Comparison of 3D bone models of the knee joint derived from CT and 3T MR imaging

Neubert, Aleš; Wilson, Katharine J.; Engstrom, Craig; Surowiec, Rachel K.; Paproki, Anthony; Johnson, Nicholas S.; Crozier, Stuart; Fripp, Jürgen; Ho, Charles P.

doi:10.1016/j.ejrad.2017.05.042

Cited by 32 publications

(47 citation statements)

References 21 publications

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“…This is consistent with the several studies looking at comparisons of the knee joint, where on average, the CT models were slightly larger than the MRI models. Neubert el al 3 . found this difference due to the lack of hydrogen in cortical bone thereby reducing its profile.…”

Section: Discussionmentioning

confidence: 98%

“…In this cadaveric study, the transverse processes and vertebral bodies which were in contact with air were excluded from the segmentations, as the spines were already harvested from the donors. This is a problem which occurs in the clinical setting in general orthopaedics in areas where bone does not have much soft tissue coverage, 3 and even when automatic segmentation has been attempted 13 . Fortunately, these surfaces are not used for surface matching of patient‐specific guides in the lumbar spine.…”

Section: Discussionmentioning

confidence: 99%

“…CT is currently the gold-standard modality used to create spine biomodels and patient-specific guides, although magnetic resonance imaging (MRI) is being used for this purpose in other orthopaedic fields such as knee arthroplasty. [2][3][4] Currently, clinical spine MRIs are not precise enough to create 3D biomodels (Figure 1d-f), due to excessive noise at the bone-soft tissue interface. 1 Spine CT scans are often not required in the elective surgical setting, as MRI gives more pertinent information regarding the neural elements.…”

Section: Introductionmentioning

confidence: 99%

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Geometric and volumetric relationship between human lumbar vertebrae and “Black‐bone” MRI‐based models

Kanawati

Fernandes

Gee

et al. 2021

Robotics Computer Surgery

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Background This study will examine the differences between human lumbar vertebrae, three‐dimensional (3D) scans of these bones, 3D models based on ‘Black‐bone’ magnetic resonance imaging (MRI) scans, and 3D‐printed models. Materials and Methods 3D mesh models were created from the “Black‐bone” MRI data from two cadaveric human spines, and then 3D printed. Four models were analysed and compared: anatomic bones, 3D‐scanned models, MRI models and 3D‐printed models. Results There was no significant difference between when comparing the average of all measurements between all model types (p = 0.81). The mean dice coefficient was 0.91 (SD 0.016) and the mean Hausdorff distance was 0.37 mm (SD 0.04 mm) when comparing the MRI model to the 3D‐scanned model. The mean volumes for the MRI model and the 3D scanned model were 10.42 and 10.04 ml (p = 0.085), respectively. Conclusions The ‘Black‐bone’ MRI could be a valid radiation‐free alternative to computed tomography for the 3D printing of lumbar spinal biomodels.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Geometric and volumetric relationship between human lumbar vertebrae and “Black‐bone” MRI‐based models

Kanawati

Fernandes

Gee

et al. 2021

Robotics Computer Surgery

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“…The versatility of SMILI makes it ideally suited for a range of biomedical studies and has already been successfully utilised for a number of other applications since its release. It has been employed for advanced 3D in vivo visualisation of structures and models in musculoskeletal radiology [28,29], computing and visualising surface distances between models and bone shape [30,31], as well as in recent work on deformation fields in radiotherapy treatment planning [32,33]. The latter has clinical value in not only understanding the effects of…”

Section: Custom Clinical Applicationmentioning

confidence: 99%

A lightweight rapid application development framework for biomedical image analysis

Chandra

Dowling

Engstrom

et al. 2018

Computer Methods and Programs in Biomedicine

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Biomedical imaging analysis typically comprises a variety of complex tasks requiring sophisticated algorithms and visualising high dimensional data. The successful integration and deployment of the enabling software to clinical (research) partners, for rigorous evaluation and testing, is a crucial step to facilitate adoption of research innovations within medical settings. In this paper, we introduce the Simple Medical Imaging Library Interface (SMILI), an object oriented open-source framework with a compact suite of objects geared for rapid biomedical imaging (cross-platform) application development and deployment. SMILI supports the development of both command-line (shell and Python scripting) and graphical applications utilising the same set of processing algorithms. It provides a substantial subset of features when compared to more complex packages, yet it is small enough to ship with clinical applications with limited overhead and has a license suitable for commercial use. After describing where SMILI fits within the existing biomedical imaging software ecosystem, by comparing it to other state-of-the-art offerings, we demonstrate its capabilities in creating a clinical application for manual measurement of cam-type lesions of the femoral head-neck region for the investigation of femoro-acetabular impingement (FAI) from three dimensional (3D) magnetic resonance (MR) images of the hip. This application for the investigation of FAI proved to be convenient for radiological analyses and resulted in high intra (ICC=0.97) and inter-observer (ICC=0.95) reliabilities for measurement of α-angles of the femoral head-neck region. We believe that SMILI is particularly well suited for prototyping biomedical imaging applications requiring user interaction and/or visualisation of 3D mesh, scalar, vector or tensor data.

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“…To evaluate this error, comparison of the MR bone segmentations against segmentations obtained from CT images (gold standard) is required. This was not performed as part of this thesis, however, a recent study comparing manual segmentations of the knee bones in CT and MR images suggested that MR imaging was a suitable alternative to CT for bone segmentation and obtained comparable accuracy, although slightly inferior [Neubert 2016c] (abstract) [Neubert 2016b] (under review). Overall, this segmentation error did not directly affect the accuracy of the kinematic parameters estimated, which mainly relied on the contact surfaces of the cartilage plates and gross bone features.…”

Section: Discussionmentioning

confidence: 99%

Automated Image Analysis of High-field and Dynamic Musculoskeletal MRI

Paproki¹

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Comparison of 3D bone models of the knee joint derived from CT and 3T MR imaging

Cited by 32 publications

References 21 publications

Geometric and volumetric relationship between human lumbar vertebrae and “Black‐bone” MRI‐based models

Geometric and volumetric relationship between human lumbar vertebrae and “Black‐bone” MRI‐based models

A lightweight rapid application development framework for biomedical image analysis

Automated Image Analysis of High-field and Dynamic Musculoskeletal MRI

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