Assessing the fit of 3D printed bolus from CT, optical scanner and photogrammetry methods

Additive manufacturing of patient specific bolus for radiotherapy: large scale production and quality assurance

Bolus is commonly used to improve dose distributions in radiotherapy in particular if dose to skin must be optimised such as in breast or head and neck cancer. We are documenting four years of experience with 3D printed bolus at a large cancer centre. In addition to this we review the quality assurance (QA) program developed to support it. More than 2000 boluses were produced between Nov 2018 and Feb 2023 using fused deposition modelling (FDM) printing with polylactic acid (PLA) on up to five Raise 3D printers. Bolus is designed in the radiotherapy treatment planning system (Varian Eclipse), exported to an STL file followed by pre-processing. After checking each bolus with CT scanning initially we now produce standard quality control (QC) wedges every month and whenever a major change in printing processes occurs. A database records every bolus printed and manufacturing details. It takes about 3 days from designing the bolus in the planning system to delivering it to treatment. A 'premium' PLA material (Spidermaker) was found to be best in terms of homogeneity and CT number consistency (80 HU +/- 8HU). Most boluses were produced for photon beams (93.6%) with the rest used for electrons. We process about 120 kg of PLA per year with a typical bolus weighing less than 500 g and the majority of boluses 5 mm thick. Print times are proportional to bolus weight with about 24 h required for 500 g material deposited. 3D printing using FDM produces smooth and reproducible boluses. Quality control is essential but can be streamlined.

Three-dimensional printing of the human lung pleural cavity model for PDT malignant mesothelioma

OBJECTIVE

The primary aim was to investigate emerging 3D printing and optical acquisition technologies to refine and enhance photodynamic therapy (PDT) dosimetry in the management of malignant pleural mesothelioma (MPM).

MATERIALS AND METHODS

A rigorous digital reconstruction of the pleural lung cavity was conducted utilizing 3D printing and optical scanning methodologies. These reconstructions were systematically assessed against CT-derived data to ascertain their accuracy in representing critical anatomic features and post-resection topographical variations.

RESULTS

The resulting reconstructions excelled in their anatomical precision, proving instrumental translation for precise dosimetry calculations for PDT. Validation against CT data confirmed the utility of these models not only for enhancing therapeutic planning but also as critical tools for educational and calibration purposes.

CONCLUSION

The research outlined a successful protocol for the precise calculation of light distribution within the complex environment of the pleural cavity, marking a substantive advance in the application of PDT for MPM. This work holds significant promise for individualizing patient care, minimizing collateral radiation exposure, and improving the overall efficiency of MPM treatments.

Evaluation of 3D-printed bolus for radiotherapy using megavoltage X-ray beams

A radiotherapy bolus is a tissue-equivalent material placed on the skin to adjust the surface dose of megavoltage X-ray beams used for treatment. In this study, the dosimetric properties of two 3D-printed filament materials, polylactic acid (PLA) and thermoplastic polyether urethane (TPU), used as radiotherapy boluses, were investigated. The dosimetric properties of PLA and TPU were compared with those of several conventional bolus materials and RMI457 Solid Water. Percentage depth-dose (PDD) measurements in the build-up region were performed for all materials using 6 and 10 MV photon treatment beams on Varian linear accelerators. The results showed that the differences in the PDDs of the 3D-printed materials from the RMI457 Solid Water were within 3%, whereas those of the dental wax and SuperFlab gel materials were within 5%. This indicates that PLA and TPU 3D-printed materials are suitable radiotherapy bolus materials.

Synthetic cranial MRI from 3D optical surface scans using deep learning for radiation therapy treatment planning

BACKGROUND

Optical scanning technologies are increasingly being utilised to supplement treatment workflows in radiation oncology, such as surface-guided radiotherapy or 3D printing custom bolus. One limitation of optical scanning devices is the absence of internal anatomical information of the patient being scanned. As a result, conventional radiation therapy treatment planning using this imaging modality is not feasible. Deep learning is useful for automating various manual tasks in radiation oncology, most notably, organ segmentation and treatment planning. Deep learning models have also been used to transform MRI datasets into synthetic CT datasets, facilitating the development of MRI-only radiation therapy planning.

AIMS

To train a pix2pix generative adversarial network to transform 3D optical scan data into estimated MRI datasets for a given patient to provide additional anatomical data for a select few radiation therapy treatment sites. The proposed network may provide useful anatomical information for treatment planning of surface mould brachytherapy, total body irradiation, and total skin electron therapy, for example, without delivering any imaging dose.

METHODS

A 2D pix2pix GAN was trained on 15,000 axial MRI slices of healthy adult brains paired with corresponding external mask slices. The model was validated on a further 5000 previously unseen external mask slices. The predictions were compared with the "ground-truth" MRI slices using the multi-scale structural similarity index (MSSI) metric. A certified neuro-radiologist was subsequently consulted to provide an independent review of the model's performance in terms of anatomical accuracy and consistency. The network was then applied to a 3D photogrammetry scan of a test subject to demonstrate the feasibility of this novel technique.

RESULTS

The trained pix2pix network predicted MRI slices with a mean MSSI of 0.831 ± 0.057 for the 5000 validation images indicating that it is possible to estimate a significant proportion of a patient's gross cranial anatomy from a patient's exterior contour. When independently reviewed by a certified neuro-radiologist, the model's performance was described as "quite amazing, but there are limitations in the regions where there is wide variation within the normal population." When the trained network was applied to a 3D model of a human subject acquired using optical photogrammetry, the network could estimate the corresponding MRI volume for that subject with good qualitative accuracy. However, a ground-truth MRI baseline was not available for quantitative comparison.

CONCLUSIONS

A deep learning model was developed, to transform 3D optical scan data of a patient into an estimated MRI volume, potentially increasing the usefulness of optical scanning in radiation therapy planning. This work has demonstrated that much of the human cranial anatomy can be predicted from the external shape of the head and may provide an additional source of valuable imaging data. Further research is required to investigate the feasibility of this approach for use in a clinical setting and further improve the model's accuracy.

Evaluation of the accuracy of dental casts manufactured with 3D printing technique in the All-on-4 treatment concept

PURPOSE

The aim of this study is to compare the casts obtained by using conventional techniques and liquid crystal display (LCD) three-dimensional (3D) print techniques in the All-on-4 treatment concept of the edentulous mandibular jaw.

MATERIALS AND METHODS

In this study, a completely edentulous mandibular acrylic cast (typodont) with bone-level implants placed with the All-on-4 technique served as a reference cast. In this typodont, impressions were taken with the conventional technique and dental stone casts were obtained. In addition, after scanning the acrylic cast in a dental laboratory scanner and obtaining the Standard Tessellation Language (STL) data, 3D printed casts were manufactured with a 3D printing device based on the design. The stone and 3D printed casts were scanned in the laboratory scanner and STL data were obtained, and then the interimplant distances were measured using Geomagic Control X v2020 (3D Systems, Rock Hill, SC, USA) analysis software (n = 60). The obtained data were statistically evaluated with one-way analysis of variance (ANOVA) and Tukey's pairwise comparison tests.

RESULTS

As a result of the one-way ANOVA test, it was determined that the stone casts, 3D printed casts, and reference cast values in all distance intervals conformed to the normal distribution and these values had a significant difference among them in all distance intervals. In Tukey pairwise comparison test, significant differences were found between casts at all distance intervals. In all analyses, the level of significance was determined as .05.

CONCLUSION

3D printed casts obtained with a 3D LCD printing device can be an alternative to stone casts when implants are placed in edentulous jaws.

Evaluation of optical 3D scanning system for radiotherapy use

INTRODUCTION

Optical three-dimensional scanning devices can produce geometrically accurate, high-resolution models of patients suitable for clinical use. This article describes the use of a metrology-grade structured light scanner for the design and production of radiotherapy medical devices and synthetic water-equivalent computer tomography images.

METHODS

Following commissioning of the device by scanning objects of known properties, 173 scans were performed on 26 volunteers, with observations of subjects and operators collected.

RESULTS

The fit of devices produced using these scans was assessed, and a workflow for the design of complex devices using a treatment planning system was identified.

CONCLUSIONS

Recommendations are provided on the use of the device within a radiation oncology department.

A dosimetric comparison of CT- and photogrammetry- generated 3D printed HDR brachytherapy surface applicators

In this study, we investigate whether an acceptable dosimetric plan can be obtained for a brachytherapy surface applicator designed using photogrammetry and compare the plan quality to a CT-derived applicator. The nose region of a RANDO anthropomorphic phantom was selected as the treatment site due to its high curvature. Photographs were captured using a Nikon D5600 DSLR camera and reconstructed using Agisoft Metashape while CT data was obtained using a Canon Aquillion scanner. Virtual surface applicators were designed in Blender and printed with PLA plastic. Treatment plans with a prescription dose of 3.85 Gy × 10 fractions with 100% dose to PTV on the bridge of the nose at 2 mm depth were generated separately using AcurosBV in the Varian BrachyVision TPS. PTV D_98%, D_90% and V_100%, and OAR D_0.1cc, D_2cc and V_50% dose metrics and dwell times were evaluated, with the applicator fit assessed by air-gap volume measurements. Both types of surface applicators were printed with minimal defects and visually fitted well to the target area. The measured air-gap volume between the photogrammetry applicator and phantom surface was 44% larger than the CT-designed applicator, with a mean air gap thickness of 3.24 and 2.88 mm, respectively. The largest difference in the dose metric observed for the PTV and OAR was the PTV V_100% of - 1.27% and skin D_0.1cc of - 0.28%. PTV D_98% and D_90% and OAR D_2cc and V_50% for the photogrammetry based plan were all within 0.5% of the CT based plan. Total dwell times were also within 5%. A 3D printed surface applicator for the nose was successfully constructed using photogrammetry techniques. Although it produced a larger air gap between the surface applicator and phantom surface, a clinically acceptable dose plan was created with similar PTV and OAR dose metrics to the CT-designed applicator. Additional future work is required to comprehensively evaluate its suitability in a clinically environment.

Determining tolerance levels for quality assurance of 3D printed bolus for modulated arc radiotherapy of the nose

Given the existing literature on the subject, there is obviously a need for specific advice on quality assurance (QA) tolerances for departments using or implementing 3D printed bolus for radiotherapy treatments. With a view to providing initial suggested QA tolerances for 3D printed bolus, this study evaluated the dosimetric effects of changes in bolus geometry and density, for a particularly common and challenging clinical situation: specifically, volumetric modulated arc therapy (VMAT) treatment of the nose. Film-based dose verification measurements demonstrated that both the AAA and the AXB algorithms used by the Varian Eclipse treatment planning system (Varian Medical Systems, Palo Alto, USA) were capable of providing sufficiently accurate dose calculations to allow this planning system to be used to evaluate the effects of bolus errors on dose distributions from VMAT treatments of the nose. Thereafter, the AAA and AXB algorithms were used to calculate the dosimetric effects of applying a range of simulated errors to the design of a virtual bolus, to identify QA tolerances that could be used to avoid clinically significant effects from common printing errors. Results were generally consistent, whether the treatment target was superficial and treated with counter-rotating coplanar arcs or more-penetrating and treated with noncoplanar arcs, and whether the dose was calculated using the AAA algorithm or the AXB algorithm. The results of this study suggest the following QA tolerances are advisable, when 3D printed bolus is fabricated for use in photon VMAT treatments of the nose: bolus relative electron density variation within [Formula: see text] (although an action level at [Formula: see text] may be permissible); bolus thickness variation within [Formula: see text] mm (or 0.5 mm variation on opposite sides); and air gap between bolus and skin [Formula: see text] mm. These tolerances should be investigated for validity with respect to other treatment modalities and anatomical sites. This study provides a set of baselines for future comparisons and a useful method for identifying additional or alternative 3D printed bolus QA tolerances.