Next-generation imaging of the skeletal system and its blood supply

Grüneboom, Anika; Kling, Lasse; Christiansen, Silke; Mill, Leonid; Maier, Andreas; Engelke, Klaus; Quick, Harald H.; Schett, Georg; Gunzer, Matthias

doi:10.1038/s41584-019-0274-y

Cited by 61 publications

(50 citation statements)

References 197 publications

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“…Vasculatures exist in three dimensional environments with vessels extending across all dimensions, may vary in density, and can resemble other non-vascular tissue elements (e.g., ducts, cell bundles, etc.). A variety of imaging modalities are employed to visualize the vasculature in both the laboratory and the clinic (Spaide et al, 2015;Grüneboom et al, 2019;Bautista et al, 2020). Usually, a contrast agent that fills the blood space and/or labels vessel cells directly is involved, as inherent contrast between the vessel and the surrounding tissue is often low.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning

Strobel¹,

Schultz

Moss³

et al. 2021

Front. Physiol.

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Given the considerable research efforts in understanding and manipulating the vasculature in tissue health and function, making effective measurements of vascular density is critical for a variety of biomedical applications. However, because the vasculature is a heterogeneous collection of vessel segments, arranged in a complex three-dimensional architecture, which is dynamic in form and function, it is difficult to effectively measure. Here, we developed a semi-automated method that leverages machine learning to identify and quantify vascular metrics in an angiogenesis model imaged with different modalities. This software, BioSegment, is designed to make high throughput vascular density measurements of fluorescent or phase contrast images. Furthermore, the rapidity of assessments makes it an ideal tool for incorporation in tissue manufacturing workflows, where engineered tissue constructs may require frequent monitoring, to ensure that vascular growth benchmarks are met.

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Section: Introductionmentioning

confidence: 99%

Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning

Strobel¹,

Schultz

Moss³

et al. 2021

Front. Physiol.

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“…As is known to all, X-ray-based imaging including computed tomography (CT) have long been widely applied for investigating bone structures for almost one century in clinic. However, owing to radiation and low resolution (at ~ mm level), X-ray imaging is considered not suitable for a long-time study of bone tissues from an organ level to a molecular level [4,5]. Therefore, it is in urgent need to develop a multi-scale imaging approach to visualize the morphology of bones on microscale, even nanoscale.…”

Section: Read Full License Introductionmentioning

confidence: 99%

In vivo live imaging of bone using shortwave infrared fluorescent quantum dots

et al. 2020

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“…Fracture healing is a frequently investigated process in basic research using different small animal models. In clinical studies, advanced imaging technologies like positron-emission-tomography combined with computed-tomography (PET/CT), microcomputer-tomography (µCT) and magnetic-resonance-imaging (MRI) are used frequently and contribute to a new understanding of bone tissue composition, regeneration and disease 1 – 3 . Technical progress with regard to increased resolution and the development of contrast agents and labeling techniques provides new insights also in small animal analysis of musculoskeletal tissues, now 4 – 6 .…”

Section: Introductionmentioning

confidence: 99%

A novel MRI compatible mouse fracture model to characterize and monitor bone regeneration and tissue composition

Schmitz

Timmen²,

Kostka³

et al. 2020

Sci Rep

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Over the last years, murine in vivo magnetic resonance imaging (MRI) contributed to a new understanding of tissue composition, regeneration and diseases. Due to artefacts generated by the currently used metal implants, MRI is limited in fracture healing research so far. In this study, we investigated a novel MRI-compatible, ceramic intramedullary fracture implant during bone regeneration in mice. Three-point-bending revealed a higher stiffness of the ceramic material compared to the metal implants. Electron microscopy displayed a rough surface of the ceramic implant that was comparable to standard metal devices and allowed cell attachment and growth of osteoblastic cells. MicroCT-imaging illustrated the development of the callus around the fracture site indicating a regular progressing healing process when using the novel implant. In MRI, different callus tissues and the implant could clearly be distinguished from each other without any artefacts. Monitoring fracture healing using MRI-compatible implants will improve our knowledge of callus tissue regeneration by 3D insights longitudinal in the same living organism, which might also help to reduce the consumption of animals for future fracture healing studies, significantly. Finally, this study may be translated into clinical application to improve our knowledge about human bone regeneration.

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Next-generation imaging of the skeletal system and its blood supply

Cited by 61 publications

References 197 publications

Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning

Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning

In vivo live imaging of bone using shortwave infrared fluorescent quantum dots

A novel MRI compatible mouse fracture model to characterize and monitor bone regeneration and tissue composition

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