[go: up one dir, main page]

WO2016199051A1 - Système et procédé de modélisation tridimensionnelle à partir d'images scannées - Google Patents

Système et procédé de modélisation tridimensionnelle à partir d'images scannées Download PDF

Info

Publication number
WO2016199051A1
WO2016199051A1 PCT/IB2016/053389 IB2016053389W WO2016199051A1 WO 2016199051 A1 WO2016199051 A1 WO 2016199051A1 IB 2016053389 W IB2016053389 W IB 2016053389W WO 2016199051 A1 WO2016199051 A1 WO 2016199051A1
Authority
WO
WIPO (PCT)
Prior art keywords
femur
tibia
segmentation
images
regions
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.)
Ceased
Application number
PCT/IB2016/053389
Other languages
English (en)
Inventor
Harikrishnan RAMARAJU
Gineesh SUKUMARAN
Bharath SHIVAPURAM
Param RAJPURA
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
L&T Technology Services Ltd
Original Assignee
L&T Technology Services Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by L&T Technology Services Ltd filed Critical L&T Technology Services Ltd
Publication of WO2016199051A1 publication Critical patent/WO2016199051A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/505—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of bone
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
    • G06T19/006—Mixed reality
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
    • G06T19/20—Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A61B6/032—Transmission computed tomography [CT]

Definitions

  • the invention generally relates to a system and method for 3D modelling and specifically 3D modelling from scanned images.
  • Arthroplasty is a surgical procedure to restore the integrity and function of a joint. It typically involves an orthopedic surgery where the articular surface of a musculoskeletal joint is replaced, remodeled, or realigned by osteotomy or some other procedure. The replacement includes using artificial man-made pieces.
  • the arthroplasty process involves studying the damage part of the bones by studying X-ray/ MRI / CT scan images, developing/designing replacement bone component based on the information extracted from the X-ray/ MRI / CT scan images and replacing the same by surgical procedure.
  • the designing or the development of the replacement bone component is typically performed by manually studying the X-ray/ MRI / CT scan images that leaves a lot of scope of error. Hence in most of the cases, surgeons have to perform alterations either on the replacement components or on the bones to adjust the components perfectly. In fact in some cases the joint do not work properly even after doing alterations. Also if the replacement part is made up of metal then typically alteration is performed on bone part that results in unnecessary removal of a healthy bone part. Some solutions are known that may study the images by processing the images and may develop component with better precision. However most of these solutions depend on the quality of images received from the scan. Moreover identifying the bone region is currently done by manual intervention. This is laborious, time consuming and prone to error.
  • the present invention is directed to overcoming one or more of the problems as set forth above.
  • a system and method of developing a three- dimensional (3D) model of a knee bone from a plurality of images includes selecting one or more images from the plurality of images, identifying Femur, Tibia and Cortical bone regions in the selected one or more images, pre-processing the identified images for Femur, Tibia and Cortical bone segmentation using separate image processing actions respectively, segmenting the Femur, Tibia and Cortical bone regions using a separate segmentation process respectively for the Femur, Tibia and Cortical bone regions, merging the Femur, Tibia and Cortical bone segmented regions and post processing the merged segmented regions to develop the three-dimensional (3D) model of the knee bone.
  • Figure 1 illustrates a block diagram of a process flow for creating a 3D model of the knee bone from a set of scanned images according to exemplary embodiments of the invention
  • Figure 2 illustrates a block diagram of a process flow for image processing of Femur bone regions
  • Figure 3 illustrates a block diagram of a process flow for image processing of Tibia bone regions
  • Figure 4 illustrates a block diagram of a process flow for image processing of Cortical bone regions
  • Figure 5 illustrates an exemplary system for creating a 3D model of the knee bone from a set of scanned images, according to one embodiment of the invention.
  • a system and method of creating a 3D model of the bone that may be used to design a custom orthopaedic implant from a set of MRI / CT scanned images is disclosed.
  • the bone is a knee bone.
  • Figure 1 illustrates a block diagram of a process 100 for developing a three-dimensional (3D) model of a knee bone from scanned images according to embodiments of the invention.
  • one or more images may be selected from a plurality of images.
  • the plurality of images may be magnetic resonance imaging (MRI) images or computed tomography (CT) images.
  • the MRI images may be directly obtained from an MRI instrument or with a DICOM reader.
  • the CT images may be obtained from a CT scan instrument or with a DICOM reader.
  • the MRI or CT images may be obtained from a repository containing a plurality of MRI images or CT images.
  • the images may comply with the Digital Imaging and Communications in Medicine (DICOM) standards.
  • DICOM Digital Imaging and Communications in Medicine
  • Femur, Tibia and Cortical bone regions in the selected one or more images may be identified.
  • the Femur, Tibia and Cortical bone regions may be identified based on at least one seed selection in the selected one or more images.
  • the seed selection is the starting point for further processing.
  • the seed may be selected using any input device such as, but not limited to, computer mouse, digital pen etc.
  • the image processing for the Femur bone regions may be performed.
  • Figure 2 illustrates exemplary steps involved in image processing of the Femur bone regions.
  • the image processing step 106 of the Femur bone regions may include a pre-processing step 200, a segmentation step 210 and a post-processing step 212.
  • the pre-processing step 200 for Femur bone region may include an image smoothing step 202, a sharpening step 204, an edge enhancement step 206 or/and an intensity transformations step 208.
  • the illustrated pre-processing steps 200 that is image smoothing step 202, sharpening step 204, edge enhancement step 206 or/and intensity transformations may be performed in any sequence.
  • the segmentation step 210 for Femur bone regions may be performed after the pre-processing step 200.
  • the segmentation step 210 for Femur bone regions may include an edge based Active Contour segmentation process.
  • the post processing step 212 of the segmented images for Femur bone regions may be carried out after segmentation step 210 of the Femur bone regions.
  • the post processing 212 of the segmented images for Femur bone regions may be based on a threshold value.
  • the threshold value used may be a global threshold that is a single value threshold may be applied on the entire image.
  • the image processing for the Tibia bone regions may be performed.
  • Figure 3 illustrates the exemplary steps involved in image processing of the Tibia bone regions.
  • the image processing of the Tibia bone regions may include a pre-processing step 300, a segmentation step 310 and a post-processing step 312.
  • the pre-processing step 300 of the Tibia bone regions may include an image smoothing step 302, a sharpening step 304, a contrast enhancement step 306 and/or an intensity transformations step 308.
  • the illustrated pre-processing steps 300 that are image smoothing step 302, sharpening step 304, contrast enhancement step 306 and/or intensity transformations step 308 may be performed in any sequence.
  • the segmentation step 310 for Tibia bone regions may be performed after the pre-processing step 300.
  • the segmentation step 310 for Tibia bone regions may include a region based Active Contour segmentation process.
  • the post processing step 312 for Tibia bone regions may be carried out after segmentation step 310 on the segmented image of the Tibia bone region.
  • the post processing step 312 for Tibia bone regions may be based on a threshold value.
  • the threshold value used may be a global threshold that is a single value threshold may be applied on the entire image.
  • the image processing for the Cortical bone regions may be performed.
  • Figure 4 illustrates exemplary steps involved in the image processing of the Cortical bone regions.
  • the image processing of the Cortical bone regions may include a pre-processing step 400, a segmentation step 402 and a post-processing step 404.
  • the pre-processing step 400 of Cortical bone regions may include an image smoothing process.
  • the segmentation step 402 for Cortical bone regions may be performed after the pre-processing step 400.
  • the segmentation step 402 may include a region based Local Auto Threshold based segmentation process.
  • the post processing step 404 of the segmented images for Cortical bone regions may be carried out after segmentation step 402 of the Cortical bone regions.
  • the post processing 404 of the segmented images for Cortical bone regions may include a filtering process.
  • a user may manually set the parameters for segmentation process of the Femur, Tibia and Cortical bone regions.
  • the Femur bone, Tibia bone and Cortical bone segmented regions may be merged after subjecting to separate image processing as illustrated in figure 2, figure 3 and figure 4.
  • the merging of the segmented bones may be automatic and may not require user input.
  • the merged segmented regions may be post-processed to develop a three- dimensional (3D) model of a knee bone.
  • a user may edit the 3D model by adding an appropriate region or removing an inappropriate region using drawing tools for making minor corrections.
  • Figure 5 illustrates an exemplary system 500 for creating a 3D model of the bone from a set of scanned images, according to one embodiment of the invention.
  • the scanned images may be stored in a repository 502.
  • the repository may contain MRI images or CT images.
  • the system may include a processor 504 for creating a 3D model of the bone from a set of scanned images.
  • One or more images may be selected from the repository and provided as input to the processor 504.
  • the processor 504 may select scanned images from the repository 502 and may identify Femur, Tibia and Cortical bone regions in the selected scanned images based on at least one seed selection in the scanned images.
  • the seed may be selected using any input device such as, but not limited to, computer mouse, digital pen etc.
  • the processor 504 has a pre-processing module 506, segmentation module 514 and postprocessing module 522.
  • the pre-processing module 506 may perform pre-processing of the selected images for segmentation of the identified bone regions.
  • the preprocessing module 506 may have a sub module 508 for pre-processing Femur bone regions, a sub module 510 for pre-processing Tibia bone regions and a sub module 512 for pre-processing Cortical bone regions.
  • the pre-processing of Femur bone regions may be performed in sub module 508.
  • the sub module 508 may perform image processing actions including image smoothing, sharpening, edge enhancement and intensity transformations.
  • the pre-processing of Tibia bone regions may be performed in sub module 510.
  • the sub module 510 may perform image processing actions including image smoothing, sharpening, contrast enhancement and intensity transformations.
  • the pre-processing of Cortical bone regions may be performed in sub module 508.
  • the sub module 508 may perform image processing actions including image smoothing.
  • the segmentation module 514 may perform segmentation process for the Femur, Tibia and Cortical bone regions. According to an exemplary embodiment, the segmentation module 514 may have a sub module 516 for performing segmentation process of the Femur bone regions, a sub module 518 for performing segmentation process of the Tibia bone regions and a sub module 520 for performing segmentation process of the Cortical bone regions.
  • the segmentation process of Femur bone regions may be performed in sub module 516.
  • the sub module 516 may perform an edge based Active Contour segmentation process.
  • the segmentation process for Tibia bone regions may be performed in sub module 518.
  • the sub module 518 may perform a region based Active Contour segmentation process.
  • the segmentation process for Cortical bone regions may be performed in sub module 520.
  • the sub module 520 may perform a Local Auto Threshold based segmentation process.
  • a user may manually set the parameters for segmentation of Femur, Tibia and Cortical bone regions.
  • the post-processing module 522 may perform post processing of the segmented images for Femur, Tibia and Cortical bone regions. According to an exemplary embodiment, the postprocessing module 522 may have a sub module 524 for post-processing Femur bone regions, a sub module 526 for post-processing Tibia bone regions and a sub module 528 for post processing Cortical bone regions.
  • the post-processing of the segmented images of the Femur and Tibia bone regions may be performed in sub modules 524 and 526 respectively.
  • post processing of the segmented images for Femur and Tibia bone regions may be based on a threshold value.
  • the threshold value used may be a global threshold i.e. single value threshold is applied on the entire image.
  • the sub module 528 may perform a post processing of the segmented images of the Cortical bone regions.
  • the post processing of the segmented images for Cortical bone regions may include filtering technique.
  • the processor 504 may further perform merging of Femur bone, Tibia bone and Cortical bone segmented regions and post-processing of the merged segmented regions to generate a 3D model of the knee bone.
  • the processor 504 may generate output in the form of a 3D model of a knee bone on a display device 530.
  • the user may edit the 3D model on the display device 530 by adding an appropriate region or removing an inappropriate region using drawing tools for making minor corrections.
  • the display device may be any display such as but not limited to Cathode ray tube display (CRT), Light-emitting diode display (LED), Electroluminescent display (ELD), Plasma display panel (PDP) etc.
  • the display may include a graphical user interface (GUI).
  • GUI graphical user interface
  • the embodiments of the invention further encompass computer apparatus, computing systems and machine-readable media configured to carry out the foregoing systems and methods.
  • the present invention may be conveniently implemented using a conventional general purpose or a specialized digital computer or microprocessor programmed according to the teachings of the present disclosure, as will be apparent to those skilled in the computer art.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Software Systems (AREA)
  • Veterinary Medicine (AREA)
  • Molecular Biology (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Biophysics (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Computer Graphics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Optics & Photonics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Surgery (AREA)
  • Primary Health Care (AREA)
  • Orthopedic Medicine & Surgery (AREA)
  • Architecture (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Dentistry (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
  • Image Processing (AREA)

Abstract

L'invention concerne un système et un procédé de mise au point d'un modèle tridimensionnel (3D) d'un os du genou à partir d'une pluralité d'images. Le procédé consiste à sélectionner une ou plusieurs images parmi la pluralité d'images et à identifier les régions de l'os du fémur, de l'os du tibia et de l'os cortical dans lesdites une ou plusieurs images sélectionnées. Les images identifiées sont prétraitées en vue de la segmentation de l'os du fémur, de l'os du tibia et de l'os cortical par des actions séparées de traitement d'image, respectivement. La segmentation des régions de l'os du fémur, de l'os du tibia et de l'os cortical s'effectue par un processus de segmentation séparé, respectivement pour les régions de l'os du fémur, de l'os du tibia et de l'os cortical. Les régions segmentées de l'os du fémur, de l'os du tibia et de l'os cortical sont fusionnées puis les régions segmentées fusionnées sont post-traitées de façon à mettre au point le modèle tridimensionnel (3D) de l'os du genou.
PCT/IB2016/053389 2015-06-09 2016-06-09 Système et procédé de modélisation tridimensionnelle à partir d'images scannées Ceased WO2016199051A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
IN2867/CHE/2015 2015-06-09
IN2867CH2015 2015-06-09

Publications (1)

Publication Number Publication Date
WO2016199051A1 true WO2016199051A1 (fr) 2016-12-15

Family

ID=57503525

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2016/053389 Ceased WO2016199051A1 (fr) 2015-06-09 2016-06-09 Système et procédé de modélisation tridimensionnelle à partir d'images scannées

Country Status (1)

Country Link
WO (1) WO2016199051A1 (fr)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110197712A (zh) * 2019-06-05 2019-09-03 桂林电子科技大学 一种医学图像储存系统及储存方法
CN111640093A (zh) * 2020-05-20 2020-09-08 上海联影智能医疗科技有限公司 医学图像的质控方法和计算机可读存储介质
CN111714145A (zh) * 2020-05-27 2020-09-29 浙江飞图影像科技有限公司 基于弱监督分割的股骨颈骨折检测方法及系统
US11351007B1 (en) 2018-01-22 2022-06-07 CAIRA Surgical Surgical systems with intra-operative 3D scanners and surgical methods using the same
US11432882B2 (en) 2019-09-17 2022-09-06 CAIRA Surgical System and method for medical object tracking
WO2024049613A1 (fr) * 2022-08-29 2024-03-07 Smith & Nephew, Inc. Segmentation automatisée pour planification opératoire de révision d'acl

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5802134A (en) * 1997-04-09 1998-09-01 Analogic Corporation Nutating slice CT image reconstruction apparatus and method

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5802134A (en) * 1997-04-09 1998-09-01 Analogic Corporation Nutating slice CT image reconstruction apparatus and method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
RAVIKANT KAMAL ET AL.: "Three-Dimensional(3D) modeling of Knee and Designing of custom made Knee Implant Using Mimic Software", INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND TECHNOLOGY, vol. 3, no. 2, June 2013 (2013-06-01), pages 327 - 330, ISSN: 2277 -4106 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11351007B1 (en) 2018-01-22 2022-06-07 CAIRA Surgical Surgical systems with intra-operative 3D scanners and surgical methods using the same
CN110197712A (zh) * 2019-06-05 2019-09-03 桂林电子科技大学 一种医学图像储存系统及储存方法
CN110197712B (zh) * 2019-06-05 2023-09-15 桂林电子科技大学 一种医学图像储存系统及储存方法
US11432882B2 (en) 2019-09-17 2022-09-06 CAIRA Surgical System and method for medical object tracking
US11510739B2 (en) 2019-09-17 2022-11-29 CAIRA Surgical System and method for medical object tracking
US11896319B2 (en) 2019-09-17 2024-02-13 CAIRA Surgical System and method for medical object tracking
CN111640093A (zh) * 2020-05-20 2020-09-08 上海联影智能医疗科技有限公司 医学图像的质控方法和计算机可读存储介质
CN111714145A (zh) * 2020-05-27 2020-09-29 浙江飞图影像科技有限公司 基于弱监督分割的股骨颈骨折检测方法及系统
WO2024049613A1 (fr) * 2022-08-29 2024-03-07 Smith & Nephew, Inc. Segmentation automatisée pour planification opératoire de révision d'acl

Similar Documents

Publication Publication Date Title
Van Eijnatten et al. CT image segmentation methods for bone used in medical additive manufacturing
WO2016199051A1 (fr) Système et procédé de modélisation tridimensionnelle à partir d'images scannées
JP6355766B2 (ja) 医用イメージングのための骨セグメンテーションにおけるユーザ誘導される形状モーフィング
US11062449B2 (en) Method and system for extracting vasculature
CN110047128B (zh) 从几个x射线放射照片3d重建x射线ct体积和分割掩模的方法和系统
JP6215851B2 (ja) ２ｄ−３ｄ画像位置合わせを支援するための方法およびシステム
US10966789B2 (en) Method and node for manufacturing a surgical kit for cartilage repair
US7616794B2 (en) System and method for automatic bone extraction from a medical image
EP1636753A1 (fr) Segmentation d'images en 3d
Baier et al. Forensic 3D printing from micro-CT for court use-process validation
CN107752979B (zh) 人工投影的自动生成方法、介质和投影图像确定装置
EP3433862B1 (fr) Poursuite binaire sur des images médicales
Xie et al. Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs
Al Abboodi et al. Supervised Transfer Learning for Multi Organs 3D Segmentation With Registration Tools for Metal Artifact Reduction in CT Images
Gamage et al. 3D reconstruction of patient specific bone models from 2D radiographs for image guided orthopedic surgery
Nysjö BoneSplit-a 3D texture painting tool for interactive bone separation in CT images
JP2005218528A (ja) 画像処理方法および装置並びにプログラム
Yang et al. Automatic 3D reconstruction of the Femur
Salah et al. Preoperative planning of a complete mastoidectomy: semiautomatic segmentation and evaluation
Liang Atlas-based segmentation of temporal bone anatomy
Calvo et al. Semiautomatic Modeling of Bone Tissue from Medical Image for Finite Element Method Based Biomechanical Studies
CN117115354A (zh) 一种基于三维影像的骨骼分割重建方法
Mu et al. Segmentation of knee joints in x-ray images using decomposition-based sweeping and graph search
Paulano et al. Fractured bone identification from ct images, fragment separation and fracture zone detection
Yuksel et al. Automatic segmentation of bone tissue in x-ray hand images

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16806989

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 16806989

Country of ref document: EP

Kind code of ref document: A1