JP6457785B2 - Biological texture model production method and biological texture model production program - Google Patents
Biological texture model production method and biological texture model production program Download PDFInfo
- Publication number
- JP6457785B2 JP6457785B2 JP2014229242A JP2014229242A JP6457785B2 JP 6457785 B2 JP6457785 B2 JP 6457785B2 JP 2014229242 A JP2014229242 A JP 2014229242A JP 2014229242 A JP2014229242 A JP 2014229242A JP 6457785 B2 JP6457785 B2 JP 6457785B2
- Authority
- JP
- Japan
- Prior art keywords
- pixel
- mechanical property
- model
- data
- dimensional
- 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.)
- Active
Links
- 238000004519 manufacturing process Methods 0.000 title claims description 16
- 235000019587 texture Nutrition 0.000 claims description 36
- 210000000988 bone and bone Anatomy 0.000 claims description 25
- 235000019589 hardness Nutrition 0.000 claims description 23
- 210000001519 tissue Anatomy 0.000 claims description 22
- 239000000463 material Substances 0.000 claims description 19
- 230000037182 bone density Effects 0.000 claims description 16
- 238000009826 distribution Methods 0.000 claims description 15
- 238000000034 method Methods 0.000 claims description 15
- 230000003287 optical effect Effects 0.000 claims description 11
- 238000000465 moulding Methods 0.000 claims description 10
- 239000011347 resin Substances 0.000 claims description 8
- 229920005989 resin Polymers 0.000 claims description 8
- 210000004872 soft tissue Anatomy 0.000 claims description 7
- 238000010030 laminating Methods 0.000 claims description 4
- 238000002360 preparation method Methods 0.000 claims description 4
- 210000000577 adipose tissue Anatomy 0.000 claims description 3
- 238000007493 shaping process Methods 0.000 claims 1
- 238000002591 computed tomography Methods 0.000 description 41
- 238000002595 magnetic resonance imaging Methods 0.000 description 11
- 210000000056 organ Anatomy 0.000 description 10
- 239000000843 powder Substances 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 239000003086 colorant Substances 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000002091 elastography Methods 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 239000007943 implant Substances 0.000 description 2
- 210000004185 liver Anatomy 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 1
- 210000003484 anatomy Anatomy 0.000 description 1
- 230000004071 biological effect Effects 0.000 description 1
- 210000004204 blood vessel Anatomy 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 229920005570 flexible polymer Polymers 0.000 description 1
- 229910052602 gypsum Inorganic materials 0.000 description 1
- 239000010440 gypsum Substances 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000003475 lamination Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000035807 sensation Effects 0.000 description 1
- 235000019615 sensations Nutrition 0.000 description 1
- 229910052710 silicon Inorganic materials 0.000 description 1
- 239000010703 silicon Substances 0.000 description 1
- 239000007779 soft material Substances 0.000 description 1
Landscapes
- Instructional Devices (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Description
本発明は、X線CT(Computed Tomography)、MRI(Magnetic Resonance Imaging)、超音波診断装置(エコー)などの医用画像診断装置および光学系画像装置(光学顕微鏡、光学系軟質質感分析装置など)から得られた断面像を用いて、3次元の生体質感モデルを作製する技術に関する。 The present invention is based on medical image diagnostic apparatuses such as X-ray CT (Computed Tomography), MRI (Magnetic Resonance Imaging), and ultrasonic diagnostic apparatus (echo), and optical system image apparatuses (such as an optical microscope and an optical soft texture analyzer). The present invention relates to a technique for producing a three-dimensional living body texture model using the obtained cross-sectional image.
医療分野における患部や身体の特定部位の3次元視覚化は、インフォームドコンセント、 診療方針の決定、医療教育、医学研究の場などでのニーズが高まっている。特に、3次元造形モデルを利用した3次元視覚化の場合、視覚だけでなく、立体形状を実際に手に触れて見ることで、コンピュータ画像では伝えきれない多くの情報を伝えることができる。 The need for 3D visualization of affected areas and specific parts of the body in the medical field is increasing in the fields of informed consent, decision on medical policy, medical education, and medical research. In particular, in the case of three-dimensional visualization using a three-dimensional modeling model, a lot of information that cannot be communicated by a computer image can be conveyed by actually touching and viewing a three-dimensional shape as well as vision.
従来から、X線CT、MRIなどの医用画像診断装置の標準規格であるDICOM(Digital Imaging and Communications in Medicine)を用い、3次元形状データを製作し、それをもとにして、粉末積層式造型機により石膏ベースの材料にて、高速、高精度の医療用の3次元造形モデルを作製することが知られている。
しかしながら、内部構造を有する肝臓などの複雑な臓器の柔らかさの模擬や、臓器等を扱う医師や看護師に対する臓器等の感触情報の提供が行えないといった問題があった。
Conventionally, using DICOM (Digital Imaging and Communications in Medicine), which is a standard for medical diagnostic imaging equipment such as X-ray CT and MRI, three-dimensional shape data is produced, and based on that, powder lamination molding It is known that a high-speed and high-precision medical three-dimensional modeling model is produced from a gypsum-based material by a machine.
However, there has been a problem that it is impossible to simulate the softness of complex organs such as the liver having an internal structure and to provide touch information on organs to doctors and nurses who handle the organs.
また一方で、複数樹脂の同時噴射により、硬性樹脂と柔軟性樹脂を組み合わせ、機械的性質の異なる樹脂を用いた3次元造形モデルを作製できる3次元プリンタが知られている。かかる3次元プリンタを用いて、その形状構造に関して表面のみならずその内部構造まで再現できるようになっている。しかしながら、内部構造を有する肝臓などの複雑な臓器の柔軟性、若しくは骨などの硬さを再現できるものは少ない。 On the other hand, there is known a three-dimensional printer capable of producing a three-dimensional modeling model using a resin having different mechanical properties by combining a hard resin and a flexible resin by simultaneous injection of a plurality of resins. Using such a three-dimensional printer, not only the surface but also its internal structure can be reproduced with respect to its shape structure. However, few can reproduce the flexibility of complex organs such as the liver having an internal structure or the hardness of bones.
先行技術では、網構造の中に造形用のモデル材を保持できる保持シートに一層の複数種類のモデル材をそれぞれの造形位置に保持して固定し、固定された一層のモデル材の上に次層の保持シートを載置し、次層の保持シートに次層の複数種類のモデル材をそれぞれの造形位置に保持して固定し、モデル材の固定を順次に上の層について繰り返し行い、各層を積層した後に保持シートを溶解除去することにより、多色・多材料で、堅さが部分により異なる複雑な構造の立体物を造形することのできる3次元造形物の作製方法がある(特許文献1を参照。)。しかしながら、特許文献1の方法では、堅さが部分により異なるものは、その部分の堅さに応じた硬いあるいは柔らかいモデル材を準備する必要があった。 In the prior art, a plurality of types of model materials are held and fixed at each modeling position on a holding sheet that can hold the model material for modeling in the net structure, and the next is placed on the fixed model material. A layer holding sheet is placed, and a plurality of types of model materials of the next layer are held and fixed at the respective molding positions on the holding sheet of the next layer, and the model material is sequentially fixed on the upper layer repeatedly. There is a method for producing a three-dimensional structure that can form a three-dimensional object with a multi-color / multi-material structure with different hardness depending on the part by dissolving and removing the holding sheet after laminating layers (Patent Literature) 1). However, in the method of Patent Document 1, if the hardness varies depending on the part, it is necessary to prepare a hard or soft model material corresponding to the hardness of the part.
また、立体物の外部構造および外部色彩ならびに外部から観察不可能な内部構造および内部色彩を表した3次元立体模型であって、切り開くことにより内部構造および内部色彩を観察することができる軟質素材でできた3次元立体模型が知られている(特許文献2を参照。)。特許文献2に開示された技術は、外部から観察不可能な内部構造および内部色彩を表しているものの、力学特性を再現するものではない。 In addition, it is a 3D solid model that represents the external structure and external colors of solid objects and the internal structures and internal colors that cannot be observed from the outside, and is a soft material that can observe the internal structure and internal colors by opening them. A produced three-dimensional solid model is known (see Patent Document 2). Although the technique disclosed in Patent Document 2 represents an internal structure and an internal color that cannot be observed from the outside, it does not reproduce the mechanical characteristics.
また、医用画像診断装置で撮影された断層画像データから目的の部位のデータを抽出する画像処理手段と、抽出された断層画像データから3次元画像データを生成する画像処理手段と、生成された3次元画像データを模型造形用断層画像データに変換する画像処理手段と、模型造成用断層データにより粉体を積層して精密粉体模型を作製する手段と、作製した精密粉体模型を鋳型として透明又は半透明のシリコンなど柔軟性を持つポリマーからなる血管及び臓器の個別対応型3次元模型の作製方法が知られている(特許文献3を参照。)。特許文献3に開示された技術は、医用画像診断装置で撮影された断層画像データを鋳型の作成に利用しているものであり、モデルの質感の再現を断層画像データに基づいて行うものではない。 Further, image processing means for extracting data of a target part from tomographic image data photographed by the medical image diagnostic apparatus, image processing means for generating three-dimensional image data from the extracted tomographic image data, and the generated 3 Image processing means for converting dimensional image data to tomographic image data for modeling, means for producing a precision powder model by laminating powder using tomographic data for model building, and transparent using the prepared precision powder model as a mold Alternatively, there is known a method for producing an individually corresponding three-dimensional model of blood vessels and organs made of a flexible polymer such as translucent silicon (see Patent Document 3). The technique disclosed in Patent Document 3 uses tomographic image data captured by a medical image diagnostic apparatus for creating a template, and does not reproduce the texture of a model based on tomographic image data. .
従来の3次元造形モデルの目的は、形状確認に重点が置かれていた。滑らかな形状や細かいパーツまで再現できる高精度の造形モデルが、製品デザイナーや製品設計者が求めるものであったが、形状だけからは触感や使用感を想像するのは困難である。形状だけでなく、そのものの触感、使用感、質感など実際のものに近似した造形モデルが要求されている。
また、モデルの質感は医師の感覚よるものであり定性的データに基づくのではなく、生体に近い力学特性を有し、生体に埋入するインプラントの力学試験に使用できるモデルが要望されている。
The purpose of the conventional three-dimensional modeling model has been focused on shape confirmation. A high-precision modeling model that can reproduce even smooth shapes and fine parts is what product designers and product designers demand, but it is difficult to imagine the tactile sensation and usability from the shape alone. There is a demand for a modeling model that approximates not only the shape but also the actual touch, feel, and texture.
In addition, the texture of the model depends on the sense of the doctor, and is not based on qualitative data, but there is a demand for a model that has mechanical characteristics close to that of a living body and can be used for a mechanical test of an implant that is implanted in the living body.
上記状況に鑑みて、本発明は、CT、MRI、エコーなどの医用画像診断装置および光学系画像装置(光学顕微鏡、光学系軟質質感分析装置など)などの断面画像のCT値又は輝度値の2次元分布データから、医師などの専門家が有している生体臓器の感触のみならず、生体に近い力学特性を再現できる生体質感モデル作製方法を提供することを目的とする。 In view of the above situation, the present invention provides a CT value or luminance value of 2 for cross-sectional images of medical image diagnostic apparatuses such as CT, MRI, and echo, and optical system image apparatuses (such as an optical microscope and an optical soft texture analyzer). An object of the present invention is to provide a method for producing a living body texture model that can reproduce not only the feel of living organs possessed by specialists such as doctors but also mechanical characteristics close to the living body from the dimension distribution data.
上記課題を解決すべく、本発明の生体質感モデル作製方法は、下記1)〜5)のステップを少なくとも備え、生体に近い力学特性を再現する。
1)医用画像診断装置により得られた断面像からCT値の2次元分布データを取得する。
本発明では、CT、MRI、エコーなどの医用画像診断装置および光学系画像装置(光学顕微鏡、光学系軟質質感分析装置など)の医用画像診断装置の断面像から、CT値又は輝度値の2次元分布データを得る。なお、MRI画像は、CT画像よりも軟部組織の分解能が高いので、多くの組織の物性値が取得可能である。
2)段階的に硬度の異なる最小基本単位を予め作製し、各々の最小基本単位の少なくとも硬度、ヤング率および破断係数の力学特性データを取得する。
ここで、最小基本単位は、四面体、六面体、円柱、楕円柱、多角柱、球体、くさび形状体、角錐、円錐など幾何学的な基本形状のものと、基本形状の組み合わせた形状や独自形状など応用形状のプリミティブな形状を有する。プリミティブな形状の種類や大きさ、混ぜ合わせる個数・量、混ぜ合わせ方、用いる樹脂材を調整して柔らかさを加減して求める生体質感を作り出す。そして、段階的に硬度の異なる最小基本単位について、各々の力学特性データを測定し、データベース化する。力学特性データは、少なくとも硬度、ヤング率および破断係数を用いるのが良い。なお、MRIエラストグラフィーやエコーを用いて弾性率を推定できることから、力学特性データに弾性率を加えても良い。
In order to solve the above problems, the living body texture model manufacturing method of the present invention includes at least the following steps 1) to 5), and reproduces mechanical characteristics close to those of a living body.
1) Two-dimensional distribution data of CT values is acquired from a cross-sectional image obtained by a medical image diagnostic apparatus.
In the present invention, two-dimensional CT values or luminance values are obtained from cross-sectional images of medical image diagnostic apparatuses such as CT, MRI, and echo, and medical image diagnostic apparatuses of optical system image apparatuses (such as an optical microscope and an optical soft texture analyzer). Obtain distribution data. Since MRI images have higher resolution of soft tissues than CT images, it is possible to acquire physical properties of many tissues.
2) Minimum basic units having different hardnesses are prepared in stages, and mechanical characteristic data of at least the hardness, Young's modulus, and fracture modulus of each minimum basic unit is acquired.
Here, the minimum basic unit is a geometrical basic shape such as tetrahedron, hexahedron, cylinder, elliptical column, polygonal column, sphere, wedge shape, pyramid, cone, etc. It has a primitive shape of applied shape. Adjust the type and size of the primitive shape, the number and amount to mix, how to mix, and the resin material to be used to create the desired living body texture by adjusting the softness. Then, each mechanical characteristic data is measured and made into a database for the smallest basic unit having different hardnesses in stages. As the mechanical property data, at least hardness, Young's modulus, and fracture coefficient may be used. Since the elastic modulus can be estimated using MRI elastography or echo, the elastic modulus may be added to the mechanical property data.
3)CT値の2次元分布データから骨組織を推定して骨組織領域を判別する。
CT値から骨組織を推定する骨組織領域を判別する。骨組織以外の軟組織領域と脂肪組織領域についても必要に応じて判別する。
4)骨組織領域の各画素のCT値から骨密度を求め、骨密度から各画素における少なくとも硬度、ヤング率および破断係数の力学特性データの推定し、同じ或は最も近似する力学特性データを有する最小基本単位を各画素に割り当てる。
CT値から骨密度を算出できることから、骨組織領域の各画素のCT値から骨密度を求める。そして、骨密度から各画素における少なくとも硬度、ヤング率および破断係数の力学特性データを推定する。上記2)でデータベース化された段階的に硬度の異なる最小基本単位の力学特性データと比較して、同じ或は最も近似する力学特性データを有する最小基本単位を各画素に割り当てる。
5)断面像を積層して3次元データとし、各画素に割り当てられた最小基本単位を用いて3次元プリンタにより造形する。
3) The bone tissue is estimated from the two-dimensional distribution data of the CT value to discriminate the bone tissue region.
A bone tissue region in which the bone tissue is estimated from the CT value is determined. A soft tissue region other than a bone tissue and an adipose tissue region are also determined as necessary.
4) Obtain the bone density from the CT value of each pixel in the bone tissue region, estimate at least the hardness, Young's modulus and fracture coefficient mechanical property data of each pixel from the bone density, and have the same or the most approximate mechanical property data A minimum basic unit is assigned to each pixel.
Since the bone density can be calculated from the CT value, the bone density is obtained from the CT value of each pixel in the bone tissue region. Then, the mechanical characteristic data of at least the hardness, Young's modulus, and rupture coefficient in each pixel is estimated from the bone density. The minimum basic unit having the same or the most approximate mechanical characteristic data is assigned to each pixel in comparison with the mechanical characteristic data of the minimum basic unit having different degrees of hardness in a stepwise manner that is stored in the database in 2).
5) Laminate cross-sectional images to form three-dimensional data, and form the image with a three-dimensional printer using the minimum basic unit assigned to each pixel.
また、本発明の生体質感モデル作製方法は、上記の作製方法によって作製された造形モデルと、目的とする組織の力学特性と同じ或は最も近似する力学特性データを有する素材を用いて成形された成形モデルとを組合せて3次元モデルを組み立てることにより行う。 In addition, the living body texture model manufacturing method of the present invention is formed using a modeling model manufactured by the above manufacturing method and a material having mechanical property data that is the same as or closest to the mechanical property of the target tissue. This is done by assembling a three-dimensional model in combination with a molding model.
本発明によれば、生体に近い力学特性を持つモデル(骨、臓器など)を作製することができる。また、生体で使用するインプラントでは、所定の認可を受ける必要があるが、本発明を用いてモデルを作製し、実験を行えば、認可取得のためのデータを揃えることが容易となる。さらに、本発明を用いてモデルを作製し、リアルな術前トレーニング、医学教育に活用することができる。すなわち、術前トレーニングや教育の現場で、実際にモデルを手にとって生体臓器の立体構造や感触を理解し、感触を疑似体験し、手技の修練に有効利用できる。 According to the present invention, a model (bone, organ, etc.) having mechanical characteristics close to that of a living body can be produced. In addition, for an implant used in a living body, it is necessary to obtain a predetermined approval. However, if a model is produced using the present invention and an experiment is performed, it is easy to prepare data for obtaining the approval. Furthermore, a model can be created using the present invention and can be utilized for realistic preoperative training and medical education. In other words, in pre-operative training and education, the model can be used as a hand to understand the three-dimensional structure and feel of living organs, to experience the feel in a simulated manner, and can be used effectively for training of the technique.
以下、本発明の実施形態について、図面を参照しながら詳細に説明していく。なお、本発明の範囲は、以下の実施例や図示例に限定されるものではなく、幾多の変更及び変形が可能である。 Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. The scope of the present invention is not limited to the following examples and illustrated examples, and many changes and modifications can be made.
図1は、実施例1の生体質感モデル作製方法のフローを示している。以下、ステップ毎に順を追って説明する。
<ステップ1(S01)>
医用画像診断装置により得られた断面画像からCT値の2次元分布データを取得する。取得した2次元分布データはコンピュータのメモリに記憶される。
<ステップ2(S02)>
段階的に硬度の異なる最小基本単位を予め作製し、各々の最小基本単位の力学特性データを取得する。各々の最小基本単位の力学特性データはコンピュータのメモリに記憶される。最小基本単位(Minimally Essential Unit:MEU、或は、セルと呼ぶ)は、円柱、円錐、球などのプリミティブ形状を有しており、多種多様の樹脂から1種あるいは2種以上が混合されたもので予め作製され、実測により力学特性データ値が既知である。
FIG. 1 shows a flow of the biological texture model production method of the first embodiment. Hereinafter, the steps will be described step by step.
<Step 1 (S01)>
Two-dimensional distribution data of CT values is acquired from a cross-sectional image obtained by the medical image diagnostic apparatus. The acquired two-dimensional distribution data is stored in a computer memory.
<Step 2 (S02)>
Minimum basic units having different hardnesses are prepared in advance, and mechanical property data of each minimum basic unit is acquired. The mechanical characteristic data of each minimum basic unit is stored in a computer memory. The minimum essential unit (MEU, or cell) has a primitive shape such as a cylinder, cone, or sphere, and is a mixture of one or more of a wide variety of resins. The mechanical property data values are known by actual measurement.
<ステップ3(S03)>
CT値の2次元分布データから骨組織を推定して骨組織領域を判別する。人体内組織のCT値を表1に示す。CT値に基づいて骨組織領域を判別することは可能である。
A bone tissue is estimated from the two-dimensional distribution data of CT values to discriminate a bone tissue region. Table 1 shows CT values of human tissues. It is possible to determine the bone tissue region based on the CT value.
<ステップ4(S04)>
骨組織領域の各画素のCT値から骨密度を算出する。CT画像から骨のCT値を算出し、骨密度の評価を行うことは既に様々検討されている。また、X線CT装置内に被験物をセットして、高感度放射線エネルギーセンサーであるイメージングプレート上に撮影し、コンピュータを用いて画像解析を行い、骨密度を算出できることも知られている。
<ステップ5(S05)>
算出した骨密度から各画素における力学特性データを推定する。
<ステップ6(S06)>
同じ或は最も近似する力学特性データを有する最小基本単位を各画素に割り当てる。力学特性データが近似するとは、硬度や弾性率が所定の差異内にあることを意味する。
<Step 4 (S04)>
The bone density is calculated from the CT value of each pixel in the bone tissue region. Various studies have already been made to calculate bone CT values from CT images and evaluate bone density. It is also known that a test object can be set in an X-ray CT apparatus, photographed on an imaging plate that is a high-sensitivity radiation energy sensor, and subjected to image analysis using a computer to calculate bone density.
<Step 5 (S05)>
The mechanical characteristic data in each pixel is estimated from the calculated bone density.
<Step 6 (S06)>
A minimum basic unit having the same or the most approximate mechanical characteristic data is assigned to each pixel. The approximation of the mechanical property data means that the hardness and elastic modulus are within a predetermined difference.
<ステップ7(S07)>
断面画像を積層して3次元データとし、各画素に割り当てられた最小基本単位を用いて3次元プリンタにより生体質感モデルを造形する。なお、医用画像診断装置の2次元データの輝度情報から造形対の3次元形状を抽出している。すなわち、医用画像診断装置であるCTやMRI装置からDICOMフォーマットの輝度情報を含む断層画像のドット情報を取得し、それらの断層画像を積層して造形対象の生体部位の3次元形状を抽出する。そして、市販されている3次元プリンタを活用して3次元造形モデルを作製する。
<Step 7 (S07)>
Cross-sectional images are stacked to form three-dimensional data, and a biological texture model is formed by a three-dimensional printer using the minimum basic unit assigned to each pixel. Note that the three-dimensional shape of the modeling pair is extracted from the luminance information of the two-dimensional data of the medical image diagnostic apparatus. That is, tomographic image dot information including luminance information in DICOM format is obtained from a CT or MRI apparatus, which is a medical image diagnostic apparatus, and the three-dimensional shape of a living body part to be modeled is extracted by stacking these tomographic images. And a 3D modeling model is produced using a commercially available 3D printer.
次に、図2を参照して、CT画像から特定部位の力学特性値を算出するやり方を説明する。CTやMRI装置で取得した断面画像において、画素毎にCT値を求める。マトリックスの各画素にCT値が割り当てられることになる。組織が骨領域であれば骨密度を算出し、骨密度から当該組織の力学特性を推定できる。
組織が骨領域以外の組織、例えば、脂肪、皮膚、臓器などであっても、CT値から組織が推定できる。事前に、新鮮検体から、脂肪、皮膚、臓器などの組織のCT値と力学特性データを実測し、当該組織のCT値と力学特性をデータベース化しておく。
また一方で、事前に、段階的に硬度の異なる最小基本単位(セル)を予め作製して、各セルの力学特性データを実測しておく。
これにより、断面画像のCT値から組織を判別し、判別した組織の力学特性と同じか類似している力学特性を有するセルを決定し、セルNo.のマトリックスを準備することが可能になる。
Next, with reference to FIG. 2, a method for calculating the mechanical characteristic value of the specific part from the CT image will be described. In a cross-sectional image acquired by CT or MRI apparatus, a CT value is obtained for each pixel. A CT value is assigned to each pixel of the matrix. If the tissue is a bone region, the bone density can be calculated, and the mechanical characteristics of the tissue can be estimated from the bone density.
Even if the tissue is a tissue other than the bone region, such as fat, skin, organ, etc., the tissue can be estimated from the CT value. In advance, CT values and mechanical property data of tissues such as fat, skin, and organs are actually measured from a fresh specimen, and the CT values and mechanical properties of the tissues are stored in a database.
On the other hand, minimum basic units (cells) having different hardness in stages are prepared in advance, and mechanical characteristic data of each cell is measured.
Thereby, the tissue is discriminated from the CT value of the cross-sectional image, and a cell having a mechanical characteristic that is the same as or similar to the mechanical characteristic of the discriminated tissue is determined. It becomes possible to prepare a matrix.
図3に示されるように、CTやMRI装置から、断面画像を取得し(S21)、取得画像の画素毎のCT値を取得し(S22)、CT値から骨密度を算出、力学特性を推定する(S23)。そして、データベース化されたセルNo.テーブルを参照し、推定した力学特性と同一又は類似の特性を有するセルNo.を選択する(S24)。断面画像の各画素にセルNo.を対応付ける(S25)。取得した全ての断面画像に対して繰り返し(S26)、断面画像を積層して3次元形状化して、3Dプリンタで造形する(S27)。 As shown in FIG. 3, a cross-sectional image is acquired from a CT or MRI apparatus (S21), a CT value for each pixel of the acquired image is acquired (S22), bone density is calculated from the CT value, and mechanical characteristics are estimated. (S23). Then, the cell numbers stored in the database are stored. Referring to the table, the cell No. having the same or similar characteristics as the estimated mechanical characteristics. Is selected (S24). For each pixel in the cross-sectional image, cell No. (S25). It repeats with respect to all the acquired cross-sectional images (S26), a cross-sectional image is laminated | stacked, it is made three-dimensional shape, and it models with a 3D printer (S27).
図4に示されるように、最小基本単位を規定(Minimally Essential Unit: MEU or セル)して、段階的に硬さの異なるセルを作製して力学特性を取得する。一方で、新鮮献体を用いた測定、例えば、CT値(Hounsfield値)や、MRIエラストグラフィーや超音波診断装置(エコー)を用いて弾性率を測定し、対応する組織の力学特性を取得する。力学特性データに基づいて、画像データにセルを対応させることで、生体の力学特性を有する生体質感モデルを作製することができることになる。 As shown in FIG. 4, a minimum essential unit (MEU or cell) is specified, and cells having different hardnesses are produced in stages to obtain mechanical characteristics. On the other hand, the elastic modulus is measured using a fresh donation, for example, a CT value (Hounsfield value), MRI elastography, or an ultrasonic diagnostic apparatus (echo), and the mechanical characteristics of the corresponding tissue are acquired. By associating the cell with the image data based on the mechanical property data, it is possible to create a biological texture model having the biological properties of the biological body.
図5は、生体質感モデル作製用コンピュータのブロック図を示している。図4に示されるように、生体質感モデル作製用コンピュータは、CPU,メモリ,画面表示部、操作入力部、HDD、通信I/Fを備える一般的なコンピュータである。具体的には、操作入力部はタッチパネルであり、画面表示部は液晶ディスプレイである。メモリはデータを記憶するものであり、CT値の2次元分布データと、CT値の力学特性データと、セルの力学特性データを備える。
生体質感モデル作製プログラムは、HDDからメモリ上に読みだしてコンピュータが実行するものである。
FIG. 5 shows a block diagram of a computer for producing a biological texture model. As shown in FIG. 4, the biological texture model creation computer is a general computer including a CPU, a memory, a screen display unit, an operation input unit, an HDD, and a communication I / F. Specifically, the operation input unit is a touch panel, and the screen display unit is a liquid crystal display. The memory stores data and includes two-dimensional distribution data of CT values, dynamic characteristic data of CT values, and dynamic characteristic data of cells.
The biological texture model creation program is read from the HDD onto the memory and executed by the computer.
図6は、実施例2の生体質感モデル作製方法のフローを示している。
実施例2の生体質感モデル作製方法では、目的とする組織の力学特性と同じ或は最も近似する力学特性データを有する素材を用いて成形された成形モデルと、実施例1の生体質感モデル作製方法を用いて作製された造形モデルを組合せて3次元モデルを組み立てる。
図6に示されるように、目的とする組織の断面画像を取得し(S31)、断面画像のCT値の2次元分布データから、骨組織、軟組織、脂肪組織など各組織領域を判別する(S32)。実施例1の生体質感モデル作製方法のように、セルテーブルを参照し、CT値から推定される力学特性と同一・類似のセルを選択して(S33)、3D造形でモデル作製する(S34)ことにより、リアリティーのある感触が再現できる場合もあるが、組織によっては、3D造形が困難なものや、既知の素材でリアリティーのある感触が再現できることが知られている組織も存在する。そこで、そのような組織の場合、素材テーブルを参照し、CT値から推定される力学特性と同一・類似の素材を選択して(S35)、成形でモデル作製する(S36)ことにし、3D造形で作製したモデルと、成形で作製したモデルを組合せて生体疑似組織を組立てる(S37)ことにする。
これにより、3D造形でリアリティーのある感触を再現する組織を全て作製することが困難であっても、従来の成形で作製したモデルを補完させることにより、リアリティーのある感触を再現する生体疑似組織を作製できる。
FIG. 6 shows a flow of the biological texture model production method of the second embodiment.
In the biological texture model preparation method of the second embodiment, a molded model formed using a material having mechanical property data that is the same as or closest to the mechanical characteristics of the target tissue, and the biological texture model preparation method of the first embodiment. A three-dimensional model is assembled by combining the modeling models produced using the.
As shown in FIG. 6, a cross-sectional image of the target tissue is acquired (S31), and each tissue region such as bone tissue, soft tissue, adipose tissue is discriminated from the two-dimensional distribution data of CT values of the cross-sectional image (S32). ). Like the living body texture model creation method of the first embodiment, the cell table is referred to, a cell having the same or similar mechanical characteristics as estimated from the CT value is selected (S33), and the model is created by 3D modeling (S34). In some cases, a realistic feel can be reproduced. However, depending on the structure, there is a structure in which 3D modeling is difficult or a known material can reproduce a realistic feel. Therefore, in the case of such a structure, the material table is referred to, a material that is the same as or similar to the mechanical property estimated from the CT value is selected (S35), and a model is created by molding (S36), and 3D modeling is performed. The living body pseudo-tissue is assembled by combining the model manufactured in (1) and the model manufactured by molding (S37).
As a result, even if it is difficult to produce all tissues that reproduce a realistic feel in 3D modeling, a biological pseudo-tissue that reproduces a realistic feel can be obtained by complementing a model produced by conventional molding. Can be made.
本発明により作製される生体質感モデルは、リアリティーのある術前トレーニング、解剖学や外科学の教材として有用である。 The living body texture model produced by the present invention is useful as a material for realistic preoperative training, anatomy and external science.
Claims (8)
2)段階的に硬度の異なる最小基本単位を予め作製し、各々の最小基本単位の少なくとも硬度、ヤング率および破断係数の力学特性データを取得するステップと、
3)前記CT値の2次元分布データから骨組織を推定して骨組織領域を判別するステップと、
4)骨組織領域の各画素のCT値から骨密度を求め、骨密度から各画素における少なくとも硬度、ヤング率および破断係数の力学特性データの推定し、同じ或は最も近似する力学特性データを有する前記最小基本単位を各画素に割り当てるステップと、
5)前記断面像を積層して3次元データとし、各画素に割り当てられた前記最小基本単位を用いて3次元プリンタにより造形するステップと、
を備えたことを特徴とする生体質感モデル作製方法。 1) obtaining two-dimensional distribution data of CT values or luminance values from a cross-sectional image obtained by a medical image diagnostic apparatus or an optical system image apparatus;
2) A step of preparing minimum basic units having different hardnesses step by step and obtaining mechanical property data of at least the hardness, Young's modulus, and fracture modulus of each minimum basic unit;
3) estimating a bone tissue from the two-dimensional distribution data of the CT value and discriminating a bone tissue region;
4) Obtain the bone density from the CT value of each pixel in the bone tissue region, estimate at least the hardness, Young's modulus and fracture coefficient mechanical property data of each pixel from the bone density, and have the same or the most approximate mechanical property data Assigning the minimum basic unit to each pixel;
5) Laminating the cross-sectional images to form three-dimensional data, and shaping with a three-dimensional printer using the minimum basic unit assigned to each pixel;
A biological texture model production method characterized by comprising:
軟組織領域と脂肪組織領域の各画素における少なくとも硬度、ヤング率および破断係数の力学特性データの推定し、同じ或は最も近似する力学特性データを有する前記最小基本単位を各画素に割り当てるステップと、
を更に備えたことを特徴とする請求項1の生体質感モデル作製方法。 Discriminating a soft tissue region and a fat tissue region other than a bone tissue from the two-dimensional distribution data of the CT value or the luminance value;
Estimating at least the hardness, Young's modulus and fracture modulus mechanical property data in each pixel of the soft tissue region and adipose tissue region, and assigning the minimum basic unit having the same or the most approximate mechanical property data to each pixel;
The living body texture model manufacturing method according to claim 1, further comprising:
少なくとも1種類の樹脂材から成ることを特徴とする請求項1又は2の生体質感モデル作製方法。 The minimum basic unit has one or more primitive shapes selected from a tetrahedron, a hexahedron, a cylinder, and a sphere,
The living body texture model manufacturing method according to claim 1 or 2, comprising at least one kind of resin material.
前記成形ステップにより成形された成形モデルと、請求項1〜3の何れかの作製方法を用いて作製された造形モデルとを組合せて3次元モデルを組み立てるステップと、
を備えたことを特徴とする生体質感モデル作製方法。 Molding with a material having mechanical property data that is the same as or closest to the mechanical properties of the target tissue;
Assembling a three-dimensional model by combining the molding model molded by the molding step and the modeling model manufactured by using the manufacturing method according to any one of claims 1 to 3;
A biological texture model production method characterized by comprising:
1)医用画像診断装置あるいは光学系画像装置により得られた断面像からCT値又は輝度値の2次元分布データを入力する手順と、
2)段階的に硬度の異なる最小基本単位の各々の少なくとも硬度、ヤング率および破断係数の力学特性データを入力する手順と、
3)前記CT値の2次元分布データから骨組織を推定して骨組織領域を判別する手順と、
4)骨組織領域の各画素のCT値から骨密度を算出する手順と、
5)骨密度から各画素における少なくとも硬度、ヤング率および破断係数の力学特性データの推定する手順と、
6)同じ或は最も近似する力学特性データを有する前記最小基本単位を各画素に割り当てる手順と、
7)前記断面像を積層して3次元データとし、各画素に割り当てられた前記最小基本単位の情報を3次元プリンタに出力する手順と、
を実行させるための生体質感モデル作製プログラム。 On the computer,
1) a procedure for inputting two-dimensional distribution data of CT values or luminance values from a cross-sectional image obtained by a medical image diagnostic apparatus or an optical system image apparatus;
2) a procedure for inputting mechanical property data of at least the hardness, Young's modulus, and fracture modulus of each of the minimum basic units having different hardness in stages;
3) A procedure for estimating a bone tissue from the two-dimensional distribution data of the CT value and discriminating a bone tissue region;
4) a procedure for calculating the bone density from the CT value of each pixel in the bone tissue region;
5) A procedure for estimating at least the hardness, Young's modulus, and mechanical property data of the fracture coefficient in each pixel from the bone density;
6) a procedure for assigning to each pixel the minimum basic unit having the same or the most approximate mechanical property data;
7) Laminating the cross-sectional images into three-dimensional data, and outputting the information of the minimum basic unit assigned to each pixel to a three-dimensional printer;
Living body texture model creation program for executing
前記CT値又は輝度値の2次元分布データから、骨組織以外の軟組織領域と脂肪組織領域を判別する手順と、
軟組織領域と脂肪組織領域の各画素における少なくとも硬度、ヤング率および破断係数の力学特性データの推定し、同じ或は最も近似する力学特性データを有する前記最小基本単位を各画素に割り当てる手順と、
を実行させるための請求項5の生体質感モデル作製プログラム。 Before the procedure of 7) above,
A procedure for discriminating a soft tissue region and a fat tissue region other than bone tissue from the two-dimensional distribution data of the CT value or luminance value;
Estimating at least the hardness, Young's modulus, and fracture coefficient mechanical property data in each pixel of the soft tissue region and the fat tissue region, and assigning the minimum basic unit having the same or the most approximate mechanical property data to each pixel;
The biological texture model production program of Claim 5 for performing.
少なくとも1種類の樹脂材から成ることを特徴とする請求項5又は6の生体質感モデル作製プログラム。 The minimum basic unit has one or more primitive shapes selected from a tetrahedron, a hexahedron, a cylinder, and a sphere,
The biological texture model production program according to claim 5 or 6 , wherein the biological texture model production program is made of at least one kind of resin material.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2014229242A JP6457785B2 (en) | 2014-11-11 | 2014-11-11 | Biological texture model production method and biological texture model production program |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2014229242A JP6457785B2 (en) | 2014-11-11 | 2014-11-11 | Biological texture model production method and biological texture model production program |
Publications (2)
Publication Number | Publication Date |
---|---|
JP2016087359A JP2016087359A (en) | 2016-05-23 |
JP6457785B2 true JP6457785B2 (en) | 2019-01-23 |
Family
ID=56015756
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP2014229242A Active JP6457785B2 (en) | 2014-11-11 | 2014-11-11 | Biological texture model production method and biological texture model production program |
Country Status (1)
Country | Link |
---|---|
JP (1) | JP6457785B2 (en) |
Families Citing this family (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP7140513B2 (en) * | 2018-03-05 | 2022-09-21 | キヤノンメディカルシステムズ株式会社 | Image processing device and 3D molding device |
EP3806068A4 (en) | 2018-06-08 | 2022-01-19 | Fasotec Co., Ltd. | Multi-purpose fixture tool for manipulation training |
CN109512449A (en) * | 2018-09-26 | 2019-03-26 | 沈阳东软医疗***有限公司 | Physiological parameter output method and device |
WO2022168925A1 (en) * | 2021-02-04 | 2022-08-11 | 株式会社Kompath | Information processing system, program, and information processing method |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH01310484A (en) * | 1988-06-08 | 1989-12-14 | Toshiba Corp | Picture processor |
WO2011044048A2 (en) * | 2009-10-05 | 2011-04-14 | Board Of Regents, The University Of Texas System | Endo-rhinal endoscopic-technique trainer |
EP2692509A4 (en) * | 2011-03-31 | 2014-10-22 | Nat Univ Corp Univ Kobe | Method for manufacturing three-dimensional molded model and support tool for medical treatment, medical training, research, and education |
US9805624B2 (en) * | 2011-09-30 | 2017-10-31 | Regents Of The University Of Minnesota | Simulated, representative high-fidelity organosilicate tissue models |
JP5829921B2 (en) * | 2012-01-06 | 2015-12-09 | 学校法人 龍谷大学 | Computer operating method for fracture risk assessment |
-
2014
- 2014-11-11 JP JP2014229242A patent/JP6457785B2/en active Active
Also Published As
Publication number | Publication date |
---|---|
JP2016087359A (en) | 2016-05-23 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
JP5239037B2 (en) | 3D modeling model creation method and medical / medical / research / educational support tool | |
US20180168730A1 (en) | System and method for medical procedure planning | |
US20220137593A1 (en) | Method for fabricating a physical simulation device, simulation device and simulation system | |
CN108352132A (en) | ultrasonic simulation method | |
JP6457785B2 (en) | Biological texture model production method and biological texture model production program | |
JP7222399B2 (en) | brace | |
Lambrecht et al. | Haptic model fabrication for undergraduate and postgraduate teaching | |
Visentin et al. | Iterative simulations to estimate the elastic properties from a series of MRI images followed by MRI-US validation | |
Bhatti et al. | A novel breast software phantom for biomechanical modeling of elastography | |
Levine et al. | Mesh processing in medical-image analysis—a tutorial | |
Bertolini et al. | Additive manufacturing of a compliant multimaterial heart model | |
Welsh et al. | A novel method of anatomical data acquisition using the Perception ScanWorks V5 scanner | |
Khatri et al. | Unfolding the mysterious path of forensic facial reconstruction: Review of different imaging modalities | |
Guy et al. | Toward emulating human movement: adopting a data-driven bitmap-based “voxel” multimaterial workflow to create a flexible 3D printed neonatal lower limb | |
Cheung et al. | Magnetic resonance imaging properties of multimodality anthropomorphic silicone rubber phantoms for validating surgical robots and image guided therapy systems | |
WO2015037978A1 (en) | An anatomical model | |
Sánchez et al. | Constructing detailed subject-specific models of the human masseter | |
Park et al. | Development of a three-dimensional-printed heart model replicating the elasticity, tear resistance, and hardness of pig heart using agilus and tango | |
Kapakin | Stereolithographic biomodelling to create tangible hard copies of the ethmoidal labyrinth air cells based on the visible human project | |
Selvamanikkam et al. | High-Resolution Stereoscopic Visualization of Pediatric Echocardiography Data on Microsoft HoloLens 2 | |
KR102367095B1 (en) | A method for preparing 3d bone model reflecting 2d image of target bone and a 3d bone model prepared thereby | |
Robertson et al. | 3D printed ultrasound phantoms for clinical training | |
Moon et al. | Standardizing 3D medical imaging | |
Tabernero-Rico et al. | Mangement of a 3D visualization program from radiological images in neuroradiology | |
Wang | FEA-based simulation of breast deformation in real-time using artificial neural network |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20171113 |
|
A977 | Report on retrieval |
Free format text: JAPANESE INTERMEDIATE CODE: A971007 Effective date: 20180724 |
|
A131 | Notification of reasons for refusal |
Free format text: JAPANESE INTERMEDIATE CODE: A131 Effective date: 20180831 |
|
A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20180915 |
|
TRDD | Decision of grant or rejection written | ||
A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20181205 |
|
A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20181221 |
|
R150 | Certificate of patent or registration of utility model |
Ref document number: 6457785 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |
|
R250 | Receipt of annual fees |
Free format text: JAPANESE INTERMEDIATE CODE: R250 |