SIST-TP 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 standard includes creation of optimized data for Medical Additive Manufacturing (MAM) which is
generated from static modalities like Magnetic resonance images (MRI), Computed Tomogram (CT), Positron Emission Tomogram (PET), SPECT and Dynamic modalities like ultrasound and optical image
data. It addresses medical-specific data quality requirements and medical image data acquisition
processing approaches for accurate solid medical models and devices based on real human
information. Also this data can be used for animal surgeries (Veterinary surgery).
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
SIST-TP CEN ISO/ASTM/TR 52916:2022
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
SIST-TP CEN ISO/ASTM/TR 52916:2022 en,fr,de
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.
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SIST-TP CEN ISO/ASTM/TR 52916:2022
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SIST-TP CEN ISO/ASTM/TR 52916:2022
CEN ISO/ASTM/TR
TECHNICAL REPORT
52916
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.
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CEN ISO/ASTM/TR 52916:2022 (E)
Contents Page
European foreword . 3
2
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CEN ISO/ASTM/TR 52916:2022 (E)
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.
3
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SIST-TP CEN ISO/ASTM/TR 52916:2022
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
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SIST-TP CEN ISO/ASTM/TR 52916:2022
ISO/ASTM TR 52916:2022(E)
COPYRIGHT PROTECTED DOCUMENT
© ISO/ASTM International 2022
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester. In the United States, such requests should be sent to ASTM International.
ISO copyright office ASTM International
CP 401 • Ch. de Blandonnet 8 100 Barr Harbor Drive, PO Box C700
CH-1214 Vernier, Geneva West Conshohocken, PA 19428-2959, USA
Phone: +41 22 749 01 11 Phone: +610 832 9634
Fax: +610 832 9635
Email: copyright@iso.org Email: khooper@astm.org
Website: www.iso.org Website: www.astm.org
Published in Switzerland
ii
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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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11.4 Tolerance error situations . 15
Annex A (informative) Medical CAD for additive manufacturing tolerance .16
Bibliography .24
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ISO/ASTM TR 52916:2022(E)
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out
through ISO technical committees. Each member body interested in a subject for which a technical
committee has been established has the right to be represented on that committee. International
organizations, governmental and non-governmental, in liaison with ISO, also take part in the work.
ISO collaborates closely with the International Electrotechnical Commission (IEC) on all matters of
electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are
described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the
different types of ISO documents should be noted. This document was drafted in accordance with the
editorial rules of the ISO/IEC Directives, Part 2 (see www.iso.org/directives).
Attention is drawn to the possibility that some of the elements of this document may be the subject of
patent rights. ISO shall not be held responsible for identifying any or all such patent rights. Details of
any patent rights identified during the development of the document will be in the Introduction and/or
on the ISO list of patent declarations received (see www.iso.org/patents).
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and
expressions related to conformity assessment, as well as information about ISO's adherence to
the World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT), see
www.iso.org/iso/foreword.html.
This document was prepared by ISO/TC 261, Additive manufacturing, in cooperation with ASTM
Committee F42, Additive Manufacturing Technologies, on the basis of a partnership agreement between
ISO and ASTM International with the aim to create a common set of ISO/ASTM standards on additive
manufacturing. and in collaboration with the European Committee for Standardization (CEN) Technical
Committee CEN/TC 438, Additive manufacturing, in accordance with the Agreement on technical
cooperation between ISO and CEN (Vienna Agreement).
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html.
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ISO/ASTM TR 52916:2022(E)
Introduction
This document has been developed in close cooperation of ISO/TC 261 and ASTM F 42 on basis of a
partnership agreement between ISO and ASTM international with the aim to create a common set of
ISO/ASTM standards on additive manufacturing.
Digital imaging and communications in medicine (DICOM) image files cannot be used directly for 3D
printing; further steps are necessary to make them readable by additive manufacturing system. In
particular, as the thickness of the computed tomography slice increases, there is a problem that the
error in 3D reconstruction of the anatomical structure increases. Therefore, the focus of this technical
report is to automatically reconfigure the slice interval through the application of isotropic conversion
technology to utilize the existing dicom file and visualization and editing software as it is. In addition,
in order to present a method for optimized medical image data for additive manufacturing, tomography
metadata without compression is used by editing and processing the output format file without loss in
the AM equipment system, or tomography within the maximum allowable range of radiation. Consider
reducing the spacing of slices as much as possible and increasing the resolution per image as much as
possible.
This document benefits from the direction of development and high quality additive manufacturing
output through the technical optimization of medical imaging for additive manufacturing: medical
academics, clinic and industry fields for AM like as anatomical measurements, 3D analysis, finite
element analysis and surgical planning or simulation, patient-specific implant and device design. There
are many affected stakeholder like as medical AM system manufacturer, AM feedstock manufacturer,
AM feedstock supplier and vendor, medical AM hardware manufacturer, medical AM software
manufacturer, medical AM system manufacturer, medical AM platform manufacturer, AM based medical
device manufacturer, medical 3D scanning and digitizing device manufacturer, surgical simulation AM
model manufacturer, AM surgical implant manufacturer, AM surgical guide manufacturer, AM physical
model for clinical education and diagnostic treatment, disposable medical AM consumable devices.
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SIST-TP CEN ISO/ASTM/TR 52916:2022
TECHNICAL REPORT ISO/ASTM TR 52916:2022(E)
Additive manufacturing for medical — Data — Optimized
medical image data
1 Scope
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.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content
constitutes requirements of this document. For dated references, only the edition cited applies. For
undated references, the latest edition of the referenced document (including any amendments) applies.
ISO/ASTM 52900, Additive manufacturing — General principles — Fundamentals and vocabulary
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/ASTM 52900 and the
following apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
CT
computed tomography
computed axial tomography
radiographic scanning technique that uses a number of CT projections of an object at different angles in
order to allow calculation of a CT image
[SOURCE: ISO 15708-1:2017, 3.7]
3.2
MRI
magnetic resonance image
imaging technique that uses static and time varying magnetic fields to provide images of tissue by the
magnetic resonance of nuclei
[SOURCE: ISO 14630:2012, 3.5]
1
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ISO/ASTM TR 52916:2022(E)
3.3
polygon
planar surface defined by one exterior boundary and by zero or more interior boundaries
Note 1 to entry: Each interior boundary describes a hole in the surface.
Note 2 to entry: A single or group of polygons can be used to define a treatment zone.
[SOURCE: ISO 11783-10:2015, 3.13]
3.4
reconstruction
process of transforming a set of CT projections into a CT image
[SOURCE: ISO 15708-1:2017, 3.25]
3.5
rendering
action of transforming from a scene description to a specific output description/device
[SOURCE: ISO 19262:2015, 3.213]
3.6
ROI
region of interest, sub-volume within an object or a CT image
[SOURCE: ISO 15708-1:2017, 3.26]
3.7
segmentation
method which partitions a surface or volume into distinct regions
[SOURCE: SOURCE: ISO 25178-2:2012, 3.3.6, modified — ISO 25178-2:2012 had “scale-limited surface”
in the definition.]
3.8
volume data
data of a volume in a 3D space
Note 1 to entry: The description can be performed on the basis of density differences inside the three-dimensional
space.
[SOURCE: ISO 18739:2016, 3.1.42]
3.9
voxel
volume pixel
three-dimensional cuboid representing the minimum unit comprising a three-dimensional image
[SOURCE: ISO/TR 16379:2014, 2.17, modified — "volume pixel" has been added as a second term.]
3.10
2D
geometry in a xy-plane, where all the geometry's points have only x and y coordinates
[SOURCE: ISO 14649-10:2004, 3.1]
2
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ISO/ASTM TR 52916:2022(E)
3.11
DICOM
digital imaging and communications in medicine
international standard for medical images and related information
Note 1 to entry: It defines the formats for medical images that can be exchanged with the data and quality
necessary for clinical use.
Note 2 to entry: The Medical Imaging Technology Association (MITA), a division of NEMA, serves as the DICOM
Secretariat. The current DICOM standard may be found at: https:// www .dicomstandard .org/ current.
4 Medical images generation for AM
4.1 General medical image data generation
The start for image generation is to collect raw image data. This collects raw information about the
inside of the human body and becomes the basic object of all subsequent image processing tasks. In the
end, regardless of the image format, the data collection process detects physical factors, pre-processes
the collected signals and then digitizes them (see Figure 1).
Figure 1 — Process from medical image to medical additive manufacturing
4.2 General error occurrence steps in medical images generation
With gradual technological advancement, many solutions for medical additive manufacturing are
emerging. However, research into the cause for resolving errors in medical additive manufacturing
output is still ongoing. The cause of additive manufacturing accuracy error occurs in the process
of converting the raw data to medical images and the process of converting 3D model data. Error
generation factors that occur during this conversion process are described in 4.3 for the most common
tomography systems.
3
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ISO/ASTM TR 52916:2022(E)
Additional errors may be generated by the process of converting DICOM or PACS data to the
computational formats used within segmentation editing software and saving the STL 3D mesh format
for use in additive manufacturing systems. When saving a customized STL file, all meta data that
defined colour, material, surface textures are lost. The lack of accuracy and precision for 3D data from
the scan systems, editing and modelling software can reduce the quality of an additive manufactured
medical device.
NOTE 1 There can be other factors in creating errors when utilizing other image capture modalities, such as
ultrasound, digital microscopy, etc. not covered in 4.3.
4.3 Medical image extraction
4.3.1 Introduction of medical image extraction
The quality of a medical image depends on the degree to which the microscopic structure of the human
body can be accurately represented. According to the needs of the medical professional who requested
the tomography, the layer spacing between the cross-sectional images is adjusted and photographed.
Based on the captured meta data, reconstruction through 3D visualization is performed to extract the
data of the region of interest. In this process, the medical imaging tomography technology, imaging
conditions, and data conversion process will continue to affect the medical additive manufacturing
output resolution.
4.3.2 CT image error generation factors
CT modality images use absorption coefficient parameters that visualize the density of an image.
The contrast of hard tissue is more clearly expressed than soft tissue. Since sequential image layers
are output as a series, 3D reconstruction is possible. The important factors that determine the image
quality are the accuracy of the CT reduction coefficient, which expresses the degree of attenuation
of a substance, noise, uniformity, spatial resolution, contrast resolution, and radiation dose. It is
recommended that the patient's exposure dose is small, but it is very difficult to control the exposure
dose and image quality because it is directly related to image noise and density resolution. Adjustment
of radiation dose for each body part according to the patient's condition follows the clinical experience
and medical recommendations of the radiologist. This is an external factor that affects the medical
image data homogeneity.
— CT matrix size: The digital medical image is stored as 2D pixels, and each pixel is converted into the
number of bits matched by the number of gray levels and represented. The CT image size depends on the
anatomy being examined. Typically, CT images have a matrix size of 512 pixels × 512 pixels × 8 bytes
(12 bits), and gray levels range from 512 pixels (28 bits) to 4 096 pixels (212 bits). A single CT section
requires 512 pixels × 512 pixels × 2 bytes = 524,288 bytes of storage on the computer.
— CT reduction coefficient: The tissue weighting factor (W ) is a relative measure of the risk of
T
stochastic effects that might result from irradiation of that specific tissue. It accounts for the
variable radiosensitivities of organs and tissues in the body to ionizing radiation. To calculate the
effective dose, the individual organ equivalent dose values are multiplied by the respective tissue
weighting factor and the products added. The sum of the weighting factors is 1.
— Based on the values of tissue weighting factors, tissues are grouped into following to assess the
carcinogenic risk:
high risk (W = 0,12): stomach, colon, lung, red bone marrow;
T
moderate risk (W = 0,05): urinary bladder, oesophagus, breast, liver, thyroid;
T
low risk (W = 0,01): bone s
...
SLOVENSKI STANDARD
kSIST-TP FprCEN ISO/ASTM TR 52916:2021
01-november-2021
Aditivna proizvodnja za medicino - Formati datotek - Optimizirani medicinski
slikovni posnetki (ISO/ASTM DTR 52916:2021)
Additive manufacturing for medical - Data - Optimized medical image data (ISO/ASTM
DTR 52916:2021)
Additive Fertigung - Datenformate - Normspezifikation für optimierte medizinische
Bilddaten (ISO/ASTM DTR 52916:2021)
Fabrication additive dans le secteur médical - Données - Données d'images médicales
optimisées (ISO/ASTM DTR 52916:2021)
Ta slovenski standard je istoveten z: FprCEN/TR/ISO/ASTM 52916
ICS:
11.040.99 Druga medicinska oprema Other medical equipment
25.030 3D-tiskanje Additive manufacturing
kSIST-TP FprCEN ISO/ASTM TR en,fr,de
52916:2021
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.
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kSIST-TP FprCEN ISO/ASTM TR 52916:2021
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kSIST-TP FprCEN ISO/ASTM TR 52916:2021
TECHNICAL ISO/ASTM TR
REPORT 52916
First edition
Additive manufacturing for medical —
Data — Optimized medical image data
Fabrication additive dans le secteur médical — Données — Données
d'images médicales optimisées
PROOF/ÉPREUVE
Reference number
ISO/ASTM TR 52916:2021(E)
©
ISO/ASTM International 2021
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kSIST-TP FprCEN ISO/ASTM TR 52916:2021
ISO/ASTM TR 52916:2021(E)
COPYRIGHT PROTECTED DOCUMENT
© ISO/ASTM International 2021
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may be
reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester. In the United States, such requests should be sent to ASTM International.
ISO copyright office ASTM International
CP 401 • Ch. de Blandonnet 8 100 Barr Harbor Drive, PO Box C700
CH-1214 Vernier, Geneva West Conshohocken, PA 19428-2959, USA
Phone: +41 22 749 01 11 Phone: +610 832 9634
Fax: +610 832 9635
Email: copyright@iso.org Email: khooper@astm.org
Website: www.iso.org Website: www.astm.org
Published in Switzerland
ii PROOF/ÉPREUVE© ISO/ASTM International 2021 – All rights reserved
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kSIST-TP FprCEN ISO/ASTM TR 52916:2021
ISO/ASTM TR 52916:2021(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 .15
11.3 Equipment error .15
11.3.1 Background.15
11.3.2 Standard computational mesh model data creation for an evaluation method .15
11.4 Tolerance error situations .16
© ISO/ASTM International 2021 – All rights reserved PROOF/ÉPREUVE iii
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kSIST-TP FprCEN ISO/ASTM TR 52916:2021
ISO/ASTM TR 52916:2021(E)
Annex A (informative) Medical CAD for additive manufacturing tolerance .17
Bibliography .25
iv PROOF/ÉPREUVE© ISO/ASTM International 2021 – All rights reserved
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kSIST-TP FprCEN ISO/ASTM TR 52916:2021
ISO/ASTM TR 52916:2021(E)
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out
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This document was prepared by ISO/TC 261, Additive manufacturing, in cooperation with ASTM
Committee F42, Additive Manufacturing Technologies, on the basis of a partnership agreement between
ISO and ASTM International with the aim to create a common set of ISO/ASTM standards on additive
manufacturing. and in collaboration with the European Committee for Standardization (CEN) Technical
Committee CEN/TC 438, Additive manufacturing, in accordance with the Agreement on technical
cooperation between ISO and CEN (Vienna Agreement).
Any feedback or questions on this document should be directed to the user’s national standards body. A
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Introduction
This document has been developed in close cooperation of ISO/TC 261 and ASTM F 42 on basis of a
partnership agreement between ISO and ASTM international with the aim to create a common set of
ISO/ASTM standards on additive manufacturing.
Digital imaging and communications in medicine (DICOM) image files cannot be used directly for 3D
printing; further steps are necessary to make them readable by additive manufacturing system. In
particular, as the thickness of the computed tomography slice increases, there is a problem that the
error in 3D reconstruction of the anatomical structure increases. Therefore, the focus of this technical
report is to automatically reconfigure the slice interval through the application of isotropic conversion
technology to utilize the existing dicom file and visualization and editing software as it is. In addition,
in order to present a method for optimized medical image data for additive manufacturing, tomography
metadata without compression is used by editing and processing the output format file without loss in
the AM equipment system, or tomography within the maximum allowable range of radiation. Consider
reducing the spacing of slices as much as possible and increasing the resolution per image as much as
possible.
This document benefits from the direction of development and high quality additive manufacturing
output through the technical optimization of medical imaging for additive manufacturing: medical
academics, clinic and industry fields for AM like as anatomical measurements, 3D analysis, finite
element analysis and surgical planning or simulation, patient-specific implant and device design. There
are many affected stakeholder like as medical AM system manufacturer, AM feedstock manufacturer,
AM feedstock supplier and vendor, medical AM hardware manufacturer, medical AM software
manufacturer, medical AM system manufacturer, medical AM platform manufacturer, AM based medical
device manufacturer, medical 3D scanning and digitizing device manufacturer, surgical simulation AM
model manufacturer, AM surgical implant manufacturer, AM surgical guide manufacturer, AM physical
model for clinical education and diagnostic treatment, disposable medical AM consumable devices.
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Additive manufacturing for medical — Data — Optimized
medical image data
1 Scope
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.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content
constitutes requirements of this document. For dated references, only the edition cited applies. For
undated references, the latest edition of the referenced document (including any amendments) applies.
ISO/ASTM 52900, Additive manufacturing — General principles — Terminology
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/ASTM 52900 and the
following apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www. iso. org/o bp
— IEC Electropedia: available at https:// www.e lectropedia. org/
3.1
CT
computed tomography
computed axial tomography
radiographic scanning technique that uses a number of CT projections of an object at different angles in
order to allow calculation of a CT image
[SOURCE: ISO 15708-1:2017, 3.7]
3.2
MRI
magnetic resonance image
imaging technique that uses static and time varying magnetic fields to provide images of tissue by the
magnetic resonance of nuclei
[SOURCE: ISO 14630:2012, 3.5]
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3.3
polygon
planar surface defined by one exterior boundary and by zero or more interior boundaries
Note 1 to entry: Each interior boundary describes a hole in the surface.
Note 2 to entry: A single or group of polygons can be used to define a treatment zone.
[SOURCE: ISO 11783-10:2015, 3.13]
3.4
reconstruction
process of transforming a set of CT projections into a CT image
[SOURCE: ISO 15708-1:2017, 3.25]
3.5
rendering
action of transforming from a scene description to a specific output description/device
[SOURCE: ISO 19262:2015, 3.213]
3.6
ROI
region of interest, sub-volume within an object or a CT image
[SOURCE: ISO 15708-1:2017, 3.26]
3.7
segmentation
method which partitions a surface or volume into distinct regions
[SOURCE: SOURCE: ISO 25178-2:2012, 3.3.6, modified — ISO 25178-2:2012 had “scale-limited surface”
in the definition.]
3.8
volume data
data of a volume in a 3D space
Note 1 to entry: The description can be performed on the basis of density differences inside the three-dimensional
space.
[SOURCE: ISO 18739:2016, 3.1.42]
3.9
voxel
volume pixel
three-dimensional cuboid representing the minimum unit comprising a three-dimensional image
[SOURCE: ISO/TR 16379:2014, 2.17, modified — "volume pixel" has been added as a second term.]
3.10
2D
geometry in a xy-plane, where all the geometry's points have only x and y coordinates
[SOURCE: ISO 14649-10:2004, 3.1]
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3.11
DICOM
digital imaging and communications in medicine
international standard for medical images and related information
Note 1 to entry: It defines the formats for medical images that can be exchanged with the data and quality
necessary for clinical use.
Note 2 to entry: The Medical Imaging Technology Association (MITA), a division of NEMA, serves as the DICOM
Secretariat. The current DICOM standard may be found at: https:// www .dicomstandard .org/ current.
4 Medical images generation for AM
4.1 General medical image data generation
The start for image generation is to collect raw image data. This collects raw information about the
inside of the human body and becomes the basic object of all subsequent image processing tasks. In the
end, regardless of the image format, the data collection process detects physical factors, pre-processes
the collected signals and then digitizes them (see Figure 1).
Figure 1 — Process from medical image to medical additive manufacturing
4.2 General error occurrence steps in medical images generation
With gradual technological advancement, many solutions for medical additive manufacturing are
emerging. However, research into the cause for resolving errors in medical additive manufacturing
output is still ongoing. The cause of additive manufacturing accuracy error occurs in the process
of converting the raw data to medical images and the process of converting 3D model data. Error
generation factors that occur during this conversion process are described in 4.3 for the most common
tomography systems.
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Additional errors may be generated by the process of converting DICOM or PACS data to the
computational formats used within segmentation editing software and saving the STL 3D mesh format
for use in additive manufacturing systems. When saving a customized STL file, all meta data that
defined colour, material, surface textures are lost. The lack of accuracy and precision for 3D data from
the scan systems, editing and modelling software can reduce the quality of an additive manufactured
medical device.
NOTE 1 There can be other factors in creating errors when utilizing other image capture modalities, such as
ultrasound, digital microscopy, etc. not covered in 4.3.
4.3 Medical image extraction
4.3.1 Introduction of medical image extraction
The quality of a medical image depends on the degree to which the microscopic structure of the human
body can be accurately represented. According to the needs of the medical professional who requested
the tomography, the layer spacing between the cross-sectional images is adjusted and photographed.
Based on the captured meta data, reconstruction through 3D visualization is performed to extract the
data of the region of interest. In this process, the medical imaging tomography technology, imaging
conditions, and data conversion process will continue to affect the medical additive manufacturing
output resolution.
4.3.2 CT image error generation factors
CT modality images use absorption coefficient parameters that visualize the density of an image.
The contrast of hard tissue is more clearly expressed than soft tissue. Since sequential image layers
are output as a series, 3D reconstruction is possible. The important factors that determine the image
quality are the accuracy of the CT reduction coefficient, which expresses the degree of attenuation
of a substance, noise, uniformity, spatial resolution, contrast resolution, and radiation dose. It is
recommended that the patient's exposure dose is small, but it is very difficult to control the exposure
dose and image quality because it is directly related to image noise and density resolution. Adjustment
of radiation dose for each body part according to the patient's condition follows the clinical experience
and medical recommendations of the radiologist. This is an external factor that affects the medical
image data homogeneity.
— CT matrix size: The digital medical image is stored as 2D pixels, and each pixel is converted into the
number of bits matched by the number of gray levels and represented. The CT image size depends on the
anatomy being examined. Typically, CT images have a matrix size of 512 pixels × 512 pixels × 8 bytes
(12 bits), and gray levels range from 512 pixels (28 bits) to 4 096 pixels (212 bits). A single CT section
requires 512 pixels × 512 pixels × 2 bytes = 524,288 bytes of storage on the computer.
— CT reduction coefficient: The tissue weighting factor (W ) is a relative measure of the risk of
T
stochastic effects that might result from irradiation of that specific tissue. It accounts for the
variable radiosensitivities of organs and tissues in the body to ionizing radiation. To calculate the
effective dose, the individual organ equivalent dose values are multiplied by the respective tissue
weighting factor and the products added. The sum of the weighting factors is 1.
— Based on the values of tissue weighting factors, tissues are grouped into following to assess the
carcinogenic risk:
high risk (W = 0,12): stomach, colon, lung, red bone marrow;
T
moderate risk (W = 0,05): urinary bladder, oesophagus, breast, liver, thyroid;
T
low risk (W = 0,01): bone surface, skin.
T
— Spatial resolution: Ability to image small objects that have high subject contrast, CT has moderate
spatial resolution 20 lp/cm.
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— Contrast resolution: Ability to distinguish between and image similar tissues, CT has excellent low
contrast detectability 0,25 % to 0,5 % difference in tissue attenuation.
4.3.3 MRI Image error generation factors
MRI uses a magnetic field that is harmless to the human body and radio frequency, which is non-ionizing
radiation. The principle is to image the density and physicochemical properties of the atomic nucleus
by causing nuclear magnetic resonance phenomenon in the atomic nucleus inside the human body. The
advantage and difference are that it has several imaging parameters compared to CT. Four factors, such
as the density of the hydrogen atom nucleus, T1 relaxation time, T2 relaxation time, and blood flow, are
important parameters that determine the shading of the image. However, not only the distribution of
the hydrogen atom nucleus, but also the molecular state of the contained tissue or the physical state of
the image varies. The MRI image looks at the distribution of spin density and is further affected by the
T1 T2 relaxation time associated with the NMR (Nuclear Magnetic Resonance) phenomenon. However,
due to parameter elements for each MRI device, standard parameter settings are different for each MRI
imaging personnel and are external factors affecting image data homogeneity.
MRI image quality depends on resolution (matrix, field of view, slice thickness), signal noise ratio,
contrast, artefacts. Especially contrast depends on the MRI scan parameter.
MRI resolution is the size of an individual pixel, the smaller it is, the higher the resolution. The MRI
matrix size is the number of pixels in the images. To improve the MRI resolution, increase the matrix,
decrease the FOV, and decrease the slice thickness.
In the field of orthopedic surgery, MRI scan parameters are applied in the following ranges of maximum
and minimum values of FOV, slice thickness, interslice gap, and matrix size.
Table 1 — Musculoskeletal MRI scan parameters
Scan section
Parameters
Shoulder Elbow Wrist Hip Knee Ankle
Field of view
≥16 10 to 16 6 to 12 16 to 20 ≥16 ≥14
(cm)
Slice thickness
≥3 3 to 4 ≥3 3 to 4 ≥3 ≥3
(mm)
Slice gap (%) ≥10 ≥33 ≥33 ≥33 ≥10 ≥10
Matrix size
≤ 256 × 192 ≤ 256 × 256 ≤ 256 × 192 ≤ 512 × 384 ≤ 256 × 192 ≤ 256 × 192
(pixel)
— Signal noise ratio: The signal noise ratio is a measure that compares the level of a desired signal
to the level of background noise. For data acquired through magnetic resonance imaging, this
quantification is typically used to allow comparison between imaging hardware, imaging protocols
...
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