Advanced Image Segmentation and Modeling - A Review of the 2021-2022 Thematic Series

Medical 3D printing is a form of manufacturing that benefits patient care, particularly when the 3D printed part is patient-specific and either enables or facilitates an intervention for a specific condition. Most of the patient-specific medical 3D printing begins with volume based medical images of the patient. Several digital manipulations are typically performed to prescribe a final anatomic representation that is then 3D printed. Among these are image segmentation where a volume of interest such as an organ or a set of tissues is digitally extracted from the volumetric imaging data. Image segmentation requires medical expertise, training, software, and effort. The theme of image segmentation has a broad intersection with medical 3D printing. The purpose of this editorial is to highlight different points of that intersection in a recent thematic series within 3D Printing in Medicine.

A systematic evaluation of medical 3D printing accuracy of multi-pathological anatomical models for surgical planning manufactured in elastic and rigid material using desktop inverted vat photopolymerization

PURPOSE

The dimensional accuracy of three-dimensional (3D) printed anatomical models is essential to correctly understand spatial relationships and enable safe presurgical planning. Most recent accuracy studies focused on 3D printing of a single pathology for surgical planning. This study evaluated the accuracy of medical models across multiple pathologies, using desktop inverted vat photopolymerization (VP) to 3D print anatomic models using both rigid and elastic materials.

METHODS

In the primary study, we 3D printed seven models (six anatomic models and one reference cube) with volumes ranging from ~2 to ~209 cc. The anatomic models spanned multiple pathologies (neurological, cardiovascular, abdominal, musculoskeletal). Two solid measurement landing blocks were strategically created around the pathology to allow high-resolution measurement using a digital micrometer and/or caliper. The physical measurements were compared to the designed dimensions, and further analysis was conducted regarding the observed patterns in accuracy. All of the models were printed in three resins: Elastic, Clear, and Grey Pro in the primary experiments. A full factorial block experimental design was employed and a total of 42 models were 3D printed in 21 print runs. In the secondary study, we 3D printed two of the anatomic models in triplicates selected from the previous six to evaluate the effect of 0.1 mm vs 0.05 mm layer height on the accuracy.

RESULTS

In the primary experiment, all dimensional errors were less than 1 mm. The average dimensional error across the 42 models was 0.238  ±  0.219 mm and the relative error was 1.10  ±  1.13%. Results from the secondary experiments were similar with an average dimensional error of 0.252  ±  0.213 mm and relative error of 1.52%  ±  1.28% across 18 models. There was a statistically significant difference in the relative errors between the Elastic resin and Clear resin groups. We explained this difference by evaluating inverted VP 3D printing peel forces. There was a significant difference between the Solid and Hollow group of models. There was a significant difference between measurement landing blocks oriented Horizontally and Vertically. In the secondary experiments, there was no difference in accuracy between the 0.10 and 0.05 mm layer heights.

CONCLUSIONS

The maximum measured error was less than 1 mm across all models, and the mean error was less than 0.26mm. Therefore, inverted VP 3D printing technology is suitable for medical 3D printing if 1 mm is considered the cutoff for clinical use cases. The 0.1 mm layer height is suitable for 3D printing accurate anatomical models for presurgical planning in a majority of cases. Elastic models, models oriented horizontally, and models that are hollow tend to have relatively higher deviation as seen from experimental results and mathematical model predictions. While clinically insignificant using a 1 mm cutoff, further research is needed to better understand the complex physical interactions in VP 3D printing which influence model accuracy.

Understanding the relationship between slicing and measured fill density in material extrusion 3D printing towards precision porosity constructs for biomedical and pharmaceutical applications

Background

Fill density is a critical parameter affecting the functional performance of 3D printed porous constructs in the biomedical and pharmaceutical domain. Numerous studies have reported the impact of fill density on the mechanical properties, diffusion characteristics and content release rates of constructs. However, due to the way in which slicing toolpath calculations are performed, there is substantial deviation between the measured and slicing fill density for relatively small sized constructs printed at low fill densities (high porosities). The purpose of the current study was to investigate this discrepancy using a combination of mathematical modeling and experimental validation.

Methods

The open source slicer Slic3r was used to 3D print 20 mm × 20 mm × 5 mm constructs at three identified slicing fill density values, 9.58%, 20.36% and 32.33% (exact values entered into software), in triplicates. A mathematical model was proposed to accurately predict fill density, and the measured fill density was compared to both the predicted as well as the slicing fill density. The model was further validated at two additional slicing fill densities of 15% and 40%. The total material within the construct was analyzed from the perspective of material extruded within the beads as well as the bead to bead interconnects using the predictive model.

Results

The slicing fill density deviated substantially from measured fill density at low fill densities with absolute errors larger than 26% in certain instances. The proposed model was able to predict fill density to within 5% of the measured fill density in all cases. The average absolute error between predicted vs. measured fill density was 3.5%, whereas that between slicing vs. measured fill density was 13%. The material extruded in the beads varied from 86.5% to 95.9%, whereas that extruded in the interconnects varied from 13.5% to 4.1%.

Conclusions

The proposed model and approach was able to predict fill density to a reasonable degree of accuracy. Findings from the study could prove useful in applications where controlling construct fill density in relatively small sized constructs is important for achieving targeted levels of functional criteria such as mechanical strength, weight loss and content release rate.

3D Printing for the Clinic: Examining Contemporary Polymeric Biomaterials and Their Clinical Utility

The advent of additive manufacturing offered the potential to revolutionize clinical medicine, particularly with patient-specific implants across a range of tissue types. However, to date, there are very few examples of polymers being used for additive processes in clinical settings. The state of the art with regards to 3D printable polymeric materials being exploited to produce novel clinically relevant implants is discussed here. We focus on the recent advances in the development of implantable, polymeric medical devices and tissue scaffolds without diverging extensively into bioprinting. By introducing the major 3D printing techniques along with current advancements in biomaterials, we hope to provide insight into how these fields may continue to advance while simultaneously reviewing the ongoing work in the field.