In situ 4D tomography image analysis framework to follow sintering within 3D-printed glass scaffolds

We propose a novel image analysis framework to automate analysis of X-ray microtomography images of sintering ceramics and glasses, using open-source toolkits and machine learning. Additive manufacturing (AM) of glasses and ceramics usually requires sintering of green bodies. Sintering causes shrinkage, which presents a challenge for controlling the metrology of the final architecture. Therefore, being able to monitor sintering in 3D over time (termed 4D) is important when developing new porous ceramics or glasses. Synchrotron X-ray tomographic imaging allows in situ, real-time capture of the sintering process at both micro and macro scales using a furnace rig, facilitating 4D quantitative analysis of the process. The proposed image analysis framework is capable of tracking and quantifying the densification of glass or ceramic particles within multiple volumes of interest (VOIs) along with structural changes over time using 4D image data. The framework is demonstrated by 4D quantitative analysis of bioactive glass ICIE16 within a 3D-printed scaffold. Here, densification of glass particles within 3 VOIs were tracked and quantified along with diameter change of struts and interstrut pore size over the 3D image series, delivering new insights on the sintering mechanism of ICIE16 bioactive glass particles in both micro and macro scale

Detection and tracking volumes of interest in 3D printed tissue engineering scaffolds using 4D imaging modalities.

Additive manufacturing (AM) platforms allow the production of patient tissue engineering scaffolds with desirable architectures. Although AM platforms offer exceptional control on architecture, post-processing methods such as sintering and freeze-drying often deform the printed scaffold structure. In-situ 4D imaging can be used to analyze changes that occur during post-processing. Visualization and analysis of changes in selected volumes of interests (VOIs) over time are essential to understand the underlining mechanisms of scaffold deformations. Yet, automated detection and tracking of VOIs in the 3D printed scaffold over time using 4D image data is currently an unsolved image processing task. This paper proposes a new image processing technique to segment, detect and track volumes of interest in 3D printed tissue engineering scaffolds. The method is validated using a 4D synchrotron sourced microCT image data captured during the sintering of bioactive glass scaffolds in-situ. The proposed method will contribute to the development of scaffolds with controllable designs and optimum properties for the development of patient-specific scaffolds

In situ 4D tomography image analysis framework to follow sintering within 3D-printed glass scaffolds

We propose a novel image analysis framework to automate analysis of X-ray microtomography images of sintering ceramics and glasses, using open-source toolkits and machine learning. Additive manufacturing (AM) of glasses and ceramics usually requires sintering of green bodies. Sintering causes shrinkage, which presents a challenge for controlling the metrology of the final architecture. Therefore, being able to monitor sintering in 3D over time (termed 4D) is important when developing new porous ceramics or glasses. Synchrotron X-ray tomographic imaging allows in situ, real-time capture of the sintering process at both micro and macro scales using a furnace rig, facilitating 4D quantitative analysis of the process. The proposed image analysis framework is capable of tracking and quantifying the densification of glass or ceramic particles within multiple volumes of interest (VOIs) along with structural changes over time using 4D image data. The framework is demonstrated by 4D quantitative analysis of bioactive glass ICIE16 within a 3D-printed scaffold. Here, densification of glass particles within 3 VOIs were tracked and quantified along with diameter change of struts and interstrut pore size over the 3D image series, delivering new insights on the sintering mechanism of ICIE16 bioactive glass particles in both micro and macro scales

Semantic segmentation of micro-CT images to analyze bone ingrowth into biodegradable scaffolds.

The healing of bone fractures is a complex and well-orchestrated physiological process, but normal healing is compromised when the fracture is large. These large non-union fractures often require a template with surgical intervention for healing. The standard treatment, autografting, has drawbacks such as donor site pain and limited availability. Biodegradable scaffolds developed using biomaterials such as bioactive glass are a potential solution. Investigation of bone ingrowth into biodegradable scaffolds is an important aspect of their development. Micro-CT (μ-CT) imaging is widely used to evaluate and quantify tissue ingrowth into scaffolds in 3D. Existing segmentation techniques have low accuracy in differentiating bone and scaffold, and need improvements to accurately quantify the bone in-growth into the scaffold using μ-CT scans. This study proposes a novel 3-stage pipeline for better outcome. The first stage of the pipeline is based on a convolutional neural network for the segmentation of the scaffold, bone, and pores from μ-CT images to investigate bone ingrowth. A 3D rigid image registration procedure was employed in the next stage to extract the volume of interest (VOI) for the analysis. In the final stage, algorithms were developed to quantitatively analyze bone ingrowth and scaffold degradation. The best model for segmentation produced a dice similarity coefficient score of 90.1, intersection over union score of 83.9, and pixel accuracy of 93.1 for unseen test data