METHODS: Cardiac insert volumes were segmented from CT image datasets, derived from an anthropomorphic chest phantom of Lungman N-01 (Kyoto Kagaku, Japan). These segmented datasets were converted to a virtual 3D-isosurface of heart-shaped shell, while two other removable inserts were included using computer-aided design (CAD) software program. This newly designed cardiac insert phantom was later printed by using a fused deposition modelling (FDM) process via a Creatbot DM Plus 3D printer. Then, several selected filling materials, such as contrast media, oil, water and jelly, were loaded into designated spaces in the 3D-printed phantom. The 3D-printed cardiac insert phantom was positioned within the anthropomorphic chest phantom and 30 repeated CT acquisitions performed using a multi-detector scanner at 120-kVp tube potential. Attenuation (Hounsfield Unit, HU) values were measured and compared to the image datasets of real-patient and Catphan® 500 phantom.
RESULTS: The output of the 3D-printed cardiac insert phantom was a solid acrylic plastic material, which was strong, light in weight and cost-effective. HU values of the filling materials were comparable to the image datasets of real-patient and Catphan® 500 phantom.
CONCLUSIONS: A novel and cost-effective cardiac insert phantom for anthropomorphic chest phantom was developed using volumetric CT image datasets with a 3D printer. Hence, this suggested the printing methodology could be applied to generate other phantoms for CT imaging studies.
METHODS: The 3D-printed cardiac insert phantom was positioned into a chest phantom and scanned with a 16-slice CT scanner. Acquisitions were performed with CCTA protocols using 120 kVp at four different tube currents, 300, 200, 100 and 50 mA (protocols A, B, C and D, respectively). The image data sets were reconstructed with a filtered back projection (FBP) and three different IR algorithm strengths. The image quality metrics of image noise, signal-noise ratio (SNR) and contrast-noise ratio (CNR) were calculated for each protocol.
RESULTS: Decrease in dose levels has significantly increased the image noise, compared to FBP of protocol A (P
Methods: A patient-specific 3D-printed breast model was generated using 3D-printing techniques for the construction of the hollow skin and fibroglandular region shells. Then, the T1 relaxation times of the five selected materials (agarose gel, silicone rubber with/without fish oil, silicone oil, and peanut oil) were measured on a 3T MRI system to determine the appropriate ones to represent the MR imaging characteristics of fibroglandular and adipose tissues. Results were then compared to the reference values of T1 relaxation times of the corresponding tissues: 1,324.42±167.63 and 449.27±26.09 ms, respectively. Finally, the materials that matched the T1 relaxation times of the respective tissues were used to fill the 3D-printed hollow breast shells.
Results: The silicone and peanut oils were found to closely resemble the T1 relaxation times and imaging characteristics of these two tissues, which are 1,515.8±105.5 and 405.4±15.1 ms, respectively. The agarose gel with different concentrations, ranging from 0.5 to 2.5 wt%, was found to have the longest T1 relaxation times.
Conclusions: A patient-specific 3D-printed breast phantom was successfully designed and constructed using silicone and peanut oils to simulate the MR imaging characteristics of fibroglandular and adipose tissues. The phantom can be used to investigate different MR breast imaging protocols for the quantitative assessment of breast density.
OBJECTIVES: The aim of the study was to evaluate the deviation of implant placement performed with a surgical guide fabricated by means of the rapid prototyping technique (the PolyJet™ technology).
MATERIAL AND METHODS: Twenty sheep mandibles were used in the study. Pre-surgical cone-beam computed tomography (CBCT) scans were acquired for the mandibles by using the Kodak 9000 3D cone-beam system. Two implants with dimensions of 4 mm in diameter and 10 mm in length were virtually planned on the 3D models of each mandible by using the Mimics software, v. 16.0. Twenty surgical guides were designed and printed using the PolyJet technology. A total of 40 implants were placed using the surgical guides, 1 on each side of the mandible (2 implants per mandible). The post-surgical CBCT scans of the mandibles were performed and superimposed on the pre-surgical CBCT scans. The amount of deviation between the virtually planned placement and the actual implant placement was measured, and a descriptive analysis was done.
RESULTS: The results showed that the mean deviation at the implant coronal position was 1.82 ±0.74 mm, the mean deviation at the implant apex was 1.54 ±0.88 mm, the mean depth deviation was 0.44 ±0.32 mm, and the mean angular deviation was 3.01 ±1.98°.
CONCLUSIONS: The deviation of dental implant placement performed with a 3D-printed surgical guide (the PolyJet technology) is within the acceptable 2-millimeter limit reported in the literature.