JP2022505511A - 超音波信号の適応ビームフォーミングの方法及びシステム - Google Patents
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Abstract
本発明はまた、超音波信号の適応ビームフォーミングに有用な人工ニューラルネットワーク16を訓練する方法、並びに関連するコンピュータプログラム及びシステムに関する。
Description
である。共分散行列Rの反転は、特に大きな行列の場合、計算的に非常に高価であるため、リアルタイムの実行には適していない。
ここで、μxは任意の時間に活性化層を通って伝播される入力値のベクトル又はバッチの平均であり、||x-μx||は、L2ノルム又は分散である。従って、活性化関数は、活性化前にL2正規化を適用することが好ましい。この正規化は、活性化が信号に対してより対称的に働くことを可能にする。更に、L2正規化は、トレーニングがより迅速に収束するという利点を有する。
ここで、b(1)及びb(2)は、バイアスベクトルであり、W(1)及びW(2)は、重み行列であり、G及びsは、活性化関数である。一般的に使用される活性化関数は、シグモイド関数の形である:
ここで、λは、0から1への遷移の傾きを示す。重み行列W(1)及びW(2)は、Levenberg‐Marquardtあるいはバックプロパゲーションアルゴリズムのような訓練アルゴリズムを用いて計算される。
Claims (15)
- 超音波信号の適応ビームフォーミングのための方法であって、
a)超音波送信に応答して複数の超音波トランスデューサ素子によって取得されたRF信号を受信するステップと、
b)訓練済み人工ニューラルネットワークを前記RF信号に適用することによって、前記RF信号をビームフォーミングするためのコンテント適応アポダイゼーション重みを決定するステップと、
を有する方法。 - 前記訓練済み人工ニューラルネットワークの入力ノードの数及び出力ノードの数は、寄与するRF信号の数に対応する、請求項1に記載の方法。
- c)前記コンテント適応アポダイゼーション重みを前記RF信号に適用して、ビームフォーミングされた出力信号を計算するステップ、を更に有する、請求項1又は2に記載の方法。
- 前記訓練済み人工ニューラルネットワークは、正及び負の入力値の両方を無境界の出力値で伝播させる活性化関数を含む少なくとも1つの活性化層を有する、請求項1乃至3のいずれか1項に記載の方法。
- 前記訓練済みニューラルネットワークは、入力値の前記正の部分と前記負の部分とを連結する活性化関数を含む少なくとも1つの活性化層を含む、請求項1乃至4のいずれか1項に記載の方法。
- 前記人工ニューラルネットワークが、最大で4つの完全結合層を有する、請求項1乃至5のいずれか1項に記載の方法。
- 前記人工ニューラルネットワークは、最大で3つの活性化層を有する、請求項1乃至6のいずれか1項に記載の方法。
- 前記ビームフォーミングされた出力信号は、視野の超音波画像を再構成するために使用され、前記超音波画像の1つ又は多くとも数ピクセルに関する前記RFデータが前記人工ニューラルネットワークによって1つ又は複数のバッチで処理されるように、前記RF信号が、前記訓練済み人工ニューラルネットワークを適用する前に再配置される、請求項1乃至7のいずれか1項に記載の方法。
- 前記人工ニューラルネットワークが、1つ又は複数の完全結合層に加えて、又はその代替として、少なくとも1つの畳み込み層を有する、請求項1乃至8のいずれか1項に記載の方法。
- 前記人工ニューラルネットワークは、反復ニューラルネットワークの一部である、請求項1乃至9のいずれか1項に記載の方法。
- 前記人工ニューラルネットワークの前記重みの一部又は全部が量子化され又は1~4ビットに量子化される、請求項1乃至10のいずれか1項に記載の方法。
- 前記人工ニューラルネットワークは、前記人工ニューラルネットワークの入力層及び/又は出力層よりも少ない数のノードを有する少なくとも1つの隠れ層を有する、請求項1乃至11のいずれか1項に記載の方法。
- 超音波信号のコンテント適応ビームフォーミングに有用な訓練済み人工ニューラルネットワークを提供する方法であって、
(a)入力トレーニングデータ又は超音波送信に応答して複数の超音波トランスデューサ素子によって取得されたRF信号を受信するステップと、
(b)出力トレーニングデータを受信するステップであって、前記出力トレーニングデータは、コンテント適応アポダイゼーション重みであり、前記コンテント適応アポダイゼーション重みが、前記コンテント適応ビームフォーミングアルゴリズム、特に最小分散アルゴリズムによってRF信号から計算されるか、又は前記出力トレーニングデータが前記コンテント適応ビームフォーミングアルゴリズムによって前記RF信号から計算されるビームフォーミングされた出力信号である、ステップと、
(c)前記入力トレーニングデータ及び前記出力トレーニングデータを使用することによって人工ニューラルネットワークを訓練するステップと、
(d)訓練済み人工ニューラルネットワークを提供するステップと、
を有する方法。 - コンピュータユニットによってコンピュータプログラムが実行される場合に、前記コンピュータユニットに請求項1乃至13のいずれか1項に記載の方法を実行させる命令を有するコンピュータプログラム。
- 超音波信号の適応ビームフォーミングのためのシステムであって、
a)超音波送信に応答して複数の超音波トランスデューサ素子によって取得されるRF信号を受信する第1のインターフェースと、
b)前記RF信号に訓練済み人工ニューラルネットワークを適用し、それによって前記RF信号をビームフォーミングするためのコンテント適応アポダイゼーション重みが生成され、前記RF信号に前記コンテント適応アポダイゼーション重みを適用してビームフォーミングされた出力信号を計算するように構成された計算ユニットと、
c)前記ビームフォーミングされた出力信号を出力する第2のインターフェースと、
を有するシステム。
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