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Experimental investigations of the PMMA bone cement distribution inside a model of lumbar vertebrae

Bonik Artur, Tyfa Zbigniew, Ciupik Lechosław Franciszek, Sobczak Krzysztof, Kierzkowska Agnieszka, Obidowski Damian Stanisław, Witkowski Dariusz, Pałczyński Tomasz, Elgalal Marcin, Komorowski Piotr, Słoński Edward, Jóźwik Krzysztof, Walkowiak Bogdan

Engineering of Biomaterials

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2022

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vol. 25, no. 165

23--30

EN

The use of bone cement in procedures such as vertebroplasty and kyphoplasty can reduce pain and mechanically support the spine. This study aimed to evaluate whether air entrapped within bone cement affected its distribution in a vertebral body model. The study included 3D printed anatomical models of vertebrae together with their internal trabecular structure. Aeration was achieved by mixing the bone cement using three different altered procedures, whilst the control sample was prepared according to the manufacturer’s instructions. The further two samples were prepared by reducing or increasing the number of cycles required to mix the bone cement. A test rig was used to administer the prepared bone cement and introduce it into the vertebral model. Each time the injection was stopped when either the cement started to flow out of the vertebral model or when the entire cement volume was administered. The computer tomography (CT) scanning was performed to evaluate aerification and its influence on the bone cement distribution in each of the patient-specific models. The large number of small pores visible within the cement, especially in the cannula vicinity, indicated that the cement should not be treated as a homogenous liquid. These results suggest that a high level of aerification can influence the further cement distribution.

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Design and manufacture of customized medical implants - final report

Elgalal Marcin, Komorowski Piotr, Makowski Krzysztof, Styczyński Andrzej, Rokita Bożena, Kubiak Tomasz, Sawicki Jacek, Walkowiak Bogdan

Engineering of Biomaterials

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2021

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Vol. 24, no. 163

24

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Design and manufacture of customized medical implants - third report

Elgalal Marcin, Komorowski Piotr, Makowski Krzysztof, Styczyński Andrzej, Ulański Piotr, Kubiak Tomasz, Sawicki Jacek, Domżalski Marcin, Walkowiak Bogdan

Engineering of Biomaterials

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2020

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Vol. 23, no. 158 spec. iss.

31

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CNN-based superresolution reconstruction of 3D MR images using thick-slice scans

Jurek Jakub, Kocinski Marek, Materka Andrzej, Elgalal Marcin, Majos Agata

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

111--125

EN

Due to inherent physical and hardware limitations, 3D MR images are often acquired in the form of orthogonal thick slices, resulting in highly anisotropic voxels. This causes the partial volume effect, which introduces blurring of image details, appearance of staircase artifacts and significantly decreases the diagnostic value of images. To restore high resolution isotropic volumes, we propose to use a convolutional neural network (CNN) driven by patches taken from three orthogonal thick-slice images. To assess the validity and efficiency of this postprocessing approach, we used 1x1x1 mm3-voxel brain images of different modalities, available via the well known BrainWeb database. They served as a high resolution reference and were numerically preprocessed to create input images of different slice thickness and anatomical orientation, for CNN training, validation and testing. The visual quality of reconstructed images was indeed superior, compared to images obtained by fusion of interpolated thick-slice images, or to images reconstructed with the CNN using a single input MR scan. The significant increase of objectively computed figures of merit, e.g. the Structural Similarity Image Metric, was also noticed. Keeping in mind that any single value of such quality metrics represents a number of psychophysical effects, we applied the CNN trained on brain images for superresolution reconstruction of synthetic and acquired blood vessel tree images. We then used the restored superresolution volumes for estimation of vessel radii. It was demonstrated that vessel radius values derived from superresolution images of simulated vessel trees are significantly more accurate than those obtained from a standard fusion of interpolated thick-slice orthogonal scans. Superiority of the CNN-based superresolution images was also observed for scanner-acquired MR scans according to the evaluated parameters. These three experiments show the efficiency of CNN-based image reconstruction for qualitative and quantitative improvement of its diagnostic quality, as well as illustrates the practical usefulness of transfer learning - networks trained on example images of one kind can be used to restore superresolution images of physically different objects.

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Design and manufacture of customized medical implants - continued

Elgalal Marcin, Komorowski Piotr, Makowski Krzysztof, Styczyński Andrzej, Ulański Piotr, Kubiak Tomasz, Sawicki Jacek, Domżalski Marcin, Walkowiak Bogdan

Engineering of Biomaterials

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2019

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Vol. 22, no. 153 spec. iss.

100