Abstract
The knee menisci are essential for maintaining joint stability and load distribution, with circumferential collagen fibres playing a critical biomechanical role. Degenerative or traumatic injuries to the meniscus can require implants to restore function. This study aimed to develop a 3D-printable meniscus implant that could be virtually tested prior to production. A novel staining and preparation protocol using Lugol’s solution and freeze-drying was applied to six intact human menisci, enabling high-resolution micro-CT imaging. Quantitative analysis revealed that approximately 48% of the meniscal volume consists of circumferential fibres. Based on this, a two-volumetric printable stereolithography (STL) model was created, with an inner volume representing 48% of the total structure. A custom Python script was developed to translate the G-code from this model into a two-volumetric finite element (FE) model -overcoming the limitations of conventional software. The two-volumetric implant was then evaluated in a virtual knee joint using thermoplastic polyurethane materials with elastic moduli of 54 MPa and 205 MPa. The results confirmed the feasibility of simulating and optimizing patient-specific meniscal implants prior to fabrication.
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Introduction
The knee menisci play a pivotal role in maintaining the stability and function of a healthy knee joint1. Composed primarily of water (65–70%), type I and II collagen (20–25%), and proteoglycans (1–2%), the meniscal fibrocartilage exhibits a distinct collagen fibre arrangement. This structure comprises a superficial layer characterized by a meshwork of thin fibrils, a lamellar layer composed of collagen fibres, and a deep layer featuring circumferentially oriented collagen fibres2,3,4 (Fig. 1A). Specifically, the central layer comprising circumferential collagen fibres stands out as the primary biomechanical component of the meniscus5,6,7. It is known that these fibres extend from 210 μm on the outer side to 20 μm on the inner side, beneath the meniscus surface7,8,9,10. Its arrangement enables the meniscus to convert downward compressive forces into encircling hoop stresses, facilitating shock absorption and load distribution within the knee joint7,8,11,12,13,14. Consequently, the functionality of the meniscus is primarily compromised when the circumferential collagen fibres are severed due to traumatic meniscal tear or degenerative meniscal injury5,6.
As of current medical understanding, the prevailing consensus emphasizes the preservation of the meniscus rather than its resection4,15,16,17,18. Despite the acknowledgment of the imperative to preserve the meniscus, conventional treatment modalities face inherent limitations. Surgical techniques and allograft transplantation, which were initially considered primary interventions3,15,16,19,20frequently confront challenges in clinical settings, compelling clinicians to opt for partial or complete meniscal removal21,22,23. Recognizing this clinical dilemma, a pressing need arises for innovative and effective repair or replacement methods.
Commercially available meniscus implants, designed to treat meniscal injuries, currently lack the capability to faithfully replicate the intricate three-layered architecture found in the human meniscus24. This deficiency compromises both the effectiveness and durability of these implants in clinical applications25.
To overcome this limitation and replicate the native collagen fibre orientation of menisci, 3D-printing technologies have emerged as promising solutions. These technologies offer the potential to precisely mimic the complex structural characteristics of the meniscus, thereby enhancing the functionality and longevity of engineered replacements18,23. However, while it is known that circumferential fibres are a key structural element, the exact percentage of these fibres in the total meniscal volume remains unclear. To create a printable meniscus model that accurately mimics the central core of circumferential collagen fibres, it is crucial to quantify their percentage in human menisci. We hypothesize that this can be achieved through microcomputed tomography (µCT). µCT has been instrumental in advancing our understanding of regenerative processes in mineralized tissues, such as bone26,27. However, its application in evaluating soft tissues has been limited because non-mineralized tissues exhibit low X-ray attenuation. Lugol’s solution, a non-toxic and cost-effective iodine-based dye, serves as a simple yet effective medium for improving soft tissue contrast in µCT imaging28,29. Lugol’s solution works by binding to glycogen and other polysaccharides present in soft tissues, which increases the X-ray attenuation and provides the necessary contrast for detailed imaging30,31. This method has been adapted for a variety of biological specimens and tissues, including the vascular system and peripheral nerve tissues, allowing for precise 3D reconstructions without the need for destructive histological sectioning. Several studies have demonstrated the effectiveness of Lugol’s solution for enhancing the visibility of soft tissues in µCT imaging32,33. In the context of cartilage and menisci, staining with Lugol’s solution has been less common compared to its use in bone and vascular studies.
Moving forward, the first objective of our study was to leverage this method to quantitatively assess the percentage of circumferential collagen fibres in human menisci. This data enabled the derivation of a 3D-printable model encoded in a stereolithography (STL) file34,35 for meniscus implants. Specifically, the printable model encompassed two distinct volumes: a core that accurately represented the proportion of circumferential collagen fibres in a human meniscus using a circumferential infill pattern, and an outer shell that replicated the patient’s meniscal geometry.
In the context of 3D printing and implant production, biomechanical testing of implants is crucial. Traditionally, this testing has been conducted through in-vitro and in-vivo methods36. In recent years though, a novel approach that has gained traction is in-silico testing via Finite Element Analysis (FEA)37. FEA is a computational technique used extensively in engineering and industrial design to simulate how a product reacts to real-world forces, vibration, heat, fluid flow, and other physical effects. Despite its widespread use in these fields, FEA has been relatively underutilized in medical applications38.
When evaluating 3D printed meniscus implants within a virtual knee joint, a significant challenge arises: STL files, which are essential for 3D printing, only provide surface geometry data39. In contrast, G-code, a machine code detailing 3D-printer movement (e.g. print speed, nozzle size, infill patterns,… ) and material description, contains the comprehensive volumetric information required for creating 3D models used in FEA simulations40. However, to the best of our knowledge, no existing or publicly available software can convert G-code directly into a volumetric FE model41. To address the challenge of in-silico testing due to the incompatibility of STL files with FE models, our second objective was to develop a novel approach for converting 3D printable files into FE models for simulations. We aimed at leveraging G-code, which contains the necessary volumetric data to create accurate FE models for virtual analysis. This approach allowed for the screening of biomaterial formulations for biomedical implants prior to physical testing, enabling an early assessment of their biomechanical feasibility. To validate our modelling approach, a comparative analyses of an intact knee joint simulation, particularly in terms of contact pressure, stress distribution, and meniscal displacement was conducted (Fig. 1).
Introduction and project outline. (A) Medial meniscus, posterior horn, cross-section: The collagen fibres are oriented in a three-layered structure: ① a superficial grid layer, a lamellar layer just below with layers of collagen fibrils, and ② a central core primarily consisting of circular-oriented bundles of collagen fibrils with ③ occasional radial-tie fibres. (B) Meniscal damage affects the knee equilibrium, progressively contributing to cartilage disruption up to the development of osteoarthritis (OA). (C) Workflow overview.
Results
Lugol’s staining combined with freeze-drying provided clear visualization of collagen fibres in meniscal tissue while preserving its structural integrity
Our study evaluated a staining protocol for visualizing collagen fibres in the posterior horn of a degenerative medial meniscus from a total knee joint replacement patient (Fig. 2A). Initially, the native meniscal tissue was examined using µCT imaging at isotropic resolutions of 31 μm and 20.7 μm. At both resolutions, the intrinsic structure of the meniscus was not discernible. Staining the tissue with Lugol’s solution, which was intended to enhance contrast, did not successfully highlight the collagen fibres. This was likely due to the high water content of the meniscal tissue, which resulted in general staining of soft tissue rather than clear visualization of collagen structures. However, applying Lugol’s staining followed by freeze-drying effectively revealed the collagen fibres. This two-step approach reduced the tissue’s water content and concentrated the staining agents, thereby enhancing the contrast between the collagen fibres and the surrounding tissue. Further volumetric analysis via the Segment Statistics42 module in 3D Slicer Version 5.2.243 confirmed that the tissue volume after staining had increased by 4% (1574.44 mm3 in unstained versus 1640.98 mm3 in stained tissue).
To confirm that the freeze-drying process did not alter the structural integrity of the meniscus tissue, scanning electron microscopy (SEM) was performed. Two intact, unstained, freeze-dried human menisci - medial and lateral from the right knee of a body donor - were examined (Fig. 2B). The scans revealed the intact meniscal surface at a magnification of 70x. Beneath the intact surface, measuring 20 μm on the inner and up to 210 μm on the outer side, the deep circumferential layer was clearly discernible. At a high magnification of 1.00k, even radial tie-fibres were observed, further supporting the preservation of structural integrity.
Enhancing meniscus tissue visualization: effects of Lugol staining and freeze-drying on µCT imaging and SEM. (A) Effects of Lugol staining and freeze-drying on human meniscus. (1) Posterior horn of a degenerative medial meniscus. (2) µCT scan of native meniscus tissue – intrinsic structures are not visible. (3) µCT scan of meniscus tissue stained with Lugol solution – contrast enhancement observed, but collagen fibres are not visible. (4) µCT scan of meniscus tissue after staining with Lugol solution and freeze-drying – visualization of collagen fibres achieved. (B) SEM images depicting a cross-sectional view of intact freeze-dried human meniscus at the pars intermedia. (1) Intact meniscal surface. (2–5) Discernible superficial (red arrow) and lamellar layers (green arrow) with thickness discrepancies between the outer and inner sides. (5/a-b) Higher magnification reveals finer details, including intact radial tie-fibres (blue arrow).
Segmentation and volume proportion assessment of circumferential fibres in human menisci
To assess the volume proportion of circumferential collagen fibres in total menisci, high-resolution µCT scans of six Lugol-stained and freeze-dried intact human menisci were obtained. The scans, captured at an isotropic resolution of 10 μm and 20.7 μm, respectively, provided detailed insights into the meniscal structure, highlighting its integrity and intricate collagen fibre organization. The µCT scans demonstrated the preservation of the meniscal surface, showcasing its smooth and continuous nature. The µCT images revealed the meniscus’s three-layered architecture, with each layer exhibiting a unique collagen fibre arrangement (Fig. 3A). The sagittal sections of the scans allowed for clear identification of the surface layer, lamellar layer and circumferential fibres.
To assess the volume proportion of circumferential fibres within the entire human meniscus, image segmentation was conducted using the Segment Editor42 module in 3D Slicer. This process resulted in the creation of two distinct segments (Fig. 3B/1–2).
The first segment encompassed the entire meniscus, achieved through manual global grayscale thresholding to differentiate the tissue from the background. The second segment specifically targeted the region presumed to contain circumferential fibres. Based on published literature, deep circumferential fibres extend from 210 μm at the outer part to 20 μm at the inner part, beneath the meniscus surface7,8,9,10. Using this information, a region of interest (ROI) was manually delineated with the Drawing Tool to ensure consistency across samples. This ROI was defined in every 10th image slice and subsequently interpolated to generate a volumetric representation of the segmented areas, enabling a more precise structural analysis of the meniscus.
The culmination of the image segmentation and ROI definition process resulted in a virtual 3D volumetric representation of the circumferential fibres within the meniscus (Fig. 3B/2). This representation provided a detailed visualization of the structural characteristics of the meniscus, facilitating further analysis and interpretation.
Employing the Segment Statistics module in 3D Slicer, volumes and cross-sectional areas were determined. Comparison of absolute circumferential and total volumes of medial and lateral menisci revealed no significant differences (F (1, 8) = 0.8493; p = 0.3837) (Fig. 3C/1). As shown in Fig. 3C/2, the mean volume proportion of circumferential fibres in medial menisci was 46.09% (± 1.32% SD), while circumferential fibres occupied 50.23% (± 0.31 SD) of lateral menisci volume, which was significantly different (t(4) = 5.267; p = 0.0062). On average, the overall proportion of circumferential fibres constituted 48.16% (± 2.43 SD) in both medial and lateral menisci across all six specimens.
As depicted in Fig. 3C/3–4, the average cross-sectional area of estimated circumferential fibres in medial menisci was 44.41% (± 5.03% SD), while in lateral menisci, it was 36.63% (± 10.56% SD). On average, the mean cross-sectional area of estimated circumferential fibres constituted 40.52% (± 8.54 SD) in both medial and lateral menisci across all six specimens.
µCT-scan of stained and freeze-dried human meniscus and results of image segmentation. (A) The scanned meniscus, with a resolution of 10 μm, displays various structural layers that are distinguishable: the surface layer (red arrow), which remained intact, the lamellar layer (green arrow) and bundles of circumferential fibres (yellow arrow). (B) Segmentation, (1) encompassing the total meniscus, (2), the expected volume of circumferential collagen fibres (pink volume) within the total meniscus. (C/1–2) Volumetric segmentation analysis. (C/3–4) Cross-sectional area analysis. Data from three biological replicates (three medial & three lateral menisci) ± SD are given. ** p < 0.01.
3D printing of prototype
Based on the acquired data indicating that 48% of the total meniscus volume was occupied by circumferential collagen fibres, a representative prototype of a meniscus implant was created to demonstrate printability. The design process began by importing the stereolithography (STL) data of the medial meniscus from patient 9, a healthy 34-year-old male from the OpenKnee(s) project44 into Ultimaker Cura 5.4.0 (Fig. 4A–B). This 3D slicing software is commonly used for preparing models for 3D printing by generating printable G-code files. The geometrical data from the MRI scans enabled accurate replication of the outer shape and size of the medial meniscus for our implant design. Within Ultimaker Cura, the internal structure of the implant was manually designed, focusing on creating a core volume with a circumferential infill pattern to occupy 48% of the total implant volume. This approach replicated the natural proportion of circumferential collagen fibres observed in the native meniscus. The circumferential infill pattern was selected to simulate the orientation of collagen fibres in the meniscus, ensuring that the 3D-printed implant mirrored the natural structural characteristics of the meniscus. The final 3D model consisted of an outer shell that replicated the patient-specific meniscal geometry and a core volume, making up 48% of the total meniscal volume, designed with a circumferential infill pattern.
For the 3D printing process, a Prusa MK3S equipped with a 400 μm nozzle was used, and SUNLU polylactide (PLA) filament (1.75 mm) served as the printing material. The following print settings were based on the MK3S template within Ultimaker Cura: a layer height of 0.2 mm, a line width of 0.4 mm. This prototype was created solely to demonstrate printability, with the material choice not yet optimized for the actual production of a meniscus prosthesis (Fig. 4C).
Conversion of 3D-printable meniscus model into FE model
To facilitate in-silico biomechanical analysis of our 3D printable meniscus implant, which comprised two distinct volumes, a Python script was developed to convert these volumes into a single finite element (FE) model for processing in Abaqus 2020 Version 6.2045 via specific G-code (Fig. 4D). This approach addressed the limitations of available software, which lack the capability to convert G-codes from a 3D slicer into FE models. Abaqus is a suite of software applications for finite element analysis (FEA) and computer-aided engineering (CAE) used extensively in industries such as aerospace, automotive, civil engineering, and biomedical engineering for designing and testing products, optimizing performance, and reducing the need for physical prototypes.
Our Python script operated by reading a G-Code file generated with the Ultimaker Cura 5.4.0 slicer software as input, extracting all necessary information for volumetric model creation, and organizing coordinates into registers, ordered by layers. As the G-code represents the 3D printable file in full detail, this method allowed us to digitally analyse the mechanical properties of the 3D model and simulate the use of different materials to determine the mechanical strength of the final 3D model. Within Abaqus, wire poly-lines were established and loft operations were executed, interconnecting consecutive wire poly-lines to forge a coherent three-dimensional model. The execution time for this function ranged between 50 and 60 min. To manage system memory efficiently, a new part in Abaqus was initiated every 20 layers, mitigating the substantial burden on memory. The gradual sluggishness in generating new geometries was addressed through random-access memory (RAM) release achieved exclusively through part creation, followed by the consolidation of individual parts into a unified entity through merging. The detailed geometry information generated from G-code was successfully meshed for use in Abaqus 2020 (Fig. 4D and E). A tetrahedral mesh with quadratic shape functions was employed, and scripting enabled the assignment of a volume mesh to sub-model regions based on the applied material from the G-code. The connection between these distinct volumes was achieved through a tie (glue) contact formulation. This approach facilitated the accurate representation of the implant’s two-volumetric structure, essential for subsequent comprehensive computational modelling and analysis.
Process of 3D slicing, 3D printing and finite element (FE) modelling. (A) The meniscus STL file utilized in this procedure originated from the OpenKnee(s) project44. (B) 3D slicing was conducted using Ultimaker Cura 5.4.0 software; red lines: shell; green lines: inner walls; yellow lines: circumferential infill pattern, which constitutes 48% of the total volume. (C) 3D-printed model, fabricated from PLA; green area: circumferential infill pattern; (*): 3D printing support structure. (D) The two-volumetric FE model was created in Abaqus 2020 via Python scripting and was implemented in (E) a virtual knee joint for subsequent FEA.
Biomechanical in-silico analysis of 3D printed meniscus implant
First, a FEA was conducted on the healthy knee joint from patient 9 of the OpenKnee(s) project44 to simulate short-term loading at full knee extension. The simulation demonstrated that the results for the normal human knee joint closely matched existing literature46,47,48,49,50,51,52,53,54. Following this, the medial meniscus of the same knee joint was replaced with our 3D-printed meniscus implant. The FEA was then conducted again to evaluate the mechanical performance of the implanted meniscus (Fig. 5). In this analysis, the two-volumetric implant virtually comprised two different thermoplastic polyurethanes (TPU): the inner core consisted of a TPU with an E-modulus of 54 MPa, and the outer shell was made of a stiffer TPU with an E-modulus of 205 MPa. When comparing the knee joint with the 3D-printed two-volumetric medial meniscus implant to the native human meniscus, we observed that the peak cartilage contact pressures on the femur and tibia were comparable to those of the native meniscus. The von Mises stress and Tresca shear stress on the femoral and tibial cartilage for the medial meniscus implant were lower compared to those observed in the native meniscus. Notably, the peak compression stress (CPRESS) was higher in the implant compared to the native medial meniscus, while the von Mises stress was also higher in the implant but lower in the lateral meniscus compared to the native menisci. Despite these differences, the displacement of the meniscus/implant was found to be similar between the two conditions. Numerical values of FEA results are given in Tables 1 and 2.
Distribution of peak compression [MPa] and shear stress [MPa] in knee joint models. Peak compression (CPRESS) [MPa] and shear stress (Tresca stress) [MPa] results are shown for an intact knee joint and a knee joint with a medial meniscus implant. The colour gradient from red to deep blue represents varying levels of stress, from high (red) to low (blue). The panels illustrate stress distributions in: (A) femoral cartilage, (B) menisci, (C) medial tibial cartilage, and (D) lateral tibial cartilage. The finite element illustrations were generated from our own analysis using Abaqus.
Discussion
Meniscal injuries, whether traumatic or degenerative, disrupt the critical circumferential collagen fibre network responsible for load absorption and distribution in the knee joint, ultimately contributing to joint degeneration and early onset osteoarthritis (OA)7,55. While arthroscopic partial meniscectomy (APM) has traditionally been a common treatment, clinical guidelines increasingly recommend meniscus preservation as the first-line intervention due to poorer long-term outcomes associated with APM15,16. In cases where preservation is not feasible, meniscus implants have emerged as alternatives to meniscus allograft transplantation (MAT)56. Currently available implants fall into two main categories: collagen-based scaffolds, such as Menaflex CMI®, and synthetic polymer-based scaffolds like Actifit® and NUsurface®18. These implants are designed for partial or total meniscus replacement and are intended to promote fibrocartilage ingrowth and restore load-bearing function. However, they possess notable limitations. Menaflex CMI® and Actifit® are designed for partial defects and rely on host cell colonization without pre-seeding, while NUsurface® is a free-floating, discoid-shaped prosthesis made from polycarbonate-urethane (PCU) reinforced with UHMWPE fibres to replicate pressure distribution within the medial compartment57,58,59. Despite these design considerations, clinical failure rates remain significant - up to 12% for Menaflex CMI®, 32% for Actifit®, and 17% for NUsurface® - with complications including mechanical failure, infection, and reoperation17,60,61. A critical shortcoming of these implants is their inability to replicate the anisotropic fibre architecture of the native meniscus, as most are casted and lack spatially oriented fibre structures essential for physiological load transfer18. There is growing consensus that an effective implant must closely replicate the biomechanical properties and structural complexity of native meniscal tissue to achieve long-term functionality and integration62,63. In light of the limitations associated with current meniscus implants - particularly their inability to replicate the native collagen fibre architecture - our study aimed to develop a 3D-printable meniscus implant that accurately reflected the volume proportion and orientation of circumferential fibres found in native meniscal tissue. Rather than relying solely on physical prototyping, we implemented a virtual testing approach by simulating implant performance within an in-silico knee joint model prior to fabrication.
In this study, our first objective was to leverage soft-tissue staining and µCT imaging to quantitatively assess the percentage of circumferential collagen fibres in human menisci, thereby enabling the generation of a 3D-printable STL-file for meniscus implants. This was achieved by staining six non-degenerative and non-traumatically altered human menisci with Lugol’s solution, followed by freeze-drying and µCT imaging. Compared to traditional imaging techniques such as episcopic microscopy, µCT imaging offered significant advantages, primarily due to its non-invasive nature32. Unlike traditional methods that require physical sectioning of tissue samples, µCT imaging enabled high-resolution visualization of the entire meniscus structure while preserving its integrity. Additionally, the use of Lugol’s solution as a contrast agent presented further benefits, as it was both cost-effective and non-toxic, unlike commonly used alternatives such as osmium or gold-based staining agents64,65,66,67. A common concern with staining soft tissues was the potential for osmotic processes to cause volumetric changes due to immersion in a non-physiological solution68. However, this was likely not a significant issue for meniscal tissues. Existing literature demonstrated that tendons, which have a composition and structural arrangement similar to menisci10did not experience significant volume changes when stained with Lugol’s solution32. Our volumetric analysis using the Segment Statistics module in 3D Slicer confirmed this, showing that the tissue volume of a posterior horn of a degenerative meniscus increased by 4% after Lugol staining (from 1574.44 mm3 to 1640.98 mm3). This minimal volume change indicated that the staining process did not significantly affect tissue volume and preserved the overall structure. Another potential source of artifacts was the freeze-drying process, which could lead to tissue shrinkage and structural deformation due to dehydration. However, research involving freeze-dried porcine meniscus samples has demonstrated that this method preserved the native architecture effectively, as evidenced by high-resolution micro-CT imaging69. In our study, SEM analyses revealed well-maintained collagen fibre organization and surface morphology, indicating that the freeze-drying process did not compromise the structural fidelity of the meniscal tissue. In summary, both Lugol staining and freeze-drying introduced minimal volumetric and structural changes to meniscal tissue. These findings supported the reliability of our dual-step protocol in preserving the native microarchitecture of the meniscus, thereby ensuring the accuracy of subsequent micro-CT and SEM imaging analyses..
In our study, image segmentation was conducted to identify regions occupied by circumferential fibres in individual digital cross-sections based on published layer thicknesses7,8,9,10 and manually drawing regions of interest (ROIs) using 3D Slicer software. A stack of 2D images was divided into two distinct segments using the Segment Editor module in 3D Slicer: the first segment encompassed the entire meniscus, achieved through thresholding. Due to the complexity of biological tissue at the micron level, manual global grayscale thresholding upon visual inspection was applied as attempts to develop an auto-thresholding approach did not yield satisfying results. This approach allowed for precise adjustments, ensuring that the entire meniscus was accurately encompassed and accommodating the intricate variations in the imaging data. The second segment targeted the region presumed to contain circumferential fibres. Although the second segment was selected based on published literature7,8,9,10a manual approach was necessary rather than an automated selection based on gray value differences as no automated procedures currently exist for this specific task to our knowledge. Therefore, performing statistics that would quantify the accuracy of the selection or computation of a p-value could not be performed.
The respective study confirmed the published layer thickness in two unstained freeze-dried non-degenerative and non-traumatically altered human menisci via SEM. A relevant consideration was the rationale for using published data instead of determining layer thicknesses directly from the individual digital slices obtained through µCT. One advantage of µCT is damage-free visualisation of anatomical structures, which was performed at up to 10-micron resolution in this study. However, SEM has a superior resolution down to a few nanometres compared to µCT. Therefore, the published layer thicknesses confirmed via SEM were used in the segmentation process to define ROIs. The ROIs were drawn manually, however, an automated procedure would be favourable to ensure repeatability, avoid inter-operator variability and reduce the time required to establish a print model.
A prerequisite for automated image processing is high quality of µCT scan data which was ensured via the developed staining procedure. Based on this, an image processing algorithm could be developed that takes into consideration different granularity of visualized structures to define ROIs in individual slices. Alternatively, the algorithm could also borrow principles from roughness analysis to characterise textures implemented by different spatial organisation of collagen fibres70,71,72,73. Nevertheless, implementation of these analysis approaches exceeded the scope of this study.
Subsequently the acquired data enabled generation of a printable STL-file, comprising two distinct volumes: a core that represented the proportion of circumferential collagen fibres using a circumferential infill pattern, and an outer shell that replicated the patient’s meniscal geometry. This approach not only allowed for anatomically accurate modelling of meniscal structures but also offered significant clinical potential through patient-specific customization. By leveraging medical imaging data (e.g., MRI or CT), the design could be tailored to individual anatomy, pathology, and biomechanics, enhancing the fit and function of the implant within the joint space74.
Moreover, the same design and fabrication principles could be extended beyond meniscal reconstruction to other orthopaedic applications, such as cartilage and ligament repair. Articular cartilage structures, which vary greatly in shape and thickness across individuals and joint compartments, could benefit from similar personalized scaffold generation75. Likewise, ligamentous tissues - such as the anterior cruciate ligament (ACL) - which exhibit highly organized collagen fibre architecture, could be modelled with fibre-reinforced core structures that mimic native alignment, stiffness, and load-bearing behaviour76. Therefore, this approach has the potential to serve as a versatile platform for developing patient-specific, mechanically optimized implants for a range of musculoskeletal soft tissue repairs.
While this approach facilitated the fabrication of a biomimetic meniscus implant, a significant limitation arose from the resolution constraints of the 3D printing process. The use of a 400 μm nozzle did not allow for the accurate replication of the fine collagen fibre architecture found in native meniscal tissue, where fibre diameters typically range from 3 to 10 μm14. This disparity limited the structural fidelity of the printed fibres and may have reduced the implant’s ability to mimic the anisotropic mechanical properties of the native meniscus, particularly in terms of tensile strength and load distribution77,78. Reducing the nozzle size to 200 μm or smaller could significantly enhance print resolution, enabling the fabrication of finer fibre structures that better replicate the native collagen network. Improved resolution would contribute to greater structural precision, potentially enhancing the mechanical integrity and physiological function of the implant under joint loading conditions79.
In addition, while the circumferential infill pattern used in the current design reflected the predominant fibre orientation of the native meniscus, it did not fully recapitulate its complex internal architecture. In native tissue, circumferential fibres are interspersed with radial tie-fibres, which play a critical role in resisting hoop stresses and preventing tissue extrusion8. The current STL model employed a uniformly distributed infill and lacked spatial variation or radial reinforcement. This simplification may have compromised the implant’s ability to replicate the native biomechanical response, particularly under complex multi-axial loading47,80. However, replicating the hierarchical and regionally specific fibre architecture is crucial for functional meniscus repair or replacement81. Future work will aim to address these limitations by integrating advanced print path strategies and multi-material approaches to more accurately mimic the zonal organization and mechanical behaviour of the native meniscus.
To further evaluate the implants biomechanical characteristics, preliminary finite element analyses (FEAs) of the implant in a virtual knee joint were conducted. One significant challenge in evaluating 3D-printed meniscus implants within a virtual knee joint is that STL files provide only surface geometry data, whereas G-code contains comprehensive volumetric information necessary for FEA simulations. Neither commercially available software nor conventional tools have the functionality to convert G-code from a slicer into a computable volume.
Unlike traditional methods that create an STL file from µCT images for subsequent FEA, this method converted a G-code generated by a slicer for 3D printing into an FE model by a uniquely developed Python script. This was necessary because our 3D printed artificial meniscus implant consisted of two different volumes with distinct properties, which needed to be accurately represented in a simulation model. This G-code approach was novel and allowed for precise implementation of the two-volumetric 3D-printed meniscus in a virtual knee joint. This approach offered distinct advantages: efficiency and accuracy for customization, flexibility and user-friendliness, and potential for refinement through adjustable layer height and post-processing techniques. Additionally, a “fastmode” feature significantly accelerated script testing and debugging, promoting iterative development. While acceptable accuracy was achieved, comparisons with studies like Rodriguez-Vila et al. (2017)82 indicated potential for further improvement through advanced meshing and defeaturing techniques83,84.
Our study introduced a two-volumetric finite element model of a 3D-printable meniscus scaffold. This digital model allowed for the simulation of various material compositions - synthetic, biological, or hybrid - within an in-silico knee joint before physical fabrication, offering a valuable tool for preclinical optimization. While TPU was selected for initial testing due to its favourable printability, cost-effectiveness, and adjustable mechanical properties (with tested formulations demonstrating elastic moduli of 54 MPa and 205 MPa to approximate the native meniscus’s compressive and tensile stiffness), ongoing research in tissue engineering highlights a broad range of alternative materials with potential to further improve biomechanical performance18 .
Synthetic polymers, such as polycaprolactone (PCL), polylactic acid (PLA), polycarbonate urethane (PCU), and silicone elastomers, are widely utilized in 3D printing due to their structural stability, tunable degradation rates, and ease of manufacturing85,86,87,88. However, these materials are not without limitations; they may trigger adverse immune responses, release acidic degradation products, and lack the biological cues necessary for effective tissue regeneration89. In contrast, natural hydrogels - such as collagen, gelatine, alginate, hyaluronic acid (HA), silk fibroin, and decellularized extracellular matrices (dECMs) - offer superior biocompatibility, bioactivity, and cell-binding capacity, making them attractive for promoting meniscal cell attachment, proliferation, and differentiation90,91,92,93. Despite these advantages, their mechanical weakness, low shape fidelity, and rapid degradation remain significant barriers to load-bearing applications such as meniscus reconstruction.
To reconcile these trade-offs, hybrid material strategies that combine synthetic polymers with bioactive hydrogels are increasingly explored. These composites aim to leverage the mechanical strength and printability of synthetic materials with the biological advantages of natural components, enhancing both functional performance and tissue integration94. Moreover, recent studies have shown that incorporating biological cues - such as growth factors or stem cell-laden hydrogels - can guide lineage-specific differentiation and matrix deposition, further improving regenerative outcomes91,95.
Biocompatibility is a critical consideration in the design of any implantable scaffold. While TPU has shown promising results in various biomedical applications, its long-term performance in the intra-articular environment of the knee joint requires further investigation, particularly regarding wear particle generation, local immune responses, and integration with host tissue96,97.
In our study, we conducted two preliminary FEA simulations to assess the feasibility of our approach and evaluate the biomechanical performance of the implant on femoral and tibial cartilage. The first simulation applied an axial compressive load to a healthy knee joint, while the second simulation involved replacing the medial meniscus in the same knee joint with our two-volumetric implant. The initial simulation assessed the model’s validity by comparing its outcomes with previously published data46,47,48,49,50,51,52,53,54. Specifically, the peak contact pressures and meniscal displacements obtained in this study were evaluated against established values in the scientific literature. Earlier studies examining knee joint biomechanics under compressive loads between 1000 and 2700 N, where the femur was subjected to loading while the tibia remained fixed and muscle forces were not considered, have reported peak contact pressures ranging from 2.75 to 9.3 MPa. In the current simulation, the maximum tibial contact pressure measured was 7.66 MPa, aligning well with this documented range (see Table 3).
Previous studies examining meniscus extrusion in static, upright, non-flexed postures have documented medial meniscus extrusion ranging from 1.1 to 1.8 mm and lateral meniscus extrusion between 1.26 and 2.46 mm. In the present simulation, the predicted medial meniscus extrusion was 1.08 mm, while the lateral extrusion was 1.46 mm, both of which fall within these established ranges (see Table 4). These results indicate that the model effectively captured both contact pressures and meniscal displacements under balanced standing conditions, reinforcing the accuracy and reliability of the FE simulation.
The second simulation demonstrated the feasibility of using two different TPUs in the implant. When comparing the knee joint with the 3D-printed medial meniscus implant to the native menisci, we observed comparable peak cartilage contact pressures on the femoral and tibial cartilage, indicating that the implant effectively mimics the natural load distribution of the meniscus.
Additionally, the von Mises and Tresca shear stresses on the femoral and tibial cartilage were lower for the implant compared to the native meniscus. This indicated that the implant reduced the risk of cartilage damage and wear under load. Lower stress values suggested that the implant more evenly distributed forces across the cartilage, potentially leading to a reduced likelihood of stress concentrations that could cause cartilage degeneration over time.
Interestingly, the peak compression stress (CPRESS) was higher in the implant than in the native meniscus, while the von Mises stress was higher in the implant but lower in the lateral meniscus. The higher CPRESS in the implant indicated that the implant experienced greater localized compressive forces than the native meniscus, potentially leading to areas of higher stress concentration that may have affected its durability and longevity. Additionally, the increased von Mises stress in the implant suggested that the material or design led to more significant overall stress within the implant itself, likely due to differences in material properties or geometry. Conversely, the reduction of von Mises stress in the lateral meniscus when the implant was used suggested that the implant helped redistribute some of the load away from the lateral meniscus, potentially reducing the risk of overloading and associated damage in the lateral compartment of the knee.
Despite these differences, the displacement of the meniscus/implant was found to be similar between the two conditions, demonstrating that the implant performed comparably to the natural meniscus in terms of mechanical displacement under the simulated load conditions.
The conducted FEA simulations had some limitations. First, the simulation was limited to a balanced standing position without flexion in the femur, necessary initially to focus on material testing. Once appropriate materials are chosen, future studies will incorporate complex and combined movements. Second, only two volumes with different materials were tested, without modelling infill structures, as this exceeded the current study’s scope. Future research will aim to accurately mimic different infill patterns, like circumferential structures. Third, the study primarily focused on the effects of the implant on femoral and tibial cartilage, with bones modelled as rigid bodies. Using rigid bodies for bones reduces computational complexity and time because bones, being much stiffer than soft tissues, undergo negligible deformation under physiological loads. This approach allows the focus to remain on the more critical interactions and deformations of the femoral and tibial cartilage and implant in the knee joint99,100. Naturally, subsequent research will involve detailed analysis of bones and ligaments, including the anterior and posterior cruciate ligaments, as well as the medial and lateral collateral ligaments.
Additionally, while our virtual testing approach provided valuable insight into the mechanical behaviour of different materials and design strategies, experimental validation remains essential. The current study served as a foundational step in the design of a 3D-printed meniscus implant, but mechanical testing - such as uniaxial or biaxial compression tests on printed prototypes - will be critical to verify the predictive accuracy of the FEA results. These tests will help assess how closely simulated stress and strain values match real-world mechanical performance. Experimental validation will not only confirm the material behaviour under load but also guide refinement of both model parameters and implant design.
Future research will also focus on testing different materials, such as bioinks and synthetics to enhance biocompatibility, cell/vascular growth, and mechanical stability. Based on these results, a prototype will be printed.
Conclusion
This study presented a comprehensive and innovative framework for the development of a biomimetic, 3D-printable meniscus implant, combining advanced imaging, computational modelling, and virtual testing. Compared to prior research, our approach offered a significant advancement by integrating high-resolution visualization of soft tissue microarchitecture -achieved through Lugol’s staining and freeze-drying - with subject-specific geometry to enable precise digital reconstruction of collagen fibre orientation within the meniscus. This level of detail provided a more accurate basis for developing functionally relevant implants.
A key innovation of this study lied in the direct translation of the 3D-printable model into a finite element volumetric mesh, effectively bridging the gap between design and simulation. This enabled in-silico testing of mechanical performance under physiological conditions prior to fabrication, reducing trial-and-error in physical prototyping and increasing the likelihood of clinical success. Furthermore, we demonstrated the feasibility of using different material moduli in a dual-volume construct, opening new possibilities for mechanical tuning based on anatomical region or patient-specific requirements.
While the current work focused on meniscal tissue, the imaging, modelling, and simulation methodologies developed here are adaptable and can be extended to other soft tissues, such as articular cartilage or ligament structures, which also exhibit complex fibre architectures. By enabling patient-specific customization, our workflow contributed to the growing field of personalized medicine and functional tissue engineering.
To translate these findings toward clinical application, future studies should include mechanical testing of printed prototypes to validate simulation outputs, followed by animal studies or pilot clinical trials to assess long-term biocompatibility, functional integration, and implant durability in vivo. These steps are critical for confirming the efficacy and safety of the proposed implant system in real-world conditions and for paving the way toward next-generation soft tissue replacements in orthopaedics.
Methods
The methods for this study were structured into four distinct but interconnected components aimed at advancing the visualization and to determine the volume percentage of circumferential collagen fibres in the human meniscus and applying these findings to the development of 3D printed meniscus implants.
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1.
Visualization of collagen fibres
To visualize collagen fibres in the human meniscus, we established a comprehensive protocol that involved staining with Lugol’s solution followed by freeze-drying. Initially, meniscal tissue samples were obtained and stained with Lugol’s solution. The samples were then subjected to a freeze-drying process to remove water content, thereby preserving the structural integrity of the collagen fibres and enabling their detailed examination under µCT imaging.
Sample collection
The study protocol received a positive ethics vote from the Ethics Committee of Lower Austria, Austria (EK # GS1-EK-4/775–2022) and the Ethics Committee of the Medical University of Vienna, Austria (EK # 2120/2022).
To establish the staining and freeze-drying protocol, one medial degenerative meniscus, classified as “surgical waste” of one patient (female, age 59) who received a total left knee joint replacement, was collected at the University Hospital Krems, Austria. After harvesting, the meniscus was stored in 4% neutral buffered formalin at 4 °C for 24 h to ensure proper fixation and preservation of the specimens prior to staining, freeze-drying and/or µCT scanning. The inclusion criteria for participants required them to be between 18 and 100 years old and to have provided written informed consent. Participants were excluded if they had a positive viral test for hepatitis B, hepatitis C, or HIV; had an active systemic or chronic infection; had undergone chemotherapy or taken immunosuppressive drugs (excluding corticosteroids) in the past six months; or had received radiation therapy in the past six months.
To virtually segment the menisci for the expected circumferential fibres region (six menisci) and to conduct SEM scans (two menisci), eight menisci in total (both medial and lateral) were harvested from the right knees of four deceased human cadavers (one male, three females; age 76,5 ± 9,95) at the Centre for Anatomy and Cell Biology of the Medical University of Vienna, Austria. The inclusion criteria for this study comprised menisci obtained from both female and male body donors, aged between 18 and 100 years. Exclusion criteria encompassed menisci with degenerative or acute traumatic alterations (Fig. 6). Each excised meniscus was macroscopically immaculate, with no detectable macroscopic cartilage defects on the tibia or femur, as verified by a specialist in anatomy and a resident in orthopaedic and trauma surgery. Within 24 h after the demise of the donors, the knees were carefully separated from the rest of the body. Subsequently, the isolated knee specimens were placed in a freezer maintained at a temperature of -20 °C to facilitate preservation until further resection. After the menisci were harvested, the tissue was stored in 4% neutral buffered formalin at 4 °C for 24 h to allow for fixation and preservation of the specimens before subsequent staining or freeze-drying.
Sample preparation. (A) Eight menisci were obtained from four body donors who did not have osteoarthritis or lifelong traumatic knee joint injuries. (B) Resected menisci from a right knee joint that were used for the study.
Staining and freeze-drying procedure
Lugol’s solution is one part iodine and two parts potassium iodide in an aqueous solution. To produce a concentration of 1% (w/v), 0,4% (w/v) iodine and 0,6% (w/v) potassium iodide is dissolved in double-distilled water.
Six intact menisci (three medial and three lateral) and one medial degenerative meniscus were stained in 50 ml 1% (w/v) Lugol’s solution for one week at 4 °C on a rotation device. Following the staining process, the menisci underwent an overnight freezing step at -20 °C. Subsequently, they were placed into a freeze-drying apparatus, where the water content within the frozen meniscus was removed through sublimation under a vacuum pressure of 0.6 mBar and at a temperature of -50 °C. This freeze-drying procedure extended over a duration of 5 days.
Additionally, two menisci were resected from a knee joint after preservation in a freezer at -20 °C for a week. Subsequently, they were stored in 4% neutral buffered formalin at 4 °C for 24 h, and then only freeze-dried according to the aforementioned protocol for subsequent SEM.
µCT-imaging
µCT scans were performed using a SCANCO µCT 50 (SCANCO Medical AG, Brutistellen, Switzerland) specimen µCT scanner. To prevent any potential disturbances caused by movement, the menisci were carefully put within 15 ml conical centrifuge tubes.
The six stained and freeze-dried menisci were initially scanned for an overview using 55 kVp (200 µA, 0.5 mm Al filter, 850 projections, 440 ms integration time), at an isotropic resolution of 20.7 μm. Additionally, one of the six menisci was subjected to a higher resolution scan, using an isotropic resolution of 10 μm, for enhanced visualization.
For the degenerative meniscus, three different scans were conducted. The first scan, in its native state and scanned in aqueous 4% neutral buffered formalin, was performed using 45 kVp (59 µA, 0.1 mm Al filter, 850 projections, 295 ms integration time) at an isotropic resolution of 31.0 μm. The second scan, conducted after staining the tissue for one week in 1% Lugol solution and scanned in the remaining aqueous Lugol solution, used 55 kVp (176 µA, 0.1 mm Al filter, 850 projections, 880 ms integration time) at an isotropic resolution of 20.7 μm. The third scan, performed after Lugol staining and subsequent freeze-drying for 5 days, also used 55 kVp (176 µA, 0.1 mm Al filter, 850 projections, 880 ms integration time) at an isotropic resolution of 20.7 μm.
Scanning electron microscopy (SEM)
SEM was conducted on two unstained, freeze-dried intact human menisci to visualize the collagen fibres after the freeze-drying process and to evaluate whether freeze-drying damaged the structural integrity of the collagen fibres. Thin slices of two unstained freeze-dried human menisci underwent gold sputter-coating with a Quorum Q150RES (Quorum Technologies Ltd, East Sussex, United Kingdom). Subsequently they were observed under a FlexSEM 1000 micrsocope (Hitachi, Tokyo, Japan). The SEM was conducted with a working distance of 7.4 mm, an accelerating voltage of 20 kV, and magnifications of 70, 45, and 1.0 k, respectively.
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2.
Quantification of circumferential collagen fibres
The proportion of circumferential collagen fibres within the human meniscus was quantified using six tissue samples, including both medial and lateral menisci from the right knees of three biological donors. These samples were analysed to determine the percentage of the total meniscal volume occupied by circumferential collagen fibres.
Image processing and segmentation
The µCT scans were saved as reconstructed DICOM stacks from the µCT system and then imported into the free open-software application 3D Slicer Version 5.2.243, Boston, Massachusetts USA, www.slicer.org, for further processing. The volume of circumferential fibres was evaluated in six human menisci (both medial and lateral) from three body donors (Fig. 7).
The initial step involved the creation of 3D reconstructions of the menisci using the module Volume Rendering101. This module is a visualization technique that enables the direct display of image volumes as 3D objects without the need for segmentation.
Next, the stack of 2D images was divided into two distinct segments using the Segment Editor42 module: the first segment encompassed the entire meniscus, achieved through manual global grayscale thresholding.
The second segment was designed to capture the expected circumferential fibres region. According to published literature, the deep circumferential fibres extends from 210 μm on the outer side to 20 μm on the inner side, beneath the meniscus surface7,8,9. Using that information, we manually defined a region of interest (ROI) by hand using the Drawing Tool that extended from 210 μm on the outer side to 20 μm on the inner side, beneath the meniscus surface. This ROI was consistently determined in every 10th image slice and subsequently interpolated to generate a volumetric representation for both segmentation areas.
Process of image segmentation. A series of 2D µCT-scans were virtually segmented into two regions: (A, B) delineation of the Region of Interest (ROI) to capture the expected circumferential fibres region, (C) interpolation of the ROI, and (D) presentation of the virtual 3D volumetric representation showcasing the expected proportion of circumferential fibres within the meniscus.
Measurements and statistical evaluation
The Segment Statistics102 module was employed for the calculation of both the volume and surface area of the entire meniscus, as well as the expected circumferential fibres region. Data were analysed via unpaired t-test and statistical significance was accepted for p < 0.05.
Descriptive statistics were calculated using GraphPad Prism version 9 for Windows, GraphPad Software, Boston, Massachusetts USA, www.graphpad.com.
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3.
Application to 3D printed meniscus implant
The design of the 3D printed meniscus implants was guided by the quantitative finding that approximately 48% of the meniscal volume is composed of circumferential collagen fibres. We utilized patient-specific outward geometries of the medial meniscus obtained from magnetic resonance imaging (MRI) data through the OpenKnee(s)44 project to inform the design of our implant. This MRI data provided detailed, three-dimensional geometric information of the meniscus, including the outer contours and spatial dimensions of the meniscal structure, which served as the foundation for creating the implant model.
3D slicing and 3D printing
To demonstrate printability, a prototype was created. To begin the design process, the STL data of the medial meniscus of patient 9 (male, age 34, no history of traumatic or degenerative knee joint injuries) from the OpenKnee(s)44 project was imported into Ultimaker Cura 5.4.0, Geldermalsen, Netherlands, www.ultimaker.com, a 3D slicing software commonly used for preparing models for 3D printing. The geometrical data from the MRI scans allowed us to accurately replicate the outer shape and size of the medial meniscus for our implant design. Within Ultimaker Cura, we manually designed the internal structure of the implant, focusing on creating a core volume with a specific circumferential infill pattern, that would occupy 48% of the total implant volume. This approach replicates the natural proportion of circumferential collagen fibres observed in the native meniscus. The circumferential infill pattern was chosen to simulate the orientation of collagen fibres in the meniscus, thereby ensuring that the 3D printed implant mirrored the natural structural characteristics of the meniscus. The final 3D model consisted of an outer shell that replicated the patient-specific meniscal geometry and a core volume designed with a circumferential infill pattern.
The 3D printing process utilized a Prusa MK3S (Prusa Research, Prag, Czech Republic, www.prusa3d.com) with a 400 μm nozzle and Polylactide (PLA) filament (1.75 mm) (SUNLU, Guangdong, China, www.sunlu.com) as the printing material.
In adherence to the printing setup, the resulting printable STL file was characterized by the following parameters: a layer height of 0.2 mm, line width of 0.4 mm, linear pattern for top/bottom surfaces, 48% infill area of the total volume, and a concentric infill pattern.
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4.
Development of 3D finite element model & conducting finite element analysis
To evaluate the efficacy of the 3D printed meniscus implants in-silico, we developed a Python script that converts surface geometries and internal volume information (e.g. printing structure, material, etc.) from STL files into detailed 3D FE models for mechanical analysis. This script reads G-Code files generated by Ultimaker Cura 5.4.0 and extracts critical information such as infill volume and layer coordinates to create a comprehensive volumetric model. The resulting FE model was integrated into a virtual knee joint simulation, allowing for the assessment of mechanical performance and enabling design refinements before final production.
Overview of finite element modelling
A methodology has been developed, that directly generates a volumetric body from an STL file by reconstructing geometries through G-code. This approach circumvents the traditional conversion step, approximating a volumetric representation that preserves the original surface geometry of the STL file. The required G-code is generated using UltiMaker Cura 5.4.0, and a subsequent Python script extracts essential coordinates, organizing them into two registers as distinct objects for each layer. The FEA software SIMULIA Abaqus 2020 Version 6.20, Dassault Systèmes Deutschland GmbH, Stuttgart, Germany, www.3ds.com, utilizes its scripting interface to produce curves for each layer, employing wire-poly lines to connect individual coordinates and facilitate the generation of these curves. Subsequently, volumes are synthesized between these curves using the Solid-Loft feature, culminating in the comprehensive creation of the volumetric representation of the meniscus for FEA (Fig. 8).
Python scripting
Initialization and imports: The Python script, employing an object-oriented approach with the Factory Method pattern, is designed for efficient parsing of G-code (by GCodeProcessor) and creation of geometric models in Abaqus (by AbaqusProcessor). This separation enhances the modularity and clarity of the code, allowing each part to be developed, maintained, and scaled independently. A notable advantage of this script is its design for compatibility with a standard Python environment. The script can be run directly in an IDE (Integrated Development Environment) to generate the data registers only. Executing the script in the Abaqus Scripting Interface enables the generation of geometric models.
Class G-code-processor: The G-code-Processor class provides the main functionality of the Python script, tasked with the parsing and processing of G-code files. It leverages regular expressions (RegEx) to read through G-code, line by line, extracting critical coordinate data (X, Y, Z axes). This extraction process recognizes and categorizes different types of structural elements within the G-code, such as Inner Walls, Outer Walls, Skins, Fills, and Skirts. Each category of data is systematically organized into two primary registers — one dedicated to the outer geometry and another for the inner geometry of the model. This dual-register system ensures a structured and precise organization of the coordinate data, which is crucial for the accurate reconstruction of the model’s geometry in the subsequent stages of processing.
Beyond simple data extraction, the G-code-Processor class is engineered to tackle the challenges associated with modelling complex geometries. It employs strategies to manage layers featuring open curves or containing only a single geometry point. G-code-Processor’s functionality handles the processing of layers with multiple curves. In scenarios where a single layer includes more than one distinct geometric feature (e.g., the anterior and posterior horns of a meniscus), the class generates additional layers with slightly modified Z coordinates. This approach maintains the integrity of the model’s complex geometry without compromising on the precision of the volumetric representation.
This class exemplifies a robust approach to data processing, enabling the accurate and efficient transition from G-code to a structured dataset ready for 3D modelling. By providing registers filled with organized layer coordinates, the G-code-Processor class lays a solid foundation for the creation of precise and detailed volumetric models, showing its critical role in bridging the gap between digital design and physical simulation.
Class Abaqus-processor: The Abaqus-Processor class acts as the bridge between G-code data and the creation of 3D models in Abaqus. It is initialized with a G-code-Processor instance and employs the processed G-code data for modelling. The primary function of this class is twofold: firstly, it generates 2D curves for each layer from the register’s coordinates using the Wire-Poly-Line operation; secondly, it creates volumetric bodies between these curves facilitating the Solid-Loft operation. This class is integral to the script’s functionality, enabling the seamless creation of volumetric models from G-code data. It demonstrates sophisticated handling of the Abaqus Scripting Interface, from generating curves to lofting solids, underscoring the script’s capability to bridge the gap between computational design and physical simulation.
Flowcharts of Python scripting. (A) G-code processing for register generation and (B) volumetric part creation.
Finite element analysis of 3D printable meniscus implant
First, a FEA was conducted on a healthy knee joint to simulate a short-term loading at full knee joint extension. Subsequently, the medial meniscus of the same knee joint was replaced with our 3D printed two-volumetric meniscus implant. The FEA was then performed again to evaluate the mechanical performance of the implanted meniscus.
Data acquisition and model import
The knee model used for the creation of this FE model was derived from patient 9 (male, age 34, no history of traumatic or degenerative knee joint injuries) of the OpenKnee(s) project44. The knee geometry data in STL file format, was converted into a solid body format using Ansys SpaceClaim 2020.R2, Canonsburg, Pennsylvania USA, www.ansys.com before being imported into Abaqus 2020. The model included bones (femur, tibia, fibula, patella), ligaments (cruciate ligaments, collateral ligaments, patellar tendon, quadriceps tendon), and cartilage (femoral cartilage, tibial cartilage, patellar cartilage).
Creation of FE knee joint model and definition of material properties
The FE model of the knee joint was implemented using Abaqus 2020 Version 6.20. The knee assembly was based on the geometric references from the OpenKnee(s) model. The bones were defined with a Young’s modulus of 300,000 MPa to replicate a rigid body. The native menisci were defined with a Young’s modulus of 59 MPa, the anterior cruciate ligament with 116 MPa, the posterior cruciate ligament with 87 MPa, the collateral ligaments with 48 MPa, and the femoral and tibial cartilage with 20 MPa. The Poisson’s ratios were defined as 0.48 for the menisci, 0.46 for the cartilage, and 0.3 for the remaining components98,103. All components were modelled with ideal-elastic material behaviour and meshed with C3D10 tetrahedral elements. Fixed connections were defined with tie constraints, while contact interactions were used for relative movements between components44.
The medial 3D-printed meniscus implant was modelled with two distinct volumes, each utilizing different materials. The inner volume was composed of thermoplastic polyurethane (TPU) (Ultrafuse, BASF, Ludwigshafen am Rhein, Pfalz, Germany), which has a Young’s modulus of 54 MPa and a Poisson’s ratio of 0.48. The outer volume consisted of a TPU with a Young’s modulus of 205 MPa and a Poisson’s ratio of 0.48.
Boundary conditions and load distribution
The boundary conditions included fixing the lower end of the tibia and fibula, as well as the upper end of the quadriceps tendon. The menisci were fixed at their posterior and anterior horns to the tibia plateau104,105. A load was applied to the top of the femur at 0° flexion, with an axial compressive load of 1150 N and an anteroposterior load of 134 N. This load simulates the average force exerted on the knee joint during the stance phase of walking, when the foot fully contacts the ground and bears the body’s weight46,98,106.
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
We would like to express our gratitude to Patrick Heimel from Ludwig Boltzmann Institute, Vienna, Austria for his valuable contributions regarding µCT imaging and image analysis. We also extend our appreciation to Julia Maier from Johannes Kepler University, Linz, Austria for her insightful discussions on the use of CAD software. Their expertise and guidance significantly contributed to the success of this research project.
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The project was funded by Gesellschaft für Forschungsförderung (GFF) Niederösterreich (LS20-033).
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Each author contributed to the composition of the article and critically reviewed it for substantial intellectual content. All authors provided their approval for the final version to be published after thorough examination and approval of the manuscript.
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Human menisci were obtained from two institutions: the Medical University of Vienna, Centre for Anatomy and Cell Biology (EK # 2120/2022), and University Hospital Krems, Austria (EK # GS1-EK-4/775–2022), following positive ethics approval. Preliminary studies focused on the staining, freeze-drying, and scanning procedures were performed using a degeneratively altered meniscus collected from a patient undergoing total knee joint replacement at the University Hospital Krems. Additionally, image segmentation and verification of the freeze-drying process were carried out on eight intact menisci donated to the Body Donation Program at the Medical University of Vienna. Individuals provided written informed consent to participate in the Body Donation Program.
This research adhered to the ethical principles established by regional and national research committees, aligning with the 1964 Helsinki Declaration and its subsequent revisions, or analogous ethical standards.
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Moser, AC., Fritz, J., Otahal, A. et al. Freeze drying and Lugol staining of human menisci reveal circumferential fibre volumes to guide meniscus implant design and virtual simulation. Sci Rep 15, 22798 (2025). https://doi.org/10.1038/s41598-025-05004-1
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DOI: https://doi.org/10.1038/s41598-025-05004-1
