Blood Pool Segmentation Results in Superior Virtual Cardiac Models than Myocardial Segmentation for 3D Printing

• Published in: Pediatric cardiology 2016-08, Vol.37 (6), p.1028-1036
• Main Authors: Farooqi, Kanwal M., Lengua, Carlos Gonzalez, Weinberg, Alan D., Nielsen, James C., Sanz, Javier
• Format: Article
• Language: English
• Subjects: 3D printing; Cardiac Surgery; Cardiology; Heart; Humans; Imaging, Three-Dimensional; Magnetic Resonance Angiography; Medicine; Medicine & Public Health; Original Article; Printing, Three-Dimensional; Reproducibility of Results; Vascular Surgery
• ISSN: 0172-0643; 1432-1971
• DOI: 10.1007/s00246-016-1385-8

The method of cardiac magnetic resonance (CMR) three-dimensional (3D) image acquisition and post-processing which should be used to create optimal virtual models for 3D printing has not been studied systematically. Patients ( n  = 19) who had undergone CMR including both 3D balanced steady-state free precession (bSSFP) imaging and contrast-enhanced magnetic resonance angiography (MRA) were retrospectively identified. Post-processing for the creation of virtual 3D models involved using both myocardial (MS) and blood pool (BP) segmentation, resulting in four groups: Group 1—bSSFP/MS, Group 2—bSSFP/BP, Group 3—MRA/MS and Group 4—MRA/BP. The models created were assessed by two raters for overall quality (1—poor; 2—good; 3—excellent) and ability to identify predefined vessels (1–5: superior vena cava, inferior vena cava, main pulmonary artery, ascending aorta and at least one pulmonary vein). A total of 76 virtual models were created from 19 patient CMR datasets. The mean overall quality scores for Raters 1/2 were 1.63 ± 0.50/1.26 ± 0.45 for Group 1, 2.12 ± 0.50/2.26 ± 0.73 for Group 2, 1.74 ± 0.56/1.53 ± 0.61 for Group 3 and 2.26 ± 0.65/2.68 ± 0.48 for Group 4. The numbers of identified vessels for Raters 1/2 were 4.11 ± 1.32/4.05 ± 1.31 for Group 1, 4.90 ± 0.46/4.95 ± 0.23 for Group 2, 4.32 ± 1.00/4.47 ± 0.84 for Group 3 and 4.74 ± 0.56/4.63 ± 0.49 for Group 4. Models created using BP segmentation (Groups 2 and 4) received significantly higher ratings than those created using MS for both overall quality and number of vessels visualized ( p