WO2012066494A2 - Procédé et appareil de compensation d'un mouvement intra-fractionnel - Google Patents

Procédé et appareil de compensation d'un mouvement intra-fractionnel Download PDF

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Publication number
WO2012066494A2
WO2012066494A2 PCT/IB2011/055143 IB2011055143W WO2012066494A2 WO 2012066494 A2 WO2012066494 A2 WO 2012066494A2 IB 2011055143 W IB2011055143 W IB 2011055143W WO 2012066494 A2 WO2012066494 A2 WO 2012066494A2
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WIPO (PCT)
Prior art keywords
motion
internal
target object
external
model
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PCT/IB2011/055143
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English (en)
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WO2012066494A3 (fr
Inventor
Jens-Christoph Georgi
Michael Johannes Eble
Irene Torres Espallardo
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Koninklijke Philips Electronics N.V.
Philips Intellectual Property & Standards Gmbh
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Publication of WO2012066494A2 publication Critical patent/WO2012066494A2/fr
Publication of WO2012066494A3 publication Critical patent/WO2012066494A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1048—Monitoring, verifying, controlling systems and methods
    • A61N5/1049—Monitoring, verifying, controlling systems and methods for verifying the position of the patient with respect to the radiation beam
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1048—Monitoring, verifying, controlling systems and methods
    • A61N5/1064—Monitoring, verifying, controlling systems and methods for adjusting radiation treatment in response to monitoring
    • A61N5/1065—Beam adjustment
    • A61N5/1067—Beam adjustment in real time, i.e. during treatment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1048—Monitoring, verifying, controlling systems and methods
    • A61N5/1064—Monitoring, verifying, controlling systems and methods for adjusting radiation treatment in response to monitoring
    • A61N5/1069—Target adjustment, e.g. moving the patient support
    • A61N5/107—Target adjustment, e.g. moving the patient support in real time, i.e. during treatment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/087—Measuring breath flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • A61B5/1135—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing by monitoring thoracic expansion
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1048—Monitoring, verifying, controlling systems and methods
    • A61N5/1049—Monitoring, verifying, controlling systems and methods for verifying the position of the patient with respect to the radiation beam
    • A61N2005/1052—Monitoring, verifying, controlling systems and methods for verifying the position of the patient with respect to the radiation beam using positron emission tomography [PET] single photon emission computer tomography [SPECT] imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N2005/1085—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy characterised by the type of particles applied to the patient
    • A61N2005/1087—Ions; Protons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/103—Treatment planning systems
    • A61N5/1037—Treatment planning systems taking into account the movement of the target, e.g. 4D-image based planning
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/103—Treatment planning systems
    • A61N5/1039—Treatment planning systems using functional images, e.g. PET or MRI
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1042—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy with spatial modulation of the radiation beam within the treatment head
    • A61N5/1045—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy with spatial modulation of the radiation beam within the treatment head using a multi-leaf collimator, e.g. for intensity modulated radiation therapy or IMRT
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20172—Image enhancement details
    • G06T2207/20201—Motion blur correction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30061—Lung

Definitions

  • the present invention relates to a method, apparatus, imaging device and computer program product for compensating intra-fractional motion in radiation therapy by means of motion estimation from a data acquisition or imaging device that acquires images of a moving object.
  • suitable imaging devices comprise a magnetic resonance unit (MR), a computer tomography unit (CT), an ultra-sound unit (US), a positron emission tomography device (PET), a single photon emitting computer tomography (SPECT), or any combination thereof.
  • radiotherapy planning is based predominantly on X-ray CT imaging to outline target structures and organs at risk.
  • biological information gained from PET imaging gets used increasingly.
  • the clinical target volume is extended by general safety margins to account for not yet visible tumor spread, for inter-fractional uncertainties in patient positioning, and also for intra-fractional motion of the tumor.
  • the position of the target e.g. tumor
  • the related movements can be divided into two parts: 1) motion- inducing action during irradiation causes intra-fractional movements and 2) change of mean target position between two treatment fractions causes inter-fractional movements.
  • Intra- fractional motion is especially important for radiotherapy of lung tumors, but also tumors in the liver region are affected by breathing motion.
  • lung mean amplitudes of motion in the lower lobes have been investigated to be on the magnitude of more than 1 cm in the cranial-caudal direction.
  • Solutions to account for or to compensate for intra-fractional motion are highly desired.
  • a reliable, robust and precise method would improve therapy outcome and reduce side effects.
  • solutions for this problem are desirable.
  • the aim is to adapt the MLC according to the tumor's predicted motion in real-time and thereby get more dose to the target while sparing surrounding normal tissue.
  • the internal motion may be determined by a motion estimation part of a local motion estimation algorithm. This allows obtaining a three- dimensional motion vector for specifying the internal motion.
  • the external indication comprises at least one of a breathing air flow, a motion of markers on said target object, signals from a chest belt, and a thorax surface laser tracking system. These external indications can be easily measured and correlated with the internal motion.
  • self-learning may be applied based e.g. on neuronal networking to obtain the motion model.
  • an object-individual learning process can be implemented.
  • the motion compensation mechanism may be adapted to control radiation beams of said imaging device or any therapeutic radiation device by adjusting at least one of a multi-leaf collimator, a gantry magnet and a patient table.
  • a straight forward and robust mechanism for compensating intra-fractional motion can be provided.
  • the motion compensation mechanism may be adapted to control gating of a follow-up imaging display.
  • advanced gating of subsequent evaluations is allowed.
  • the motion compensation mechanism may be controlled in real-time. This allows real-time compensation of intra-fractional motion during the imaging process.
  • the apparatus may be implemented as a chip module, chip set, circuit or software-controlled processor, and may be provided in a network node (i.e. wireless node) or in a station management entity for the wireless mesh network.
  • a network node i.e. wireless node
  • a station management entity for the wireless mesh network.
  • Fig. 1 shows a schematic block diagram of an imaging device with intra- fractional motion compensation according to a first embodiment
  • Fig. 2 shows schematic diagrams indicating the principle of local motion estimation
  • Fig. 3 shows a schematic flow diagram of an intra- fractional motion
  • PET based tumor motion estimation from a local motion compensation algorithm is used as an input to train a motion model for prediction of intra-fractional tumor motion caused by breathing.
  • the derived breathing motion model can subsequently be used for real-time beam adjustment in external photon radiotherapy or hadrontherapy (protons, neutrons or positive ions) or for improved gating in follow up imaging studies.
  • robust external markers are linked to the real internal tumor motion (derived via the local motion estimation from PET imaging) in the breathing motion model.
  • the obtained connection between internal motion and external markers allows reliable prediction of tumor position based on the external markers alone.
  • Fig. 1 shows a schematic block diagram of an imaging device (ID) 10 (such as a PET unit) with a patient internal motion estimation functionality which may be
  • ID imaging device
  • a patient internal motion estimation functionality which may be
  • the imaging device 10 is adapted to image an internal target (e.g. a tumor or an organ at risk) of a target object (T) 20 which may be a body of a living human or animal.
  • the motion compensation functionality comprises a marker extraction function or marker extractor 30, a motion model generation function or motion model generator (MMG) 40 and a motion compensation function or motion compensator (MC) 50 and may be integrated in the imaging device 10 or provided as a separate unit.
  • the marker extractor 30, motion model generator 40 and motion compensator 50 may be implemented as separate or combined hardware circuits or as software routines controlling a processor or computing device which may be arranged separately or integrated in the imaging device 10. It is noted that a separate therapy unit of a therapy delivery system may be provided, which is not shown in Fig. 1. The therapy delivery system may as well be controlled to some extend by the motion compensator 50.
  • the marker extractor 30 comprises a detecting function or detector for detecting an external marker or indicator of the target object 10 so as to derive an external motion signal or output.
  • the external marker may be a breathing air flow, a motion of at least one marker attached to the target object (e.g. a marker on the patient's chest), an output from a chest belt, or any other indicator of a motion-inducing action of the target object 10.
  • the model is trained based on patient specific data containing both, the external marker characteristics and the internal motion of the internal target, which may be recorded synchronously.
  • the internal motion can be determined for example by means of a motion estimation part of a local motion compensation algorithm.
  • Fig. 2 shows schematic diagrams for explaining the principle of local motion estimation.
  • a sequence of images of the lesion or target of interest over time t is
  • the motion model generator 40 correlates the obtained internal motion with the external motion output of the marker extractor 30 to derive a model for predicting the internal motion (i.e. intra-fractional motion) based on the observed external motion.
  • the results of the motion prediction model can then be used by the motion compensator 50 a motion compensation mechanism of the imaging device 10, e.g., to control beams in photon radiotherapy (e.g. by adjusting multi-leaf collimators in real-time) or for particle beams by adjusting the gantry magnets. It could also be used to adjust a patient table or for advanced gating in follow-up imaging studies.
  • Fig. 3 shows a schematic flow diagram of an intra-fractional motion compensation procedure according to a second embodiment. More specifically, a principal application flow for motion prediction based on external and internal (PET based) motion information is shown.
  • PET based external and internal
  • the procedure comprises an initial model preparation part (MP) with steps S100 to S120 and a subsequent model application part (MA) with steps S200 to S230.
  • MP initial model preparation part
  • MA model application part
  • step SI 00 a dedicated acquisition of the target (without additional dose for the patient) is performed using parameters like the following: bed position, lesion centered in this bed position, 10 min acquisition time, list- mode data storage, etc.
  • Parallel to the imaging study (PET) external surrogate markers are acquired simultaneously in an external marker acquisition (EMA) step and the data from imaging and external markers is recorded synchronously in a synchronous data collection (SDC) to allow later correlation.
  • EMA external marker acquisition
  • SDC synchronous data collection
  • DA data analysis
  • the obtained internal and external motion data is analyzed and a local motion estimation algorithm is used to derive a 3D motion vector of the tumor or other internal target from the list mode data.
  • a (breathing) motion model is derived as a base model (BM), e.g. by self-learning based on neuronal networking, which builds a link between derived external motion (observation of external marker motion) and measured internal target motion (e.g. motion vector).
  • BM base model
  • the motion model allows to predict motion in the model application part based on input from the external motion markers only.
  • step S200 of the application part external marker data (EMD) is obtained and an individualized motion model (IM) is derived in step S210 based on the available motion base model (derived in the model preparation part) and additional model training (e.g. modification by self-learning etc.).
  • the internal target motion is then estimated or predicted in step S220 to obtain a predicted motion (PM) based on the individualized motion model.
  • various control or compensation mechanisms multi-leaf collimators, patient table, magnets
  • CM compensation mechanism for compensating for the estimated intra-fractional target motion.
  • the beam of the imaging device is directed to where the internal target (e.g. lesion or organ of interest) is expected to be.
  • steps S100 to S120 and S200 to S230 may be implemented as software routines controlling a processor or computing device which may be arranged separately or integrated in the imaging device.
  • the steps may be performed by software code portions of a computer program product for a computer when the product is run on the computer.
  • the above embodiments could be implemented as (a) dedicated module(s) in a radiotherapy planning software. Furthermore, they could be used to control MLCs in realtime to adjust the beam according to the presumed lesion position. Due to the sharp Bragg peak this application is of special importance in particle radiotherapy, as well.
  • the embodiments could also be used for improved gated acquisitions in follow-up imaging studies (as already mentioned, a first imaging study would always be needed to train the model).
  • the present invention is not restricted to the above embodiments and implementations. Rather, the proposed estimation and compensation of intra-fractional motion may be used in any PET, SPECT, PET/CT, SPECT/CT, PET/MR, SPECT/MR, MRI systems.
  • the therapeutic unit is not necessarily controlled by an imaging device in combination with the motion compensation mechanism, but the motion compensation algorithm or mechanism, once trained for a specific patient, can itself control the therapeutic device, only based on the external motion markers.
  • the present invention proposes to couple easy-to-measure, robust external markers to a real internal tumor motion in a breathing model.
  • a reliable prediction of tumor position based on external markers alone is possible.
  • Suitable models have already been developed and could be used and adapted right away.
  • the model is trained based on patient specific data containing both, the external marker characteristics and the internal motion, recorded synchronously.
  • the internal motion can be determined by means of the motion estimation part of the local motion compensation algorithm.
  • the present invention proposes to couple easy to measure, robust external markers to a real internal tumor motion in a breathing model.
  • a reliable prediction of tumor position based on external markers alone is possible.
  • Suitable models have already been developed and could be used and adapted right away.
  • the model is trained based on patient specific data containing both, the external marker characteristics and the internal motion, recorded synchronously.
  • the internal motion can be determined by means of the motion estimation part of the local motion compensation algorithm.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Animal Behavior & Ethology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Pathology (AREA)
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  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Quality & Reliability (AREA)
  • Medical Informatics (AREA)
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Abstract

La présente invention concerne couplage de marqueurs externes, robustes et faciles à mesurer à un mouvement réel d'une tumeur interne dans un modèle de respiration. Avec une telle relation entre le mouvement interne et les marqueurs externes, il est possible de prédire de manière fiable la position de la tumeur en se basant uniquement sur les marqueurs externes. Des modèles adaptés ont déjà été mis au point et pourraient être utilisés et adaptés immédiatement. Dans une étape d'apprentissage, le modèle est appris en se basant sur des données spécifiques d'un patient contenant à la fois les caractéristiques des marqueurs externes et le mouvement interne, enregistrées de manière synchrone. Le mouvement interne peut être déterminé au moyen de la partie d'estimation du mouvement de l'algorithme de compensation du mouvement local.
PCT/IB2011/055143 2010-11-19 2011-11-17 Procédé et appareil de compensation d'un mouvement intra-fractionnel WO2012066494A2 (fr)

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EP10306277.4 2010-11-19

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Cited By (7)

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WO2017036774A1 (fr) * 2015-08-28 2017-03-09 Koninklijke Philips N.V. Appareil permettant la détermination d'une relation de mouvement
WO2017132522A1 (fr) * 2016-01-29 2017-08-03 Elekta Ltd. Commande de traitement utilisant une prédiction de mouvement sur la base d'un modèle de mouvement cyclique
CN110201319A (zh) * 2013-05-21 2019-09-06 瓦里安医疗系统国际股份公司 用于自动产生剂量预测模型以及作为云服务的疗法治疗计划的系统和方法
WO2020124583A1 (fr) * 2018-12-21 2020-06-25 四川省肿瘤医院 Système de surveillance de la position de tumeursen temps réel et procédé de surveillance associé
CN111954559A (zh) * 2018-03-15 2020-11-17 爱可瑞公司 在处置递送期间限制成像放射剂量和提高图像质量
US20210290167A1 (en) * 2020-03-19 2021-09-23 Siemens Healthcare Gmbh Deformation model for a tissue
WO2022117883A3 (fr) * 2020-12-04 2022-07-14 Elekta Instruments Ab Méthodes de radiothérapie adaptative

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