JP2021520970A - 異なる繰返し時間を有する2つのMRI画像から導出される低周波数(<1MHz)交流導電率推定 - Google Patents
異なる繰返し時間を有する2つのMRI画像から導出される低周波数(<1MHz)交流導電率推定 Download PDFInfo
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Abstract
Description
本出願は、参照によりその全体が本明細書に組み込まれている、米国仮出願第62/655,670号(2018年4月10日出願)の利益を主張するものである。
Claims (19)
- 所与の周波数における解剖学的体積の交流導電率または抵抗率の3Dモデルを作成する方法であって、前記方法が、
前記解剖学的体積の第1のMRI画像を取得するステップであって、前記第1のMRI画像が、関連する第1の繰返し時間を有する、ステップと、
前記解剖学的体積の第2のMRI画像を取得するステップであって、前記第2のMRI画像が、前記第1の繰返し時間とは異なる関連する第2の繰返し時間を有する、ステップと、
前記解剖学的体積内の各ボクセルについて、前記第2のMRI画像内の対応するボクセルの強度に対する前記第1のMRI画像内の対応するボクセルの強度の比IRを計算するステップと、
前記解剖学的体積内の各ボクセルに関する前記計算されたIRを、前記所与の周波数における交流導電率または抵抗率の3Dモデルの対応するボクセルにマッピングするステップであって、前記所与の周波数が、1MHz未満である、ステップと
を含む、方法。 - 前記所与の周波数が、100kHz〜300kHzの間である、請求項1に記載の方法。
- 前記所与の周波数が、180kHz〜220kHzの間である、請求項1に記載の方法。
- 前記第1のMRI画像が、T1画像であり、前記第2のMRI画像が、T1画像である、請求項1に記載の方法。
- 前記第1のMRI画像が、T1画像であり、前記第2のMRI画像が、プロトン密度画像である、請求項1に記載の方法。
- 前記第1の繰返し時間が、400ミリ秒〜800ミリ秒の間であり、前記第2の繰返し時間が、2秒〜5秒の間である、請求項1に記載の方法。
- 前記解剖学的体積が、脳の白質および灰白質を備える、請求項1に記載の方法。
- 前記交流導電率または抵抗率の3Dモデルが、交流導電率の3Dモデルである、請求項1に記載の方法。
- 被験者の身体上に配置される複数の電極の位置を最適化する方法であって、前記電極が、所与の周波数における解剖学的体積内の標的組織内に電場を課すために使用され、前記方法が、
前記解剖学的体積の第1のMRI画像を取得するステップであって、前記第1のMRI画像が、関連する第1の繰返し時間を有する、ステップと、
前記解剖学的体積の第2のMRI画像を取得するステップであって、前記第2のMRI画像が、前記第1の繰返し時間とは異なる関連する第2の繰返し時間を有する、ステップと、
前記解剖学的体積内の各ボクセルについて、前記第2のMRI画像内の対応するボクセルの強度に対する前記第1のMRI画像内の対応するボクセルの強度の比IRを計算するステップと、
前記解剖学的体積内の各ボクセルに関する前記計算されたIRを、前記所与の周波数における導電率または抵抗率の3Dモデルの対応するボクセルにマッピングするステップであって、前記所与の周波数が、1MHz未満である、ステップと、
前記解剖学的体積内の前記標的組織の位置を識別するステップと、
前記マッピングするステップにおいて生成された前記所与の周波数における前記導電率または抵抗率の3Dモデルと、前記識別するステップにおいて識別された前記標的組織の前記位置とに基づいて、前記電極の位置を決定するステップと
を含む、方法。 - 前記所与の周波数が、100kHz〜300kHzの間である、請求項9に記載の方法。
- 前記所与の周波数が、180kHz〜220kHzの間である、請求項9に記載の方法。
- 前記第1のMRI画像が、T1画像であり、前記第2のMRI画像が、T1画像である、請求項9に記載の方法。
- 前記第1のMRI画像が、T1画像であり、前記第2のMRI画像が、プロトン密度画像である、請求項9に記載の方法。
- 前記第1の繰返し時間が、400ミリ秒〜800ミリ秒の間であり、前記第2の繰返し時間が、2秒〜5秒の間である、請求項9に記載の方法。
- 前記決定するステップにおいて決定された前記位置において前記被験者の身体に前記電極を固定するステップと、
前記標的組織内に前記電場を課すために、前記固定するステップに続いて前記電極間に電気信号を印加するステップと
をさらに含む、請求項9に記載の方法。 - 前記解剖学的体積が、脳の白質および灰白質を備える、請求項9に記載の方法。
- 前記解剖学的体積が、脳であり、
前記電極の位置の前記決定が、一定の導電率を有する少なくとも1つのシェルのモデルによって前記脳の前記導電率または抵抗率の3Dモデルが囲まれている複合モデルに基づく、
請求項9に記載の方法。 - 前記解剖学的体積が、脳脊髄液によって囲まれている脳であり、
前記電極の位置の前記決定が、一定の導電率を有する少なくとも1つのシェルのモデルによって前記脳の前記導電率または抵抗率の3Dモデルが囲まれている複合モデルに基づく、
請求項9に記載の方法。 - 前記導電率または抵抗率の3Dモデルが、導電率の3Dモデルである、請求項9に記載の方法。
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PCT/IB2019/052931 WO2019197999A1 (en) | 2018-04-10 | 2019-04-09 | Low frequency (<1 mhz) ac conductivity estimates derived from two mri images having different repetition times |
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US10779875B2 (en) | 2013-05-06 | 2020-09-22 | Novocure Gmbh | Optimizing treatment using TTfields by changing the frequency during the course of long term tumor treatment |
US10188851B2 (en) | 2015-10-28 | 2019-01-29 | Novocure Limited | TTField treatment with optimization of electrode positions on the head based on MRI-based conductivity measurements |
US10821283B2 (en) | 2016-04-04 | 2020-11-03 | Novocure Gmbh | Reducing motility of cancer cells using tumor treating fields (TTFields) |
EP4147747A1 (en) | 2016-06-30 | 2023-03-15 | Novocure GmbH | Arrays for longitudinal delivery of ttfields to a body |
EP3571503A1 (en) | 2017-01-19 | 2019-11-27 | Moshe Giladi | System for viewing cell cultures under a microscope whilst applying ttfields |
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US11650277B2 (en) | 2023-05-16 |
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EP3775956B1 (en) | 2022-03-23 |
JP7225373B2 (ja) | 2023-02-20 |
WO2019197999A1 (en) | 2019-10-17 |
KR20200141478A (ko) | 2020-12-18 |
EP3775956A1 (en) | 2021-02-17 |
CA3096429A1 (en) | 2019-10-17 |
CA3096429C (en) | 2023-10-17 |
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