CEN ISO/ASTM/TR 52916:2022
(Main)Additive manufacturing for medical - Data - Optimized medical image data (ISO/ASTM TR 52916:2022)
Additive manufacturing for medical - Data - Optimized medical image data (ISO/ASTM TR 52916:2022)
This document includes the creation of optimized data for medical additive manufacturing (MAM). These data are generated from static modalities, such as magnetic resonance imaging (MRI), computed tomography (CT). This document addresses improved medical image data, and medical image data acquisition processing and optimization approaches for accurate solid medical models, based on real human and animal data.
Solid medical models are generally created from stacked 2D images output from medical imaging systems. The accuracy of the final model depends on the resolution and accuracy of the original image data. The main factors influencing accuracy are the resolution of the image, the amount of image noise, the contrast between the tissues of interest and artefacts inherent in the imaging system.
Additive Fertigung - Datenformate - Normspezifikation für optimierte medizinische Bilddaten (ISO/ASTM TR 52916:2022)
Fabrication additive dans le secteur médical - Données - Données d'images médicales optimisées (ISO/ASTM TR 52916:2022)
Le présent document comprend la création de données optimisées pour la fabrication additive médicale (FAM). Ces données sont générées à partir de modes opératoires statiques tels que l'imagerie par résonance magnétique (IRM), la tomographie informatisée (TI). Le présent document traite des données améliorées d'image médicale et des approches du procédé d'acquisition et d'optimisation des données d'image médicale pour des modèles médicaux solides précis, basés sur des données humaines et animales réelles.
Les modèles médicaux solides sont généralement créés à partir d'images 2D empilées provenant de systèmes d'imagerie médicale. L'exactitude du modèle final dépend de la résolution et de l'exactitude des données originales d'image. Les principaux facteurs influençant l'exactitude sont la résolution de l'image, la quantité de bruit d'image, le contraste entre les tissus d'intérêt et les artefacts inhérents au système d'imagerie.
Aditivna proizvodnja za medicino - Formati datotek - Optimizirani medicinski slikovni posnetki (ISO/ASTM TR 52916:2022)
Ta standard vključuje oblikovanje optimiziranih posnetkov za medicinsko aditivno proizvodnjo (MAM), ustvarjenih na podlagi statičnih modalitet, kot so magnetnoresonančne slike (MRI), računalniški tomogram (CT), pozitronski emisijski tomogram (PET) in slike SPECT, ter dinamičnih modalitet, kot so ultrazvočni in optični slikovni posnetki. Obravnava zahteve za kakovost podatkov, specifičnih za medicino, ter načine obdelave zajetih medicinskih slikovnih
posnetkov za natančne trdne medicinske modele in pripomočke, izdelane na podlagi podatkov o dejanskih osebah. Te podatke je mogoče uporabiti tudi pri operacijah živali (veterinarska kirurgija).
General Information
Standards Content (Sample)
SLOVENSKI STANDARD
01-september-2022
Aditivna proizvodnja za medicino - Formati datotek - Optimizirani medicinski
slikovni posnetki (ISO/ASTM TR 52916:2022)
Additive manufacturing for medical - Data - Optimized medical image data (ISO/ASTM
TR 52916:2022)
Additive Fertigung - Datenformate - Normspezifikation für optimierte medizinische
Bilddaten (ISO/ASTM TR 52916:2022)
Fabrication additive dans le secteur médical - Données - Données d'images médicales
optimisées (ISO/ASTM TR 52916:2022)
Ta slovenski standard je istoveten z: CEN ISO/ASTM/TR 52916:2022
ICS:
11.040.99 Druga medicinska oprema Other medical equipment
25.030 3D-tiskanje Additive manufacturing
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.
CEN ISO/ASTM/TR
TECHNICAL REPORT
RAPPORT TECHNIQUE
TECHNISCHER BERICHT
February 2022
ICS 25.030
English Version
Additive manufacturing for medical - Data - Optimized
medical image data (ISO/ASTM TR 52916:2022)
Fabrication additive dans le secteur médical - Données Additive Fertigung - Datenformate - Normspezifikation
- Données d'images médicales optimisées (ISO/ASTM für optimierte medizinische Bilddaten (ISO/ASTM TR
TR 52916:2022) 52916:2022)
This Technical Report was approved by CEN on 18 January 2022. It has been drawn up by the Technical Committee CEN/TC 438.
This European Standard was corrected and reissued by the CEN-CENELEC Management Centre on 09 March 2022.
CEN members are the national standards bodies of Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia,
Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway,
Poland, Portugal, Republic of North Macedonia, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey and
United Kingdom.
EUROPEAN COMMITTEE FOR STANDARDIZATION
COMITÉ EUROPÉEN DE NORMALISATIO N
EUROPÄISCHES KOMITEE FÜR NORMUN G
CEN-CENELEC Management Centre: Rue de la Science 23, B-1040 Brussels
© 2022 CEN All rights of exploitation in any form and by any means reserved Ref. No. CEN ISO/ASTM/TR 52916:2022 E
worldwide for CEN national Members.
Contents Page
European foreword . 3
European foreword
This document (CEN ISO/ASTM/TR 52916:2022) has been prepared by Technical Committee ISO/TC
261 "Additive manufacturing" in collaboration with Technical Committee CEN/TC 438 “Additive
Manufacturing” the secretariat of which is held by AFNOR.
Attention is drawn to the possibility that some of the elements of this document may be the subject of
patent rights. CEN shall not be held responsible for identifying any or all such patent rights.
Any feedback and questions on this document should be directed to the users’ national standards
body/national committee. A complete listing of these bodies can be found on the CEN website.
Endorsement notice
The text of ISO/ASTM TR 52916:2022 has been approved by CEN as CEN ISO/ASTM/TR 52916:2022
without any modification.
TECHNICAL ISO/ASTM TR
REPORT 52916
First edition
2022-01
Additive manufacturing for medical —
Data — Optimized medical image data
Fabrication additive dans le secteur médical — Données — Données
d'images médicales optimisées
Reference number
ISO/ASTM TR 52916:2022(E)
© ISO/ASTM International 2022
ISO/ASTM TR 52916:2022(E)
© ISO/ASTM International 2022
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
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Published in Switzerland
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© ISO/ASTM International 2022 – All rights reserved
ISO/ASTM TR 52916:2022(E)
Contents Page
Foreword .v
Introduction . vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Medical images generation for AM .3
4.1 General medical image data generation. 3
4.2 General error occurrence steps in medical images generation . 3
4.3 Medical image extraction . 4
4.3.1 Introduction of medical image extraction . 4
4.3.2 CT image error generation factors . 4
4.3.3 MRI Image error generation factors . 5
5 Image segmentation . 6
5.1 Introduction of segmentation . 6
5.2 Segmentation techniques . 6
5.2.1 Thresholding algorithm . 6
5.2.2 Region growing algorithm . 6
5.2.3 Morphological image algorithm . 7
5.2.4 Level-set algorithm . 7
5.2.5 Other partial segmentation algorithm . 7
6 Reconstruction . 7
6.1 Introduction of reconstruction . 7
6.2 Reconstruction process . 7
7 Smoothing .8
7.1 Marching cubes . 8
7.2 Mesh smoothing . 8
8 3D visualization method . 8
8.1 Surface rendering . . 8
8.1.1 Introduction of surface shaded rendering. 8
8.1.2 Surface shaded rendering feature . 9
8.2 Volume rendering . 9
8.2.1 Introduction of volume rendering . 9
8.2.2 Volume rendering feature . 9
8.2.3 Ray casting techniques . 9
8.2.4 3D texture mapping techniques . 9
9 Additional processing for additive manufacturing .10
10 Methods .10
10.1 Image isotropic conversion . 10
10.2 Image enhancement . 11
10.3 Image segmentation .12
11 Minimizing error of software and equipment .14
11.1 Introduction of software and equipment error . 14
11.2 Software error . 14
11.2.1 Background . 14
11.2.2 Verification method using main inflection . 14
11.2.3 Improving accuracy and precision . 14
11.3 Equipment error . 15
11.3.1 Background . 15
11.3.2 Standard computational mesh model data creation for an evaluation
method .15
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© ISO/ASTM International 2022 – All rights reserved
ISO/ASTM TR 52916:2022(E)
11.4 Tolerance error situations . 15
Annex A (inf
...
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