Quality and Safety of 3D-Printed Medical Models

Tested geometry

Printing technology

Absolute difference; mean ± SD (range) [mm, unless otherwise noted]

Relative difference; mean ± SD (range) [%]

Skull and mandible (El-Katatny et al. 2010)

Professional FDM

0.1 ± 0.1 (0.0–0.2)

0.2 ± 0.2% (0.0–0.6%)

Skull and mandible (Salmi et al. 2013)

SLS, polyamide

0.9 ± 0.4 (max: 1.9)

0.8 ± 0.3% (max: 1.4%)

Binder jet

0.8 ± 0.53 (max: 1.7)

0.7 ± 0.4% (max: 1.6%)

Material jet

0.2 ± 0.1 (max: 0.5)

0.2 ± 0.1% (max: 0.5%)

Geometric models defined in ISO 12836 for dental restoration (Braian et al. 2016)

SLS, polyamide

Dimensions: 0.06 ± 0.06 (0–0.2)

Angles: 0.56 ± 0.47° (0.07°–1.23°)

Dimensions: 0.9 ± 1.2% (0.0–4.1%)

Angles: 3.4 ± 2.73% (0.4–7.2%)

Material jet (equipment A)

Dimensions: 0.02 ± 0.04 (0.0–0.18)

Angles: 0.34 ± 0.24° (0.08°–0.64°)

Dimensions: 0.2 ± 0.1% (0.0–0.4%)

Angles: 2.0 ± 1.4% (0.5–3.7%)

Material jet (equipment B)

Dimensions: 0.04 ± 0.03 (0–0.09)

Angles: 0.53 ± 0.37° (0.23°–1.05°)

Dimensions: 0.5 ± 0.4% (0–1.39%)

Angles: 3.2 ± 2.1% (1.4–6%)

Complex geometric model (Teeter et al. 2015)

SLS, stainless steel

0.01 ± 0.02 (0–0.09)a

1.5 ± 3.2% (0–17.8%)a

Abbreviations: SLS selective laser sintering; FDM fused deposition material

aExcluding features <0.3 mm

Specific technical procedures that implement the basic methodology developed in these phantom-based studies have already been described in the medical literature toward establishing an in-hospital clinical 3D printer QC program (Matsumoto et al. 2015; Leng et al. 2017; Wake et al. 2017). In these procedures, QC phantoms containing features of sizes and shapes relevant for medical 3D printing have been digitally designed with precisely known dimensions in a computer-aided design (CAD) program . These digital QC models can be printed either at regular intervals (for preventive maintenance) or along with every patient model. Physical measurements of the printed QC phantom are then compared with the (design) dimensions of the digital model (Matsumoto et al. 2015; Wake et al. 2017). The first QC phantom proposed for medical 3D printing (Matsumoto et al. 2015; Leng et al. 2017) contained 0.5–2 linear pair resolution bars per mm (Fig. 11.1). “Second-generation” phantoms have been developed to address more complex shapes, including spherical, cylindrical, hexagonal, conical, and spiral features, both extruding and negative-shaped (i.e., holes of the prescribed shape) (Leng et al. 2017). Whenever possible, manual Vernier caliper measurements should be replaced by more precise and more numerous dimensional measurements of the printed phantoms, for example, via the use of 3D laser scanning or CNC coordinate measuring machines (Liacouras 2017).


Fig. 11.1
Example of phantom for implementing 3D printing equipment quality control procedures developed at the Mayo Clinic. Reproduced with Permission from Leng S et al., 3D Printing in Medicine, 2017:in press

Recently, QC phantoms composed of two components that contain mirror features (i.e., positive and corresponding negative) have been proposed (Leng et al. 2017). Such phantoms enable a fit test to be used instead of physical measurements (Leng et al. 2017), simply inserting the positive half of the phantom (with features extruding) into the negative side of the phantom (with the corresponding depressions). A successful fit with no visible gaps would presumably attest to printer accuracy. This approach should not be used without some physical measurements, as phantoms printed with an incorrect scaling factor will still pass a fit test. An alternative we propose is to have one half of the fit test QC phantom manufactured using legacy manufacturing (e.g., injection molding, computer numerically controlled [CNC] milling , or laser cutting) and printing the other half with the 3D printer. A successful fit of these two halves would additionally confirm dimensional accuracy of the printed model.

It is important that QC phantoms for medical 3D printing contain features that extend in all three axes and that they also include overhangs that extend in all three axes, as different printer technologies have different accuracy characteristics for such features (George et al. 2017; Pang et al. 1995; Teeter et al. 2015). Furthermore, QC phantoms should ideally be printed using the same materials as the specific medical application for which quality control is being performed (Wake et al. 2017; Teeter et al. 2015), including color (Wake et al. 2017) as this may be achieved using different material chemistries.

11.2 Mathematical Metrics of Quality Control

Comparing agreement between two models of a tissue is a second approach toward establishing quality and safety of medical 3D printing. The two models can be two STL models, each derived from a different segmentation of a tissue depicted in a single DICOM image data set, for example each segmentation performed by a different radiologist. This scenario is useful for quality assurance (QA). The two STL files can also be the initially designed STL to be printed, and a digitized version of the printed model. This scenario is useful for QC of the individual print. A printed model can be digitized, for example, using 3D laser scanning, or tomographic imaging such as CT, and potentially even MRI (George et al. 2017; Mitsouras et al. 2017). Optical scanners are preferred as they have much higher precision (<0.01 mm) compared to CT and MRI, but they are limited to only assessing the outer surface of a model. Once the two STL models to compare are obtained there are two mathematical procedures that can be used to perform such comparisons.

11.2.1 Model Surface Distances

The first approach is to compare the “distance” between STL models. Conceptually, there is a minimum distance from an arbitrary point located on one STL surface to the other STL surface. This distance can be computed for any number of representative points (typically the nodes of the triangular STL mesh), thereby yielding a distribution of distances that possesses an average and standard deviation that together convey a quantitative assessment of the overall difference between the two models (Fig. 11.2).


Fig. 11.2
Humerus segmented from CT by two different operators; segmentation 1 was fully automated (bone 226 Hounsfield Unit threshold), while segmentation 2 was manually edited. The former model is missing a portion of the humeral head. Comparing the two models using an STL distance metric to quantitatively assess model agreement is not meaningful; the mean distance from model 1 to model 2 is −0.36 ± 0.43 mm (range, −2.72–2.22 mm), while that from model 2 to model 1 is 1.24 ± 2.48 mm (range, −3.28–16.41 mm). The metric can potentially be used to readily determine qualitative agreement vs disagreement using an acceptable cutoff (e.g., <|1.5| mm in this figure)

This approach provides a simple comparison between STL models (George et al. 2017; Leng et al. 2017; Mitsouras et al. 2017) that can be used for QC of individual printed models. Individual printed model QC is necessary since an anatomic model may fail to print in a given printing technology (Fig. 11.3), for example, one that requires appropriate support structures such as SLA or FDM (see Chap. 2). The same model may print successfully using a different technology that fully surrounds the model being printed with support material, such as binder jetting, but forces exerted during cleaning of a model printed with those technologies may then lead to breakage of important anatomic features (Fig. 11.3). Visual inspection of a printed model should always be used as part of standard operating QC procedures to ensure that each finished medical model reflects the intended, segmented anatomy. Visual inspection is nonetheless prone to operator variability. The distance metric between STLs offers an alternative that is less prone to operator error. Specifically, the printed model can be scanned with CT in air, and the resulting images can be segmented to produce an STL model. This STL model can be aligned to the initial design STL that was sent to the 3D printer and the distance between the digitized model and original intended model calculated. Using, for example, a prespecified distance cutoff that is likely to capture missing anatomy (that failed to print) can be used as a QC procedure to detect bulk errors in the printed anatomy (Fig. 11.2).


Fig. 11.3
Glenoid component models printed with bottom-up stereolithography printer (left panel) and bilateral renal artery aneurysms model printed with a binder jet printer (right-hand panel) exemplifying the need for per-model quality control procedures. A portion of the glenoid component failed to print (red arrows) due to large forces exerted during detachment of the model from the vat floor; additional supports (green arrow) enabled more of the component to successfully print but a portion still failed. Small renal artery in the binder jet model broke during removal of the model from the printer. These failures are model specific and likely would not have occurred if the models had been printed with different printer technologies; for example, the glenoid would not have failed in a binder jet system, and the renal artery would not have broken off if printed with stereolithography which uses stronger acrylic-based materials. A QC phantom printed at the same time as either of the models would have likely printed correctly, failing to capture these model-specific failures

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Nov 14, 2017 | Posted by in NEUROSURGERY | Comments Off on Quality and Safety of 3D-Printed Medical Models
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