Computer methods and programs in biomedicine · 2021
Assessment of transfer of morphological characteristics of Anomalous Aortic Origin of a Coronary Artery from imaging to patient specific 3D Printed models: A feasibility study.
СтатьяНазвание и аннотация: Перевод в очереди
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Parthasarathy Jayanthi, Hatoum Hoda, Flemister Dorma C, Krull Carly M, Walter Benjamin A, Zhang Wei, Mery Carlos M, Molossi Silvana, Jadhav Siddharth, Dasi Lakshmi Prasad, Krishnamurthy Rajesh
Аннотация
BACKGROUND AND OBJECTIVE: This study aims to determine the accuracy of patient specific 3D printed models in capturing pathological anatomical characteristics derived from CT angiography (CTA) in children with anomalous aortic origin of a coronary artery (AAOCA).
METHODS & MATERIALS: Following institutional regulatory approval, a standardized protocol for CTA of AAOCA was utilized for imaging. Blood volume of the aorta and coronaries were segmented from the DICOM images. A total of 10 models from 8 AAOCA patients were created, including 2 post-operative models. Mechanical properties of Agilus30 a flexible photopolymer coated with a thin layer of parylene, polyurethane (PU) and silicone and native aortic tissue from a postmortem specimen were compared. AAOCA models with wall thicknesses of 2mm aorta and 1.5mm coronaries were 3D printed in Agilus30 and coated with PU. CT of the printed models was performed, and 3D virtual models were generated. Transfer of anatomical characteristics and geometric accuracy were compared between the patient model virtual models.
RESULTS: Dynamic modulus of Agilus30 at 2mm thickness was found to be close to native aortic tissue. Structured reporting of anatomical characteristics by imaging experts showed good concordance between patient and model CTA Comparative patient and virtual model measurements showed Pearson's correlation (r) of 0.9959 for aorta (n=70) and 0.9538 for coronaries (n=60) linear, and 0.9949 for aorta (n=30) and 0.9538 for coronaries (n=30) cross-sectional, dimensions. Surface contour map mean difference was 0.08 ± 0.29mm.
CONCLUSIONS: Geometrically accurate AAOCA models preserving morphological characteristics, essential for risk stratification and decision-making, can be 3D printed from a patient's CTA.
METHODS & MATERIALS: Following institutional regulatory approval, a standardized protocol for CTA of AAOCA was utilized for imaging. Blood volume of the aorta and coronaries were segmented from the DICOM images. A total of 10 models from 8 AAOCA patients were created, including 2 post-operative models. Mechanical properties of Agilus30 a flexible photopolymer coated with a thin layer of parylene, polyurethane (PU) and silicone and native aortic tissue from a postmortem specimen were compared. AAOCA models with wall thicknesses of 2mm aorta and 1.5mm coronaries were 3D printed in Agilus30 and coated with PU. CT of the printed models was performed, and 3D virtual models were generated. Transfer of anatomical characteristics and geometric accuracy were compared between the patient model virtual models.
RESULTS: Dynamic modulus of Agilus30 at 2mm thickness was found to be close to native aortic tissue. Structured reporting of anatomical characteristics by imaging experts showed good concordance between patient and model CTA Comparative patient and virtual model measurements showed Pearson's correlation (r) of 0.9959 for aorta (n=70) and 0.9538 for coronaries (n=60) linear, and 0.9949 for aorta (n=30) and 0.9538 for coronaries (n=30) cross-sectional, dimensions. Surface contour map mean difference was 0.08 ± 0.29mm.
CONCLUSIONS: Geometrically accurate AAOCA models preserving morphological characteristics, essential for risk stratification and decision-making, can be 3D printed from a patient's CTA.