Three‐dimensional (3D) printing technology has demonstrated a huge potential for the future of medicine. Since its introduction, it has been used in various areas, for example building anatomical models, personalising medical devices and implants, aiding in precision medical interventions and the latest development, 3D bioprinting. This commentary is provided to outline the current use of 3D printing in medical imaging and its future directions for advancing the healthcare services.

Introduction: An ideal organ‐specific insert phantom should be able to simulate the anatomical features with appropriate appearances in the resultant computed tomography (CT) images. This study investigated a 3D printing technology to develop a novel and cost‐effective cardiac insert phantom derived from volumetric CT image datasets of anthropomorphic chest phantom.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.

Image enhancement techniques for digital mammograms can preserve the contrast to distinguish the diagnostic features such as masses and microcalcifications. However, mammogram images suffer low contrast and high image noise due to low exposure radiation used. As a result, the diagnostic features are difficult to detect and analyse by the radiologist. In this article, we propose a novel Fuzzy Anisotropic Diffusion Histogram Equalisation Contrast Adaptive Limited (FADHECAL) enhancement technique to reduce the noise of the mammogram images while preserving contrast and brightness. The FADHECAL technique also applies Fuzzy Clipped Inference System (FCIS) which automatically select clip-limit during the enhancement process. The mammogram images were retrieved from Mini-Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) from the University of South Florida database. The results have shown that FADHECAL has the most superior results among other selected enhancement techniques with 6.502 ± 1.855 of AMBE, 0.934 ± 0.034 of SSIM, 15.742 ± 1.217 of MAE, 26.843 ± 2.541 of PSNR, 0.969 ± 0.021 of UIQI and 1.151 ± 0.147 of RMSE values. This FADHECAL can be used as an ideal platform of image enhancement for mammogram images to detect the breast cancer lesions with better noise reduction and preserving the image details.

Purpose: The purpose of this study was to investigate the effect of increasing iterative reconstruction (IR) algorithm strength at different tube voltages in coronary computed tomography angiography (CCTA) protocols using a three‐dimensional (3D)‐printed and Catphan^® 500 phantoms.Methods: A 3D‐printed cardiac insert and Catphan 500 phantoms were scanned using CCTA protocols at 120 and 100 kVp tube voltages. All CT acquisitions were reconstructed using filtered back projection (FBP) and Adaptive Statistical Iterative Reconstruction (ASIR) algorithm at 40% and 60% strengths. Image quality characteristics such as image noise, signal–noise ratio (SNR), contrast–noise ratio (CNR), high spatial resolution, and low contrast resolution were analyzed.Results: There was no significant difference (P > 0.05) between 120 and 100 kVp measures for image noise for FBP vs ASIR 60% (16.6 ± 3.8 vs 16.7 ± 4.8), SNR of ASIR 40% vs ASIR 60% (27.3 ± 5.4 vs 26.4 ± 4.8), and CNR of FBP vs ASIR 40% (31.3 ± 3.9 vs 30.1 ± 4.3), respectively. Based on the Modulation Transfer Function (MTF) analysis, there was a minimal change of image quality for each tube voltage but increases when higher strengths of ASIR were used. The best measure of low contrast detectability was observed at ASIR 60% at 120 kVp.Conclusions: Changing the IR strength has yielded different image quality noise characteristics. In this study, the use of 100 kVp and ASIR 60% yielded comparable image quality noise characteristics to the standard CCTA protocols using 120 kVp of ASIR 40%. A combination of 3D‐printed and Catphan^® 500 phantoms could be used to perform CT dose optimization protocols.

Introduction: 3D‐printed imaging phantoms are now increasingly available and used for computed tomography (CT) dose optimisation study and image quality analysis. The aim of this study was to evaluate the integrated 3D‐printed cardiac insert phantom when evaluating iterative reconstruction (IR) algorithm in coronary CT angiography (CCTA) protocols.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 < 0.001). As a result, the SNR and CNR were significantly decreased (P < 0.001). For FBP, the highest noise with poor SNR and CNR was protocol D with 19.0 ± 1.6 HU, 18.9 ± 2.5 and 25.1 ± 3.6, respectively. For IR algorithm, the highest strength (AIDR3D_strong) yielded the lowest noise with excellent SNR and CNR.Conclusions: The use of IR algorithm and increasing its strengths have reduced noise significantly and thus increased the SNR and CNR when compared to FBP. Therefore, this integrated 3D‐printed phantom approach could be used for dose optimisation study and image quality analysis in CCTA protocols.