心領域を表示することを更に備える請求項12に記載の方法。
- 前記複数の脳画像の患者固有のトレーニングセットを取得することは、臨床医がユーザインターフェースを操作することによる手動選択と画像検索構成要素による自動選択のうちの1つ又は複数を含む、請求項1に記載の方法。
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