Development of a 3D printed anthropomorphic lung phantom for image quality assessment in CT

Tailoring the Mass Density of 3D Printing Materials for Accurate X-ray Imaging Simulation by Controlled Underfilling for Radiographic Phantoms

Additive manufacturing and 3D printing allow for the design and rapid production of radiographic phantoms for X-ray imaging, including CT. These are used for numerous purposes, such as patient simulation, optimization of imaging procedures and dose levels, system evaluation and quality assurance. However, standard 3D printing polymers do not mimic X-ray attenuation properties of tissues like soft, adipose, lung or bone tissue, and standard materials like liquid water. The mass density of printing polymers-especially important in CT-is often inappropriate, i.e., mostly too high. Different methods can be applied to reduce mass density. This work examines reducing density by controlled underfilling either realized by using 3D printing materials expanded through foaming during heating in the printing process, or reducing polymer flow to introduce microscopic air-filled voids. The achievable density reduction depends on the base polymer used. When using foaming materials, density is controlled by the extrusion temperature, and ranges from 33 to 47% of the base polymer used, corresponding to a range of -650 to -394 HU in CT with 120 kV. Standard filaments (Nylon, modified PLA and modified ABS) allowed density reductions by 20 to 25%, covering HU values in CT from -260 to 77 (Nylon), -230 to -20 (ABS) and -81 to 143 (PLA). A standard chalk-filled PLA filament allowed reproduction of bone tissue in a wide range of bone mineral content resulting in CT numbers from 57 to 460 HU. Controlled underfilling allowed the production of radiographic phantom materials with continuously adjustable attenuation in a limited but appropriate range, allowing for the reproduction of X-ray attenuation properties of water, adipose, soft, lung, and bone tissue in an accurate, predictable and reproducible manner.

3D-printed iodine-ink CT phantom for radiomics feature extraction - advantages and challenges

BACKGROUND

To test and validate novel CT techniques, such as texture analysis in radiomics, repeat measurements are required. Current anthropomorphic phantoms lack fine texture and true anatomic representation. 3D-printing of iodinated ink on paper is a promising phantom manufacturing technique. Previously acquired or artificially created CT data can be used to generate realistic phantoms.

PURPOSE

To present the design process of an anthropomorphic 3D-printed iodine ink phantom, highlighting the different advantages and pitfalls in its use. To analyze the phantom's X-ray attenuation properties, and the influences of the printing process on the imaging characteristics, by comparing it to the original input dataset.

METHODS

Two patient CT scans and artificially generated test patterns were combined in a single dataset for phantom printing and cropped to a size of 26 × 19 × 30 cm3 . This DICOM dataset was printed on paper using iodinated ink. The phantom was CT-scanned and compared to the original image dataset used for printing the phantom. The water-equivalent diameter of the phantom was compared to that of a patient cohort (N = 104). Iodine concentrations in the phantom were measured using dual-energy CT. 86 radiomics features were extracted from 10 repeat phantom scans and the input dataset. Features were compared using a histogram analysis and a PCA individually and overall, respectively. The frequency content was compared using the normalized spectrum modulus.

RESULTS

Low density structures are depicted incorrectly, while soft tissue structures show excellent visual accordance with the input dataset. Maximum deviations of around 30 HU between the original dataset and phantom HU values were observed. The phantom has X-ray attenuation properties comparable to a lightweight adult patient (∼54 kg, BMI 19 kg/m2 ). Iodine concentrations in the phantom varied between 0 and 50 mg/ml. PCA of radiomics features shows different tissue types separate in similar areas of PCA representation in the phantom scans as in the input dataset. Individual feature analysis revealed systematic shift of first order radiomics features compared to the original dataset, while some higher order radiomics features did not. The normalized frequency modulus |f(ω)| of the phantom data agrees well with the original data. However, all frequencies systematically occur more frequently in the phantom compared to the maximum of the spectrum modulus than in the original data set, especially for mid-frequencies (e.g., for ω = 0.3942 mm-1 , |f(ω)|original  = 0.09 * |fmax |original and |f(ω)|phantom  = 0.12 * |fmax |phantom ).

CONCLUSIONS

3D-iodine-ink-printing technology can be used to print anthropomorphic phantoms with a water-equivalent diameter of a lightweight adult patient. Challenges include small residual air enclosures and the fidelity of HU values. For soft tissue, there is a good agreement between the HU values of the phantom and input data set. Radiomics texture features of the phantom scans are similar to the input data set, but systematic shifts of radiomics features in first order features, due to differences in HU values, need to be considered. The paper substrate influences the spatial frequency distribution of the phantom scans. This phantom type is of very limited use for dual-energy CT analyses.

A customized anthropomorphic 3D-printed phantom to reproducibility assessment in computed tomography: an oncological case study

Introduction

Studies on computed tomography (CT) reproducibility at different acquisition parameters have to take into account radiation dose administered and related ethical issues. 3D-printed phantoms provide the possibility to investigate these features deeply and to foster CT research, also taking advantage by outperforming new generation scanners. The aim of this study is to propose a new anthropomorphic 3D-printed phantom for chest lesions, tailored on a real patient CT scan, to investigate the variability of volume and Hounsfield Unit (HU) measurements at different CT acquisition parameters.

Methods

The chest CT of a 75-year-old patient with a paramediastinal lung lesion was segmented based on an eight-compartment approach related to HU ranges (air lung, lung interstitium, fat, muscle, vascular, skin, bone, and lesion). From each mask produced, the 3D.stl model was exported and linked to a different printing infill value, based on a preliminary test and HU ratios derived from the patient scan. Fused deposition modeling (FDM) technology printing was chosen with filament materials in polylactic acid (PLA). Phantom was acquired at 50 mAs and three different tube voltages of 80, 100, and 120 kVp on two different scanners, namely, Siemens Somatom Force (Siemens Healthineers, Erlangen, Germany; same setting of real patient for 80 kVp acquisition) and GE 750 HD CT (GE Healthcare, Chicago, IL). The same segmentation workflow was then applied on each phantom acquisition after coregistration pipeline, and Dice Similarity Coefficient (DSC) and HU averages were extracted and compared for each compartment.

Results

DSC comparison among real patient versus phantom scans at different kVp, and on both CT scanners, demonstrated a good overlap of different compartments and lesion vascularization with a higher similarity for lung and lesion masks for each setting (about 0.9 and 0.8, respectively). Although mean HU was not comparable with real data, due to the PLA material, the proportion of intensity values for each compartment remains respected.

Discussion

The proposed approach demonstrated the reliability of 3D-printed technology for personalized approaches in CT research, opening to the application of the same workflow to other oncological fields.

Towards using a multi-material, pellet-fed additive manufacturing platform to fabricate novel imaging phantoms

The design freedom afforded by additive manufacturing (AM) is now being leveraged across multiple applications, including many in the fields of imaging for personalised medicine. This study utilises a pellet-fed, multi-material AM machine as a route to fabricating new imaging phantoms, used for developing and refining algorithms for the detection of subtle soft tissue anomalies. Traditionally comprising homogeneous materials, higher-resolution scanning now allows for heterogeneous, multi-material phantoms. Polylactic acid (PLA), a thermoplastic urethane (TPU) and a thermoplastic elastomer (TPE) were investigated as potential materials. Manufacturing accuracy and precision were assessed relative to the digital design file, whilst the potential to achieve structural heterogeneity was evaluated by quantifying infill density via micro-computed tomography. Hounsfield units (HU) were also captured via a clinical scanner. The PLA builds were consistently too small, by 0.2 - 0.3%. Conversely, TPE parts were consistently larger than the digital file, though by only 0.1%. The TPU components had negligible differences relative to the specified sizes. The accuracy and precision of material infill were inferior, with PLA exhibiting greater and lower densities relative to the digital file, across the 3 builds. Both TPU and TPE produced infills that were too dense. The PLA material produced repeatable HU values, with poorer precision across TPU and TPE. All HU values tended towards, and some exceeded, the reference value for water (0 HU) with increasing infill density. These data have demonstrated that pellet-fed AM can produce accurate and precise structures, with the potential to include multiple materials providing an opportunity for more realistic and advanced phantom designs. In doing so, this will enable clinical scientists to develop more sensitive applications aimed at detecting ever more subtle variations in tissue, confident that their calibration models reflect their intended designs.

Observer-study-based approaches to quantitatively evaluate the realism of synthetic medical images

Objective.Synthetic images generated by simulation studies have a well-recognized role in developing and evaluating imaging systems and methods. However, for clinically relevant development and evaluation, the synthetic images must be clinically realistic and, ideally, have the same distribution as that of clinical images. Thus, mechanisms that can quantitatively evaluate this clinical realism and, ideally, the similarity in distributions of the real and synthetic images, are much needed.Approach.We investigated two observer-study-based approaches to quantitatively evaluate the clinical realism of synthetic images. In the first approach, we presented a theoretical formalism for the use of an ideal-observer study to quantitatively evaluate the similarity in distributions between the real and synthetic images. This theoretical formalism provides a direct relationship between the area under the receiver operating characteristic curve, AUC, for an ideal observer and the distributions of real and synthetic images. The second approach is based on the use of expert-human-observer studies to quantitatively evaluate the realism of synthetic images. In this approach, we developed a web-based software to conduct two-alternative forced-choice (2-AFC) experiments with expert human observers. The usability of this software was evaluated by conducting a system usability scale (SUS) survey with seven expert human readers and five observer-study designers. Further, we demonstrated the application of this software to evaluate a stochastic and physics-based image-synthesis technique for oncologic positron emission tomography (PET). In this evaluation, the 2-AFC study with our software was performed by six expert human readers, who were highly experienced in reading PET scans, with years of expertise ranging from 7 to 40 years (median: 12 years, average: 20.4 years).Main results.In the ideal-observer-study-based approach, we theoretically demonstrated that the AUC for an ideal observer can be expressed, to an excellent approximation, by the Bhattacharyya distance between the distributions of the real and synthetic images. This relationship shows that a decrease in the ideal-observer AUC indicates a decrease in the distance between the two image distributions. Moreover, a lower bound of ideal-observer AUC = 0.5 implies that the distributions of synthetic and real images exactly match. For the expert-human-observer-study-based approach, our software for performing the 2-AFC experiments is available athttps://apps.mir.wustl.edu/twoafc. Results from the SUS survey demonstrate that the web application is very user friendly and accessible. As a secondary finding, evaluation of a stochastic and physics-based PET image-synthesis technique using our software showed that expert human readers had limited ability to distinguish the real images from the synthetic images.Significance.This work addresses the important need for mechanisms to quantitatively evaluate the clinical realism of synthetic images. The mathematical treatment in this paper shows that quantifying the similarity in the distribution of real and synthetic images is theoretically possible by using an ideal-observer-study-based approach. Our developed software provides a platform for designing and performing 2-AFC experiments with human observers in a highly accessible, efficient, and secure manner. Additionally, our results on the evaluation of the stochastic and physics-based image-synthesis technique motivate the application of this technique to develop and evaluate a wide array of PET imaging methods.

Development of a patient-specific chest computed tomography imaging phantom with realistic lung lesions using silicone casting and three-dimensional printing

The validation of the accuracy of the quantification software in computed tomography (CT) images is very challenging. Therefore, we proposed a CT imaging phantom that accurately represents patient-specific anatomical structures and randomly integrates various lesions including disease-like patterns and lesions of various shapes and sizes using silicone casting and three-dimensional (3D) printing. Six nodules of various shapes and sizes were randomly added to the patient's modeled lungs to evaluate the accuracy of the quantification software. By using silicone materials, CT intensities suitable for the lesions and lung parenchyma were realized, and their Hounsfield unit (HU) values were evaluated on a CT scan of the phantom. As a result, based on the CT scan of the imaging phantom model, the measured HU values for the normal lung parenchyma, each nodule, fibrosis, and emphysematous lesions were within the target value. The measurement error between the stereolithography model and 3D-printing phantoms was 0.2 ± 0.18 mm. In conclusion, the use of 3D printing and silicone casting allowed the application and evaluation of the proposed CT imaging phantom for the validation of the accuracy of the quantification software in CT images, which could be applied to CT-based quantification and development of imaging biomarkers.

Design and CT imaging of casper, an anthropomorphic breathing thorax phantom

The goal of this work was to build an anthropomorphic thorax phantom capable of breathing motion with materials mimicking human tissues in x-ray imaging applications. The thorax phantom, named Casper, was composed of resin (body), foam (lungs), glow polyactic acid (bones) and natural polyactic acid (tumours placed in the lungs). X-ray attenuation properties of all materials prior to manufacturing were evaluated by means of photon-counting computed tomography (CT) imaging on a table-top system. Breathing motion was achieved by a scotch-yoke mechanism with diaphragm motion frequencies of 10-20 rpm and displacements of 1 to 2 cm. Casper was manufactured by means of 3D printing of moulds and ribs and assembled in a complex process. The final phantom was then scanned using a clinical CT scanner to evaluate material CT numbers and the extent of tumour motion. Casper CT numbers were close to human CT numbers for soft tissue (46 HU), ribs (125 HU), lungs (-840 HU) and tumours (-45 HU). For a 2 cm diaphragm displacement the largest tumour displacement was 0.7 cm. The five tumour volumes were accurately assessed in the static CT images with a mean absolute error of 4.3%. Tumour sizes were either underestimated for smaller tumours or overestimated for larger tumours in dynamic CT images due to motion blurring with a mean absolute difference from true volumes of 10.3%. More Casper information including a motion movie and manufacturing data can be downloaded from http://web.uvic.ca/~bazalova/Casper/.

Dual-energy CT evaluation of 3D printed materials for radiotherapy applications

Objective. There is a continuous increase in 3D printing applications in several fields including medical imaging and radiotherapy. Although there are numerous advantages of using 3D printing for the development of customized phantoms, bolus, quality assurance devices and other clinical applications, material properties are not well known and printer settings can affect considerably the properties (e.g. density, isotropy and homogeneity) of the printed parts. This study aims to evaluate several materials and printer properties to identify a range of tissue-mimicking materials.Approach. Dual-energy CT was used to obtain the effective atomic number (Z_eff) and relative electron density (RED) for thirty-one different materials including different colours of the same filament from the same manufacturer and the same type of filament from different manufacturers. In addition, a custom bone equivalent filament was developed and evaluated since a high-density filament with a composition similar to bone is not commercially available. Printing settings such as infill density, infill pattern, layer height and nozzle size were also evaluated.Main results. Large differences were observed for HU (288), RED (>10%) andZ_eff(>50%) for different colours of the same filament due to the colour pigment. Results show a wide HU variation (-714 to 1104), RED (0.277 to 1.480) andZ_eff(5.22 to 12.39) between the printed samples with some materials being comparable to commercial tissue-mimicking materials and good substitutes to a range of materials from lung to bone. Printer settings can result in directional dependency and significantly affect the homogeneity of the samples.Significance. The use of DECT to extract RED, andZ_effallows for quantitative imaging and dosimetry using 3D printed materials equivalent to certified tissue-mimicking tissues.

Attenuation coefficient in the energy range 14–36 keV of 3D printing materials for physical breast phantoms

Objective. To measure the monoenergetic x-ray linear attenuation coefficient, μ, of fused deposition modeling (FDM) colored 3D printing materials (ABS, PLAwhite, PLAorange, PET and NYLON), used as adipose, glandular or skin tissue substitutes for manufacturing physical breast phantoms. Approach. Attenuation data (at 14, 18, 20, 24, 28, 30 and 36 keV) were acquired at Elettra synchrotron radiation facility, with step-wedge objects, using the Lambert–Beer law and a CCD imaging detector. Test objects were 3D printed using the Ultimaker 3 FDM printer. PMMA, Nylon-6 and high-density polyethylene step objects were also investigated for the validation of the proposed methodology. Printing uniformity was assessed via monoenergetic and polyenergetic imaging (32 kV, W/Rh). Main results. Maximum absolute deviation of μ for PMMA, Nylon-6 and HD-PE was 5.0%, with reference to literature data. For ABS and NYLON, μ differed by less than 6.1% and 7.1% from that of adipose tissue, respectively; for PET and PLAorange the difference was less than 11.3% and 6.3% from glandular tissue, respectively. PLAorange is a good substitute of skin (differences from −9.4% to +1.2%). Hence, ABS and NYLON filaments are suitable adipose tissue substitutes, while PET and PLAorange mimick the glandular tissue. PLAwhite could be printed at less than 100% infill density for matching the attenuation of glandular tissue, using the measured density calibration curve. The printing mesh was observed for sample thicknesses less than 60 mm, imaged in the direction normal to the printing layers. Printing dimensional repeatability and reproducibility was less 1%. Significance. For the first time an experimental determination was provided of the linear attenuation coefficient of common 3D printing filament materials with estimates of μ at all energies in the range 14–36 keV, for their use in mammography, breast tomosynthesis and breast computed tomography investigations.

X-ray attenuation of bone, soft and adipose tissue in CT from 70 to 140 kV and comparison with 3D printable additive manufacturing materials

Additive manufacturing and 3D printing are widely used in medical imaging to produce phantoms for image quality optimization, imaging protocol definition, comparison of image quality between different imaging systems, dosimetry, and quality control. Anthropomorphic phantoms mimic tissues and contrasts in real patients with regard to X-ray attenuation, as well as dependence on X-ray spectra. If used with different X-ray energies, or to optimize the spectrum for a certain procedure, the energy dependence of the attenuation must replicate the corresponding energy dependence of the tissues mimicked, or at least be similar. In the latter case the materials’ Hounsfield values need to be known exactly to allow to correct contrast and contrast to noise ratios accordingly for different beam energies. Fresh bovine and porcine tissues including soft and adipose tissues, and hard tissues from soft spongious bone to cortical bone were scanned at different energies, and reference values of attenuation in Hounsfield units (HU) determined. Mathematical model equations describing CT number dependence on kV for bones of arbitrary density, and for adipose tissues are derived. These data can be used to select appropriate phantom constituents, compare CT values with arbitrary phantom materials, and calculate correction factors for phantoms consisting of materials with an energy dependence different to the tissues. Using data on a wide number of additive manufacturing and 3D printing materials, CT numbers and their energy dependence were compared to those of the tissues. Two commercially available printing filaments containing calcium carbonate powder imitate bone tissues with high accuracy at all kV values. Average adipose tissue can be duplicated by several off-the-shelf printing polymers. Since suitable printing materials typically exhibit a too high density for the desired attenuation of especially soft tissues, controlled density reduction by underfilling might improve tissue equivalence.

Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study

Background

Recently, computed tomography (CT) manufacturers have developed deep‐learning‐based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels.

Purpose

To assess the impact of an artificial intelligence deep‐learning reconstruction (AI‐DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications.

Methods

Acquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU‐41 phantoms were performed at five dose levels (CTDI_vol: 9.5/7.5/6/2.5/0.4 mGy) used for chest CT conditions. Raw data were reconstructed using filtered backprojection, two levels of IR (iDose⁴ levels 4 (i4) and 7 (i7)), and five levels of AI‐DLR (Precise Image; Smoother, Smooth, Standard, Sharp, Sharper). Noise power spectrum (NPS), task‐based transfer function, and detectability index (d′) were computed: d′‐modeled detection of a soft tissue mediastinal nodule (low‐contrast soft tissue chest nodule within the mediastinum [LCN]), ground‐glass opacity (GGO), or high‐contrast pulmonary (HCP) lesion. The subjective image quality of chest anthropomorphic phantom images was independently evaluated by two radiologists. They assessed image noise, image smoothing, contrast between vessels and fat in the mediastinum for mediastinal images, visual border detection between bronchus and lung parenchyma for parenchymal images, and overall image quality using a commonly used four‐ or five‐point scale.

Results

From Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: −66.3% ± 0.5% for mediastinal images and −63.1% ± 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: −35.3% ± 2.2% for mediastinal images and −13.3% ± 2.2% for parenchymal images), and the detectability (d′) of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low‐contrast polyethylene insert and the opposite for the high‐contrast air insert. Compared to the i4 used in clinical practice, d′ values were higher using Smoother (mean for all dose levels: 338.7% ± 29.4%), Smooth (103.4% ± 11.2%), and Standard (34.1% ± 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% ± 53.2% for GGO and 136.9% ± 1.6% for HCP) and Smooth (36.4% ± 22.1% and 24.1% ± 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required.

Conclusion

With AI‐DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.

PixelPrint: Three-dimensional printing of realistic patient-specific lung phantoms for CT imaging

Phantoms are essential tools for assessing and verifying performance in computed tomography (CT). Realistic patient-based lung phantoms that accurately represent textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D-printing solution to create patient-specific lung phantoms with accurate contrast and textures. PixelPrint converts patient images directly into printer instructions, where density is modeled as the ratio of filament to voxel volume to emulate local attenuation values. For evaluation of PixelPrint, phantoms based on four COVID-19 pneumonia patients were manufactured and scanned with the original (clinical) CT scanners and protocols. Density and geometrical accuracies between phantom and patient images were evaluated for various anatomical features in the lung, and a radiomic feature comparison was performed for mild, moderate, and severe COVID-19 pneumonia patient-based phantoms. Qualitatively, CT images of the patient-based phantoms closely resemble the original CT images, both in texture and contrast levels, with clearly visible vascular and parenchymal structures. Regions-of-interest (ROIs) comparing attenuation demonstrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist revealed a high degree of geometrical correlation between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the images. Radiomic feature analysis revealed high correspondence, with correlations of 0.95-0.99 between patient and phantom images. Our study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate geometry, texture, and contrast that will enable protocol optimization, CT research and development advancements, and generation of ground-truth datasets for radiomic evaluations.

Three-dimensional printing of patient-specific lung phantoms for CT imaging: Emulating lung tissue with accurate attenuation profiles and textures

PURPOSE

Phantoms are a basic tool for assessing and verifying performance in CT research and clinical practice. Patient-based realistic lung phantoms accurately representing textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D printing solution to create patient-based lung phantoms with accurate attenuation profiles and textures.

METHODS

PixelPrint, a software tool, was developed to convert patient digital imaging and communications in medicine (DICOM) images directly into FDM printer instructions (G-code). Density was modeled as the ratio of filament to voxel volume to emulate attenuation profiles for each voxel, with the filament ratio controlled through continuous modification of the printing speed. A calibration phantom was designed to determine the mapping between filament line width and Hounsfield units (HU) within the range of human lungs. For evaluation of PixelPrint, a phantom based on a single human lung slice was manufactured and scanned with the same CT scanner and protocol used for the patient scan. Density and geometrical accuracy between phantom and patient CT data were evaluated for various anatomical features in the lung.

RESULTS

For the calibration phantom, measured mean HU show a very high level of linear correlation with respect to the utilized filament line widths, (r > 0.999). Qualitatively, the CT image of the patient-based phantom closely resembles the original CT image both in texture and contrast levels (from -800 to 0 HU), with clearly visible vascular and parenchymal structures. Regions of interest comparing attenuation illustrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist reveal a high degree of geometrical correlation of details between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the scans.

CONCLUSION

The present study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate organ geometry, image texture, and attenuation profiles. PixelPrint will enable applications in the research and development of CT technology, including further development in radiomics.

Experimental validation of daily adaptive proton therapy

Anatomical changes during proton therapy require rapid treatment plan adaption to mitigate the associated dosimetric impact. This in turn requires a highly efficient workflow that minimizes the time between imaging and delivery. At the Paul Scherrer Institute, we have developed an online adaptive workflow, which is specifically designed for treatments in the skull-base/cranium, with the focus set on simplicity and minimizing changes to the conventional workflow. The dosimetric and timing performance of this daily adaptive proton therapy (DAPT) workflow has been experimentally investigated using an in-house developed DAPT software and specifically developed anthropomorphic phantom. After a standard treatment preparation, which includes the generation of a template plan, the treatment can then be adapted each day, based on daily imaging acquired on an in-room CT. The template structures are then rigidly propagated to this CT and the daily plan is fully re-optimized using the same field arrangement, DVH constraints and optimization settings of the template plan. After a dedicated plan QA, the daily plan is delivered. To minimize the time between imaging and delivery, clinically integrated software for efficient execution of all online adaption steps, as well as tools for comprehensive and automated QA checks, have been developed. Film measurements of an end-to-end validation of a multi-fraction DAPT treatment showed high agreement to the calculated doses. Gamma pass rates with a 3%/3 mm criteria were >92% when comparing the measured dose to the template plan. Additionally, a gamma pass rate >99% was found comparing measurements to the Monte Carlo dose of the daily plans reconstructed from the logfile, accumulated over the delivered fractions. With this, we experimentally demonstrate that the described adaptive workflow can be delivered accurately in a timescale similar to a standard delivery.

3D Printing of Physical Organ Models: Recent Developments and Challenges

Physical organ models are the objects that replicate the patient-specific anatomy and have played important roles in modern medical diagnosis and disease treatment. 3D printing, as a powerful multi-function manufacturing technology, breaks the limitations of traditional methods and provides a great potential for manufacturing organ models. However, the clinical application of organ model is still in small scale, facing the challenges including high cost, poor mimicking performance and insufficient accuracy. In this review, the mainstream 3D printing technologies are introduced, and the existing manufacturing methods are divided into "directly printing" and "indirectly printing", with an emphasis on choosing suitable techniques and materials. This review also summarizes the ideas to address these challenges and focuses on three points: 1) what are the characteristics and requirements of organ models in different application scenarios, 2) how to choose the suitable 3D printing methods and materials according to different application categories, and 3) how to reduce the cost of organ models and make the process simple and convenient. Moreover, the state-of-the-art in organ models are summarized and the contribution of 3D printed organ models to various surgical procedures is highlighted. Finally, current limitations, evaluation criteria and future perspectives for this emerging area are discussed.

Evaluation of a no-reference image quality metric for projection X-ray imaging using a 3D printed patient-specific phantom

PURPOSE

Feasability of a no-reference image quality metric was assessed on patient-like images using a patient-specific phantom simulating a frame of a coronary angiogram.

METHODS

One background and one contrast-filled frame of a coronary angiogram, acquired using a clinical imaging protocol, were selected from a Philips Integris Allura FD (Philips Healthcare, Best, The Netherlands). The background frame's pixels were extruded to a thickness proportional to their grey value. One phantom was 3D printed using composite 80% bronze filament (max. thickness of 5.1 mm), the other was a custom PMMA cast (max thickness of 8.5 cm). A vessel mold was created from the contrast-filled frame and injected with a solution of 320 mg I/ml contrast fluid (75%), water and gelatin. Still X-ray frames of the vessel mold + background phantom + 16 cm PMMA were acquired at manually selected different exposure settings using a Philips Azurion (Philips Healthcare, Best, The Netherlands) in User Quality Control Mode and were exported as RAW images. The signal-difference-to-noise-ratio-squared (SDNR²) and a spatial-domain-equivalent of the noise equivalent quanta (NEQ_SDE) were calculated. The Spearman's correlation of the latter parameters with a no-reference perceptual image quality metric (NIQE) was investigated.

RESULTS

The bronze phantom showed better resemblance to the original patient frame selected from a coronary angiogram of an actual patient, with better contrast and less blur than the PMMA phantom. Both phantoms were imaged using a comparable imaging protocol to the one used to acquire the original frame. The bronze phantom was hence used together with the vessel mold for image quality measurements on the 165 still phantom frames. A strong correlation was noted between NEQ_SDE and NIQE (SROCC = -0.99, p < 0.0005) and between SDNR² and NIQE (SROCC = -0.97, p < 0.0005).

CONCLUSION

Using a cost-effective and easy to realize patient-specific phantom we were able to generate patient-like X-ray frames. NIQE as a no-reference image quality model has the potential to predict physical image quality from patient images.

Simulating Tissues with 3D-Printed and Castable Materials

Manufacturing technologies continue to be developed and utilized in medical prototyping, simulations, and imaging phantom production. For radiologic image-guided simulation and instruction, models should ideally have similar imaging characteristics and physical properties to the tissues they replicate. Due to the proliferation of different printing technologies and materials, there is a diverse and broad range of approaches and materials to consider before embarking on a project. Although many printed materials' biomechanical parameters have been reported, no manufacturer includes medical imaging properties that are essential for realistic phantom production. We hypothesize that there are now ample materials available to create high-fidelity imaging anthropomorphic phantoms using 3D printing and casting of common commercially available materials. A material database of radiological, physical, manufacturing, and economic properties for 29 castable and 68 printable materials was generated from samples fabricated by the authors or obtained from the manufacturer and scanned with CT at multiple tube voltages. This is the largest study assessing multiple different parameters associated with 3D printing to date. These data are being made freely available on GitHub, thus affording medical simulation experts access to a database of relevant imaging characteristics of common printable and castable materials. Full data available at: https://github.com/nmcross/Material-Imaging-Characteristics .

Additively Manufactured Patient-Specific Anthropomorphic Thorax Phantom With Realistic Radiation Attenuation Properties

Conventional medical imaging phantoms are limited by simplified geometry and radiographic skeletal homogeneity, which confines their usability for image quality assessment and radiation dosimetry. These challenges can be addressed by additive manufacturing technology, colloquially called 3D printing, which provides accurate anatomical replication and flexibility in material manipulation. In this study, we used Computed Tomography (CT)-based modified PolyJet^TM 3D printing technology to print a hollow thorax phantom simulating skeletal morphology of the patient. To achieve realistic heterogenous skeletal radiation attenuation, we developed a novel radiopaque amalgamate constituting of epoxy, polypropylene and bone meal powder in twelve different ratios. We performed CT analysis for quantification of material radiodensity (in Hounsfield Units, HU) and for identification of specific compositions corresponding to the various skeletal structures in the thorax. We filled the skeletal structures with their respective radiopaque amalgamates. The phantom and isolated 3D printed rib specimens were rescanned by CT for reproducibility tests regarding verification of radiodensity and geometry. Our results showed that structural densities in the range of 42–705HU could be achieved. The radiodensity of the reconstructed phantom was comparable to the three skeletal structures investigated in a real patient thorax CT: ribs, ventral vertebral body and dorsal vertebral body. Reproducibility tests based on physical dimensional comparison between the patient and phantom CT-based segmentation displayed 97% of overlap in the range of 0.00–4.57 mm embracing the anatomical accuracy. Thus, the additively manufactured anthropomorphic thorax phantom opens new vistas for imaging- and radiation-based patient care in precision medicine.

Anthropomorphic phantom for deformable lung and liver CT and MR imaging for radiotherapy

In this study, a functioning and ventilated anthropomorphic phantom was further enhanced for the purpose of CT and MR imaging of the lung and liver. A deformable lung, including respiratory tract was 3D printed. Within the lung's inner structures is a solid region shaped from a patient's lung tumour and six nitro-glycerine capsules as reference landmarks. The full internal mesh was coated, and the tumour filled, with polyorganosiloxane based gel. A moulded liver was created with an external casing of silicon filled with polyorganosiloxane gel and flexible plastic internal structures. The liver, fitted to the inferior portion of the right lung, moves along with the lung's ventilation. In the contralateral side, a cavity is designed to host a dosimeter, whose motion is correlated to the lung pressure. A 4DCT of the phantom was performed along with static and 4D T1 weighted MR images. The CT Hounsfield units (HU) for the flexible 3D printed material were -600-100 HU (lung and liver structures), for the polyorganosiloxane gel 30-120 HU (lung coating and liver filling) and for the silicon 650-800 HU (liver casing). The MR image intensity units were 0-40, 210-280 and 80-130, respectively. The maximum range of motion in the 4D imaging for the superior lung was 1-3.5 mm and 3.5-8 mm in the inferior portion. The liver motion was 5.5-8.0 mm at the tip and 5.7-10.0 mm at the dome. No measurable drift in motion was observed over a 2 h session and motion was reproducible over three different sessions for sin²(t), sin⁴(t) and a patient-like breathing curve with the interquartile range of amplitudes for all breathing cycles within 0.5 mm. The addition of features within the lung and of a deformable liver will allow the phantom to be used for imaging studies such as validation of 4DMRI and pseudo CT methods.

Development of a CT imaging phantom of anthromorphic lung using fused deposition modeling 3D printing

Development of patient-specific CT imaging phantoms with randomly incorporated lesions of various shapes and sizes for calibrating image intensity and validating quantitative measurement software is very challenging. In this investigation, a physical phantom that accurately represents a patient's specific anatomy and the intensity of lung CT images at the voxel level will be fabricated using fused deposition modeling (FDM) 3D printing. Segmentation and modeling of a patient's CT data were performed by an expert and the results were confirmed by a thoracic radiologist with more than 20 years of experience. This facilitated the extraction of the details of the patient's anatomy; various kinds of nodules with different shapes and sizes were randomly added to the modeled lung for evaluating the size-accuracy of the quantification software. To achieve these Hounsfield Units (HU) ranges for the corresponding voxels in acquired CT scans, the infill ratios of FDM 3D printing were controlled. Based on CT scans of the 3D printed phantoms, the measured HU for normal pulmonary parenchyma, ground glass opacity (GGO), and solid nodules were determined to be within target HU ranges. The accuracy of the mean absolute difference and the mean relative difference of nodules were less than 0.55 ± 0.30 mm and 3.72 ± 1.64% (mean difference ± 95 CI), respectively. Patient-specific CT imaging phantoms were designed and manufactured using an FDM printer, which could be applied for the precise calibration of CT intensity and the validation of image quantification software.

Automatic quantitative analysis of pulmonary vascular morphology in CT images

PURPOSE

Vascular remodeling is a significant pathological feature of various pulmonary diseases, which may be assessed by quantitative computed tomography (CT) imaging. The purpose of this study was therefore to develop and validate an automatic method for quantifying pulmonary vascular morphology in CT images.

METHODS

The proposed method consists of pulmonary vessel extraction and quantification. For extracting pulmonary vessels, a graph-cuts-based method is proposed which considers appearance (CT intensity) and shape (vesselness from a Hessian-based filter) features, and incorporates distance to the airways into the cost function to prevent false detection of airway walls. For quantifying the extracted pulmonary vessels, a radius histogram is generated by counting the occurrence of vessel radii, calculated from a distance transform-based method. Subsequently, two biomarkers, slope α and intercept β, are calculated by linear regression on the radius histogram. A public data set from the VESSEL12 challenge was used to independently evaluate the vessel extraction. The quantitative analysis method was validated using images of a three-dimensional (3D) printed vessel phantom, scanned by a clinical CT scanner and a micro-CT scanner (to obtain a gold standard). To confirm the association between imaging biomarkers and pulmonary function, 77 scleroderma patients were investigated with the proposed method.

RESULTS

In the independent evaluation with the public data set, our vessel segmentation method obtained an area under the receiver operating characteristic (ROC) curve of 0.976. The median radius difference between clinical and micro-CT scans of a 3D printed vessel phantom was 0.062 ± 0.020 mm, with interquartile range of 0.199 ± 0.050 mm. In the studied patient group, a significant correlation between diffusion capacity for carbon monoxide and the biomarkers, α (R = -0.27, P = 0.018) and β (R = 0.321, P = 0.004), was obtained.

CONCLUSION

In conclusion, the proposed method was validated independently using a public data set resulting in an area under the ROC curve of 0.976 and using a 3D printed vessel phantom data set, showing a vessel sizing error of 0.062 mm (0.16 in-plane pixel units). The correlation between imaging biomarkers and diffusion capacity in a clinical data set confirmed an association between lung structure and function. This quantification of pulmonary vascular morphology may be helpful in understanding the pathophysiology of pulmonary vascular diseases.