The Inner Complexities of Multimodal Medical Data : Bitmap-Based 3D Printing for Surgical Planning Using Dynamic Physiology
© Nicholas M. Jacobson et al. 2023; Published by Mary Ann Liebert, Inc..
Motivated by the need to develop more informative and data-rich patient-specific presurgical planning models, we present a high-resolution method that enables the tangible replication of multimodal medical data. By leveraging voxel-level control of multimaterial three-dimensional (3D) printing, our method allows for the digital integration of disparate medical data types, such as functional magnetic resonance imaging, tractography, and four-dimensional flow, overlaid upon traditional magnetic resonance imaging and computed tomography data. While permitting the explicit translation of multimodal medical data into physical objects, this approach also bypasses the need to process data into mesh-based boundary representations, alleviating the potential loss and remodeling of information. After evaluating the optical characteristics of test specimens generated with our correlative data-driven method, we culminate with multimodal real-world 3D-printed examples, thus highlighting current and potential applications for improved surgical planning, communication, and clinical decision-making through this approach.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
3D printing and additive manufacturing - 10(2023), 5 vom: 01. Okt., Seite 855-868 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jacobson, Nicholas M [VerfasserIn] |
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| Links: |
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| Themen: |
3D printing |
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| Anmerkungen: |
Date Revised 28.10.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1089/3dp.2022.0265 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM363784705 |
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| 520 | |a Motivated by the need to develop more informative and data-rich patient-specific presurgical planning models, we present a high-resolution method that enables the tangible replication of multimodal medical data. By leveraging voxel-level control of multimaterial three-dimensional (3D) printing, our method allows for the digital integration of disparate medical data types, such as functional magnetic resonance imaging, tractography, and four-dimensional flow, overlaid upon traditional magnetic resonance imaging and computed tomography data. While permitting the explicit translation of multimodal medical data into physical objects, this approach also bypasses the need to process data into mesh-based boundary representations, alleviating the potential loss and remodeling of information. After evaluating the optical characteristics of test specimens generated with our correlative data-driven method, we culminate with multimodal real-world 3D-printed examples, thus highlighting current and potential applications for improved surgical planning, communication, and clinical decision-making through this approach | ||
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