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From Medical Imaging to Bioprinted Tissues: The Importance of Workflow Optimisation for Improved Cell Function

Published online by Cambridge University Press:  12 September 2025

Jesús Manuel Rodríguez Rego
Affiliation:
Departamento de Expresión Gráfica, Escuela de Ingenierías Industriales, Universidad de Extremadura, Badajoz, España
Laura Mendoza Cerezo*
Affiliation:
Departamento de Expresión Gráfica, Escuela de Ingenierías Industriales, Universidad de Extremadura, Badajoz, España Departamento de Bioquímica y Biología Molecular y Genética, Facultad de Ciencias, Universidad de Extremadura, Badajoz, España
Francisco de Asís Iñesta Vaquera
Affiliation:
Departamento de Bioquímica y Biología Molecular y Genética, Facultad de Ciencias, Universidad de Extremadura, Badajoz, España
David Picado Tejero
Affiliation:
Departamento de Expresión Gráfica, Escuela de Ingenierías Industriales, Universidad de Extremadura, Badajoz, España
Alfonso Carlos Marcos Romero
Affiliation:
Departamento de Expresión Gráfica, Escuela de Ingenierías Industriales, Universidad de Extremadura, Badajoz, España
*
Corresponding author: Laura Mendoza Cerezo; Email: lmencer@unex.es
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Abstract

Background

The rapid advancement of 3D bioprinting is transforming possibilities in tissue engineering and personalised medicine, offering innovative solutions to critical biomedical challenges such as organ shortages and the need for precise 3D cellular models. To fully unlock the potential of this technology, anoptimised and comprehensive workflow is essential.

Methods

This review provides a systematic examination of the bioprinting process, covering key steps from medical image acquisition to the validation of bioprinted structures. The analysis includes biomaterial and cell type selection, conversion of DICOM images into 3D-printable models, and slicing techniques.

Results

Key factors influencing the precision, viability, and clinical relevance of bioprinted tissues are identified. Comparisons between planar and non-planar slicing algorithms highlight their impact on scaffold integrity. The review also discusses advancements in algorithm development, bioprinter technology, and biomaterial optimisation, emphasising their role in enhancing reproducibility and functionality.

Conclusions

This structured review offers actionable insights for researchers and practitioners aiming to refine bioprinting workflows. By integrating improvements across imaging, modelling, and material selection, 3D bioprinting can more effectively support the development of clinically relevant constructs, advancing regenerative medicine and personalisedhealthcare.

Information

Type
Review
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Introduction

One of the most significant challenges currently facing modern medicine is the lack of effective and scalable technologies for tissue regeneration. Major challenges include organ shortages: despite tireless efforts and diverse medical and political initiatives, the number of organs available does not meet the growing demand of patients waiting for transplantation (Ref. Reference Cippà1). The World Health Organisation estimates that only 10% of the global demand for transplantation is met (Ref. Reference Giwa, Lewis, Alvarez, Langer, Roth, Church, Markmann, Sachs, Chandraker, Wertheim, Rothblatt, Boyden, Eidbo, Lee, Pomahac, Brandacher, Weinstock, Elliott, Nelson, Acker, Uygun, Schmalz, Weegman, Tocchio, Fahy, Storey, Rubinsky, Bischof, Elliott, Woodruff, Morris, Demirci, Brockbank, Woods, Ben, Baust, Gao, Fuller, Rabin, Kravitz, Taylor and Toner2), while the United Network for Organ Sharing reports that, in the United States, an average of 20 people die every day waiting for an organ transplant (Ref. Reference Messner, Guo, Etra and Brandacher3). In addition, research on novel therapies is hampered by a lack of suitable study models to test their efficacy (Ref. Reference Singh and Ferrara4). For example, 2D monolayer cell cultures do not recreate tissue molecular mechanisms occurring in vivo, due in part to a lack of realistic physiological conditions (Ref. Reference Salinas-Vera, Valdés, Pérez-Navarro, Mandujano-Lazaro, Marchat, Ramos-Payán, Nuñez-Olvera, Pérez-Plascencia and López-Camarillo5). As a result, there is limited correlation of 2D culture results with real-life in vivo scenarios (Ref. Reference Breslin and O’Driscoll6).

To overcome these limitations, scientists are developing 3D cultures that can offer advanced test and /or organ systems with realistic tissue microenvironments. These 3D cultures have been developed in parallel with bioprinting, an additive manufacturing technology that prints living cells in a pre-designed pattern (Ref. Reference Ozbolat and Yu7). Additive manufacturing generates a three-dimensional structure by adding cross-sections in superimposed layers, without the need for a pre-existing mould. The advanced 3D cell cultures generated with this technology may potentially leads to the generation of viable tissues and organs for transplantation to patients in need, producing major advances in the field of personalised medicine.

Unlike conventional cell seeding approaches, encapsulation during deposition enables a homogeneous and spatially controlled cell distribution, which is difficult to achieve with standard seeding methods that typically result in peripheral colonisation and oxygen gradients towards the core of the construct (Ref. Reference Cidonio, Glinka, Dawson and Oreffo8). Moreover, direct bioprinting allows for the simultaneous deposition of multiple cell types and vascular channels, making it possible to generate perfusable tissues several millimetres thick that can be maintained for weeks (Ref. Reference Kolesky, Truby, Gladman, Busbee, Homan and Lewis9), while the bioink provides a biomimetic three-dimensional microenvironment that supports cell viability and function (Ref. Reference Xue, Qin and Wu10). These advantages justify the use of direct cell bioprinting, while acknowledging that cell seeding onto pre-printed scaffolds remains valuable when materials or conditions are not compatible with the printing of living cells.

There are already numerous examples demonstrating the potential of bioprinting in both clinical and preclinical settings, with successful outcomes in animal models and in humans. In animals, studies have reported the implantation of facial constructs in rats (Ref. Reference Seol, Lee, Copus, Kang, Cho, Atala, Lee and Yoo11), functional skin grafts containing six different cell types (Ref. Reference Jorgensen, Gorkun, Mahajan, Willson, Clouse, Jeong, Varkey, Wu, Walker, Molnar, Murphy, Lee, Yoo, Soker and Atala12), meniscus substitutes in rabbits (Ref. Reference Sun, Zhang, Wu, Gao, Wei, Ma, Jiang and Dai13), bioprinted muscle tissues implanted in rats (Ref. Reference Kim, Seol, Ko, Kang, Lee, Yoo, Atala and Lee14), as well as bone implants (Ref. Reference Liu, Li, Lei, Cheng, Song, Gao, Hu, Wang, Zhang, Li, Wu, Sang, Bi and Pei15), cardiac patches (Ref. Reference Hwang, Korsnick, Shen, Jin, Singh, Abdalla, Bauser-Heaton and Serpooshan16) and oesophageal substitutes (Ref. Reference Takeoka, Matsumoto, Taniguchi, Tsuchiya, Machino, Moriyama, Oyama, Tetsuo, Taura, Takagi, Yoshida, Elgalad, Matsuo, Kunizaki, Tobinaga, Nonaka, Hidaka, Yamasaki, Nakayama and Nagayasu17), all of which showed tissue integration and partial or complete functionality. In humans, a notable example is the use of autologous dermal fibroblasts bioprinted for nerve injury repair, opening the way to direct clinical applications (Ref. Reference Ikeguchi, Aoyama, Noguchi, Ushimaru, Amino, Nakakura, Matsuyama, Yoshida, Nagai-Tanima, Matsui, Arai, Torii, Miyazaki, Akieda and Matsuda18). These advances highlight that bioprinting is beginning to bridge the translational gap, although regulatory and standardisation challenges remain before widespread clinical adoption can be achieved.

Despite remarkable progress, bioprinting still faces significant challenges for clinical translation. Among the technical hurdles are the scalability of constructs and the need to generate clinically relevant tissues that maintain cell viability, the incorporation of efficient vascularisation to overcome diffusion limits and enable stable perfusion in thicker tissues (Ref. Reference Murphy and Atala19), and the mechanical stability of scaffolds and bioinks to withstand long-term physiological loads (Ref. Reference Chimene, Lennox, Kaunas and Gaharwar20). In addition, regarding clinical application, barriers remain due to the absence of specific regulations for cell-laden bioprinted products, which must currently be classified under the framework of advanced therapy medicinal products in the EU (Ref. 21) and the FDA’s guidelines for additive manufacturing (Ref. 22). Likewise, the lack of standardisation in bioink characterisation and interlaboratory variability continue to limit the reproducibility of results (Ref. Reference Sekar, Budharaju, Zennifer, Sethuraman, Vermeulen, Sundaramurthi and Kalaskar23).

This review examines the complete workflow involved in some of these emerging technologies. Every approach should meet the requirements for their intended biological function, such as appropriate bioprinting post-processing and improved cell viability within the generated structures. We discuss advanced image processing and slicing techniques, which are essential for accurately translating patient-specific anatomical data into three-dimensional bioprinted models. Finally, this review highlights the role of medical imaging in optimising the precision and viability of bioprinted scaffolds, thereby advancing the potential of 3D bioprinting for personalised medicine and organ transplantation.

Types of bioprinting

3D bioprinting encompasses a diverse set of technologies that enable the spatially precise deposition of living cells and bioinks to generate biomimetic structures. Each modality is based on distinct physical principles and offers different advantages and limitations. The main bioprinting approaches are described below, together with their most relevant benefits and drawbacks, as well as representative examples of in vivo success that illustrate their translational potential.

Medical image acquisition and processing

The acquisition of medical images is the first step in the generation of personalised scaffolds by 3D bioprinting. This step allows detailed analysis of tissue anatomy and pathology and provides a solid basis for the design of biomimetic three-dimensional structures.

Medical imaging techniques include X-ray, CT, PET, magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), digital mammography and diagnostic ultrasound (Ref. Reference Hussain, Mubeen, Ullah, Shah, Khan, Zahoor, Ullah, Khan and Sultan52). More recently, ultrasound has been successfully used in the cardiovascular field, including 3D transthoracic echocardiography (TTE) and transesophageal echocardiography (TEE). 3D rotational angiography has also been employed. In addition, the combination of different imaging modalities, such as CT and TEE, allows the creation of hybrid 3D models that capture both structural and valvular morphology (Ref. Reference Giannopoulos and Pietila53).

The biological nature of the tissue of interest will dictate the selection of a particular imaging technique. For example, in the case of computed tomography (CT), tissue densities are directly related to pixel intensities, allowing the analysis of structures with high (e.g. bones) and low (e.g. lungs) densities, while MRI is more appropriate to appreciate differences between soft tissues (such as the white and grey matter of the brain) (Ref. Reference Bücking, Hill, Robertson, Maneas, Plumb and Nikitichev54).

CT is an imaging technique that uses iterative algorithms to reconstruct the interior of large volumes using graphics processing units (GPUs) (Ref. Reference Chghaf and Gac55). Due to its higher resolution and its ability to provide information about different tissue densities, it is a more interesting technology for medical imaging for bioprinting, as it facilitates both the generation of the 3D model and the choice of the appropriate biomaterial.

DICOM image processing

3D bioprinting based on Digital Imaging and Communications in Medicine (DICOM) images starts with the conversion of 2D medical images, which are stacked, and are later processed to create a 3D printing compatible format (.STL). This process involves the segmentation of the DICOM images into a three-dimensional computer-aided design (CAD) format. From the data obtained, initial processing is performed, such as defining and adjusting the region of interest (ROI) (Ref. Reference Kamio, Suzuki, Asaumi and Kawai56).

CT or MRI images are stored in DICOM format in the hospital’s Picture Archiving and Communication System (PACS) (Ref. Reference Quan, Wang, Du, Li and Zhou57) from where they are obtained for processing and subsequent bioprinting of the desired tissue. In case you want to use open sources, some such as DICOM Library, TCIA or NIH Chest X-ray Dataset contain numerous freely accessible DICOM files, which can be downloaded following the guidelines described by each.

The first step in converting DICOM images to .STL format is to improve the quality of the images through a thresholding and segmentation process using software that allows the CT image to be converted into a 3D model (Ref. Reference Sahai and Gogoi58).

A further improvement in the processing of DICOM images came from the use of virtual topologies combined with mesh optimisation algorithms. Recent studies have shown that these techniques make it possible to reduce the number of elements in the mesh, improving the resolution of the geometry without compromising the structural quality of the 3D model, which significantly reduces processing time (Ref. Reference Fernández-Tena, Marcos, Agujetas and Ferrera59). This approach is particularly useful in complex geometries, such as airways, where anatomical details are critical for accurate simulation.

Some suitable software for this procedure includes 3D Slicer, OsiriX, ITK-Snap, Invesalius, MeshLab or Blender. In the following, the use of 3D Slicer will be explained because of its open-source status and its widespread use in DICOM image analysis.

3D Slicer

3D Slicer is an open-source 3D data analysis and visualisation platform designed to facilitate the use of medical and scientific images. The software stands out for its ability to perform advanced segmentation and analysis, allowing users to segment medical images in two and three dimensions, which facilitates the identification and isolation of specific structures, as well as allowing the manipulation of volumetric and surface models in different planes, such as axial, sagittal and coronal.

It supports multiple data formats, such as DICOM, Neuroimaging Informatics Technology Initiative (NIfTI) or sections in .dcm format, allowing the integration of images from different modalities such as CT, MRI and ultrasound. In addition, 3D models can be exported in printable formats such as STL, facilitating the creation of physical models from medical images.

Using 3D Slicer to process medical images

Image processing begins by selecting the Data button and Choose file(s) to Add in the main 3D Slicer interface, from where it is possible to import individual DICOM files or entire folders containing multiple studies. In this case, to load a lung CT in .dcm format, all files corresponding to the sections must be selected. Once the files are uploaded, patient data such as patient name, ID, gender or year of birth, among others, can be viewed. The loaded file is then selected, and the Load option is chosen and the images automatically appear in the corresponding views (axial, coronal and sagittal), allowing a detailed visualisation of the different anatomical sections. In the case of Figure 1, a lung CT file has been chosen.

Figure 1. Detailed visualisation of the different anatomical sections.

Next (Figure 2), the desired area must be cropped using the ROI, choosing the Volume Rendering module, with the Crop option activated. By clicking on the Display ROI option (the eye) it is possible to delimit the area to be processed.

Figure 2. Selection of the desired area using the ROI (region of Interest).

Subsequently, by searching the Crop Volume module, the delimited area can be accessed to facilitate segmentation, and after determining the cutting options, the Apply option is chosen. With this step, the area of interest is obtained for processing.

The next step involves adjusting contrasts to define areas that are not visible using the default settings via the Volumes module. The programme allows to analyse the images using default options, so in this case the CT-Lung option will be selected.

Next, the segmentation of the anatomical structures of interest must be carried out. To do this, access the Segment Editor module, where a new segment is created by choosing the Add option, on which the available segmentation tools will be applied (Table 1):

Table 1. Description of the tools available in 3D Slicer

One of the most widely used tools is Threshold, which allows selecting and isolating anatomical structures based on the density or intensity values of the images, such as bone or soft tissues. To do this, in the generated segment, the density of the structure of interest (in this case, the lung) must be selected with this tool, dragging until the desired area is selected, and allowing it to be differentiated from the rest of the image. The selection will be validated with the Apply option. Threshold values can be interactively adjusted to ensure accurate selection of relevant structures. If necessary, manual adjustments can be made using tools such as Paint, Erase or Scissors to ensure optimal segmentation.

Using the Show 3D option, the result of the insulation of the structure can be observed after the necessary adjustments have been made (Figure 3).

Figure 3. Isolation of the desired anatomical structure using the Threshold tool.

Once the segmentation is finished, it is possible to improve the quality of the 3D model by applying smoothing algorithms through the Smoothing tool of the segment editor. This procedure reduces the irregularities present at the edges of the structures, improving the representation of the segmented surfaces. The level of smoothing can be adjusted according to the specific requirements of each case, providing precise control over the quality of the final model.

When the segmentation has been refined, the next step is to convert it into a 3D model (Figure 4). This is done using the Export to files option within the Segmentations module. The STL format is one of the most common formats for this purpose.

Figure 4. Export of the isolated 3D object obtained from the DICOM file to 3D printable .STL format.

Although 3D Slicer provides robust tools for STL model segmentation and generation, in some cases it can be useful to perform additional file optimisation prior to printing. This can be done using post-processing tools such as Meshmixer or Blender, which can repair errors in the mesh, simplify complex geometries and improve the structure of the model to ensure a successful print.

Slicing and its impact on the final structure

For 3D models to be bioprinted, .STL files must go through two processes: slicing and optimisation of the bioprinting parameters. In 3D printing, slicing refers to the process of converting a 3D model into a series of 2D layers or slices that will be sequentially printed, using algorithms specifically designed for this function (Refs Reference Neel, Mesto and Hascoet60, Reference Zhao, Yan, Zhang, Zhang, Pan, Yuan, Xiao, Jiang, Wei, Lin and Chen61).

Types of slicing

The different slicing methods can be initially grouped into two families: planar slicing algorithm (flat) and non-planar slicing algorithm (out of plane or curved). Each of the different slicing techniques has advantages and disadvantages depending on geometry, type, amount of material and purpose.

Planar slicing algorithms

Printing occurs layer by layer, in one plane. They are the most common method of slicing because of their simplicity and because they are accessible to more types of technologies. However, they offer poor surface quality and require the use of supports so that the bioprinted structure does not collapse during printing, which increases material usage and decreases the speed with which we can obtain the final product (Ref. Reference Nayyeri, Zareinia and Bougherara62).

This type of slicing considerably limits the performance of additive manufacturing (AM) systems, causing staggered surfaces, massive support structures, non-conformity with curved substrates and reduced strength in thin structures (Ref. Reference Shan, Gan and Mao63). The most common types of Planar Slicing are:

  • Uniform slicing or Constant Layer Thickness Planar Slicing (Figure 5): the most common method in 3D printing, in which the 3D model is cut into thin flat layers of the same thickness, which are deposited one by one by the printer (Ref. Reference Shan, Gan and Mao63). The height of the layers is a key factor in the quality of the printed object, as the thinner the layers, the higher the resolution and detail of the final object. However, this flat layer-based cutting considerably limits the performance of AM systems, leading to staggered surfaces, massive support structures, non-compliance with curved substrates and reduced strength in thin shell structures (Ref. Reference Hu64).

  • Adaptive Planar Slicing (Figure 6): individual layers of the same part can have different layer heights, so details can be produced with high resolution, while non-detailed areas are produced with higher layer heights, improving production speed (Ref. Reference Pelzer and Hopmann65) although it only improves the representation of curved surfaces. In addition, because the part is still made of flat layers, the staircase effect is not eliminated. It represents an optimisation compared to the previous method, as it allows a better surface finish to be obtained by reducing the staircase effect, with an optimised printing time (Ref. Reference Pelzer and Hopmann65), and requires a much longer planning and adjustment stage than the previous techniques described (Ref. Reference Lettori, Raffaeli, Borsato, Pellicciari and Peruzzini66).

Figure 5. Uniform slicing by flat layers of the same thickness.

Figure 6. Adaptive planar slicing with layers of different heights.

Almost all fused deposition modelling (FDM) bioprinters on the market are capable of printing from planar sliced 3D models, as these are the simplest slicings. The minimum requirements are that the extrusion can be performed in an XY plane and that they have 3 Degrees of Freedom (DOF). Examples of models that allow printing from planar sliced 3D models are NovoGenTM MMX Bioprinter, developed by Organovo and BIO X by CELLINK.

Non-planar slicing algorithms

Compared to planar slicing algorithms, non-planar slicing algorithms allow more DOF at the cost of being much more complicated in terms of procedure and computational processing (Ref. Reference Zhao and Guo67) because, as the printing is not limited to a single plane, it is necessary to determine the toolpath of the print head very well to avoid collisions in its path (Ref. Reference Nayyeri, Zareinia and Bougherara62). All these techniques require the 3D bioprinter hardware to be able to print in more than one plane and to have at least 5 DOF with computerised robotic assistance, in addition to higher computing power (Ref. Reference Bhatt, Malhan, Shembekar, Yoon and Gupta68). The most common types of non-planar slicing are:

  • Curved-Layer Slicing (Figure 7): the 3D model is interpreted in curved layers instead of flat layers, achieving a higher contour fidelity of the final object and reducing the need to include structural supports (Ref. Reference Zhao and Guo69).

  • Conformal Slicing (Figure 8): the cuts are conformal to the preconstructed layer/substrate, so they can be flat or non-flat (Ref. Reference Kapil, Negi, Joshi, Sonwane, Sharma, Bhagchandani and Karunakaran70). It allows printing a 3D structure on a free-form surface on which slices and layering are performed, as the z-axis coordinate changes continuously on the same layer, depending on the complexity and topology of a slice surface (Ref. Reference Alkadi, Lee, Bashiri and Choi71). It requires the use of more sophisticated algorithms and GPU demanding software to translate 2D patterns to non-planar 3D surfaces (Ref. Reference Alkadi, Lee, Bashiri and Choi71).

  • 5-Axis Dynamic Slicing (Figure 9): uses a multitude of planes to do the slicing for the different sections of the 3D model, selecting the cutting plane that best suits the needs, presenting a method that significantly reduces the support structures and is not very costly in terms of time and processing capacity that optimises the final result (Ref. Reference Wang, Zhang, Hu, Liu and Lammer72).

  • Helical Slicing (Figure 10): forms a single continuous 3D toolpath and eliminates seam defects, where the geometry is initially cut into flat slices and then, using two consecutive flat slices, direction vectors are constructed from the current layer to the next layer (Ref. Reference Yigit and Lazoglu73).

  • Mixed-Layer Adaptive Slicing (Figure 11): combines planar and non-planar slicing methods to optimise the printing process for different target subvolumes. This method takes advantage of the benefits of planar splicing and non-planar slicing to present a much higher quality result following a much more suitable manufacturing process (Ref. Reference Zhao and Guo69).

Figure 7. Curved-layer slicing.

Figure 8. Conformal slicing where the cuts are carried out on a previously existing surface.

Figure 9. 5-Axis dynamic slicing where you can see the constant change of planes to generate the structure in the most optimal way.

Figure 10. Helical slicing where it can be seen that the trajectory is continuous and ascending.

Figure 11. Mixed-layer adaptive slicing where the combination of uniform slicing and 5-axis dynamic slicing can be observed.

Some of these algorithms require a bioprinter of at least 5 DOF, such as the RoboPrint developed by Acoustic Robotic System Lab and Maxon, or 6 DOF, such as BioAssemblyBot by Advanced Solutions.

The choice of each type of slicing depends on both the technology available and the requirements of the result.

Relationship between slicing approaches and cell viability

Slicing plays a fundamental role in the toolpath that the bioprinter will follow during the bioprinting process. From the process of converting a 3D model to STL format, a loss of topological fidelity can occur (Ref. Reference Hong, Lin, Li, Jiang, Fang, Wang, Liu, Wu and Huang74), decreasing the accuracy of the bioprinting. To solve this, several algorithms can be used to generate different toolpath patterns depending on the part of the 3D model, either on the outside (contour) or the inside (infill density) (Ref. Reference Ahsan, Xie and Khoda75), allowing one to choose the most appropriate one for each case and improving the final result.

Infill density is a critical parameter in terms of structure and porosity. A construct with high infill density will exhibit higher mechanical properties and fidelity than structures with lower infill density, which is key to matching these properties to the desired fabric type (Ref. Reference Ravi76). In 3D bioprinting, the infill density determines the degree of porosity of the interior, which directly affects how the cells will interact with the element, and can be defined as (1 – fill density ratio) (Ref. Reference Ravi76).

Designing 3D models considering the porosity of the materials determines the degree of permeability and transport of nutrients, metabolic products, oxygen and, especially, the degree of cell migration along the final construct (Refs Reference Zieliński, Gudeti, Rikmanspoel and Włodarczyk-Biegun77, Reference Bružauskaitė, Bironaitė, Bagdonas and Bernotienė78).

Cells interact with their environment to a high degree of detail. These interactions shape their phenotype depending on very specific factors such as the presence of pores, pore size and the curvature of their substrate, among others (Ref. Reference Zanotelli, Leutenegger, Lun, Georgi, de Souza and Bodenmiller79). Each cell type and tissue has its own porosity and pore size requirements. Fibroblasts, for example, exhibit optimal proliferation values in tissues with pores of 200–250 μm and 86% porosity (Ref. Reference Mandal and Kundu80). Osteoblasts, on the other hand, show good growth in small pore sizes (40 μm), while larger pore sizes (100 μm) provide greater migration capacity (Ref. Reference Akay, Birch and Bokhari81). Chondrocytes show a greater capacity for proliferation in scaffolds with pore sizes between 250 μm and 500 μm (Ref. Reference Lien, Ko and Huang82). Although each cell type possesses a preference for a range of pore sizes for optimal proliferation, it has been suggested that a suit-all pore size would be in the range of 100–700 μm (Ref. Reference Bružauskaitė, Bironaitė, Bagdonas and Bernotienė78).

In the case of curvature, it has been described that a concave geometry could favour the formation of aggregates with cell-to-cell connections, accelerating the formation of blood vessels (Ref. Reference Magnaudeix, Usseglio, Lasgorceix, Lalloue, Damia, Brie, Pascaud-Mathieu and Champion83).

It should be noted that the bioprinting process, from the acquisition of medical images to the creation of bioprinted tissues, introduces several cumulative errors that can cause the final product to differ from the initial 3D model. First, the spatial resolution of the original DICOM images, usually from CT or MRI scans, directly affects the quality of the resulting 3D model (Ref. Reference Kamio, Suzuki, Asaumi and Kawai56). In addition, segmentation and smoothing during 3D model generation can remove important details, reducing the fidelity of the model to the original anatomy (Ref. Reference Fourie, Damstra, Schepers, Gerrits and Ren84). The slicing process, where the model is converted into layers for printing, also introduces variations due to parameters such as layer resolution and model orientation, which can alter the final geometry (Ref. Reference Alexa, Hildebrand and Lefebvre85).

The quality of the mesh also influences the simulation and functionality of the final model. Research in airway airflow simulation indicates that a mesh with poorly distributed elements can lead to significant errors in predictions, underlining the importance of optimising each stage of medical image processing for bioprinting (Ref. Reference Fernández-Tena, Marcos, Agujetas and Ferrera59).

Finally, during bioprinting, factors such as viscosity, osmotic pressure, injectivity, rheological properties, bioink surface tension, print flow control, process-induced mechanical forces and in situ cross-linking mechanisms can affect the fidelity of the bioprinted tissue (Ref. Reference Ning, Gil, Hwang, Theus, Perez, Tomov, Bauser-Heaton and Serpooshan86) introducing additional errors that affect both cell shape and viability.

The accumulation of these sources of error throughout each step produces a significant deviation between the initial digital model and the final bioprinted tissue, so carefully optimising each step of the process is key to reducing these variations.

Parameter optimisation in biological models bioprinting

Once the image has been processed by slicing in the most appropriate way and considered what bioprinter is available, it is necessary to adjust the technical parameters that influence both the structural precision and the viability and functionality of the bioprinted cells and tissues. Thus, bioprinting parameters are defined as those settings or firmware inputs necessary to produce bioprinted structures in an appropriate way (Ref. Reference Sánchez87).

The range of values suitable for bioprinting depends mostly on the type of bioink used and their physicochemical properties. Therefore, it is important to extensively characterise it before determining which parameters to adjust in the bioprinter before starting the bioprinting process. In the case of extrusion bioprinters, the most widely used due to their versatility, relatively low cost and capacity to generate thicker and bulkier structures, the main parameters to be adjusted are:

  1. 1. Extrusion pressure: is defined as the force applied to the bioink to push it through the nozzle of a bioprinter during the printing process. During the extrusion process, cells are exposed to mechanical forces of different types, which can play an important role in their differentiation, motility and growth control (Ref. Reference Wong, Chan, Kamm and Tien88). Of the different forces to which the cells are exposed, the one that has the greatest impact on them is shear stress, and is the main cause of cell death (Ref. Reference Cidonio, Glinka, Dawson and Oreffo8). Therefore, because a higher pressure triggers a higher shear stress in extrusion bioprinting, causing more cell death (Ref. Reference Han, Kim, Jin and Kim89) it is advisable to reduce the pressure to the minimum allowed by the rheological properties of the bioink (Ref. Reference Thakare, Pei, Qin and Jerpseth90).

  2. 2. Printing speed: is the speed at which the nozzle moves over the bioprinter bed and is defined as the flow rate divided by the feed rate. It is a dominant factor in printing results, affecting the height, width and thickness of the printed structures (Ref. Reference Gillispie, Han, Uzun-Per, Fisher, Mikos, MKK, Yoo, Lee and Atala91). It is directly related to the previous parameter, as higher extrusion pressure requires higher printing speed, so speed adjustments must be made to minimise shear stress and maintain cell integrity (Refs Reference Fakhruddin, Hamzah and Razak92, Reference Webb and Doyle93).

  3. 3. Nozzle diameter: nozzle diameter affects bioprinting by affecting the shear stresses caused by the internal pressure of the extruder nozzles, which affects printability and cell viability (Ref. Reference Magalhães, de Oliveira, Dernowsek, Casas and Casas94). Thus, reducing the nozzle diameter in bioprinters increases the shear stress, which can lead to cell death due to mechanical damage (Ref. Reference Jentsch, Nasehi, Kuckelkorn, Gundert, Aveic and Fischer95).

  4. 4. Nozzle geometry: nozzle geometry, including radius, length and angle of convergence, significantly influences shear stress, which, in turn, influences cell viability (Refs Reference Gómez-Blanco, Pagador, Galván-Chacón, Sánchez-Peralta, Matamoros, Marcos and Sánchez-Margallo96, Reference Chand, Muhire and Vijayavenkataraman97). For example, cylindrical nozzles tend to generate the lowest shear stress, but this stress is maintained over a longer period, which can reduce cell viability (Ref. Reference Chand, Muhire and Vijayavenkataraman97) while conical and truncated conical nozzles can increase the flow rate and reduce the dispensing pressure (Ref. Reference Gómez-Blanco, Pagador, Galván-Chacón, Sánchez-Peralta, Matamoros, Marcos and Sánchez-Margallo96). On the other hand, coaxial nozzles allow the creation of microchannels within the printed structures, improving cell viability by facilitating nutrient delivery (Ref. Reference Gao, He, Fu, Liu and Ma98).

  5. 5. Temperature: influences bioink viscosity, cell viability and structural accuracy. Proper temperature control ensures that the bioink maintains a suitable viscosity for extrusion, guarantees cell survival (usually around 37 °C) and regulates the gelation of the biomaterials used, which is essential to maintain the printed structure without deformation. It also affects the final resolution and quality of the bioprinted tissues (Ref. Reference Hospodiuk, Dey, Sosnoski and Ozbolat99).

  6. 6. Layer height: critical in determining the resolution and accuracy of printed structures, as well as the cell viability and mechanical integrity of the tissue. Thinner layers allow for greater precision and definition in detail. (Ref. Reference Murphy and Atala19), but may increase printing time and generate higher mechanical stresses on cells, while thicker layers speed up the process and reduce cellular stress, but may compromise resolution and homogeneity of tissues (Ref. Reference Ozbolat and Hospodiuk100). The balance between layer height, resolution and cell viability is key to the success of the process.

Materials and cells used in bioprinting

The appropriate selection of materials and cells depends on the tissue to be bioprinted. Choosing the right components is essential for the successful reproduction of complex biological structures. Biomaterials used as bioinks must meet specific requirements, such as being biocompatible, promoting cell adhesion and possessing mechanical and functional properties comparable to the target tissue. These materials can be derived from natural sources, synthetic sources or combinations of both (Ref. Reference Groll, Boland, Blunk, Burdick, Cho, Dalton, Derby, Forgacs, Li, Mironov, Moroni, Nakamura, Shu, Takeuchi, Vozzi, Woodfield, Xu, Yoo and Malda101). In addition, selected cells must be able to proliferate, differentiate and organise themselves into 3D architectures that mimic the functions of the tissue or organ to be bioprinted. To this end, the interaction between bioinks and cells is key, ensuring that the printed structure maintains its viability and functionality, both during the printing process and after implantation or maturation (Ref. Reference Jang, Park, Gao and Cho102). The selected bioink should be tested for its compatibility with the growth and function of a particular cell type and, therefore, for the development of a particular tissue. Table 2 exemplifies successful combination of recreated tissues with cell types.

Table 2. Bioprinting of tissues and the types of bioinks and cells used

Functional test of bioprinted constructs

Testing cell viability and functionality of bioprinted constructs is an essential step to ensure the generated structures perform as expected. During the bioprinting process, cells are exposed to changing environmental conditions (e.g. temperature, viscosity, mechanical forces) that can impact their viability and function (Refs Reference Willson, Ke, Kengla, Atala, Murphy and Crook103, Reference Zhang, Haghiashtiani, Hübscher, Kelly, Lee, Lutolf, McAlpine, Yeong, Zenobi-Wong and Malda104). There exist different methods to assess cell viability. However, they were originally designed for 2D conditions and they do not often perform well in 3D conditions. We propose the combination of methods for an adequate assessment of cell viability in these conditions, including (Refs Reference Lee, O’Connell, Onofrillo, Choong, Di Bella and Duchi105, Reference O’Brien, Haskins, Taylor, Haskins and Giuliano106, Reference Bikmulina, Kosheleva, Efremov, Antoshin, Heydari, Kapustina, Royuk, Mikhaylov, Fomin, Vosough, Timashev, Rochev and Shpichka107):

  • - Live/Dead staining: a methodology for the assessment of cell viability in both 2D and 3D cultures. It is widely employed through the combination of two fluorogenic compounds: calcein acetoxymethyl ester (calcein AM) and propidium iodide (PI). Differentiation between viable and non-viable cells is based on cell metabolic activity and plasma membrane integrity. The major component of this assay is Calcein AM, a non-fluorescent molecule hydrolysed in viable cells by intracellular esterases into calcein, a highly fluorescent substance. This process occurs exclusively in cells with active metabolism and intact membranes, as only living cells possess the requisite enzymatic activity to release the acetoxymethyl group from Calcein AM and convert it into a fluorescent compound (Ref. Reference Miles, Lynch and Sikes108). The emission wavelength of calcein is approximately 515 nm, which permits its detection through the use of fluorescence microscopy or flow cytometry (Ref. Reference So, Sallin, Zhang, Chan, Sahni, Schulze, Davila, Strome and Jain109).

    In contrast, PI is a marker that only permeate to cells with compromised plasma membranes, which is indicative of non-viable cells. PI is positively charged, rendering it unable to cross intact cell membranes. However, in damaged cells, it is capable of passing through and binding to cellular DNA and RNA (Ref. Reference Liu, Gao, Li and Sun110) via electrostatic interactions with the phosphate groups of nucleic acids. The intercalating bond between propidium iodide and DNA markedly enhances the fluorescence of the latter, resulting in the emission of light at a wavelength of approximately 617 nm upon excitation.

    The live/dead staining technique is widely used to assess viability in conventional cell cultures. Its use in 3D cell cultures can be compromised due to limited penetration of the compounds, therefore to obtain a more accurate quantitative assessment of viability in 3D models, it is recommended to combine this technique with other complementary methods to improve the accuracy of in-depth detection (Ref. Reference Bikmulina, Kosheleva, Efremov, Antoshin, Heydari, Kapustina, Royuk, Mikhaylov, Fomin, Vosough, Timashev, Rochev and Shpichka107).

  • - Alamar Blue (AB) assay: a widely used technique for assessing the metabolic activity of cells based on the reduction of the non-fluorescent compound resazurin to the fluorescent compound resorufin by enzymatic reductases using reducing metabolites such as NADH, NADPH or FADH (Refs Reference Lavogina, Lust, Tahk, Laasfeld, Vellama, Nasirova, Vardja, Eskla, Salumets, Rinken and Jaal111, Reference Vieira-da-Silva and Castanho112). The resorufin produced can be quantified by absorbance, measured at a wavelength of 570 nm, or by fluorescence, using an excitation wavelength of 540 nm and an emission of 590 nm (Ref. Reference Lavogina, Lust, Tahk, Laasfeld, Vellama, Nasirova, Vardja, Eskla, Salumets, Rinken and Jaal111), allowing sensitive and quantitative detection of cell activity.

    In bioprinting applications, cell encapsulation within bioprinting gels can impact the assay’s performance. Key variables that may affect assay effectiveness include the gel’s concentration, thickness and water content. For instance, in hydrogels with high water content, such as those based on collagen, AB dye dilution can occur, reducing assay performance by up to 25% (Ref. Reference Bonnier, Keating, Wróbel, Majzner, Baranska, Garcia-Munoz, Blanco and Byrne113). To mitigate these effects, it is recommended to calibrate assay conditions according to the specific properties of the bioprinted material being used.

  • - PicoGreen Assay: this technique measures the DNA content of cells encapsulated in the gel, which can be used to allow an estimation of their proliferation. The technique involves breaking the cell membranes to release the DNA and measuring its concentration by fluorescence. Therefore, is not suitable for continuous monitoring of cell proliferation.

Therefore, for optimal gel/3D structure characterisation, the effects of hydrogel structure, water content and porosity have to be considered when selecting a cell viability assay.

Challenges and limitations for the clinical translation of 3D bioprinting

The clinical application of bioprinting still faces numerous challenges that must be addressed. With regard to immunological safety and scaffold immunogenicity, it is known that hydrogels and scaffolds can trigger innate immune responses and capsular fibrosis, impairing graft integration, perfusion and function (Ref. Reference Mariani, Lisignoli, Borzì and Pulsatelli114). In certain natural bioinks, impurities are critical determinants. For example, alginate is often contaminated with endotoxins, proteins and polyphenols capable of inducing inflammation and cellular overgrowth within capsules, making purification essential to mitigate these effects (Ref. Reference Dusseault, Tam, Ménard, Polizu, Jourdan, Yahia and Hallé115). Similarly, decellularised ECM-derived matrices provide biomimetic signals, but residual immunogenicity largely depends on meeting minimal decellularisation criteria, including the content and size of nuclear and mitochondrial DNA fragments, the presence of reactive oxygen species (ROS), and fragmented ECM components such as hyaluronic acid or fibronectin (Ref. Reference Kasravi, Ahmadi, Babajani, Mazloomnejad, Hatamnejad, Shariatzadeh, Bahrami and Niknejad116).

Sterilisation is another critical factor in the development of new bioinks for clinical use, as it can significantly alter key properties. For instance, GelMA sterilisation by autoclaving or ethylene oxide reduces stiffness, whereas gamma irradiation increases stiffness, alters pore size, and may compromise sol–gel transition, printability and 3D cell viability (Ref. Reference Rizwan, Chan, Comeau, Willett and Yim117). Consequently, sterilisation procedures for each bioink must be systematically evaluated alongside other characterisation tests to fully understand the modifications introduced during processing.

The viability of bioprinted tissues also remains constrained by limited oxygen and nutrient diffusion in the absence of a fully functional vascular network. Although printing strategies that rely on diffusion can produce perfusable channels and enhance molecular transport within constructs, maintaining stable perfusion and effective vascular integration in vivo remains a major hurdle for the development of volumetric tissues (Ref. Reference Cai, Kilian, Mejia, Rios, Ali and Heilshorn118).

The choice of cell source also represents a key translational barrier. Autologous cells minimise the risk of immune rejection but complicate logistics and extend application timelines. Allogeneic cells offer greater scalability but may elicit donor-specific antibodies and consequent immune responses (Refs Reference Sanabria-de la Torre, Quiñones-Vico, Fernández-González, Sánchez-Díaz, Montero-Vílchez, Sierra-Sánchez and Arias-Santiago119, Reference Chen, Lv, Chen and Cui120). Induced pluripotent stem cells (iPSCs) present another alternative, although potential links to tumorigenicity have been suggested, underscoring the need for further studies and rigorous genomic and functional quality controls before clinical implementation (Ref. Reference Zhong, Liu, Pan and Zhu121).

Finally, one of the most critical barriers to clinical translation is the lack of standardisation, quality control and reproducibility of bioinks. Batch-to-batch variability in natural polymers and ECM-derived materials, the degree of functionalisation, the type of crosslinking agent, the light source used in photopolymerisation and rheological properties, among others, directly affect viability, printing fidelity and mechanical performance. Therefore, the establishment of robust bioink standardisation protocols and reproducible quality control systems is essential to achieve successful clinical translation of bioprinted constructs.

Conclusion

The workflow for generating 3D models from DICOM medical images and their bioprinting represents a significant advance in addressing critical challenges in medicine, such as organ shortages and the need for more accurate models for biomedical research. However, each step of the process – from image acquisition to slicing and bioprinting – carries the potential for cumulative errors that can compromise both the accuracy and functionality of the resulting models.

Factors such as the resolution and quality of the initial images, together with the conversion to STL format, are decisive in the accuracy of the 3D model. Slicing and printing parameters also play a crucial role in the final structure and cell viability of bioprinted tissues.

Optimising each of these stages is essential to minimise deviations between the digital model and the bioprinted tissue, thus improving the quality of the generated scaffolds and their applicability in clinical and research contexts. As technological advances in bioprinting and image processing methods continue to develop, the accuracy of these models is expected to increase, enabling the creation of more complex and functional tissues, with great potential to contribute to regenerative and personalised medicine.

Furthermore, 3D models generated from medical images not only have applications in bioprinting but have also proven to be valuable in the simulation of physiological functions. For example, they have been successfully used in the reproduction of forced spirometry tests, simulating airflow dynamics in the airways of individual patients. These simulations constitute a powerful tool for the evaluation and personalisation of medical treatments, expanding the scope of 3D models beyond the realm of bioprinting.

Acknowledgements

This research was funded by project BIOIMP_ACE_MAS_6_E, co-funded by the European Union through the Interreg VI-A Spain-Portugal Programme (POCTEP) 2021–2027.

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Figure 0

Figure 1. Detailed visualisation of the different anatomical sections.

Figure 1

Figure 2. Selection of the desired area using the ROI (region of Interest).

Figure 2

Table 1. Description of the tools available in 3D Slicer

Figure 3

Figure 3. Isolation of the desired anatomical structure using the Threshold tool.

Figure 4

Figure 4. Export of the isolated 3D object obtained from the DICOM file to 3D printable .STL format.

Figure 5

Figure 5. Uniform slicing by flat layers of the same thickness.

Figure 6

Figure 6. Adaptive planar slicing with layers of different heights.

Figure 7

Figure 7. Curved-layer slicing.

Figure 8

Figure 8. Conformal slicing where the cuts are carried out on a previously existing surface.

Figure 9

Figure 9. 5-Axis dynamic slicing where you can see the constant change of planes to generate the structure in the most optimal way.

Figure 10

Figure 10. Helical slicing where it can be seen that the trajectory is continuous and ascending.

Figure 11

Figure 11. Mixed-layer adaptive slicing where the combination of uniform slicing and 5-axis dynamic slicing can be observed.

Figure 12

Table 2. Bioprinting of tissues and the types of bioinks and cells used