JP2014212820A5

JP2014212820A5 -

Info

Publication number: JP2014212820A5
Application number: JP2013089918A
Authority: JP
Filing date: 2013-04-23
Publication date: 2015-11-26
Anticipated expiration: 2033-04-23

Description

上記課題を解決するために、例えば特許請求の範囲に記載の構成を採用する。
本発明は、上記課題を解決する手段を複数含んでいるが、その一例を挙げるならば、放射線治療の治療計画を作成する治療計画装置と、治療計画画像を保存する画像サーバと、放射線を照射する対象の位置決め用の断層画像を撮像する断層撮像装置と、患者をのせるベッドと、放射線治療にあたって前記ベッドの位置決めを行う位置決めシステムであって、前記画像サーバに保存された前記治療計画画像と前記断層撮像装置によって撮像された前記断層画像との位置合せを行い、この位置合せの結果からベッド位置決めパラメータを演算する画像位置合せ部、前記断層撮像装置で撮像された前記断層画像に対して関心領域を設定する領域設定部、この領域設定部で設定された前記関心領域の位置に関する特徴量を演算する特徴量演算部、この特徴量演算部で演算された前記特徴量を学習する特徴量学習部を有する位置決めシステムとを備えたことを特徴とする。 In order to solve the above problems, for example, the configuration described in the claims is adopted.
The invention has included a plurality of means for solving the above problems, if its one example, a treatment planning system to create a treatment plan for radiotherapy, and an image server storing a therapy planning image, radiological A tomographic imaging apparatus for imaging a tomographic image for positioning a target to be irradiated, a bed on which a patient is placed, and a positioning system for positioning the bed in radiotherapy, the treatment plan stored in the image server An image alignment unit that performs alignment between an image and the tomographic image captured by the tomographic imaging apparatus, and calculates a bed positioning parameter from the alignment result, and the tomographic image captured by the tomographic imaging apparatus A region setting unit that sets a region of interest, a feature amount calculation unit that calculates a feature amount related to the position of the region of interest set by the region setting unit, Characterized by comprising a positioning system having a feature quantity learning unit that learns the feature quantity computed by the symptoms amount calculation unit.

また位置決めシステム１０４は、放射線照射システム１０内のベッド駆動装置１０５（ベッド位置制御装置）と接続され、位置決めシステム１０４にて演算された位置決めパラメータをベッド駆動装置１０５に対して送信することで、ベッド１５を所定の位置に移動させ、治療を実施する準備を行う。 The positioning system 104 is connected to a bed driving device 105 (bed position control device) in the radiation irradiation system 10, and transmits the positioning parameters calculated by the positioning system 104 to the bed driving device 105. 15 is moved to a predetermined position to prepare for the treatment.

第２画像位置合せ部１１８は、特徴量学習部１１４で学習し、学習データベース１１５に保存された特徴量と、その特徴量に対する信頼度を反映した評価式を用いて位置合せを行い、この位置合せの結果からベッド位置決めパラメータを演算する。そして、放射線照射システム１０内にあるベッド駆動装置１０５に演算したベッド位置決めパラメータを出力する。ベッド駆動装置１０５は、入力したパラメータにしたがってベッド１５を移動させ、治療準備を実施する。この第２画像位置合せ部１１８は、評価式に基づき位置合せ結果を評価する第２位置合せ評価部１１９を備える。 The second image registration unit 118 performs registration using a feature quantity learned by the feature quantity learning unit 114 and stored in the learning database 115 and an evaluation formula reflecting the reliability of the feature quantity. The bed positioning parameter is calculated from the result of the alignment. Then, the calculated bed positioning parameter is output to the bed driving device 105 in the radiation irradiation system 10. The bed driving device 105 moves the bed 15 according to the input parameters and prepares for treatment. The second image alignment unit 118 includes a second alignment evaluation unit 119 that evaluates the alignment result based on the evaluation formula.

次いで、学習データベース１１５から位置決めに用いる領域を取得する（ステップＳ５０５）。 Then, to obtain the area used for positioning from the learning database 115 (step S505).

次いで、位置決めシステム１０４において、領域抽出部１１７は、画像サーバ１０２から関心領域および学習データベース１１５から学習結果の関心領域を取得する。そして、領域抽出部１１７では、先のステップＳ５０５において取得した画像サーバ１０２から送られた関心領域および学習データベース１１５から学習結果の関心領域との整合をとり、関心領域を治療計画ＣＴ画像および３次元断層画像から抽出する（ステップＳ５０６）。
更に、領域抽出部１１７は、学習データベース１１５から位置決めに用いる領域の重みのデータを取得する（ステップＳ５０７）。 Next, in the positioning system 104, the region extraction unit 117 acquires a region of interest from the image server 102 and a region of interest as a learning result from the learning database 115. Then, the region extraction unit 117 matches the region of interest sent from the image server 102 acquired in the previous step S505 and the region of interest of the learning result from the learning database 115, and determines the region of interest as a treatment plan CT image and a three-dimensional image. Extract from the tomographic image (step S506).
Furthermore, the region extracting section 117 acquires the data of the weights of a region to be used for positioning from the learning database 115 (step S507).

次いで、演算した位置決めパラメータを放射線照射システム１０内にあるベッド駆動装置１０５に送信し（ステップＳ５１０）、終了する。 Next, the calculated positioning parameter is transmitted to the bed driving device 105 in the radiation irradiation system 10 (step S510), and the process ends.

Claims

A treatment planning device for creating a treatment plan for radiation therapy;
And image server to save the therapy plan image,
And the tomographic imaging apparatus for imaging a tomographic image for locating target irradiating the radiological,
A bed to put the patient on,
A positioning system for positioning the bed in radiotherapy, wherein the treatment plan image stored in the image server is aligned with the tomographic image captured by the tomographic imaging apparatus, and the result of the alignment An image alignment unit that calculates a bed positioning parameter from the image, a region setting unit that sets a region of interest for the tomographic image captured by the tomographic imaging apparatus, and a feature relating to the position of the region of interest set by the region setting unit A radiation therapy system comprising: a feature quantity computing unit that computes a quantity; and a positioning system that includes a feature quantity learning unit that learns the feature quantity computed by the feature quantity computation unit.

The radiation therapy system according to claim 1, wherein
The treatment planning device for setting a region of interest in the treatment plan image;
The image server for storing the region of interest set in the treatment planning device;
The radiation treatment system further comprising: the positioning system including the region setting unit that sets the region of interest set in the treatment planning apparatus with respect to the tomographic image.

The radiation therapy system according to claim 2, wherein
An image alignment unit that performs alignment between the treatment plan image stored in the image server and the tomographic image captured by the tomographic imaging apparatus using the feature amount learned by the feature amount learning unit; The radiotherapy system further comprising the positioning system.

The radiotherapy system according to claim 3.
The feature amount learning unit that calculates and learns the reliability of a feature amount from the feature amount calculated by the feature amount calculation unit, the feature amount learned by the feature amount learning unit, and the reliability The radiation therapy system further comprising: the positioning system including an image alignment unit that performs alignment between the treatment plan image stored in an image server and the tomographic image captured by the tomographic imaging apparatus.

The radiotherapy system according to any one of claims 1 to 4,
A radiotherapy system, further comprising: a display unit that displays an alignment result between the treatment plan image stored in the image server and the tomographic image captured by the tomographic imaging apparatus.

The radiotherapy system according to any one of claims 1 to 4,
A radiotherapy system, further comprising a bed position control device that moves and controls the bed based on the bed positioning parameter.