WO2016159379A1 - Appareil et procédé de construction de configuration de vaisseaux sanguins et programme logiciel informatique - Google Patents
Appareil et procédé de construction de configuration de vaisseaux sanguins et programme logiciel informatique Download PDFInfo
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- WO2016159379A1 WO2016159379A1 PCT/JP2016/060992 JP2016060992W WO2016159379A1 WO 2016159379 A1 WO2016159379 A1 WO 2016159379A1 JP 2016060992 W JP2016060992 W JP 2016060992W WO 2016159379 A1 WO2016159379 A1 WO 2016159379A1
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- blood vessel
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Images
Classifications
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computed tomography [CT]
Definitions
- the present invention relates to a blood vessel shape constructing apparatus for blood flow analysis by numerical fluid dynamics, a method thereof, and a computer software program.
- the inside / outside determination by the luminance value analysis is based on the half value of the maximum luminance value in the center line orthogonal image. Further, it is preferable that the blood vessel shape constructing unit enhances the accuracy of blood vessel region segmentation / extraction by recursively determining whether the blood vessel region is segmented / extracted.
- the means for segmenting the blood vessel having the characteristic 1-6 is a means for extracting the blood vessel by performing inside / outside blood vessel determination based on the characteristic of the luminance value of the portion corresponding to the blood vessel on the image. 1-8.
- the means for structural analysis of the blood vessel of feature 1-6 is a means for performing labeling by dividing the blood vessel extracted in feature 1-7 by center line extraction into each blood vessel and lesioned part. 1-9. Performing features 1-7 and 1-8 first is the rough extraction of blood vessels. Based on the results of the rough extraction, it is precise to repeat the features 1-7 and 1-8 to correct the results. Let's say extraction.
- the means for setting the region of the blood vessel of feature 1-6 includes the blood vessel / lesion labeled in feature 1-8 in the blood flow analysis by comparing the conditions to be satisfied as the region setting with a list of templates. It is a means for extracting only the region. 1-12.
- the means for constructing the shape of the blood vessel of the characteristic 1-6 is a means for forming the surface of the blood vessel from minute triangular elements by means of the marching cube method or the like with respect to the blood vessel set in the area of the characteristic 1-9. 1-13.
- the means for measuring the blood vessel shape of the characteristic 1-2 is a means for measuring the shape by creating an orthogonal cross section of the blood vessel at each point on the center line obtained in the characteristic 1-8. 1-14.
- the means for evaluating the quality of the blood vessel shape of the characteristic 1-2 calculates a quality score based on a luminance gradient in the vicinity of the blood vessel wall in the obtained blood vessel shape.
- the means for extracting the rough blood vessel shape by the rough inside / outside determination is a means for extracting the rough blood vessel shape by binarizing the image using a predetermined value or a reference value calculated from the luminance value histogram. It is. 4-5.
- the means for precise blood vessel extraction includes means for creating an orthogonal cross section at each point of the coarse center line, means for creating a center line orthogonal image by image interpolation, and analyzing the luminance value of the center line orthogonal image
- a blood vessel shape constructing device having a recursive blood vessel region dividing function is a recursive blood vessel region segmentation function in a blood vessel shape constructing apparatus that eliminates user dependence by fully automatically processing each step of blood vessel shape construction and enables quality control of blood vessel shape construction. Is to provide.
- a blood vessel is extracted from a medical image by segmentation, it is necessary to determine whether the blood vessel is inside or outside. The inside / outside blood vessel determination is performed by setting a certain threshold value with respect to a change in signal intensity from inside the blood vessel to the outside.
- the fifth feature of the present invention is to provide an automatic region setting function in a blood vessel shape construction device that eliminates user dependence and enables quality control of blood vessel shape construction by fully automatically processing each step of blood vessel shape construction. To do. It is necessary to set an analysis region for the blood vessel shape extracted by region division. Until now, the analysis area has been arbitrarily determined by the user, and this has been a factor in causing variation in analysis results.
- the present invention solves the problem by taking a new viewpoint of performing absolute evaluation based on blood flow information obtained from blood flow analysis results. More specifically, the wall shear stress acting on the blood vessel wall is an important index.
- the “imaging target 36” can be subdivided into “patient situation” and “imaging site”.
- “Patient status” relates to the degree of vascular lesions and the presence or absence of treatment.
- Examples of the “vascular lesion” include those caused by arteriosclerosis. When arteriosclerosis progresses, calcification is often accompanied, but in images containing such arteriosclerotic sites, local halation may occur due to calcification (or high calcification) Affects the construction of blood vessel shape.
- Presence / absence of treatment is, for example, whether or not a treatment device such as a stent, coil, clip, or the like is placed in the body.
- noise may be generated around the treatment device and the image quality may be reduced.
- the “imaging part” indicates a factor that affects the image quality in a part-dependent manner. For example, when the vicinity of the nasal cavity is imaged by MRA, the magnetic field tends to vary spatially due to the air layer. Further, when imaging around a hard tissue such as a bone by CTA, it is difficult to distinguish between the bone and the blood vessel.
- Image discrimination process (step S2)
- the image discriminating unit 12 encodes and digitizes two pieces of “image attribute information” and “image analysis information” as appropriate image information, and processes them as input images for blood flow analysis based on this. It is determined whether or not input is possible.
- the imaging target discriminating unit performs image analysis, discriminates various types of noise, quantifies and outputs as appropriate image information (step S2-2-2). Specifically, (1) device-derived ⁇ 1 , (2) contrast agent-derived ⁇ 2 , (3) blood vessel-derived ⁇ 3 , (4) hard tissue-derived ⁇ 4 , (5) site-derived ⁇ 5 , (6) Each noise of the treatment-derived ⁇ 6 is calculated. In this embodiment, noise is defined as an error factor in blood flow analysis.
- Device-derived noise is device-specific background noise included in the background of an image. There are random noise that is uniformly distributed over the entire image and bias noise that appears in a pattern such as a stripe pattern in the image local area. In this embodiment, the features of these noises are digitized by a phantom test image. More specifically, the random noise is quantified as a high frequency component by Fourier transform, and the bias noise is quantified as a low frequency component for output and discrimination.
- Contrast-agent-derived noise Contrast-agent-derived noise is detected as uneven brightness values due to poor mixing of contrast agent and blood.
- the blood vessel-derived noise is calculated based on the quantification of the blood vessel shape, and an area that is inappropriate for blood flow analysis is defined as blood vessel-derived noise and specified.
- Hard tissue-derived noise Hard tissue-derived noise is noise related to bone and calcification. Bone and calcification are observed as a high luminance signal exceeding the luminance value of the contrast agent. Because of the high luminance signal, the signal tends to be misrecognized as a part of the blood vessel lumen even though it is inside or outside the blood vessel wall. Therefore, here, signals from bone and calcification are detected as noise.
- the standard medical image, the image appropriateness level, and the image correction level are output (step S3-4-5).
- the image correction degree is the size of the area of the correction portion.
- the blood vessel shape constructing unit 14 deletes unnecessary blood vessels for constructing a blood vessel shape for blood flow analysis by filtering (step S4-4-2).
- Unnecessary blood vessels are fine blood vessels and short blood vessels that are inappropriate for blood flow analysis. Since the diameter and length of the blood vessel are obtained by thinning and graphing, filtering is performed by matching with a prescribed threshold value. Thereafter, a region for blood flow analysis is automatically extracted according to the type and site of the lesion (step S4-4-3). Here, the determination is made with reference to a template prepared in advance.
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- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
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- Veterinary Medicine (AREA)
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- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- High Energy & Nuclear Physics (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Optics & Photonics (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
L'invention concerne un appareil comportant : une unité de détermination de permission d'entrée qui reçoit une image médicale, détermine la propriété d'image de l'image médicale et le degré de pertinence de l'image médicale en tant que tel, et détermine si l'image médicale peut être traitée en tant qu'image d'entrée sur la base de cette dernière ; une unité de normalisation d'image qui modifie/corrige une image médicale qui a été déterminée comme pouvant être traitée en tant qu'image d'entrée sur la base de la propriété d'image déterminée et du degré déterminé de pertinence afin de normaliser la qualité de l'image de l'image médicale, et délivre en sortie un degré de correction indiquant le degré de modification/correction ; une unité de construction de configuration de vaisseaux sanguins qui construit une configuration de vaisseaux sanguins en trois dimensions sur la base de l'image médicale normalisée ; une unité d'évaluation de la qualité de la configuration de vaisseaux sanguins qui détermine l'exactitude de la construction de configuration de vaisseaux sanguins de la configuration de vaisseaux sanguins en trois dimensions construite, et calcule et délivre en sortie une évaluation de la qualité globale de la configuration de vaisseaux sanguins en trois dimensions construite sur la base de l'exactitude de la construction de configuration de vaisseaux sanguins, du degré de pertinence, et du degré de correction ; et une unité de sortie de données de configuration de vaisseaux sanguins qui délivre en sortie des données de configuration de vaisseaux sanguins qui représentent la configuration de vaisseaux sanguins en trois dimensions ainsi que l'évaluation de la configuration.
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| JP2017510270A JP6713690B2 (ja) | 2015-04-02 | 2016-04-04 | 血管形状構築装置、その方法及びコンピュータソフトウエアプログラム |
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| US201562142201P | 2015-04-02 | 2015-04-02 | |
| US62/142,201 | 2015-04-02 |
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| WO2016159379A1 true WO2016159379A1 (fr) | 2016-10-06 |
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| PCT/JP2016/060992 WO2016159379A1 (fr) | 2015-04-02 | 2016-04-04 | Appareil et procédé de construction de configuration de vaisseaux sanguins et programme logiciel informatique |
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| JP (1) | JP6713690B2 (fr) |
| WO (1) | WO2016159379A1 (fr) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020054019A1 (fr) * | 2018-09-13 | 2020-03-19 | 株式会社Pfu | Dispositif de traitement d'image, procédé de traitement d'image et programme |
| KR20200065923A (ko) * | 2018-11-30 | 2020-06-09 | 아주대학교산학협력단 | 기계학습용 의료 영상 데이터셋의 품질을 평가하는 방법 및 시스템 |
| KR20200141323A (ko) * | 2019-06-10 | 2020-12-18 | 포항공과대학교 산학협력단 | 초음파 영상 생성 방법 및 이를 수행하는 장치들 |
| CN112560363A (zh) * | 2020-12-15 | 2021-03-26 | 北京航空航天大学 | 一种基于映射过程的cfd计算中的网格变形质量评价方法 |
| JP2021535793A (ja) * | 2018-09-05 | 2021-12-23 | エーアイ メディック インク． | 機械学習及び画像処理アルゴリズムを用いて医用画像の血管を自動的にセグメンテーションする方法及びシステム |
| JP2022549433A (ja) * | 2019-09-18 | 2022-11-25 | トリアージ テクノロジーズ インク． | 画像および専門知識から皮膚症状を収集し特定するシステム |
| CN116942104A (zh) * | 2023-09-21 | 2023-10-27 | 首都医科大学附属北京儿童医院 | 一种用于测量在体局部循环血管的智能观测方法 |
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| JP2022549433A (ja) * | 2019-09-18 | 2022-11-25 | トリアージ テクノロジーズ インク． | 画像および専門知識から皮膚症状を収集し特定するシステム |
| JP7330372B2 (ja) | 2019-09-18 | 2023-08-21 | トリアージ テクノロジーズ インク． | 画像および専門知識から皮膚症状を収集し特定するシステム |
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| CN112560363B (zh) * | 2020-12-15 | 2022-06-21 | 北京航空航天大学 | 一种基于映射过程的cfd计算中的网格变形质量评价方法 |
| CN112560363A (zh) * | 2020-12-15 | 2021-03-26 | 北京航空航天大学 | 一种基于映射过程的cfd计算中的网格变形质量评价方法 |
| CN116942104A (zh) * | 2023-09-21 | 2023-10-27 | 首都医科大学附属北京儿童医院 | 一种用于测量在体局部循环血管的智能观测方法 |
| CN116942104B (zh) * | 2023-09-21 | 2024-01-02 | 首都医科大学附属北京儿童医院 | 一种用于测量在体局部循环血管的智能观测方法 |
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| Publication number | Publication date |
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| JP6713690B2 (ja) | 2020-06-24 |
| JPWO2016159379A1 (ja) | 2018-02-01 |
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