Validation of three-dimensional models of the distal femur created from surgical navigation point cloud data for intraoperative and postoperative analysis of total knee arthroplasty

Wilson, David; Anglin, Carolyn; Ambellan, Felix; Grewe, Carl Martin; Tack, Alexander; Lamecker, Hans; Dunbar, Michael J.; Zachow, Stefan

doi:10.1007/s11548-017-1630-5

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…69 Magnetic resonance imaging-based 3D SSM may be able to predict the need for knee joint arthroplasty (total knee arthroplasty) 70 and has been used to correlate femur shapes derived from preoperative MRI and intraoperative measurements for total knee arthroplasty patients. 71 Magnetic resonance imaging-derived 3D SSM has shown an association between the patella and femur bone shape features and the patellofemoral joint degeneration at 3 years. 72 Applied to the hip, MRI-based 3D SSM has been used to measure longitudinal variations in proximal femur shape and their associations with hip OA.…”

Section: Bone Shape Modeling and Kinematicsmentioning

confidence: 99%

“…A recent study revealed rapid bone shape changes at 3 months after acute ACL injury using MRI-based 3D SSM 69 . Magnetic resonance imaging–based 3D SSM may be able to predict the need for knee joint arthroplasty (total knee arthroplasty) 70 and has been used to correlate femur shapes derived from preoperative MRI and intraoperative measurements for total knee arthroplasty patients 71 . Magnetic resonance imaging–derived 3D SSM has shown an association between the patella and femur bone shape features and the patellofemoral joint degeneration at 3 years 72 .…”

Section: Bone Shape Modeling and Kinematicsmentioning

confidence: 99%

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Quantitative MRI for Evaluation of Musculoskeletal Disease

et al. 2022

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Magnetic resonance imaging (MRI) is a valuable tool for evaluating musculoskeletal disease as it offers a range of image contrasts that are sensitive to underlying tissue biochemical composition and microstructure. Although MRI has the ability to provide high-resolution, information-rich images suitable for musculoskeletal applications, most MRI utilization remains in qualitative evaluation. Quantitative MRI (qMRI) provides additional value beyond qualitative assessment via objective metrics that can support disease characterization, disease progression monitoring, or therapy response. In this review, musculoskeletal qMRI techniques are summarized with a focus on techniques developed for osteoarthritis evaluation. Cartilage compositional MRI methods are described with a detailed discussion on relaxometric mapping (T2, T2*, T1ρ) without contrast agents. Methods to assess inflammation are described, including perfusion imaging, volume and signal changes, contrast-enhanced T1 mapping, and semiquantitative scoring systems. Quantitative characterization of structure and function by bone shape modeling and joint kinematics are described. Muscle evaluation by qMRI is discussed, including size (area, volume), relaxometric mapping (T1, T2, T1ρ), fat fraction quantification, diffusion imaging, and metabolic assessment by 31P-MR and creatine chemical exchange saturation transfer. Other notable technologies to support qMRI in musculoskeletal evaluation are described, including magnetic resonance fingerprinting, ultrashort echo time imaging, ultrahigh-field MRI, and hybrid MRI-positron emission tomography. Challenges for adopting and using qMRI in musculoskeletal evaluation are discussed, including the need for metal artifact suppression and qMRI standardization.

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Section: Bone Shape Modeling and Kinematicsmentioning

confidence: 99%

Section: Bone Shape Modeling and Kinematicsmentioning

confidence: 99%

Quantitative MRI for Evaluation of Musculoskeletal Disease

et al. 2022

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“…4 After segmentation of the CT scan images, an SSM of the selected patient population was generated using an approach described by Lamecker and Zachow. 11,12 Thereby, common anatomical landmarks, such as the inferior, middle, and superior meatus, the nasal septum, the isthmus nasi, and the nasopharynx were manually identified in all patient-specific geometries. All patientspecific geometries were mirrored, so that 50 individual cavities per side of the nose were included in the SSM.…”

Section: Introductionmentioning

confidence: 99%

The Healthy Nasal Cavity—Characteristics of Morphology and Related Airflow Based on a Statistical Shape Model Viewed from a Surgeon's Perspective

et al. 2019

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Functional surgery on the nasal framework requires referential criteria to objectively assess nasal breathing for indication and follow-up. This motivated us to generate a mean geometry of the nasal cavity based on a statistical shape model. In this study, the authors could demonstrate that the introduced nasal cavity's mean geometry features characteristics of the inner shape and airflow, which are commonly observed in symptom-free subjects. Therefore, the mean geometry might serve as a reference-like model when one considers qualitative aspects. However, to facilitate quantitative considerations and statistical inference, further research is necessary. Additionally, the authors were able to obtain details about the importance of the isthmus nasi and the inferior turbinate for the intranasal airstream.

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