JPWO2018178272A5 - - Google Patents
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- JPWO2018178272A5 JPWO2018178272A5 JP2019553250A JP2019553250A JPWO2018178272A5 JP WO2018178272 A5 JPWO2018178272 A5 JP WO2018178272A5 JP 2019553250 A JP2019553250 A JP 2019553250A JP 2019553250 A JP2019553250 A JP 2019553250A JP WO2018178272 A5 JPWO2018178272 A5 JP WO2018178272A5
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- 210000004204 Blood Vessels Anatomy 0.000 claims 13
- 230000000004 hemodynamic Effects 0.000 claims 6
- 230000001131 transforming Effects 0.000 claims 5
- 230000002792 vascular Effects 0.000 claims 5
- 238000000034 method Methods 0.000 claims 4
- 238000004088 simulation Methods 0.000 claims 4
- 230000000875 corresponding Effects 0.000 claims 2
- 238000004590 computer program Methods 0.000 claims 1
- 238000005094 computer simulation Methods 0.000 claims 1
- 238000002595 magnetic resonance imaging Methods 0.000 claims 1
- 238000003325 tomography Methods 0.000 claims 1
Claims (12)
前記２Ｄ画像データに基づいて、第１の血管中心線に沿った複数の血管径を決定し、
前記関心領域の３次元（３Ｄ）画像データを受信し、前記３Ｄ画像データは第２の画像取得デバイスによって得られ、
前記３Ｄ画像データに基づいて、第２の血管中心線に沿った前記関心領域の第２のプロファイルを定める複数の第２の断面形状を決定し、
前記関心領域の拡張３Ｄモデルを生成し、
１つ又は複数のプロセッサと通信するディスプレイに前記拡張３Ｄモデルを出力する、
当該１つ又は複数のプロセッサを含む、血管系モデリングシステムであって、
前記拡張３Ｄモデルを生成するために、前記１つ又は複数のプロセッサは、
前記２Ｄ画像データに基づいて疑似３Ｄモデルを構築し、前記疑似３Ｄモデルは、前記第１の血管中心線に沿った前記関心領域の第１のプロファイルを定める複数の第１の断面形状を含み、前記複数の第１の断面形状は複数の円であり、前記複数の第１の断面形状のうちの第１の断面形状の各々は、前記複数の血管径のうちの対応する血管径を含み、
前記疑似３Ｄモデルを前記拡張３Ｄモデルへと変形し、
ここで、前記疑似３Ｄモデルを前記拡張３Ｄモデルへと変形するために、前記１つ又は複数のプロセッサは、前記複数の円を、前記複数の第２の断面形状となるように変形する、血管系モデリングシステム。 Two-dimensional (2D) image data including the region of interest of the vascular system is received , and the 2D image data is obtained by the first image acquisition device.
Based on the 2D image data, a plurality of blood vessel diameters along the first blood vessel center line are determined.
The 3D image data of the region of interest is received, and the 3D image data is obtained by the second image acquisition device.
Based on the 3D image data, a plurality of second cross-sectional shapes that define a second profile of the region of interest along the second blood vessel centerline are determined.
Generate an extended 3D model of the region of interest
Output the extended 3D model to a display that communicates with one or more processors.
A vasculature modeling system that includes the one or more processors.
To generate the extended 3D model, the one or more processors
A pseudo 3D model is constructed based on the 2D image data, and the pseudo 3D model includes a plurality of first cross-sectional shapes that define a first profile of the region of interest along the first blood vessel centerline. The plurality of first cross-sectional shapes are a plurality of circles, and each of the first cross-sectional shapes of the plurality of first cross-sectional shapes includes a corresponding blood vessel diameter of the plurality of blood vessel diameters.
The pseudo 3D model is transformed into the extended 3D model,
Here, in order to transform the pseudo 3D model into the extended 3D model, the one or more processors transforms the plurality of circles into the plurality of second cross-sectional shapes. System modeling system.
血管中心線データに基づく前記第２の血管中心線、
前記複数の第２の断面形状、
前記２Ｄ画像データから導出される空間解像度、又は
前記２Ｄ画像データから導出される血管中心線データに基づく前記複数の血管径
のうちの少なくとも１つを維持するための条件を含む画像レジストレーション及び画像変形技法を使用して、前記３Ｄ画像データ及び前記２Ｄ画像データを組み合わせることによって、前記関心領域の前記拡張３Ｄモデルを生成する、請求項１に記載の血管系モデリングシステム。 The one or more processors in the extended 3D model
The second blood vessel center line based on the blood vessel center line data ,
The plurality of second cross- sectional shapes,
Spatial resolution derived from the 2D image data, or
Using image registration and image transformation techniques that include conditions for maintaining at least one of the plurality of blood vessel diameters based on the blood vessel centerline data derived from the 2D image data, the 3D image data and The vasculature modeling system according to claim 1, wherein the extended 3D model of the region of interest is generated by combining the 2D image data.
前記２Ｄ画像データは血管造影画像データである、
のうちの少なくとも１つである、請求項１に記載の血管系モデリングシステム。 The 3D image data is magnetic resonance imaging (MRI) image data or computer tomography (CT) image data , or
The 2D image data is angiographic image data .
The vasculature modeling system according to claim 1, which is at least one of .
前記拡張３Ｄモデルを使用して血行動態シミュレーションを行い、及び
前記血行動態シミュレーションに基づいて少なくとも１つの血行動態パラメータを導出する、請求項１に記載の血管系モデリングシステム。 The one or more processors
Hemodynamic simulation was performed using the extended 3D model, and
The vasculature modeling system according to claim 1, wherein at least one hemodynamic parameter is derived based on the hemodynamic simulation.
前記２Ｄ画像データに基づいて、第１の血管中心線に沿った複数の血管径を決定するステップと、
前記関心領域の３次元（３Ｄ）画像データを受信するステップであって、前記３Ｄ画像データは第２の画像取得デバイスによって得られる、前記３Ｄ画像データを受信するステップと、
前記３Ｄ画像データに基づいて、第２の血管中心線に沿った前記関心領域の第２のプロファイルを定める複数の第２の断面形状を決定するステップと、
前記関心領域の拡張３Ｄモデルを生成するステップであって、当該生成するステップは、
前記２Ｄ画像データに基づいて疑似３Ｄモデルを構築するステップであって、前記疑似３Ｄモデルは、前記第１の血管中心線に沿った前記関心領域の第１のプロファイルを定める複数の第１の断面形状を含み、前記複数の第１の断面形状は複数の円であり、前記複数の第１の断面形状のうちの第１の断面形状の各々は、前記複数の血管径のうちの対応する血管径を含む、構築するステップと、
前記複数の円を、前記複数の第２の断面形状となるように変形することを含む、前記疑似３Ｄモデルを前記拡張３Ｄモデルへと変形するステップと、
を含む、生成するステップと、
１つ又は複数のプロセッサと通信するディスプレイに前記拡張３Ｄモデルを出力するステップと、を有する、血管系モデリングのためのコンピュータ実施方法。 A step of receiving two-dimensional (2D) image data including a region of interest of the vascular system , wherein the 2D image data is obtained by a first image acquisition device, and a step of receiving the 2D image data .
A step of determining a plurality of blood vessel diameters along the first blood vessel center line based on the 2D image data, and
The step of receiving the three-dimensional (3D) image data of the region of interest, wherein the 3D image data is obtained by the second image acquisition device, and the step of receiving the 3D image data.
A step of determining a plurality of second cross-sectional shapes that define a second profile of the region of interest along the second blood vessel centerline, based on the 3D image data.
The step of generating the extended 3D model of the region of interest is the step of generating.
A step of constructing a pseudo 3D model based on the 2D image data, wherein the pseudo 3D model is a plurality of first cross sections that define a first profile of the region of interest along the first blood vessel centerline. The plurality of first cross-sectional shapes including shapes are a plurality of circles, and each of the first cross-sectional shapes of the plurality of first cross-sectional shapes is a corresponding blood vessel of the plurality of blood vessel diameters. Steps to build, including diameter,
A step of transforming the pseudo 3D model into the extended 3D model, which comprises transforming the plurality of circles into the plurality of second cross-sectional shapes.
Including the steps to generate and
A computer-implemented method for vascular modeling , comprising outputting the enhanced 3D model to a display that communicates with one or more processors .
前記拡張３Ｄモデルにおいて、
前記３Ｄ画像データから導出される血管中心線データに基づく前記第２の血管中心線、
前記複数の第２の断面形状、
前記２Ｄ画像データから導出される空間解像度、又は
前記２Ｄ画像データから導出される血管中心線データに基づく前記複数の血管径
のうちの少なくとも１つを維持することを含む画像レジストレーション及び画像変形手順を使用する、請求項８に記載のコンピュータ実施方法。 The step to generate is
In the extended 3D model,
The second blood vessel center line based on the blood vessel center line data derived from the 3D image data,
The plurality of second cross- sectional shapes,
Spatial resolution derived from the 2D image data, or
8. The computer practice of claim 8 , using image registration and image transformation procedures comprising maintaining at least one of the plurality of vessel diameters based on vessel centerline data derived from the 2D image data. Method.
前記２Ｄ画像データに基づいて、２Ｄ経路を含む前記第１の血管中心線を決定し、 Based on the 2D image data, the first blood vessel center line including the 2D path is determined.
前記３Ｄ画像データに基づいて、３Ｄ経路を含む前記第２の血管中心線を決定し、 Based on the 3D image data, the second blood vessel center line including the 3D path is determined.
前記疑似３Ｄモデルは前記第１の血管中心線を含み、 The pseudo 3D model includes the first blood vessel centerline.
前記疑似３Ｄモデルを前記拡張３Ｄモデルへと変形するために、前記１つ又は複数のプロセッサはさらに、前記第１の血管中心線を、前記第２の血管中心線の前記３Ｄ経路を有するように変形する、請求項１に記載の血管系モデリングシステム。 In order to transform the pseudo 3D model into the extended 3D model, the one or more processors further have the first vessel centerline with the 3D path of the second vessel centerline. The vasculature modeling system according to claim 1, which is deformed.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP17305369.5A EP3382641A1 (en) | 2017-03-30 | 2017-03-30 | Contrast injection imaging |
| EP17305369.5 | 2017-03-30 | ||
| PCT/EP2018/058152 WO2018178272A1 (en) | 2017-03-30 | 2018-03-29 | Contrast injection imaging |
Publications (5)
| Publication Number | Publication Date |
|---|---|
| JP2020515333A JP2020515333A (en) | 2020-05-28 |
| JP2020515333A5 JP2020515333A5 (en) | 2021-05-06 |
| JPWO2018178272A5 true JPWO2018178272A5 (en) | 2022-02-02 |
| JP7053656B2 JP7053656B2 (en) | 2022-04-12 |
| JP7053656B6 JP7053656B6 (en) | 2022-06-02 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2019553250A Active JP7053656B6 (en) | 2017-03-30 | 2018-03-29 | Contrast injection imaging |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US11282170B2 (en) |
| EP (2) | EP3382641A1 (en) |
| JP (1) | JP7053656B6 (en) |
| CN (1) | CN110494889B (en) |
| WO (1) | WO2018178272A1 (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP4241694A3 (en) | 2016-05-16 | 2023-12-20 | Cathworks Ltd. | Selection of vascular paths from images |
| JP7036742B2 (en) | 2016-05-16 | 2022-03-15 | キャスワークス リミテッド | Vascular evaluation system |
| EP3948886A4 (en) | 2019-04-01 | 2022-12-21 | CathWorks Ltd. | Methods and apparatus for angiographic image selection |
| WO2021059165A1 (en) | 2019-09-23 | 2021-04-01 | Cathworks Ltd. | Methods, apparatus, and system for synchronization between a three-dimensional vascular model and an imaging device |
| CN111341420B (en) * | 2020-02-21 | 2022-08-30 | 四川大学 | Cardiovascular image recognition system and method based on whole-heart seven-dimensional model |
| US12048575B2 (en) | 2020-03-10 | 2024-07-30 | GE Precision Healthcare LLC | Systems and methods for registration of angiographic projections with computed tomographic data |
| JP7469961B2 (en) * | 2020-05-29 | 2024-04-17 | 三菱プレシジョン株式会社 | Image processing device and computer program for image processing |
| KR102283673B1 (en) * | 2020-11-30 | 2021-08-03 | 주식회사 코어라인소프트 | Medical image reading assistant apparatus and method for adjusting threshold of diagnostic assistant information based on follow-up exam |
| EP4009334A1 (en) * | 2020-12-03 | 2022-06-08 | Koninklijke Philips N.V. | Angiography derived coronary flow |
| US11869142B2 (en) * | 2021-09-27 | 2024-01-09 | Shenzhen Keya Medical Technology Corporation | Methods and devices for three-dimensional image reconstruction using single-view projection image |
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| US6389104B1 (en) | 2000-06-30 | 2002-05-14 | Siemens Corporate Research, Inc. | Fluoroscopy based 3-D neural navigation based on 3-D angiography reconstruction data |
| JP5129480B2 (en) * | 2003-09-25 | 2013-01-30 | パイエオン インコーポレイテッド | System for performing three-dimensional reconstruction of tubular organ and method for operating blood vessel imaging device |
| DE102005023167B4 (en) | 2005-05-19 | 2008-01-03 | Siemens Ag | Method and device for registering 2D projection images relative to a 3D image data set |
| RU2568635C2 (en) | 2007-12-18 | 2015-11-20 | Конинклейке Филипс Электроникс, Н.В. | Feature-based recording of two-/three-dimensional images |
| US8643642B2 (en) * | 2009-08-17 | 2014-02-04 | Mistretta Medical, Llc | System and method of time-resolved, three-dimensional angiography |
| US9292921B2 (en) | 2011-03-07 | 2016-03-22 | Siemens Aktiengesellschaft | Method and system for contrast inflow detection in 2D fluoroscopic images |
| US10595807B2 (en) * | 2012-10-24 | 2020-03-24 | Cathworks Ltd | Calculating a fractional flow reserve |
| US9858387B2 (en) * | 2013-01-15 | 2018-01-02 | CathWorks, LTD. | Vascular flow assessment |
| JP6302922B2 (en) * | 2012-11-06 | 2018-03-28 | コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． | Coronary flow reserve ratio (FFR) index |
| JP5459886B2 (en) * | 2013-01-25 | 2014-04-02 | 株式会社東芝 | Image display device |
| US9629563B2 (en) | 2013-09-04 | 2017-04-25 | Siemens Healthcare Gmbh | Method and system for functional assessment of renal artery stenosis from medical images |
| US8977339B1 (en) * | 2013-12-05 | 2015-03-10 | Intrinsic Medical Imaging Llc | Method for assessing stenosis severity through stenosis mapping |
| DE102014210591B4 (en) * | 2014-06-04 | 2022-09-22 | Siemens Healthcare Gmbh | Fluid dynamic analysis of a vascular tree using angiography |
| EP3128481B1 (en) * | 2015-08-04 | 2019-12-18 | Pie Medical Imaging BV | Method and apparatus to improve a 3d + time reconstruction |
-
2017
- 2017-03-30 EP EP17305369.5A patent/EP3382641A1/en not_active Withdrawn
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2018
- 2018-03-29 JP JP2019553250A patent/JP7053656B6/en active Active
- 2018-03-29 CN CN201880023340.2A patent/CN110494889B/en active Active
- 2018-03-29 EP EP18713689.0A patent/EP3602482B1/en active Active
- 2018-03-29 US US16/496,620 patent/US11282170B2/en active Active
- 2018-03-29 WO PCT/EP2018/058152 patent/WO2018178272A1/en unknown
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