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Felsing C, Schröder J. Update Bildgebung beim Femoroazetabulären Impingement-Syndrom. DER ORTHOPADE 2022; 51:176-186. [DOI: 10.1007/s00132-022-04223-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/01/2022] [Indexed: 10/19/2022]
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Zeng G, Degonda C, Boschung A, Schmaranzer F, Gerber N, Siebenrock KA, Steppacher SD, Tannast M, Lerch TD. Three-Dimensional Magnetic Resonance Imaging Bone Models of the Hip Joint Using Deep Learning: Dynamic Simulation of Hip Impingement for Diagnosis of Intra- and Extra-articular Hip Impingement. Orthop J Sports Med 2021; 9:23259671211046916. [PMID: 34938819 PMCID: PMC8685729 DOI: 10.1177/23259671211046916] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/23/2021] [Indexed: 11/26/2022] Open
Abstract
Background: Dynamic 3-dimensional (3D) simulation of hip impingement enables better
understanding of complex hip deformities in young adult patients with
femoroacetabular impingement (FAI). Deep learning algorithms may improve
magnetic resonance imaging (MRI) segmentation. Purpose: (1) To evaluate the accuracy of 3D models created using convolutional neural
networks (CNNs) for fully automatic MRI bone segmentation of the hip joint,
(2) to correlate hip range of motion (ROM) between manual and automatic
segmentation, and (3) to compare location of hip impingement in 3D models
created using automatic bone segmentation in patients with FAI. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: The authors retrospectively reviewed 31 hip MRI scans from 26 symptomatic
patients (mean age, 27 years) with hip pain due to FAI. All patients had
matched computed tomography (CT) and MRI scans of the pelvis and the knee.
CT- and MRI-based osseous 3D models of the hip joint of the same patients
were compared (MRI: T1 volumetric interpolated breath-hold examination
high-resolution sequence; 0.8 mm3 isovoxel). CNNs were used to
develop fully automatic bone segmentation of the hip joint, and the 3D
models created using this method were compared with manual segmentation of
CT- and MRI-based 3D models. Impingement-free ROM and location of hip
impingement were calculated using previously validated collision detection
software. Results: The difference between the CT- and MRI-based 3D models was <1 mm, and the
difference between fully automatic and manual segmentation of MRI-based 3D
models was <1 mm. The correlation of automatic and manual MRI-based 3D
models was excellent and significant for impingement-free ROM
(r = 0.995; P < .001), flexion
(r = 0.953; P < .001), and internal
rotation at 90° of flexion (r = 0.982; P
< .001). The correlation for impingement-free flexion between automatic
MRI-based 3D models and CT-based 3D models was 0.953 (P
< .001). The location of impingement was not significantly different
between manual and automatic segmentation of MRI-based 3D models, and the
location of extra-articular hip impingement was not different between CT-
and MRI-based 3D models. Conclusion: CNN can potentially be used in clinical practice to provide rapid and
accurate 3D MRI hip joint models for young patients. The created models can
be used for simulation of impingement during diagnosis of intra- and
extra-articular hip impingement to enable radiation-free and
patient-specific surgical planning for hip arthroscopy and open hip
preservation surgery.
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Affiliation(s)
- Guodong Zeng
- Sitem Center for Translational Medicine and Biomedical Entrepreneurship, University of Bern, Switzerland
| | - Celia Degonda
- Department of Orthopedic Surgery, Inselspital, University of Bern, Bern, Switzerland
| | - Adam Boschung
- Department of Orthopedic Surgery, Inselspital, University of Bern, Bern, Switzerland.,Department of Diagnostic, Interventional and Paediatric Radiology, University of Bern, Inselspital, Bern, Switzerland
| | - Florian Schmaranzer
- Department of Orthopedic Surgery, Inselspital, University of Bern, Bern, Switzerland.,Department of Diagnostic, Interventional and Paediatric Radiology, University of Bern, Inselspital, Bern, Switzerland
| | - Nicolas Gerber
- Sitem Center for Translational Medicine and Biomedical Entrepreneurship, University of Bern, Switzerland
| | - Klaus A Siebenrock
- Department of Orthopedic Surgery, Inselspital, University of Bern, Bern, Switzerland
| | - Simon D Steppacher
- Department of Orthopedic Surgery, Inselspital, University of Bern, Bern, Switzerland
| | - Moritz Tannast
- Department of Orthopedic Surgery, Inselspital, University of Bern, Bern, Switzerland.,Department of Orthopaedic Surgery and Traumatology, Cantonal Hospital, University of Fribourg, Fribourg, Switzerland
| | - Till D Lerch
- Department of Orthopedic Surgery, Inselspital, University of Bern, Bern, Switzerland.,Department of Diagnostic, Interventional and Paediatric Radiology, University of Bern, Inselspital, Bern, Switzerland
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