Development of an inkjet calibration phantom for x-ray imaging studies

Georgiev, Tihomir; Kolev, Iliyan; Dukov, Nikolay; Mavrodinova, Stanislava; Yordanova, Mariana; Bliznakova, Kristina

doi:10.14748/ssm.v0i0.7410

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…An alternative solution for the production of anthropomorphic phantoms is the fabrication of paper-based phantoms with inks doped with contrast materials (Jahnke 2017). It allows the use of parchment papers and various specialised inks, based on a regular inkjet printer utilizing inks doped with iohexol, bismuth salt, silver nanoparticles or KI dissolved (Ikejimba et al 2017a, 2017b, Georgiev et al 2021. Threedimensional breast models have also been printed by using selective laser sintering (Mainprize et use of contrast materials only for the paper-based phantom or a single material for the other two aforementioned 3D printing technologies.…”

Section: Discussionmentioning

confidence: 99%

A voxel-by-voxel method for mixing two filaments during a 3D printing process for soft-tissue replication in an anthropomorphic breast phantom

Okkalidis¹,

Bliznakova²

2022

Phys. Med. Biol.

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Objective: In this study, a novel voxel-by-voxel mixing method is presented, according to which two filaments of different material are combined during the 3D printing process. Approach: In our approach, two types of filaments were used for the replication of soft-tissues, a polylactic acid (PLA) filament and a polypropylene (PP) filament. A custom-made software was used, while a series of breast patient CT scan images were directly associated to the 3D printing process. Each phantom´s layer was printed twice, once with the PLA filament and a second time with the PP filament. For each material, the filament extrusion rate was controlled voxel-by-voxel and was based on the HU of the imported CT images. The phantom was scanned at clinical CT, breast tomosynthesis and Micro CT facilities, as the major processing was performed on data from the CT. A side by side comparison between patient´s and phantom´s CT slices by means of profile and histogram comparison was accomplished. Further, in case of profile comparison, the Pearson´s coefficients were calculated. Main results: The visual assessment of the distribution of the glandular tissue in the CT slices of the printed breast anatomy showed high degree of radiological similarity to the corresponding patient´s glandular distribution. The profile plots´ comparison showed that the Hounsfield Units (HU) of the replicated and original patient soft tissues match adequately. In overall, the Pearson´s coefficients were above 0.91, suggesting a close match of the CT images of the phantom with those of the patient. The overall HU were close in terms of HU ranges. The HU mean, median and standard deviation of the original and the phantom CT slices were -149, -167, ±65, and -121, -130, ±91, respectively. Significance: The results suggest that the proposed methodology is appropriate for manufacturing of anthropomorphic soft tissue phantoms for x-ray imaging and dosimetry purposes, since it may offer an accurate replication of these tissues.

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Section: Discussionmentioning

confidence: 99%

A voxel-by-voxel method for mixing two filaments during a 3D printing process for soft-tissue replication in an anthropomorphic breast phantom

Okkalidis¹,

Bliznakova²

2022

Phys. Med. Biol.

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“…It is important to design phantoms that are consistent with intended application. Here, we focus on the ability for XRD imaging to correctly classify cancer and benign tissues based on their XRD spectra; therefore, for the design of the phantom objects, water and polylactic acid (PLA) plastic were identified as viable simulants for cancer 42 and adipose (fat) 43–45 (p3) tissue, respectively. Shown in Figure 2a are the XRD spectra of breast cancer and adipose tissue, 1 along with XRD spectra of water and PLA that we measured within a commercial diffractometer (D2 Phaser, Bruker, MA).…”

Section: Methodsmentioning

confidence: 99%

Application of machine learning classifiers to X‐ray diffraction imaging with medically relevant phantoms

2021

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Recent studies have demonstrated the ability to rapidly produce large field of view X-ray diffraction (XRD) images, which provide rich new data relevant to the understanding and analysis of disease. However, work has only just begun on developing algorithms that maximize the performance toward decision-making and diagnostic tasks. In this study, we present the implementation of and comparison between rules-based and machine learning (ML) classifiers on XRD images of medically relevant phantoms to explore the potential for increased classification performance. Methods: Medically relevant phantoms were utilized to provide wellcharacterized ground-truths for comparing classifier performance. Water and polylactic acid (PLA) plastic were used as surrogates for cancerous and healthy tissue, respectively, and phantoms were created with varying levels of spatial complexity and biologically relevant features for quantitative testing of classifier performance. Our previously developed X-ray scanner was used to acquire co-registered X-ray transmission and diffraction images of the phantoms. For classification algorithms, we explored and compared two rules-based classifiers (cross-correlation, or matched-filter, and linear least-squares unmixing) and two ML classifiers (support vector machines and shallow neural networks). Reference XRD spectra (measured by a commercial diffractometer) were provided to the rules-based algorithms, while 60% of the measured XRD pixels were used for training of the ML algorithms. The area under the receiver operating characteristic curve (AUC) was used as a comparative metric between the classification algorithms, along with the accuracy performance at the midpoint threshold for each classifier. Results:The AUC values for material classification were 0.994 (crosscorrelation [CC]), 0.994 (least-squares [LS]), 0.995 (support vector machine [SVM]), and 0.999 (shallow neural network [SNN]). Setting the classification threshold to the midpoint for each classifier resulted in accuracy values of CC = 96.48%, LS = 96.48%, SVM = 97.36%, and SNN = 98.94%. If only considering pixels ±3 mm from water-PLA boundaries (where partial volume effects could occur due to imaging resolution limits), the classification accuracies were CC = 89.32%, LS = 89.32%, SVM = 92.03%, and SNN = 96.79%, demonstrating an even larger improvement produced by the machine-learned algorithms in spatial regions critical for imaging tasks. Classification by transmission data alone produced an AUC of 0.773 and accuracy of 85.45%, well below the performance levels of any of the classifiers applied to XRD image data. Conclusions: We demonstrated that ML-based classifiers outperformed rulesbased approaches in terms of overall classification accuracy and improved the spatially resolved classification performance on XRD images of medical phantoms. In particular, the ML algorithms demonstrated considerably improved 532

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“…The association of the pixels' intensity with the 3D printing process was achieved by calibrating the utilized amounts of inks with the corresponding grayscale values. Thus, the process can be considered simple without the need of any additional software tool, other than a paper printing software (Jahnke et al 2016, Ikejimba et al 2017, Georgiev et al 2021. The most difficult part is the production of the mixture and the calibration.…”

Section: Printing Paper-based Phantoms With Radiopaque Inkmentioning

confidence: 99%

3D printing methods for radiological anthropomorphic phantoms

Okkalidis¹

2022

Phys. Med. Biol.

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Three dimensional (3D) printing technology has been widely evaluated for the fabrication of various anthropomorphic phantoms during the last couple of decades. The demand for such high quality phantoms is constantly rising and gaining an ever-increasing interest. Although, in a short time 3D printing technology provided phantoms with more realistic features when compared to the previous conventional methods, there are still several aspects to be explored. One of these aspects is the further development of the current 3D printing methods and software devoted for radiological applications. The current 3D printing software and methods usually employ 3D models, while the direct association of medical images with the 3D printing process is needed in order to provide results of higher accuracy and closer to the actual tissues’ texture. Another aspect of high importance is the development of suitable printing materials. Ideally, those materials should be able to emulate the entire range of soft and bone tissues, while still matching the human’s anatomy. Five types of 3D printing methods have been mainly investigated so far: a) solidification of photo-curing materials; b) deposition of melted plastic materials; c) printing paper-based phantoms with radiopaque ink; d) melting or binding plastic powder; and e) bio-printing. From the first and second category, polymer jetting technology and Fused Filament Fabrication (FFF), also known as Fused Deposition Modelling (FDM), are the most promising technologies for the fulfilment of the requirements of realistic and radiologically equivalent anthropomorphic phantoms. Another interesting approach is the fabrication of radiopaque paper-based phantoms using inkjet printers. Although, this may provide phantoms of high accuracy, the utilized materials during the fabrication process are restricted to inks doped with various contrast materials. A similar condition applies to the polymer jetting technology, which despite being quite fast and very accurate, the utilized materials are restricted to those capable of polymerization. The situation is better for FFF/FDM 3D printers, since various compositions of plastic filaments with external substances can be produced conveniently. Although, the speed and accuracy of this 3D printing method is lower compared to the others, the relatively low-cost, constantly improving resolution, sufficient printing volume and plethora of materials are quite promising for the creation of human size heterogeneous phantoms and their adaptation to the treatment procedures of patients in the current health systems.

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Development of an inkjet calibration phantom for x-ray imaging studies

Cited by 4 publications

References 19 publications

A voxel-by-voxel method for mixing two filaments during a 3D printing process for soft-tissue replication in an anthropomorphic breast phantom

A voxel-by-voxel method for mixing two filaments during a 3D printing process for soft-tissue replication in an anthropomorphic breast phantom

Application of machine learning classifiers to X‐ray diffraction imaging with medically relevant phantoms

3D printing methods for radiological anthropomorphic phantoms

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