JP2020505092A - 皮質奇形識別 - Google Patents
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Abstract
Description
Claims (17)
- 大脳皮質に対するMRI走査を実行するステップと、
デジタル画像データを前記MRI走査から取得するステップと、
定量化された走査データを生成するために、前記デジタル画像データを定量的に評価するステップと、
前記定量化された走査データに基づいて皮質奇形を自動的に検出するステップと、
前記大脳皮質のマッピングされた画像を生成するために、前記大脳皮質の3次元表現を前記定量化された走査データにマッピングするステップと、
前記皮質奇形が前記マッピングされた画像内の前記大脳皮質の残りの部分と異なる色で示されるように、前記大脳皮質の前記マッピングされた画像をカラーコーディングするステップと、
前記大脳皮質のカラーコーディングされた前記マッピングされた画像を出力するステップとを有する、皮質奇形識別方法。 - 定量化された走査データを生成するために、プロセッサ及びメモリを含むコンピュータの前記プロセッサを用いて、大脳皮質に対するMRI走査からのデジタル画像データを定量的に評価するステップと、
前記定量化された走査データに基づいて皮質奇形を自動的に検出するステップと、
前記定量化された走査データに基づいて、マッピングされた画像内の前記大脳皮質の残りの部分と異なる色で前記皮質奇形が示されるように前記大脳皮質の画像をカラーコーディングするステップとを有する、皮質奇形識別方法。 - 前記皮質奇形の測定値を生成するために、前記定量化された走査データ及びカラーコーディングされた画像に基づいて前記皮質奇形を測定するステップを更に有する、請求項2に記載の皮質奇形識別方法。
- 診断される先天性状態を生成するために、前記定量化された走査データ及び前記測定値に基づいて複数の先天性状態のうちの1つを自動的に診断するステップを更に有する、請求項3に記載の皮質奇形識別方法。
- 前記診断される先天性状態は、テイラーの限局性皮質異形成、構造異形成及び/又は細胞構築異形成のうちの1つを含む皮質奇形である、請求項4に記載の皮質奇形識別方法。
- 前記カラーコーディングするステップは、前記定量化された走査データに基づいて前記大脳皮質の画像の異なる区画をカラーコーディングするステップを有し、
前記大脳皮質の異なるカラーコーディングされた区画は、手術室内で前記大脳皮質の解剖学的構造と共に記録され、
前記画像は、手術によって除去されるべき前記皮質奇形の一部分を視覚的に輪郭を描くためにカラーコーディングされている、請求項2に記載の皮質奇形識別方法。 - 前記大脳皮質のカラーコーディングされた画像は、前記皮質奇形が手術によって除去される手術室内で電子表示装置を介して表示される、請求項2に記載の皮質奇形識別方法。
- 前記皮質奇形の疾患タイプを区別するために、前記皮質奇形の前記定量的な評価から指標を抽出するステップを更に有する、請求項2に記載の皮質奇形識別方法。
- ユーザとの対話処理なしで、前記MRI走査での前記大脳皮質の3次元表現において前記大脳皮質を区分化するために、変形可能な区分化を自動的に実行するステップを更に有する、請求項2に記載の皮質奇形識別方法。
- 前記MRI走査において前記変形可能な区分化を用いて取得された前記定量化された走査データに基づいて、前記大脳皮質の組織を分類するステップを更に有する、請求項9に記載の皮質奇形識別方法。
- 前記大脳皮質の半球の3次元3角形メッシュとして内側皮質表面及び外側皮質表面の表現を生成するステップを更に有し、
前記大脳皮質の内側境界及び外側境界が、前記3次元3角形メッシュの類似の3角形表面として表現される、請求項9に記載の皮質奇形識別方法。 - 前記定量化された走査データは、前記MRI走査中に測定された信号強度の測定値を含み、
前記信号強度は、前記デジタル画像データに基づいて画像内のピクセル値として表現可能である、請求項2に記載の皮質奇形識別方法。 - 前記定量化された走査データは、それぞれ、前記大脳皮質の内側皮質表面の法線方向で、それから内側に向く信号、及び、前記大脳皮質の外側皮質表面の法線方向で、それから外側に向く信号についての信号強度の平均値を含む、請求項12に記載の皮質奇形識別方法。
- 前記定量化された走査データを前記大脳皮質の内側皮質表面及び外側皮質表面を表現する3角形メッシュにマッピングするステップを更に有する、請求項2に記載の皮質奇形識別方法。
- 異なる大脳皮質の定量化された走査データを相関させるために、前記異なる大脳皮質の内側皮質表面及び外側皮質表面の3角形メッシュによって画定された同一のパラメータ空間内の前記異なる大脳皮質からの3次元表面を比較するステップを更に有する、請求項2に記載の皮質奇形識別方法。
- 前記皮質奇形は、前記大脳皮質の左側と右側との間の定量化された走査データにおける偏差を識別することによって検出される、請求項2に記載の皮質奇形識別方法。
- 定量化された走査データを生成するために、プロセッサ及びメモリを含むコンピュータの前記プロセッサを用いて、大脳皮質に対するMRI走査からのデジタル画像データを定量的に評価するステップと、
前記定量化された走査データに基づいて皮質奇形を自動的に検出するステップと、
前記検出された皮質奇形を含む前記大脳皮質のマッピングされた画像を生成するために、前記大脳皮質の3次元表現を前記定量化された走査データにマッピングするステップとを有する、皮質奇形識別方法。
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US201762443061P | 2017-01-06 | 2017-01-06 | |
US62/443,061 | 2017-01-06 | ||
PCT/EP2018/050089 WO2018127499A1 (en) | 2017-01-06 | 2018-01-03 | Cortical malformation identification |
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US20240135558A1 (en) * | 2014-04-25 | 2024-04-25 | Thornhill Scientific Inc. | Imaging abnormalities in vascular response |
US12029575B2 (en) * | 2018-09-24 | 2024-07-09 | Koninklijke Philips N.V. | Mesial temporal lobe epilepsy classifier based on volume and shape of subcortical brain regions |
US12102489B2 (en) * | 2021-06-30 | 2024-10-01 | Clearpoint Neuro, Inc. | Image-guided surgical systems with quantitative evaluation of in vivo thermal treatments and related methods |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100189328A1 (en) * | 2007-05-31 | 2010-07-29 | Koninklijke Philips Electronics N.V. | Method of automatically acquiring magnetic resonance image data |
US20150289779A1 (en) * | 2012-10-18 | 2015-10-15 | Bruce Fischl | System and method for diagnosis of focal cortical dysplasia |
US10302714B2 (en) * | 2017-09-15 | 2019-05-28 | Siemens Healthcare Gmbh | Magnetic resonance radio frequency pulse design using machine learning |
Family Cites Families (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5320102A (en) * | 1992-11-18 | 1994-06-14 | Ciba-Geigy Corporation | Method for diagnosing proteoglycan deficiency in cartilage based on magnetic resonance image (MRI) |
WO2005025404A2 (en) * | 2003-09-08 | 2005-03-24 | Vanderbilt University | Apparatus and methods of cortical surface registration and deformation tracking for patient-to-image alignment in relation to image-guided surgery |
US7103399B2 (en) | 2003-09-08 | 2006-09-05 | Vanderbilt University | Apparatus and methods of cortical surface registration and deformation tracking for patient-to-image alignment in relation to image-guided surgery |
EP1603076A1 (en) * | 2004-05-13 | 2005-12-07 | Aalborg Universitet | Computerized cortex boundary extraction from MR images |
US20070218084A1 (en) * | 2005-12-30 | 2007-09-20 | Fondazione Pierfranco E Luisa Mariani- Onlus | Method for the Mapping of the Epileptogenic Focus in the Pre-Surgical Evaluation of Patients with Intractable Epilepsy |
US9788753B2 (en) * | 2009-02-26 | 2017-10-17 | Ramot At Tel-Aviv University Ltd. | Method and system for characterizing cortical structures |
US20100220910A1 (en) * | 2009-03-02 | 2010-09-02 | General Electric Company | Method and system for automated x-ray inspection of objects |
EP2510500B1 (en) | 2009-12-10 | 2017-08-02 | Koninklijke Philips N.V. | A system for rapid and accurate quantitative assessment of traumatic brain injury |
CN102844790B (zh) * | 2010-03-02 | 2016-06-29 | 皇家飞利浦电子股份有限公司 | 用于识别整个大脑的至少一部分的异常的方法和系统 |
US9230321B2 (en) | 2012-03-30 | 2016-01-05 | University Of Louisville Research Foundation, Inc. | Computer aided diagnostic system incorporating 3D shape analysis of the brain for identifying developmental brain disorders |
US20160166192A1 (en) * | 2013-07-30 | 2016-06-16 | Children's Hospital Los Angeles | Magnetic resonance imaging tool to detect clinical difference in brain anatomy |
WO2015075604A1 (en) | 2013-11-22 | 2015-05-28 | Koninklijke Philips N.V. | System for measuring cortical thickness from mr scan information |
CA2946619A1 (en) * | 2014-04-25 | 2015-10-29 | Joseph Fisher | Imaging abnormalities in vascular response |
US9530206B2 (en) | 2015-03-31 | 2016-12-27 | Sony Corporation | Automatic 3D segmentation and cortical surfaces reconstruction from T1 MRI |
US20160335765A1 (en) * | 2015-05-12 | 2016-11-17 | Justus W. Harris | Three dimensional modeling of health data |
US10262414B2 (en) * | 2015-07-29 | 2019-04-16 | University Of Louisville Research Foundation, Inc. | Computer aided diagnostic system for mapping of brain images |
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---|---|---|---|---|
US20100189328A1 (en) * | 2007-05-31 | 2010-07-29 | Koninklijke Philips Electronics N.V. | Method of automatically acquiring magnetic resonance image data |
US20150289779A1 (en) * | 2012-10-18 | 2015-10-15 | Bruce Fischl | System and method for diagnosis of focal cortical dysplasia |
US10302714B2 (en) * | 2017-09-15 | 2019-05-28 | Siemens Healthcare Gmbh | Magnetic resonance radio frequency pulse design using machine learning |
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