JPH0461848A - Preparation of fine multilayer model for head part - Google Patents
Preparation of fine multilayer model for head partInfo
- Publication number
- JPH0461848A JPH0461848A JP2173168A JP17316890A JPH0461848A JP H0461848 A JPH0461848 A JP H0461848A JP 2173168 A JP2173168 A JP 2173168A JP 17316890 A JP17316890 A JP 17316890A JP H0461848 A JPH0461848 A JP H0461848A
- Authority
- JP
- Japan
- Prior art keywords
- image
- boundary
- skull
- head
- images
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 210000003625 skull Anatomy 0.000 claims abstract description 24
- 210000001175 cerebrospinal fluid Anatomy 0.000 claims abstract description 17
- 210000002615 epidermis Anatomy 0.000 claims description 18
- 210000004720 cerebrum Anatomy 0.000 claims description 9
- 238000000034 method Methods 0.000 claims description 5
- 210000004556 brain Anatomy 0.000 abstract description 6
- 210000004872 soft tissue Anatomy 0.000 abstract description 3
- 230000015572 biosynthetic process Effects 0.000 abstract 1
- 230000002285 radioactive effect Effects 0.000 abstract 1
- 238000003786 synthesis reaction Methods 0.000 abstract 1
- 230000002194 synthesizing effect Effects 0.000 abstract 1
- 238000003384 imaging method Methods 0.000 description 5
- 238000013170 computed tomography imaging Methods 0.000 description 4
- 238000010521 absorption reaction Methods 0.000 description 3
- 210000000988 bone and bone Anatomy 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000005855 radiation Effects 0.000 description 3
- 210000001519 tissue Anatomy 0.000 description 3
- 230000002490 cerebral effect Effects 0.000 description 2
- 238000000537 electroencephalography Methods 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- 238000000098 azimuthal photoelectron diffraction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000005389 magnetism Effects 0.000 description 1
- 238000002582 magnetoencephalography Methods 0.000 description 1
Landscapes
- Magnetic Resonance Imaging Apparatus (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
Description
この発明は、脳磁や脳波などの研究に用いられる、頭部
多層精密モデルの作成法に関する。The present invention relates to a method for creating a multilayer precision head model used for research on magnetoencephalography, electroencephalography, etc.
脳磁針や脳波計などを用いて人間の脳の磁気あるいは電
圧を測定し、それらのデータより電流双極子を算出する
際などに、頭部の各部の導電率の違いに基づく多層精密
モデルが使用される。この人間の頭部の多層精密モデル
については、ＭＨＩ装置を用いて人間の頭部を撮影して
得たＭＲ像上で、空間−頭表皮、頭表皮−頭蓋骨、頭蓋
骨−脳実質の各境界を抽出し、３層精密モデルを作成す
ることが知られている（ Ｊ、 Ｗ、 Ｈ，Ｍｅｉ ｊ
ｓ、 ｅｔ ａｌ”ＣＯＭＰＵＴＡＴＩＩＮ ＯＦ Ｍ
ＥＧｓ ＡＮＤ ＥＥＧｓ ＬＩＳＩＮＧ＾ＲＥＡＬＩ
ＳＴＩＣＡＬＬＹ ５）（ＡＰＥＤ ＭＵＬＴＩ−ＣＯ
ＭＰＡＲＴＭＥＮＴ ＭＯＤＥＬＯＦ Ｔ）ＩＥ ＨＥ
ＡＤ”、 Ｘ ＩＶ ＩＣＢＥ ＡＮＤ Ｖｌｌ ＩＣ
）４Ｐ、ＥＳＰＯＯ。
ＦＩＮＬＡＮＤ １９８５ Ｐ３６−３７）。A multilayer precision model based on the differences in conductivity of each part of the head is used when measuring magnetism or voltage in the human brain using a magnetic needle or electroencephalograph, and calculating current dipoles from these data. be done. For this multilayered precision model of the human head, each boundary between space and head epidermis, head epidermis and skull, and skull and brain parenchyma is identified on an MR image obtained by photographing the human head using an MHI device. It is known to extract and create a three-layer precision model (J, W, H, Mei j
s, et al”COMPUTATIIN OF M
EGs AND EEGs LISING＾REALI
STICALLY 5) (APED MULTI-CO
MPARTMENT MODELOF T）IE HE
AD”, X IV ICBE AND Vll IC
) 4P, ESPOO. FINLAND 1985 P36-37).
しかしながら、上記のようにＭＲ像がら精密モデルを作
成するのでは、より正確な４層精密モデルを作成するこ
とができないという問題がある。
すなわち、より正確には、空間−頭表皮、頭表皮−頭蓋
骨、頭蓋骨−脳脊髄液、脳脊髄液−大脳の各境界を抽出
し、４層精密モデルを作成することが必要であるが、Ｍ
Ｒ像のみを用いているのでは、頭蓋骨−脳脊髄液の境界
を抽出することができない
この発明は、人間の頭部のより正確な４層精密モデルを
作成することができる、頭部多層精密モデル作成法を提
供することを目的とする。However, when creating a precise model from MR images as described above, there is a problem that a more accurate four-layer precision model cannot be created. That is, more precisely, it is necessary to extract the boundaries of space - head epidermis, head epidermis - skull, skull - cerebrospinal fluid, and cerebrospinal fluid - cerebrum, and create a four-layer precise model.
Using only the R image, it is not possible to extract the boundary between the skull and cerebrospinal fluid.This invention is a head multilayer precision model that can create a more accurate four-layer precision model of the human head. The purpose is to provide a model creation method.
上記の目的を達成するため、この発明による頭部多層精
密モデル作成法においては、ＭＲ像から空間と頭表皮と
の境界及び大脳と脳脊髄液との境界を抽出するとともに
、ＣＴ像から頭蓋骨内外輪郭を抽出し、これら各境界を
、両画像の位置合わせを行いながら１つの画像上で合成
した上で各境界に基づく多層モデルを作成することが特
徴どなっている。In order to achieve the above object, in the head multilayer precision model creation method according to the present invention, the boundary between space and the head epidermis and the boundary between the cerebrum and cerebrospinal fluid are extracted from the MR image, and the inside and outside of the skull are extracted from the CT image. The feature is that the contours are extracted, these boundaries are combined on one image while aligning both images, and then a multilayer model is created based on each boundary.
ＭＲ撮像では、主にプロトン密度などの軟部組織の相違
が画像化される。そこで、このＭＲ像では大脳の画像が
鮮明に現れるとともに、空間と区別された頭表皮画像も
鮮明に現れることになる。
そのため、このＭＲ像を用いれば、大脳と脳脊髄液との
境界、及び空間と頭表皮との境界の抽出が容易である。
他方、ＣＴ撮像では、放射線に対する吸収率の相違が画
像化されるため、ＣＴ像には骨の部分の画像が他の組織
の画像に対して鮮明に現れることになる。そこで、この
特徴を利用することにより、頭蓋骨画像の内外輪郭を求
めることは容易であり、それらが頭表皮と頭蓋骨との境
界、及び頭蓋骨と脳脊髄液との境界を表すことになる。
そこで、これらＭＲ像及びＣＴ像から得た各境界を、こ
れら両画像を位置合わせしながら、１つの画像に合成す
れば、空間−頭表皮、頭表皮−頭蓋骨、頭蓋骨−脳脊髄
液、脳脊髄液−大脳の各境界を得ることができ、この各
境界データから人間の頭部の４層精密モデルを作成する
ことができる。MR imaging mainly images differences in soft tissues such as proton density. Therefore, in this MR image, an image of the cerebrum appears clearly, and an image of the head epidermis, which is distinguished from space, also appears clearly. Therefore, using this MR image, it is easy to extract the boundary between the cerebrum and cerebrospinal fluid, and the boundary between space and the head epidermis. On the other hand, in CT imaging, differences in the absorption rate of radiation are visualized, so that images of bone parts appear more clearly in CT images than images of other tissues. Therefore, by using this feature, it is easy to obtain the inner and outer contours of the skull image, and these represent the boundary between the head epidermis and the skull, and the boundary between the skull and the cerebrospinal fluid. Therefore, by combining each boundary obtained from these MR images and CT images into one image while aligning both images, space-head epidermis, head epidermis-skull, skull-cerebrospinal fluid, cerebrospinal Each fluid-cerebral boundary can be obtained, and a four-layer precise model of the human head can be created from this boundary data.
以下、この発明の一実施例について図面を参照しながら
詳細に説明する。第１図は人間の頭部のＭＲ像の一例を
示すものであるが、この第１図に示すように、ＭＲ像で
は軟部組織の相違が画像として明瞭に現れ、頭表皮画像
１１と大脳画像１２とがはっきりと写ることになる。こ
れは、ＭＲ像がプロトン密度などに依存したデータから
作成されるという、ＭＲ撮像の原理による。
これに対して、第２図に一例を示すＣＴ＠では、頭蓋骨
画像２１が明瞭に現れることになる。この第２図に示す
ＣＴ像は、第１図と同じ人間の頭部を同じスライスで撮
像したものを示す。ＣＴ撮像では、放射線の吸収の相違
を画像化するという原理のため、他の組織と比べて骨の
部分の放射線吸収が順著であることから、他の組織の画
像に対して骨の部分の画像が明瞭に映し出されることに
なる。そのため、第２図に示すように、頭表皮画像２４
は不明瞭にしか現れない。
そこで、これらＭＲ像とＣＴ像とのそれぞれの特徴を生
かすことにより、空間−頭表皮、頭表皮−頭蓋骨、頭蓋
骨−脳脊髄液、脳脊髄液−大脳の各境界を抽出すること
とするのである。すなわち、第１図のＭＲ像において、
しきい値処理により空間と頭表皮との境界を求め、また
大脳の輪郭つまり大脳と脳脊髄液との境界をマニュアル
により求める。他方、第２図のＣＴ像を用い、しきい値
処理により頭蓋骨画像２１の内外境界、っまり頭蓋骨画
像２１の外側輪郭２２と内側輪郭２３とを求める。そし
て、これらの位置合わせを行ないながら、空間と頭表皮
との境界画像、大脳と脳脊髄液との境界画像と、頭蓋骨
画像２１の外側輪郭２２と内側輪郭２３とを１枚の画像
に合成することにより、空間−頭表皮、頭表皮−頭蓋骨
、頭蓋骨−脳脊髄液、脳脊髄液−大脳の各境界を得る。
この位置合わせに関して、この実施例では、たとえば第
３図で示すように、頭部３１に関する座標系ｘ−ｙ−ｚ
の指標となる参照点３２に、ＭＲ撮像及びＣＴ撮像で撮
影可能な微小指標を取り付け、同じスライスについてＭ
Ｒ撮像及びＣＴ撮像を行う。こうして得たＭＲ像、ＣＴ
像にはその指標画像が写っているため、この指標画像が
一致するように画像の大きさや位置・方向を合致させる
ことにより、両画像の位置合わせを行う。
こうして、４つの各境界データが得られたとき、これら
のデータを用いて、各境界上の離散点を結ぶことにより
三角形要素あるいは四角形要素などで、各境界のモデル
化を行う、これらモデル化自体は、コンピュータグラフ
ィックスにおける一つのアルゴリズムである面素構成法
によって行うことができる。Hereinafter, one embodiment of the present invention will be described in detail with reference to the drawings. Figure 1 shows an example of an MR image of a human head.As shown in Figure 1, differences in soft tissue clearly appear in the MR image, and the difference between the head epidermis image 11 and the cerebral image is clear. 12 will be clearly visible. This is based on the principle of MR imaging, in which an MR image is created from data dependent on proton density and the like. On the other hand, in CT@, an example of which is shown in FIG. 2, the skull image 21 appears clearly. The CT image shown in FIG. 2 is an image of the same human head taken in the same slice as in FIG. 1. CT imaging uses the principle of imaging differences in radiation absorption, and since radiation absorption in bone areas is more pronounced than in other tissues, the difference in bone areas compared to images of other tissues is The image will be displayed clearly. Therefore, as shown in FIG.
appears only vaguely. Therefore, by making use of the respective characteristics of these MR images and CT images, we will extract the boundaries of space - head epidermis, head epidermis - skull, skull - cerebrospinal fluid, and cerebrospinal fluid - cerebrum. . That is, in the MR image of Fig. 1,
The boundary between space and the head epidermis is determined by threshold processing, and the outline of the cerebrum, that is, the boundary between the cerebrum and cerebrospinal fluid, is determined manually. On the other hand, using the CT image of FIG. 2, the inner and outer boundaries of the skull image 21, that is, the outer contour 22 and inner contour 23 of the skull image 21, are determined by threshold processing. Then, while performing these alignments, the boundary image between space and head epidermis, the boundary image between the cerebrum and cerebrospinal fluid, and the outer contour 22 and inner contour 23 of the skull image 21 are combined into one image. By doing this, the boundaries of space-head epidermis, head epidermis-skull, skull-cerebrospinal fluid, and cerebrospinal fluid-cerebrum are obtained. Regarding this alignment, in this embodiment, for example, as shown in FIG.
A minute index that can be photographed by MR imaging and CT imaging is attached to the reference point 32 that serves as an index of M for the same slice.
Perform R imaging and CT imaging. The MR image thus obtained, CT
Since the image includes the index image, the two images are aligned by matching the sizes, positions, and directions of the images so that the index images match. In this way, when each of the four boundary data is obtained, each boundary is modeled using triangular elements or quadrilateral elements by connecting the discrete points on each boundary using these data.The modeling itself can be performed using the surface element construction method, which is an algorithm in computer graphics.
この発明の頭部多層精密モデル作成法によれば、ＭＲ像
とＣＴ像とのそれぞれの特色を生かして、容易に空間−
頭表皮、頭表皮−頭蓋骨、頭蓋骨−脳脊髄液、脳脊髄液
−大脳の各境界を抽出することができ、人間の頭部に関
する４層精密モデルを正確に作成することができる。そ
のため、脳の研究について非常に役立てることができる
。According to the head multilayer precision model creation method of the present invention, it is possible to easily create a spatial model by taking advantage of the respective characteristics of MR images and CT images.
Each boundary of the head epidermis, head epidermis-skull, skull-cerebrospinal fluid, and cerebrospinal fluid-cerebrum can be extracted, and a four-layer precision model of the human head can be accurately created. Therefore, it can be extremely useful for brain research.
第１図は一実施例にかかるＭＲ像を示す図、第２図は同
実施例にかかるＣＴ像を示す図、第３図は同実施例にお
ける頭部の参照点位置を示す図である。
１１．２４・・・頭表皮画像、１２・・・大脳画像、２
１・・・頭蓋骨画像、２２・・・頭蓋骨外側輪郭、２３
・・・頭蓋骨内側輪郭、３１・・・頭部、３２・・・参
照点。FIG. 1 is a diagram showing an MR image according to one embodiment, FIG. 2 is a diagram showing a CT image according to the same embodiment, and FIG. 3 is a diagram showing reference point positions of the head in the same embodiment. 11.24... Head epidermis image, 12... Cerebral image, 2
1... Skull image, 22... Skull outer contour, 23
... Skull inner contour, 31 ... Head, 32 ... Reference point.
Claims (1)
髄液との境界を抽出するとともに、ＣＴ像から頭蓋骨内
外輪郭を抽出し、これら各境界を、両画像の位置合わせ
を行いながら１つの画像上で合成した上で各境界に基づ
く多層モデルを作成することを特徴とする頭部多層精密
モデル作成法。(1) Extract the boundary between space and head epidermis and the boundary between the cerebrum and cerebrospinal fluid from the MR image, and extract the inner and outer contours of the skull from the CT image, and adjust these boundaries while aligning both images. A method for creating a multilayer precision model of the head, which is characterized by combining images on one image and then creating a multilayer model based on each boundary.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2173168A JPH0461848A (en) | 1990-06-30 | 1990-06-30 | Preparation of fine multilayer model for head part |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2173168A JPH0461848A (en) | 1990-06-30 | 1990-06-30 | Preparation of fine multilayer model for head part |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| JPH0461848A true JPH0461848A (en) | 1992-02-27 |
Family
ID=15955362
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2173168A Pending JPH0461848A (en) | 1990-06-30 | 1990-06-30 | Preparation of fine multilayer model for head part |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH0461848A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5999840A (en) * | 1994-09-01 | 1999-12-07 | Massachusetts Institute Of Technology | System and method of registration of three-dimensional data sets |
| WO2010101117A1 (en) * | 2009-03-01 | 2010-09-10 | 国立大学法人浜松医科大学 | Surgery assistance system |
| JP2011019768A (en) * | 2009-07-16 | 2011-02-03 | Kyushu Institute Of Technology | Image processor, image processing method and image processing program |
| JP2015128669A (en) * | 2015-03-13 | 2015-07-16 | 三菱プレシジョン株式会社 | Living body data model creation method and device, living body data model data structure, living body data model data storage device, three dimensional data model load distribution method, and device |
| JP2016165467A (en) * | 2016-03-29 | 2016-09-15 | 三菱プレシジョン株式会社 | Living body data model creation method and device thereof |
| JP2016165468A (en) * | 2016-03-29 | 2016-09-15 | 三菱プレシジョン株式会社 | Biological data model creation method and device therefor |
-
1990
- 1990-06-30 JP JP2173168A patent/JPH0461848A/en active Pending
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5999840A (en) * | 1994-09-01 | 1999-12-07 | Massachusetts Institute Of Technology | System and method of registration of three-dimensional data sets |
| WO2010101117A1 (en) * | 2009-03-01 | 2010-09-10 | 国立大学法人浜松医科大学 | Surgery assistance system |
| JP2010200869A (en) * | 2009-03-01 | 2010-09-16 | Hamamatsu Univ School Of Medicine | Surgical operation support system |
| US8666476B2 (en) | 2009-03-01 | 2014-03-04 | National University Corporation Hamamatsu University School Of Medicine | Surgery assistance system |
| JP2011019768A (en) * | 2009-07-16 | 2011-02-03 | Kyushu Institute Of Technology | Image processor, image processing method and image processing program |
| JP2015128669A (en) * | 2015-03-13 | 2015-07-16 | 三菱プレシジョン株式会社 | Living body data model creation method and device, living body data model data structure, living body data model data storage device, three dimensional data model load distribution method, and device |
| JP2016165467A (en) * | 2016-03-29 | 2016-09-15 | 三菱プレシジョン株式会社 | Living body data model creation method and device thereof |
| JP2016165468A (en) * | 2016-03-29 | 2016-09-15 | 三菱プレシジョン株式会社 | Biological data model creation method and device therefor |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy | |
| Gao et al. | The neural sources of N170: Understanding timing of activation in face‐selective areas | |
| DE102015226669B4 (en) | Method and system for outputting augmented reality information | |
| Akalin Acar et al. | Effects of forward model errors on EEG source localization | |
| Zelkowicz et al. | An examination of regional cerebral blood flow during object naming tasks | |
| CN106204562A (en) | A kind of method of the arched roof white matter segmentation merged based on fMRI Yu DTI | |
| Hironaga et al. | Proposal for a new MEG–MRI co-registration: a 3D laser scanner system | |
| Treves et al. | Three dimensional image alignment, registration and fusion | |
| Azizollahi et al. | Effect of structural complexities in head modeling on the accuracy of EEG source localization in neonates | |
| Kneöaurek et al. | Medical image registration | |
| DE102012222073B4 (en) | Method for evaluating image data sets and combination image recording device | |
| JPH0461848A (en) | Preparation of fine multilayer model for head part | |
| EP1920422A1 (en) | Method and device for navigating and measuring in a multidimensional image data set | |
| DE102016200433B4 (en) | Procedure for planning radiation treatment of a patient and radiation treatment planning unit | |
| EP2821968B1 (en) | Processing model data representing a biological body section having a defect portion | |
| JP2006158791A (en) | Analysis program of cranial capacity and local brain structure, recording medium and analysis method of cranial capacity and local brain structure | |
| Ojalammi et al. | Automated segmentation of upper airways from MRI-vocal tract geometry extraction | |
| Wierenga | MEG, EEG and the integration with magnetic resonance images | |
| Lefaucheur et al. | Targeting Lower Limb, Upper Limb, and Face Representation in the Primary Motor Cortex for the Practice of Neuronavigated Transcranial Magnetic Stimulation | |
| JP3221451B2 (en) | 3D model creation method | |
| JPH0647022A (en) | Stereoscopic model preparing method | |
| Teo et al. | Anatomically consistent segmentation of the human cortex for functional mri visualization | |
| Giorgi et al. | Neuroanatomical digital image processing in CT-guided stereotactic operations | |
| Shan et al. | A digital pediatric brain structure atlas from T1-weighted MR images | |
| Friðgeirsson et al. | 3D segmented model of head for modelling electrical activity of brain |