Multimodal-3D imaging based on μMRI and μCT techniques bridges the gap with histology in visualization of the bone regeneration process
R. Sinibaldi
Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Multimodal3D s.r.l., Rome, Italy
Search for more papers by this authorA. Conti
Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Search for more papers by this authorB. Sinjari
Department of Medical and Oral Sciences and Biotechnologies, G. D'Annunzio University of Chieti and Pescara, Chieti, Italy
Search for more papers by this authorS. Spadone
Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Search for more papers by this authorR. Pecci
Department of Technologies and Health, Istituto Superiore di Sanità, Rome, Italy
Search for more papers by this authorM. Palombo
Department of Physics, Sapienza University of Rome, Rome, Italy
CEA/DSV/I2BM, MIRCen, Fontenay-aux-Roses, France
Search for more papers by this authorV.S. Komlev
A.A. Baikov Institute of Metallurgy and Materials Science, Russian Academy of Sciences, Moscow, Russian Federation
Search for more papers by this authorM.G. Ortore
Department of Life and Environmental Science, Marche Polytechnic University, Ancona, Italy
Search for more papers by this authorS. Capuani
CNR (Institute for Complex Systems) c/o Physics Department Sapienza University of Rome, Rome, Italy
Search for more papers by this authorR. Guidotti
Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Search for more papers by this authorF. De Luca
Department of Physics, Sapienza University of Rome, Rome, Italy
Search for more papers by this authorS. Caputi
Department of Medical and Oral Sciences and Biotechnologies, G. D'Annunzio University of Chieti and Pescara, Chieti, Italy
Search for more papers by this authorT. Traini
Department of Medical and Oral Sciences and Biotechnologies, G. D'Annunzio University of Chieti and Pescara, Chieti, Italy
Search for more papers by this authorCorresponding Author
S. Della Penna
Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Institute for Advanced Biomedical Technologies, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Correspondence
Stefania Della Penna, Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Via dei Vestini 31, I-66100 Chieti, Italy; or Institute for Advanced Biomedical Technologies, G. D'Annunzio University of Chieti-Pescara, Via dei Vestini 31, I-66100 Chieti, Italy.
Email: [email protected]
Search for more papers by this authorR. Sinibaldi
Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Multimodal3D s.r.l., Rome, Italy
Search for more papers by this authorA. Conti
Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Search for more papers by this authorB. Sinjari
Department of Medical and Oral Sciences and Biotechnologies, G. D'Annunzio University of Chieti and Pescara, Chieti, Italy
Search for more papers by this authorS. Spadone
Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Search for more papers by this authorR. Pecci
Department of Technologies and Health, Istituto Superiore di Sanità, Rome, Italy
Search for more papers by this authorM. Palombo
Department of Physics, Sapienza University of Rome, Rome, Italy
CEA/DSV/I2BM, MIRCen, Fontenay-aux-Roses, France
Search for more papers by this authorV.S. Komlev
A.A. Baikov Institute of Metallurgy and Materials Science, Russian Academy of Sciences, Moscow, Russian Federation
Search for more papers by this authorM.G. Ortore
Department of Life and Environmental Science, Marche Polytechnic University, Ancona, Italy
Search for more papers by this authorS. Capuani
CNR (Institute for Complex Systems) c/o Physics Department Sapienza University of Rome, Rome, Italy
Search for more papers by this authorR. Guidotti
Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Search for more papers by this authorF. De Luca
Department of Physics, Sapienza University of Rome, Rome, Italy
Search for more papers by this authorS. Caputi
Department of Medical and Oral Sciences and Biotechnologies, G. D'Annunzio University of Chieti and Pescara, Chieti, Italy
Search for more papers by this authorT. Traini
Department of Medical and Oral Sciences and Biotechnologies, G. D'Annunzio University of Chieti and Pescara, Chieti, Italy
Search for more papers by this authorCorresponding Author
S. Della Penna
Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Institute for Advanced Biomedical Technologies, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
Correspondence
Stefania Della Penna, Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Via dei Vestini 31, I-66100 Chieti, Italy; or Institute for Advanced Biomedical Technologies, G. D'Annunzio University of Chieti-Pescara, Via dei Vestini 31, I-66100 Chieti, Italy.
Email: [email protected]
Search for more papers by this authorAbstract
Bone repair/regeneration is usually investigated through X-ray computed microtomography (μCT) supported by histology of extracted samples, to analyse biomaterial structure and new bone formation processes. Magnetic resonance imaging (μMRI) shows a richer tissue contrast than μCT, despite at lower resolution, and could be combined with μCT in the perspective of conducting non-destructive 3D investigations of bone. A pipeline designed to combine μMRI and μCT images of bone samples is here described and applied on samples of extracted human jawbone core following bone graft. We optimized the coregistration procedure between μCT and μMRI images to avoid bias due to the different resolutions and contrasts. Furthermore, we used an Adaptive Multivariate Clustering, grouping homologous voxels in the coregistered images, to visualize different tissue types within a fused 3D metastructure. The tissue grouping matched the 2D histology applied only on 1 slice, thus extending the histology labelling in 3D. Specifically, in all samples, we could separate and map 2 types of regenerated bone, calcified tissue, soft tissues, and/or fat and marrow space. Remarkably, μMRI and μCT alone were not able to separate the 2 types of regenerated bone. Finally, we computed volumes of each tissue in the 3D metastructures, which might be exploited by quantitative simulation. The 3D metastructure obtained through our pipeline represents a first step to bridge the gap between the quality of information obtained from 2D optical microscopy and the 3D mapping of the bone tissue heterogeneity and could allow researchers and clinicians to non-destructively characterize and follow-up bone regeneration.
Supporting Information
| Filename | Description |
|---|---|
| term2494-sup-0001-SI_rev.docWord document, 1.6 MB |
Figure S1 The SRμCT slices obtained by Back Projection Imaging (BPI) reconstruction without pre-processing were less detailed than the ones obtained by Phase Retrieval BPI reconstruction. These figures demonstrate that it is hard to disentangle low mineralized bone and soft tissues from bone trabeculae. As a matter of facts, the related histogram was fitted by 3 distributions (not shown) with just one common peak describing both tissues. Figure S2 A sample slice displayed at different TE. At TE = 4.8 ms (which is the one used for further processing) the highest contrast between different tissues is obtained. Table S1 Relative distance between distributions obtained from PR BPI SRμCT. The histogram on which the Gaussian populations were deconvolved was obtained over the sample volume only. The relative distance is expressed as the ratio between the absolute value of the distance of the two means and the standard deviation of the reference distribution (the first letter in each of the “Distribution” boxes). Coding: G = Green, O = Orange, P = Pink, R = Red. In bold we highlight the comparisons which were below statistical significance (p < 0.05). Distribution pairs were assumed as overlapping when the null hypothesis was verified in one of the two comparisons (a reference distribution vs the other and/or the other way round). Table S2 Relative distance between distributions obtained from μMR images. The histogram on which Gaussian population s were deconvolved was obtained over the sample volume only. The relative distance is expressed as the ratio between the absolute value of the distance of the two means and the standard deviation of the reference distribution (the first letter in each of the “Distribution” boxes). Coding: G = Green, B = Blue, C = Cyan, R = Red, V = Violet. In bold we highlight the comparisons which were below statistical significance (p < 0.05). Distribution pairs were assumed as overlapping when the null hypothesis was verified in one of the two comparisons (a reference distribution vs the other and/or the other way round). Table S3 Slice-specific volume number for each of the clusters detected by the 3D multivariate-grouping algorithm in the three samples. Figure S3 μMRI (left side) and SRμCT (right side) images of samples C-2 (a) and C-3 (b, c). The gray values in the SRμCT image of sample C-2 are lower than the other samples, possibly due to a lower mineralization degree in the bone tissues. Figure S4 Histological sections (left side) and clustering results (right side) for sample C-2 (a) and C-3 (b). The histological section of sample C-3 matches on two different slices of the 3D metastructure obtained through clustering. Figure S5 Rois selected in the histology slice to evaluate precision of clustering. (a) and (b) sample C-1, (c) sample C-2, (d) sample C-3. The latter is divided in two parts because left and right side match on two different slices of the 3D metastructure. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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